diff --git a/CMakeLists.txt b/CMakeLists.txt index 803715e48..c22bf983f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -10,8 +10,8 @@ endif() project(Caffe C CXX) # ---[ Caffe version -set(CAFFE_TARGET_VERSION "1.1.0" CACHE STRING "Caffe logical version") -set(CAFFE_TARGET_SOVERSION "1.1.0" CACHE STRING "Caffe soname version") +set(CAFFE_TARGET_VERSION "1.1.2" CACHE STRING "Caffe logical version") +set(CAFFE_TARGET_SOVERSION "1.1.2" CACHE STRING "Caffe soname version") add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION}) # ---[ Using cmake scripts and modules @@ -47,6 +47,7 @@ caffe_option(PERFORMANCE_MONITORING "Build Caffe with PERFORMANCE_MONITORING " O caffe_option(CODE_COVERAGE "Build with code coverage instrumentation" OFF) caffe_option(CO_SIM "Build readonly cpu blob data/diff pycaffe interface" OFF) caffe_option(USE_MLSL "Build Caffe with Intel(r) Machine Learning Scaling Library for multinode training" ON) +caffe_option(ICC_STATIC_BUILD "Build Intel Caffe static link with Intel compiler" OFF) # ---[ Dependencies include(cmake/MKLDNN.cmake) @@ -93,7 +94,11 @@ if(UNIX OR APPLE) # shows that it generates correct PIE anyway if only -pie is provided. set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fstack-protector -wd2196") # Auto enable SGD Fusion if use intel compiler - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DENABLE_SGD_FUSION") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DENABLE_SGD_FUSION -DENABLE_NMS_OPTIMIZATION") + # ICC static build + if(ICC_STATIC_BUILD) + set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -static-intel") + endif() endif() # Generic flags. diff --git a/Makefile b/Makefile index d67f27d83..5d3bc5654 100644 --- a/Makefile +++ b/Makefile @@ -102,7 +102,7 @@ LIB_BUILD_DIR := $(BUILD_DIR)/lib STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a DYNAMIC_VERSION_MAJOR := 1 DYNAMIC_VERSION_MINOR := 1 -DYNAMIC_VERSION_REVISION := 0 +DYNAMIC_VERSION_REVISION := 2 DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so #DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR) DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION) @@ -389,7 +389,11 @@ endif ifneq (,$(findstring icpc,$(CXX))) CXX_HARDENING_FLAGS += -fstack-protector -wd2196 #Enable SGD FUSION if use intel compiler - COMMON_FLAGS += -DENABLE_SGD_FUSION + COMMON_FLAGS += -DENABLE_SGD_FUSION -DENABLE_NMS_OPTIMIZATION + #Intel compiler static build flag + ifeq ($(ICC_STATIC_BUILD), 1) + LDFLAGS += -static-intel + endif else ifneq (,$(findstring clang++,$(CXX))) CXX_HARDENING_FLAGS += -fPIE -fstack-protector @@ -447,7 +451,7 @@ ifeq ($(DEBUG), 1) COMMON_FLAGS += -DDEBUG -g -O0 NVCCFLAGS += -G else ifneq (,$(findstring icpc,$(CXX))) - COMMON_FLAGS += -DNDEBUG -O3 -xCORE-AVX2 -no-prec-div -fp-model fast=2 + COMMON_FLAGS += -DNDEBUG -O3 -xHost -no-prec-div -fp-model fast=2 else COMMON_FLAGS += -DNDEBUG -O3 endif diff --git a/Makefile.config.example b/Makefile.config.example index 25acbe9dc..fdbfc6754 100644 --- a/Makefile.config.example +++ b/Makefile.config.example @@ -71,6 +71,8 @@ USE_MKLDNN_AS_DEFAULT_ENGINE := 1 # To customize your choice of compiler, uncomment and set the following. # N.B. the default for Linux is g++ and the default for OSX is clang++ # CUSTOM_CXX := g++ +# If you use Intel compiler, you can choose uncomment following to enable static build +# ICC_STATIC_BUILD := 1 # If you use Intel compiler define a path to newer boost if not used # already. @@ -85,6 +87,9 @@ DISABLE_CONV_RELU_FUSION:= 0 # Use Bn + ReLU fusion to boost inference DISABLE_BN_RELU_FUSION := 0 +# Use Conv + Concat fusion to boost inference. +ENABLE_CONCAT_FUSION := 0 + # Use Conv + Eltwise + Relu layer fusion to boost inference. DISABLE_CONV_SUM_FUSION := 0 diff --git a/Makefile.dlcp b/Makefile.dlcp index fc239b957..3df2092af 100644 --- a/Makefile.dlcp +++ b/Makefile.dlcp @@ -1,6 +1,6 @@ CAFFE_ROOTDIR := $(shell pwd) DLCP_IDEEPDIR := $(CAFFE_ROOTDIR)/external/ideep -DLCP_ROOTDIR := $(DLCP_IDEEPDIR)/ideep/dlcp +DLCP_ROOTDIR := $(DLCP_IDEEPDIR)/dlcp DLCP_INCLDIR := $(DLCP_ROOTDIR)/include DLCP_LIBDIR := $(DLCP_ROOTDIR)/lib IDEEP_GITHUB := https://github.com/intel/ideep.git @@ -9,7 +9,7 @@ DLCP_CXX := $(CXX) ifeq ("$(wildcard $(DLCP_INCLDIR)/dl_compression.h)", "") dlcp_download: rm -rf $(DLCP_IDEEPDIR) - git clone $(IDEEP_GITHUB) -b dlcp $(DLCP_IDEEPDIR) + git clone $(IDEEP_GITHUB) -b ideep4py $(DLCP_IDEEPDIR) dlcp_build: dlcp_download make -C $(DLCP_ROOTDIR) -j$(shell cat /proc/cpuinfo |grep 'processor'|wc -l) diff --git a/Makefile.mkldnn b/Makefile.mkldnn index f2dd30053..8a0c48b6b 100644 --- a/Makefile.mkldnn +++ b/Makefile.mkldnn @@ -22,6 +22,12 @@ ifneq (,$(findstring ccache,$(CC))) endif MKLDNN_GITHUB := https://github.com/01org/mkl-dnn.git +# Enable MKLDNN intel compiler static build +ifneq (,$(findstring icpc,$(MKLDNN_CXX))) + ifeq ($(ICC_STATIC_BUILD), 1) + MKLDNN_CMAKE_FLAGS += $(MKLDNN_SRCDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNN_INSTALLDIR) -DMKLROOT=${MKL_ROOTDIR} -B$(MKLDNN_BUILDDIR) -DCMAKE_CXX_COMPILER="$(MKLDNN_CXX)" -DCMAKE_C_COMPILER="$(MKLDNN_CC)" -DCMAKE_SHARED_LINKER_FLAGS="$(CMAKE_SHARED_LINKER_FLAGS) -static-intel" + endif +endif MKLDNN_CMAKE_FLAGS += $(MKLDNN_SRCDIR) -DCMAKE_INSTALL_PREFIX=$(MKLDNN_INSTALLDIR) -DMKLROOT=${MKL_ROOTDIR} -B$(MKLDNN_BUILDDIR) -DCMAKE_CXX_COMPILER="$(MKLDNN_CXX)" -DCMAKE_C_COMPILER="$(MKLDNN_CC)" # Disable MKLDNN download by customized setting diff --git a/cmake/DLCP.cmake b/cmake/DLCP.cmake index 83218aaaa..64e61bf06 100644 --- a/cmake/DLCP.cmake +++ b/cmake/DLCP.cmake @@ -5,20 +5,20 @@ function(Download_DLCP) elseif(HAS_ICPC) set(DLCP_CXX "icpc" PARENT_SCOPE) else() - message(WARNING "weight grad compression is disabled because intel compiler is not found") + message("weight grad compression is disabled because intel compiler is not found") return() endif() set(EXTERNAL_DIR ${CMAKE_SOURCE_DIR}/external) set(DLCP_IDEEPDIR ${EXTERNAL_DIR}/ideep) - set(DLCP_ROOTDIR ${DLCP_IDEEPDIR}/ideep/dlcp) + set(DLCP_ROOTDIR ${DLCP_IDEEPDIR}/dlcp) set(DLCP_INCLDIR "${DLCP_ROOTDIR}/include" PARENT_SCOPE) set(DLCP_LIBDIR ${DLCP_ROOTDIR}/lib PARENT_SCOPE) # Download dl compression lib source code if it doesn't exist if (NOT EXISTS ${DLCP_INCLDIR}/dl_compression.h) execute_process(COMMAND rm -rf ${DLCP_IDEEPDIR}) - execute_process(COMMAND git clone https://github.com/intel/ideep.git -b dlcp ${DLCP_IDEEPDIR}) + execute_process(COMMAND git clone https://github.com/intel/ideep.git -b ideep4py ${DLCP_IDEEPDIR}) endif() add_custom_target(DLCP_Build ALL diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake index bf8a75803..dc6668ca4 100644 --- a/cmake/Dependencies.cmake +++ b/cmake/Dependencies.cmake @@ -128,6 +128,7 @@ if(USE_MLSL) add_definitions("-DFOUNDED_MLSL_ROOT=${MLSL_ROOT}") include_directories(SYSTEM "${MLSL_ROOT}/intel64/include") link_directories(SYSTEM "${MLSL_ROOT}/intel64/lib") + set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${MLSL_ROOT}/intel64/lib/) list(APPEND Caffe_LINKER_LIBS mlsl) if(CAFFE_MLSL_SHUFFLE) @@ -151,6 +152,7 @@ if(USE_MLSL) message(STATUS "Found DLCP: ${DLCPROOT_DIR}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DENABLE_WEIGHT_GRAD_COMPRESSION") list(APPEND Caffe_LINKER_LIBS "${DLCPROOT_LIB_DIR}/libdlcomp.so") + set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${DLCP_LIBDIR}) include_directories(SYSTEM ${DLCP_INCLUDE_DIR}) else() message(STATUS "DLCP not found. DLCP_INCLUDE_DIR = ${DLCPROOT_INCLUDE_DIR} DLCP_LIB_DIR = ${DLCPROOT_LIB_DIR}") @@ -162,6 +164,7 @@ if(USE_MLSL) if(DLCP_CXX) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DENABLE_WEIGHT_GRAD_COMPRESSION") list(APPEND Caffe_LINKER_LIBS "${DLCP_LIBDIR}/libdlcomp.so") + set(CMAKE_INSTALL_RPATH ${CMAKE_INSTALL_RPATH}:${DLCP_LIBDIR}) include_directories(SYSTEM ${DLCP_INCLDIR}) endif() endif() diff --git a/cmake/MKLDNN.cmake b/cmake/MKLDNN.cmake index 43c51f7ee..e43f86612 100644 --- a/cmake/MKLDNN.cmake +++ b/cmake/MKLDNN.cmake @@ -5,6 +5,12 @@ function(Download_MKLDNN) set(MKLDNN_SOURCE_DIR ${MKLDNN_DIR}/src) set(MKLDNN_BUILD_DIR ${MKLDNN_DIR}/build) set(MKLDNN_INSTALL_DIR ${MKLDNN_DIR}/install CACHE PATH "Installation path of MKLDNN") + # Enable MKLDNN intel compiler static build + if(${CMAKE_CXX_COMPILER_ID} STREQUAL "Intel" ) + if(ICC_STATIC_BUILD) + set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -static-intel") + endif() + endif() execute_process(COMMAND cat mkldnn.commit WORKING_DIRECTORY ${CMAKE_SOURCE_DIR} OUTPUT_VARIABLE MKLDNN_COMMIT) @@ -18,13 +24,13 @@ function(Download_MKLDNN) ExternalProject_add(MKLDNN_Build SOURCE_DIR ${MKLDNN_SOURCE_DIR} - CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR} -DMKLROOT=${MKL_ROOT_DIR} + CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR} -DMKLROOT=${MKL_ROOT_DIR} -DCMAKE_SHARED_LINKER_FLAGS=${CMAKE_SHARED_LINKER_FLAGS} #--Download step GIT_REPOSITORY https://github.com/01org/mkl-dnn.git - GIT_TAG ${MKLDNN_COMMIT} + GIT_TAG ${MKLDNN_COMMIT} #--Build step BINARY_DIR ${MKLDNN_BUILD_DIR} - BUILD_COMMAND cmake ${MKLDNN_SOURCE_DIR} + BUILD_COMMAND cmake ${MKLDNN_SOURCE_DIR} #--Install step INSTALL_DIR ${MKLDNN_INSTALL_DIR} INSTALL_COMMAND make install -j${NCORE} diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake index 438e4b57b..799d35fa9 100644 --- a/cmake/Misc.cmake +++ b/cmake/Misc.cmake @@ -21,6 +21,11 @@ if(DISABLE_BN_RELU_FUSION) add_definitions("-DDISABLE_BN_RELU_FUSION") endif() +if(ENABLE_CONCAT_FUSION) + message(STATUS "conv/concat fusion is enabled!") + add_definitions("-DENABLE_CONCAT_FUSION") +endif() + if(DISABLE_CONV_SUM_FUSION) message(STATUS "conv/eltwise/relu fusion is disabled!") add_definitions("-DDISABLE_CONV_SUM_FUSION") @@ -71,10 +76,3 @@ if(UNIX OR APPLE) COMMENT "Adding symlink: /build -> ${PROJECT_BINARY_DIR}" ) endif() -# ---[ Set debug postfix -set(Caffe_DEBUG_POSTFIX "-d") - -set(Caffe_POSTFIX "") -if(CMAKE_BUILD_TYPE MATCHES "Debug") - set(Caffe_POSTFIX ${Caffe_DEBUG_POSTFIX}) -endif() diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake index 787d7f722..2a4a968fc 100644 --- a/cmake/Summary.cmake +++ b/cmake/Summary.cmake @@ -105,6 +105,9 @@ function(caffe_print_configuration_summary) caffe_status(" Git : ${Caffe_GIT_VERSION}") caffe_status(" System : ${CMAKE_SYSTEM_NAME}") caffe_status(" C++ compiler : ${CMAKE_CXX_COMPILER}") + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") + caffe_status(" ICC_STATIC_BUILD : ${ICC_STATIC_BUILD}") + endif() caffe_status(" Release CXX flags : ${__flags_rel}") caffe_status(" Debug CXX flags : ${__flags_deb}") caffe_status(" Build type : ${CMAKE_BUILD_TYPE}") diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake index 1d2df2690..d05043c9b 100644 --- a/cmake/Targets.cmake +++ b/cmake/Targets.cmake @@ -108,7 +108,6 @@ endfunction() # caffe_default_properties() function(caffe_default_properties target) set_target_properties(${target} PROPERTIES - DEBUG_POSTFIX ${Caffe_DEBUG_POSTFIX} ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib" LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib" RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin") diff --git a/docker/README.md b/docker/README.md index f945feabb..d61cb6e58 100644 --- a/docker/README.md +++ b/docker/README.md @@ -17,7 +17,7 @@ docker run -ti caffe:cpu caffe --version ``` which should show a message like: ``` -caffe version 1.1.0 +caffe version 1.1.2 ``` One can also build and run the Caffe tests in the image using: diff --git a/docker/standalone/cpu-centos/Dockerfile b/docker/standalone/cpu-centos/Dockerfile index ef28b04f8..c0dbff8d2 100644 --- a/docker/standalone/cpu-centos/Dockerfile +++ b/docker/standalone/cpu-centos/Dockerfile @@ -4,11 +4,11 @@ MAINTAINER caffe-maint@googlegroups.com #ENV http_proxy proxy:port #ENV https_proxy proxy:port -RUN rpm -iUvh http://download.fedoraproject.org/pub/epel/7/x86_64/e/epel-release-7-10.noarch.rpm - -RUN yum upgrade -y - -RUN yum install -y \ +RUN rpm -iUvh https://dl.fedoraproject.org/pub/epel/7/x86_64/Packages/e/epel-release-7-11.noarch.rpm && \ + yum upgrade -y && \ + yum install -y \ + bzip2 \ + numactl \ redhat-rpm-config \ tar \ findutils \ @@ -16,8 +16,16 @@ RUN yum install -y \ gcc-c++ \ cmake \ git \ + vim \ wget \ ssh \ + openssh.x86_64 \ + openssh-server.x86_64 \ + openssh-clients.x86_64 \ + initscripts \ + net-tools \ + ufw \ + iptables \ atlas-devel \ boost-devel \ gflags-devel \ @@ -36,24 +44,36 @@ RUN yum install -y \ python-pip \ python-scipy \ gcc-gfortran \ - libjpeg-turbo-devel + libjpeg-turbo-devel && \ + yum clean all -RUN yum clean all -ENV CAFFE_ROOT=/opt/caffe -WORKDIR $CAFFE_ROOT +# Install conda and Intel Caffe conda package +WORKDIR /root/ +RUN wget -c https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh && \ + bash Miniconda2-latest-Linux-x86_64.sh -b && \ + ./miniconda2/bin/conda config --add channels intel && \ + ./miniconda2/bin/conda install -c intel caffe && \ + rm -rf /root/miniconda2/pkgs/* && \ + rm ~/Miniconda2-latest-Linux-x86_64.sh -f && \ + echo "export PATH=/root/miniconda2/bin:$PATH" >> /root/.bashrc +WORKDIR /root/miniconda2/caffe -# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. -ENV CLONE_TAG=master +# If you want to deploy container in HOST network mode, Install SSH service and config it to Non-standard Port. Or you needn’t below rows. +RUN mkdir /var/run/sshd && \ + echo "root:intelcaffe@123" | chpasswd +RUN sed -i 's/#PermitRootLogin prohibit-password/PermitRootLogin yes/;s/#Port 22/Port 10086/' /etc/ssh/sshd_config -RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/intel/caffe.git . && \ - for req in $(cat python/requirements.txt) pydot; do pip --no-cache-dir install $req; done && \ - mkdir build && cd build && \ - cmake -DCPU_ONLY=1 -DCMAKE_BUILD_TYPE=Release .. && \ - make all -j"$(nproc)" +# SSH login fix. Otherwise user is kicked off after login +RUN sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd -ENV PYCAFFE_ROOT $CAFFE_ROOT/python -ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH -ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH -RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig +ENV NOTVISIBLE "in users profile" +RUN echo "export VISIBLE=now" >> /etc/profile -WORKDIR /workspace +EXPOSE 10086 +RUN ssh-keygen -t rsa -A && \ + mkdir ~/.ssh && \ + touch ~/.ssh/config && \ + echo "Host *" > ~/.ssh/config && \ + echo "Port 10086" >> ~/.ssh/config && \ + echo "StrictHostKeyChecking no" >> ~/.ssh/config +CMD ["/usr/sbin/sshd", "-D"] diff --git a/docker/standalone/cpu-ubuntu/Dockerfile b/docker/standalone/cpu-ubuntu/Dockerfile index 4418f133e..239a07c1b 100644 --- a/docker/standalone/cpu-ubuntu/Dockerfile +++ b/docker/standalone/cpu-ubuntu/Dockerfile @@ -4,8 +4,9 @@ MAINTAINER caffe-maint@googlegroups.com #ENV http_proxy proxy:port #ENV https_proxy proxy:port -RUN apt-get update && apt-get install -y --no-install-recommends \ - cpio \ +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + cpio \ build-essential \ cmake \ git \ @@ -38,31 +39,20 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ python-scipy && \ rm -rf /var/lib/apt/lists/* -ENV CAFFE_ROOT=/opt/caffe -WORKDIR $CAFFE_ROOT +# Install conda and Intel Caffe conda package +WORKDIR /root/ +RUN wget -c https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh && \ + bash Miniconda2-latest-Linux-x86_64.sh -b && \ + ./miniconda2/bin/conda config --add channels intel && \ + ./miniconda2/bin/conda install -c intel caffe && \ + rm -rf /root/miniconda2/pkgs/* && \ + rm ~/Miniconda2-latest-Linux-x86_64.sh -f && \ + echo "export PATH=/root/miniconda2/bin:$PATH" >> /root/.bashrc +WORKDIR /root/miniconda2/caffe -# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. -ENV CLONE_TAG=master - -RUN pip install --upgrade pip - -RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/intel/caffe.git . && \ - for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \ - mkdir build && cd build && \ - cmake -DCPU_ONLY=1 -DUSE_MLSL=1 -DCMAKE_BUILD_TYPE=Release .. && \ - make all -j"$(nproc)" - -ENV PYCAFFE_ROOT $CAFFE_ROOT/python -ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH -ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH -RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig - -WORKDIR /opt/caffe - -RUN mkdir /var/run/sshd -RUN echo 'root:123456' | chpasswd -RUN sed -i 's/PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config -RUN sed -i 's/Port 22/Port 10010/' /etc/ssh/sshd_config +RUN mkdir /var/run/sshd && \ + echo 'root:intelcaffe@123' | chpasswd +RUN sed -i 's/PermitRootLogin prohibit-password/PermitRootLogin yes/;s/Port 22/Port 10010/' /etc/ssh/sshd_config # SSH login fix. Otherwise user is kicked off after login RUN sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd @@ -71,5 +61,10 @@ ENV NOTVISIBLE "in users profile" RUN echo "export VISIBLE=now" >> /etc/profile EXPOSE 10010 - +RUN ssh-keygen -t rsa -A && \ + mkdir ~/.ssh && \ + touch ~/.ssh/config && \ + echo "Host *" > ~/.ssh/config && \ + echo "Port 10010" >> ~/.ssh/config && \ + echo "StrictHostKeyChecking no" >> ~/.ssh/config CMD ["/usr/sbin/sshd", "-D"] diff --git a/examples/faster-rcnn/lib/fast_rcnn/net_sample_utils.py b/examples/faster-rcnn/lib/fast_rcnn/net_sample_utils.py new file mode 100755 index 000000000..3933d0661 --- /dev/null +++ b/examples/faster-rcnn/lib/fast_rcnn/net_sample_utils.py @@ -0,0 +1,69 @@ +# +# All modification made by Intel Corporation: Copyright (c) 2016 Intel Corporation +# +# All contributions by the University of California: +# Copyright (c) 2014, 2015, The Regents of the University of California (Regents) +# All rights reserved. +# +# All other contributions: +# Copyright (c) 2014, 2015, the respective contributors +# All rights reserved. +# For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md +# +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of Intel Corporation nor the names of its contributors +# may be used to endorse or promote products derived from this software +# without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +import caffe +import google.protobuf.text_format as txtf +from caffe.proto import caffe_pb2 + +def update_conv_quantized_dict(conv_quantized_dict, tmp_conv_quantized_dict): + for conv in conv_quantized_dict: + if tmp_conv_quantized_dict[conv][0] > conv_quantized_dict[conv][0]: + conv_quantized_dict[conv][0] = tmp_conv_quantized_dict[conv][0] + + if tmp_conv_quantized_dict[conv][1] > conv_quantized_dict[conv][1]: + conv_quantized_dict[conv][1] = tmp_conv_quantized_dict[conv][1] + + +def create_quantized_net(raw_net, quantized_net, conv_quantized_dict): + net_param = caffe_pb2.NetParameter() + with open(raw_net) as f: + txtf.Merge(f.read(), net_param) + #skip first conv layer when quantizing net + first_conv = True + for layer_param in net_param.layer: + if layer_param.type == "Convolution": + if first_conv: + first_conv = False + continue + layer_param.quantization_param.bw_layer_in = 8 + layer_param.quantization_param.bw_layer_out = 8 + layer_param.quantization_param.bw_params = 8 + layer_param.quantization_param.scale_in.append(conv_quantized_dict[layer_param.name][0]) + layer_param.quantization_param.scale_out.append(conv_quantized_dict[layer_param.name][1]) + for param_scale in conv_quantized_dict[layer_param.name][2]: + layer_param.quantization_param.scale_params.append(param_scale) + with open(quantized_net, 'w') as f: + f.write(str(net_param)) diff --git a/examples/faster-rcnn/lib/fast_rcnn/test.py b/examples/faster-rcnn/lib/fast_rcnn/test.py index f889d0977..4ab8d1e65 100644 --- a/examples/faster-rcnn/lib/fast_rcnn/test.py +++ b/examples/faster-rcnn/lib/fast_rcnn/test.py @@ -10,14 +10,20 @@ from fast_rcnn.config import cfg, get_output_dir from fast_rcnn.bbox_transform import clip_boxes, bbox_transform_inv import argparse +from fast_rcnn import net_sample_utils from utils.timer import Timer import numpy as np import cv2 import caffe +import os +import sys +utilpath = os.path.split(os.path.realpath(__file__))[0] + "/../../../../scripts/" +print utilpath +sys.path.insert(0, utilpath) +import sampling from fast_rcnn.nms_wrapper import nms import cPickle from utils.blob import im_list_to_blob -import os def _get_image_blob(im): """Converts an image into a network input. @@ -105,7 +111,7 @@ def _get_blobs(im, rois): blobs['rois'] = _get_rois_blob(rois, im_scale_factors) return blobs, im_scale_factors -def im_detect(net, im, boxes=None): +def im_detect(net, im, boxes=None, sample_blobs = None): """Detect object classes in an image given object proposals. Arguments: @@ -152,7 +158,19 @@ def im_detect(net, im, boxes=None): else: forward_kwargs['rois'] = blobs['rois'].astype(np.float32, copy=False) blobs_out = net.forward(**forward_kwargs) - + + + + if sample_blobs != None: + for k, _ in net.blobs.items(): # top blob + output = np.array(net.blobs[k].data) + if k not in sample_blobs.keys(): + sample_blobs[k] = [output] + else: + new_outputs = sample_blobs[k] + #print "new_outputs type: " + str(type(new_outputs)) + new_outputs.append(output) + sample_blobs[k] = new_outputs if cfg.TEST.HAS_RPN: assert len(im_scales) == 1, "Only single-image batch implemented" rois = net.blobs['rois'].data.copy() @@ -224,6 +242,30 @@ def apply_nms(all_boxes, thresh): nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() return nms_boxes + +def sample_net(raw_net_prototxt, net, imdb, sampling_iterations, quant_mode, enable_first_conv = False, winograd_algo = False): + + (conv_layers, test_net_top_names, test_net_bottom_names, conv_top_blob_layer_map, conv_bottom_blob_layer_map) = sampling.get_blob_map(net, enable_first_conv) + #currently out sample_net only supports TEST with HAS_RPN and AGNOSTIC flag + num_images = len(imdb.image_index) + image_index = 0 + box_proposals = None + sample_blobs = {} + for iter_ in xrange(sampling_iterations): + im = cv2.imread(imdb.image_path_at(image_index)) + im_detect(net, im, box_proposals, sample_blobs) + image_index += 1 + + params = {} + for k, _ in net.params.items(): + if k not in conv_layers: + continue + param = np.abs(net.params[k][0].data) # ignore bias + params[k] = [param] + + (winograd_bottoms, winograd_convolutions) = sampling.get_winograd_info(raw_net_prototxt, conv_bottom_blob_layer_map, winograd_algo) + return (sample_blobs, params, test_net_top_names, test_net_bottom_names, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) + def test_net(net, imdb, max_per_image=100, thresh=0.05, vis=False): """Test a Fast R-CNN network on an image database.""" num_images = len(imdb.image_index) diff --git a/examples/faster-rcnn/tools/test_net.py b/examples/faster-rcnn/tools/test_net.py index c22db3e1b..ddb21f525 100755 --- a/examples/faster-rcnn/tools/test_net.py +++ b/examples/faster-rcnn/tools/test_net.py @@ -11,12 +11,19 @@ import _init_paths from fast_rcnn.test import test_net +from fast_rcnn.test import sample_net +from fast_rcnn import net_sample_utils from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list from datasets.factory import get_imdb import caffe + +import time, os, sys +utilpath = os.path.split(os.path.realpath(__file__))[0] + "/../../../scripts/" +sys.path.insert(0, utilpath) +import calibrator +import sampling import argparse import pprint -import time, os, sys def parse_args(): """ @@ -49,7 +56,27 @@ def parse_args(): parser.add_argument('--num_dets', dest='max_per_image', help='max number of detections per image', default=100, type=int) + parser.add_argument('--quantized_net', action='store', dest='quantized_prototxt', + default=None, type=str) + parser.add_argument('--sample_iters', action='store', dest='sample_iters', + default=100, type=int) + parser.add_argument('--quant_mode', action='store', dest='quant_mode', + default='single', type=str) + + parser.add_argument('-u', '--unsigned_range', dest='unsigned_range', action="store_true", default=False, + help='to quantize using unsigned range for activation') + parser.add_argument('-t', '--concat_use_fp32', dest='concat_use_fp32', action="store_true", default=False, + help='to use fp32 for concat') + + parser.add_argument('-f', '--unify_concat_scales', dest='unify_concat_scales', action="store_true", default=False, + help='to unify concat scales') + + parser.add_argument('-a', '--calibration_algos', dest='calibration_algos', action='store', default="DIRECT", + help='to choose the calibration alogorithm') + + parser.add_argument('-wi', '--conv_algo', dest='conv_algo', action="store_true", default=False, + help='to choose the convolution algorithm') if len(sys.argv) == 1: parser.print_help() sys.exit(1) @@ -87,4 +114,17 @@ def parse_args(): if not cfg.TEST.HAS_RPN: imdb.set_proposal_method(cfg.TEST.PROPOSAL_METHOD) - test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis) + if args.quantized_prototxt == None: + test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis) + else: + (blobs, params, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) = sample_net(args.prototxt, net, imdb, args.sample_iters, args.quant_mode) + + (inputs_max, outputs_max, inputs_min) = sampling.calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, args.calibration_algos, "SINGLE", args.conv_algo) + params_max = sampling.calibrate_parameters(params, winograd_convolutions, "DIRECT", args.quant_mode.upper(), args.conv_algo) + calibrator.generate_sample_impl(args.prototxt, args.quantized_prototxt, inputs_max, outputs_max, inputs_min, params_max, False) + compiled_net_str = caffe.compile_net(args.prototxt, caffe.TEST, "MKLDNN") + raw_net_basename = os.path.basename(args.prototxt) + compile_net_path = "./compiled_" + raw_net_basename + with open(compile_net_path, "w") as f: + f.write(compiled_net_str) + calibrator.transform_convolutions(args.quantized_prototxt, compile_net_path, top_blobs_map, bottom_blobs_map, args.unsigned_range, args.concat_use_fp32, args.unify_concat_scales, args.conv_algo, False) diff --git a/examples/model_optimization/Readme.md b/examples/model_optimization/Readme.md new file mode 100644 index 000000000..175fc8853 --- /dev/null +++ b/examples/model_optimization/Readme.md @@ -0,0 +1,22 @@ +How to refine the inference model + +A. Pruning +There are tools to generate pruned model. +Commands list: + mkdir build_1 + cd build_1 + cmake -DCPU_ONLY=1 -DUSE_MLSL=0 .. + make all -j +Put the pre-trained model (resnet_50_16_nodes_2k_batch_100_warmup3_iter_56250.caffemodel) in examples/model_optimization/models/Facebook_ResNet_50/Baseline/ directory + ./examples/model_optimization/tools/gen_shapes_Facebook_ResNet_50.sh examples/model_optimization/models/Facebook_ResNet_50/Baseline/train_val.prototxt examples/model_optimization/models/Facebook_ResNet_50/Baseline/resnet_50_16_nodes_2k_batch_100_warmup3_iter_56250.caffemodel Facebook_ResNet_50_train + python examples/model_optimization/tools/copy_params.py examples/model_optimization/models/Facebook_ResNet_50/Baseline/train_val.prototxt examples/model_optimization/models/Facebook_ResNet_50/Baseline/resnet_50_16_nodes_2k_batch_100_warmup3_iter_56250.caffemodel examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/Facebook_ResNet_50_train.85.prototxt examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/Facebook_ResNet_50_train.85.caffemodel kl +Then we can generate small size Facebook ResNet-50 models in examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/, (i.e. Facebook_ResNet_50_train.85.prototxt, Facebook_ResNet_50_train.85.caffemodel) + +B. Fine-tune the pruned model +Command: + ./scripts/run_intelcaffe.sh --hostfile host_2nodes.txt --solver examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/Facebook_ResNet_50_solver.85_2_nodes.prototxt --weights examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/Facebook_ResNet_50_train.85.caffemodel --network opa --benchmark none --engine MKLDNN +Then you can get the fine-tuned pruned model, like Facebook_resnet_50_2_nodes_256_batch_100_warmup3_15p_finest_tune_iter_439600.caffemodel + +C. Accuracy Calibration for 8 Bit Inference +Command: + python calibrator.py -r ../build/ -m examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/Facebook_ResNet_50_train.85.prototxt -w Facebook_resnet_50_2_nodes_256_batch_100_warmup3_15p_finest_tune_iter_439600.caffemodel -i 1000 -l 0.01 -d 0 -c multiple \ No newline at end of file diff --git a/examples/model_optimization/models/Facebook_ResNet_50/Baseline/Facebook_ResNet_50_solver.85_2_nodes.prototxt b/examples/model_optimization/models/Facebook_ResNet_50/Baseline/Facebook_ResNet_50_solver.85_2_nodes.prototxt new file mode 100644 index 000000000..591cfef7d --- /dev/null +++ b/examples/model_optimization/models/Facebook_ResNet_50/Baseline/Facebook_ResNet_50_solver.85_2_nodes.prototxt @@ -0,0 +1,21 @@ +net: "examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/Facebook_ResNet_50_train.85.prototxt" +test_iter: 1000 +test_interval: 100 #500 +test_initialization: false +display: 40 +base_lr: 0.1 +lr_policy: "multistep" +stepvalue:150137 +stepvalue:300274 +stepvalue:400365 +stepvalue:430000 #Use to lower the learning rate +#stepvalue:435000 #Use to lower the learning rate +gamma: 0.1 +max_iter: 500456 # 62556 is 100 epochs, 56300 is 90 epochs (16 nodes) +warmup_iter: 15014 # 1281167 / 256 * 3 epochs +warmup_start_lr: 0.1 +momentum: 0.9 +weight_decay: 0.0001 +snapshot: 100 #500 +snapshot_prefix: "Facebook_resnet_50_2_nodes_256_batch_100_warmup3_15p_finest_tune" +solver_mode: CPU diff --git a/examples/model_optimization/models/Facebook_ResNet_50/Baseline/solver_Facebook_ResNet_50_dummy.prototxt b/examples/model_optimization/models/Facebook_ResNet_50/Baseline/solver_Facebook_ResNet_50_dummy.prototxt new file mode 100644 index 000000000..c6044abec --- /dev/null +++ b/examples/model_optimization/models/Facebook_ResNet_50/Baseline/solver_Facebook_ResNet_50_dummy.prototxt @@ -0,0 +1,19 @@ +#net: "resnet_50_16_nodes_2k_batch_100_warmup3/train_val.prototxt" +test_iter: 1000 +test_interval: 625 +test_initialization: false +display: 40 +base_lr: 0.8 +lr_policy: "multistep" +stepvalue:18750 +stepvalue:37500 +stepvalue:50000 +gamma: 0.1 +max_iter: 1 #62556 #56300 +#warmup_iter: 1876 #3125 # 1281167 / 2048 * 5 epochs +#warmup_start_lr: 0.1 +momentum: 0.9 +weight_decay: 0.0001 +snapshot: 6250 +#snapshot_prefix: "resnet_50_16_nodes_2k_batch_100_warmup3" +solver_mode: CPU diff --git a/examples/model_optimization/models/Facebook_ResNet_50/Baseline/train_val.prototxt b/examples/model_optimization/models/Facebook_ResNet_50/Baseline/train_val.prototxt new file mode 100644 index 000000000..22e157b9f --- /dev/null +++ b/examples/model_optimization/models/Facebook_ResNet_50/Baseline/train_val.prototxt @@ -0,0 +1,3324 @@ +name: "ResNet-50" +bn_stats_batch_size: 32 +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mirror: true + crop_size: 224 + scale: 0.0078125 + mean_value: 104 + mean_value: 117 + mean_value: 123 + random_aspect_ratio_param { + min_area_ratio: 0.08 + max_area_ratio: 1 + aspect_ratio_change: 0.75 + resize_param { + interp_mode: CUBIC + } + } + } + data_param { + source: "/mnt/local_disk/dataset/imagenet/lmdb_raw_compressed/ilsvrc12_train_lmdb" + #source: "/lustre/dataset/lmdb/imagenet/lmdb_raw_compressed/ilsvrc12_train_lmdb" + batch_size: 128 + backend: LMDB + prefetch: 2 + shuffle: true + } +} +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mirror: false + crop_size: 224 + scale: 0.0078125 + mean_value: 104 + mean_value: 117 + mean_value: 123 + random_resize_param { + min_size: 256 + max_size: 256 + resize_param { + interp_mode: CUBIC + } + } + } + data_param { + source: "/mnt/local_disk/dataset/imagenet/lmdb_raw_compressed/ilsvrc12_val_lmdb" + #source: "/lustre/dataset/lmdb/imagenet/lmdb_raw_compressed/ilsvrc12_val_lmdb" + batch_size: 50 + backend: LMDB + } +} + +layer { + bottom: "data" + top: "conv1" + name: "conv1" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 7 + pad: 3 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "bn_conv1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "scale_conv1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "conv1" + top: "conv1" + name: "conv1_relu" + type: "ReLU" + relu_param { + } +} + +layer { + bottom: "conv1" + top: "pool1" + name: "pool1" + type: "Pooling" + pooling_param { + kernel_size: 3 + stride: 2 + pool: MAX + } +} + +layer { + bottom: "pool1" + top: "res2a_branch1" + name: "res2a_branch1" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch1" + top: "res2a_branch1" + name: "bn2a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch1" + top: "res2a_branch1" + name: "scale2a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "pool1" + top: "res2a_branch2a" + name: "res2a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "bn2a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "scale2a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2a" + name: "res2a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a_branch2a" + top: "res2a_branch2b" + name: "res2a_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "bn2a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "scale2a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2b" + name: "res2a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a_branch2b" + top: "res2a_branch2c" + name: "res2a_branch2c" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2a_branch2c" + top: "res2a_branch2c" + name: "bn2a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2a_branch2c" + top: "res2a_branch2c" + name: "scale2a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a_branch1" + bottom: "res2a_branch2c" + top: "res2a" + name: "res2a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2a" + top: "res2a" + name: "res2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2a" + top: "res2b_branch2a" + name: "res2b_branch2a" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "bn2b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "scale2b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2a" + name: "res2b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b_branch2a" + top: "res2b_branch2b" + name: "res2b_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "bn2b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "scale2b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2b" + name: "res2b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b_branch2b" + top: "res2b_branch2c" + name: "res2b_branch2c" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2b_branch2c" + top: "res2b_branch2c" + name: "bn2b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2b_branch2c" + top: "res2b_branch2c" + name: "scale2b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2a" + bottom: "res2b_branch2c" + top: "res2b" + name: "res2b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2b" + top: "res2b" + name: "res2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2b" + top: "res2c_branch2a" + name: "res2c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 64 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "bn2c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "scale2c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2a" + name: "res2c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c_branch2a" + top: "res2c_branch2b" + name: "res2c_branch2b" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "bn2c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "scale2c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2b" + name: "res2c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c_branch2b" + top: "res2c_branch2c" + name: "res2c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res2c_branch2c" + top: "res2c_branch2c" + name: "bn2c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res2c_branch2c" + top: "res2c_branch2c" + name: "scale2c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2b" + bottom: "res2c_branch2c" + top: "res2c" + name: "res2c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res2c" + top: "res2c" + name: "res2c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res2c" + top: "res3a_branch1" + name: "res3a_branch1" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch1" + top: "res3a_branch1" + name: "bn3a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch1" + top: "res3a_branch1" + name: "scale3a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res2c" + top: "res3a_branch2a" + name: "res3a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "bn3a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "scale3a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2a" + name: "res3a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a_branch2a" + top: "res3a_branch2b" + name: "res3a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "bn3a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "scale3a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2b" + name: "res3a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a_branch2b" + top: "res3a_branch2c" + name: "res3a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3a_branch2c" + top: "res3a_branch2c" + name: "bn3a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3a_branch2c" + top: "res3a_branch2c" + name: "scale3a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a_branch1" + bottom: "res3a_branch2c" + top: "res3a" + name: "res3a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3a" + top: "res3a" + name: "res3a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3a" + top: "res3b_branch2a" + name: "res3b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "bn3b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "scale3b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2a" + name: "res3b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b_branch2a" + top: "res3b_branch2b" + name: "res3b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "bn3b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "scale3b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2b" + name: "res3b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b_branch2b" + top: "res3b_branch2c" + name: "res3b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3b_branch2c" + top: "res3b_branch2c" + name: "bn3b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3b_branch2c" + top: "res3b_branch2c" + name: "scale3b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3a" + bottom: "res3b_branch2c" + top: "res3b" + name: "res3b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3b" + top: "res3b" + name: "res3b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3b" + top: "res3c_branch2a" + name: "res3c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "bn3c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "scale3c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2a" + name: "res3c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c_branch2a" + top: "res3c_branch2b" + name: "res3c_branch2b" + type: "Convolution" + convolution_param { + + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "bn3c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "scale3c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2b" + name: "res3c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c_branch2b" + top: "res3c_branch2c" + name: "res3c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3c_branch2c" + top: "res3c_branch2c" + name: "bn3c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3c_branch2c" + top: "res3c_branch2c" + name: "scale3c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3b" + bottom: "res3c_branch2c" + top: "res3c" + name: "res3c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3c" + top: "res3c" + name: "res3c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3c" + top: "res3d_branch2a" + name: "res3d_branch2a" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "bn3d_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "scale3d_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2a" + name: "res3d_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d_branch2a" + top: "res3d_branch2b" + name: "res3d_branch2b" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "bn3d_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "scale3d_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2b" + name: "res3d_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d_branch2b" + top: "res3d_branch2c" + name: "res3d_branch2c" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res3d_branch2c" + top: "res3d_branch2c" + name: "bn3d_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res3d_branch2c" + top: "res3d_branch2c" + name: "scale3d_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3c" + bottom: "res3d_branch2c" + top: "res3d" + name: "res3d" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res3d" + top: "res3d" + name: "res3d_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res3d" + top: "res4a_branch1" + name: "res4a_branch1" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch1" + top: "res4a_branch1" + name: "bn4a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch1" + top: "res4a_branch1" + name: "scale4a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res3d" + top: "res4a_branch2a" + name: "res4a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "bn4a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "scale4a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2a" + name: "res4a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a_branch2a" + top: "res4a_branch2b" + name: "res4a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "bn4a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "scale4a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2b" + name: "res4a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a_branch2b" + top: "res4a_branch2c" + name: "res4a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4a_branch2c" + top: "res4a_branch2c" + name: "bn4a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4a_branch2c" + top: "res4a_branch2c" + name: "scale4a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a_branch1" + bottom: "res4a_branch2c" + top: "res4a" + name: "res4a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4a" + top: "res4a" + name: "res4a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4a" + top: "res4b_branch2a" + name: "res4b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "bn4b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "scale4b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2a" + name: "res4b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b_branch2a" + top: "res4b_branch2b" + name: "res4b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "bn4b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "scale4b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2b" + name: "res4b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b_branch2b" + top: "res4b_branch2c" + name: "res4b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4b_branch2c" + top: "res4b_branch2c" + name: "bn4b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4b_branch2c" + top: "res4b_branch2c" + name: "scale4b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4a" + bottom: "res4b_branch2c" + top: "res4b" + name: "res4b" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4b" + top: "res4b" + name: "res4b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4b" + top: "res4c_branch2a" + name: "res4c_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "bn4c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "scale4c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2a" + name: "res4c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c_branch2a" + top: "res4c_branch2b" + name: "res4c_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "bn4c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "scale4c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2b" + name: "res4c_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c_branch2b" + top: "res4c_branch2c" + name: "res4c_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4c_branch2c" + top: "res4c_branch2c" + name: "bn4c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4c_branch2c" + top: "res4c_branch2c" + name: "scale4c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4b" + bottom: "res4c_branch2c" + top: "res4c" + name: "res4c" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4c" + top: "res4c" + name: "res4c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4c" + top: "res4d_branch2a" + name: "res4d_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "bn4d_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "scale4d_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2a" + name: "res4d_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d_branch2a" + top: "res4d_branch2b" + name: "res4d_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "bn4d_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "scale4d_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2b" + name: "res4d_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d_branch2b" + top: "res4d_branch2c" + name: "res4d_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4d_branch2c" + top: "res4d_branch2c" + name: "bn4d_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4d_branch2c" + top: "res4d_branch2c" + name: "scale4d_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4c" + bottom: "res4d_branch2c" + top: "res4d" + name: "res4d" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4d" + top: "res4d" + name: "res4d_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4d" + top: "res4e_branch2a" + name: "res4e_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "bn4e_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "scale4e_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2a" + name: "res4e_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e_branch2a" + top: "res4e_branch2b" + name: "res4e_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "bn4e_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "scale4e_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2b" + name: "res4e_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e_branch2b" + top: "res4e_branch2c" + name: "res4e_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4e_branch2c" + top: "res4e_branch2c" + name: "bn4e_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4e_branch2c" + top: "res4e_branch2c" + name: "scale4e_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4d" + bottom: "res4e_branch2c" + top: "res4e" + name: "res4e" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4e" + top: "res4e" + name: "res4e_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4e" + top: "res4f_branch2a" + name: "res4f_branch2a" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "bn4f_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "scale4f_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2a" + name: "res4f_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f_branch2a" + top: "res4f_branch2b" + name: "res4f_branch2b" + type: "Convolution" + convolution_param { + + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "bn4f_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "scale4f_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2b" + name: "res4f_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f_branch2b" + top: "res4f_branch2c" + name: "res4f_branch2c" + type: "Convolution" + convolution_param { + + num_output: 1024 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res4f_branch2c" + top: "res4f_branch2c" + name: "bn4f_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res4f_branch2c" + top: "res4f_branch2c" + name: "scale4f_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4e" + bottom: "res4f_branch2c" + top: "res4f" + name: "res4f" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res4f" + top: "res4f" + name: "res4f_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res4f" + top: "res5a_branch1" + name: "res5a_branch1" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch1" + top: "res5a_branch1" + name: "bn5a_branch1" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch1" + top: "res5a_branch1" + name: "scale5a_branch1" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res4f" + top: "res5a_branch2a" + name: "res5a_branch2a" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "bn5a_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "scale5a_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2a" + name: "res5a_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a_branch2a" + top: "res5a_branch2b" + name: "res5a_branch2b" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 2 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "bn5a_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "scale5a_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2b" + name: "res5a_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a_branch2b" + top: "res5a_branch2c" + name: "res5a_branch2c" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5a_branch2c" + top: "res5a_branch2c" + name: "bn5a_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5a_branch2c" + top: "res5a_branch2c" + name: "scale5a_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a_branch1" + bottom: "res5a_branch2c" + top: "res5a" + name: "res5a" + type: "Eltwise" + eltwise_param { + + } +} + +layer { + bottom: "res5a" + top: "res5a" + name: "res5a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5a" + top: "res5b_branch2a" + name: "res5b_branch2a" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "bn5b_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "scale5b_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2a" + name: "res5b_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b_branch2a" + top: "res5b_branch2b" + name: "res5b_branch2b" + type: "Convolution" + convolution_param { + + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "bn5b_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "scale5b_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2b" + name: "res5b_branch2b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b_branch2b" + top: "res5b_branch2c" + name: "res5b_branch2c" + type: "Convolution" + convolution_param { + + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5b_branch2c" + top: "res5b_branch2c" + name: "bn5b_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5b_branch2c" + top: "res5b_branch2c" + name: "scale5b_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5a" + bottom: "res5b_branch2c" + top: "res5b" + name: "res5b" + type: "Eltwise" + eltwise_param { + } +} + +layer { + bottom: "res5b" + top: "res5b" + name: "res5b_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5b" + top: "res5c_branch2a" + name: "res5c_branch2a" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "bn5c_branch2a" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "scale5c_branch2a" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2a" + name: "res5c_branch2a_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5c_branch2a" + top: "res5c_branch2b" + name: "res5c_branch2b" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "bn5c_branch2b" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 1 } + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "scale5c_branch2b" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2b" + name: "res5c_branch2b_relu" + type: "ReLU" + relu_param { + } +} + +layer { + bottom: "res5c_branch2b" + top: "res5c_branch2c" + name: "res5c_branch2c" + type: "Convolution" + convolution_param { + num_output: 2048 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: false + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "res5c_branch2c" + top: "res5c_branch2c" + name: "bn5c_branch2c" + type: "BatchNorm" + param { lr_mult: 0 } + param { lr_mult: 0 } + param { lr_mult: 0 } + batch_norm_param { + moving_average_fraction: 0.9 + filler { value: 0 } + } +} + +layer { + bottom: "res5c_branch2c" + top: "res5c_branch2c" + name: "scale5c_branch2c" + type: "Scale" + param { decay_mult: 0 } + param { decay_mult: 0 } + scale_param { + bias_term: true + } +} + +layer { + bottom: "res5b" + bottom: "res5c_branch2c" + top: "res5c" + name: "res5c" + type: "Eltwise" + eltwise_param { + } +} + +layer { + bottom: "res5c" + top: "res5c" + name: "res5c_relu" + type: "ReLU" + relu_param { + + } +} + +layer { + bottom: "res5c" + top: "pool5" + name: "pool5" + type: "Pooling" + pooling_param { + kernel_size: 7 + stride: 1 + pool: AVE + } +} + +layer { + bottom: "pool5" + top: "fc1000" + name: "fc1000" + type: "InnerProduct" + inner_product_param { + num_output: 1000 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + bottom: "fc1000" + bottom: "label" + top: "loss" + name: "prob" + type: "SoftmaxWithLoss" +} +layer { + name: "loss3/top-1" + type: "Accuracy" + bottom: "fc1000" + bottom: "label" + top: "loss3/top-1" +} +layer { + name: "loss3/top-5" + type: "Accuracy" + bottom: "fc1000" + bottom: "label" + top: "loss3/top-5" + accuracy_param { + top_k: 5 + } +} diff --git a/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/solver.prototxt b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/solver.prototxt new file mode 100644 index 000000000..073e2fb1c --- /dev/null +++ b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/solver.prototxt @@ -0,0 +1,16 @@ +train_net: "models/pascal_voc/VGGx2/faster_rcnn_end2end/train.prototxt" +base_lr: 0.001 +lr_policy: "step" +gamma: 0.1 +stepsize: 50000 +display: 20 +average_loss: 100 +# iter_size: 1 +momentum: 0.9 +weight_decay: 0.0005 +# We disable standard caffe solver snapshotting and implement our own snapshot +# function +snapshot: 0 +# We still use the snapshot prefix, though +snapshot_prefix: "vggx2" +iter_size: 2 diff --git a/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/test.prototxt b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/test.prototxt new file mode 100644 index 000000000..6ad800bb7 --- /dev/null +++ b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/test.prototxt @@ -0,0 +1,608 @@ +name: "VGG_ILSVRC_16_layers" + +input: "data" +input_shape { + dim: 1 + dim: 3 + dim: 224 + dim: 224 +} + +input: "im_info" +input_shape { + dim: 1 + dim: 3 +} + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 11 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 55 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 148 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 104 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 221 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 421 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#========= RPN ============ + +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + param { lr_mult: 1.0 decay_mult: 1.0 } + param { lr_mult: 2.0 decay_mult: 0 } + convolution_param { + num_output: 512 + kernel_size: 3 pad: 1 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} + +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + param { lr_mult: 1.0 decay_mult: 1.0 } + param { lr_mult: 2.0 decay_mult: 0 } + convolution_param { + num_output: 18 # 2(bg/fg) * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { lr_mult: 1.0 decay_mult: 1.0 } + param { lr_mult: 2.0 decay_mult: 0 } + convolution_param { + num_output: 36 # 4 * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + name: "rpn_cls_score_reshape" + type: "Reshape" + reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } +} + +#========= RoI Proposal ============ + +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} +layer { + name: 'rpn_cls_prob_reshape' + type: 'Reshape' + bottom: 'rpn_cls_prob' + top: 'rpn_cls_prob_reshape' + reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } +} +layer { + name: 'proposal' + type: 'Python' + bottom: 'rpn_cls_prob_reshape' + bottom: 'rpn_bbox_pred' + bottom: 'im_info' + top: 'rois' + python_param { + module: 'rpn.proposal_layer' + layer: 'ProposalLayer' + param_str: "'feat_stride': 16" + } +} + +#========= RCNN ============ + +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_w: 7 + pooled_h: 7 + spatial_scale: 0.0625 # 1/16 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 21 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 84 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "cls_prob" + type: "Softmax" + bottom: "cls_score" + top: "cls_prob" +} diff --git a/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/train.prototxt b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/train.prototxt new file mode 100644 index 000000000..e1e843c78 --- /dev/null +++ b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X2/faster_rcnn_end2end/train.prototxt @@ -0,0 +1,673 @@ +name: "VGG_ILSVRC_16_layers" +layer { + name: 'input-data' + type: 'Python' + top: 'data' + top: 'im_info' + top: 'gt_boxes' + python_param { + module: 'roi_data_layer.layer' + layer: 'RoIDataLayer' + param_str: "'num_classes': 21" + } +} + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 11 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 55 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 148 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 104 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 221 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 421 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#========= RPN ============ + +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + param { lr_mult: 1.0 } + param { lr_mult: 2.0 } + convolution_param { + num_output: 512 + kernel_size: 3 pad: 1 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} + +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + param { lr_mult: 1.0 } + param { lr_mult: 2.0 } + convolution_param { + num_output: 18 # 2(bg/fg) * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} + +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { lr_mult: 1.0 } + param { lr_mult: 2.0 } + convolution_param { + num_output: 36 # 4 * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} + +layer { + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + name: "rpn_cls_score_reshape" + type: "Reshape" + reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } +} + +layer { + name: 'rpn-data' + type: 'Python' + bottom: 'rpn_cls_score' + bottom: 'gt_boxes' + bottom: 'im_info' + bottom: 'data' + top: 'rpn_labels' + top: 'rpn_bbox_targets' + top: 'rpn_bbox_inside_weights' + top: 'rpn_bbox_outside_weights' + python_param { + module: 'rpn.anchor_target_layer' + layer: 'AnchorTargetLayer' + param_str: "'feat_stride': 16" + } +} + +layer { + name: "rpn_loss_cls" + type: "SoftmaxWithLoss" + bottom: "rpn_cls_score_reshape" + bottom: "rpn_labels" + propagate_down: 1 + propagate_down: 0 + top: "rpn_cls_loss" + loss_weight: 1 + loss_param { + ignore_label: -1 + normalize: true + } +} + +layer { + name: "rpn_loss_bbox" + type: "SmoothL1Loss" + bottom: "rpn_bbox_pred" + bottom: "rpn_bbox_targets" + bottom: 'rpn_bbox_inside_weights' + bottom: 'rpn_bbox_outside_weights' + top: "rpn_loss_bbox" + loss_weight: 1 + smooth_l1_loss_param { sigma: 3.0 } +} + +#========= RoI Proposal ============ + +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} + +layer { + name: 'rpn_cls_prob_reshape' + type: 'Reshape' + bottom: 'rpn_cls_prob' + top: 'rpn_cls_prob_reshape' + reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } +} + +layer { + name: 'proposal' + type: 'Python' + bottom: 'rpn_cls_prob_reshape' + bottom: 'rpn_bbox_pred' + bottom: 'im_info' + top: 'rpn_rois' +# top: 'rpn_scores' + python_param { + module: 'rpn.proposal_layer' + layer: 'ProposalLayer' + param_str: "'feat_stride': 16" + } +} + +#layer { +# name: 'debug-data' +# type: 'Python' +# bottom: 'data' +# bottom: 'rpn_rois' +# bottom: 'rpn_scores' +# python_param { +# module: 'rpn.debug_layer' +# layer: 'RPNDebugLayer' +# } +#} + +layer { + name: 'roi-data' + type: 'Python' + bottom: 'rpn_rois' + bottom: 'gt_boxes' + top: 'rois' + top: 'labels' + top: 'bbox_targets' + top: 'bbox_inside_weights' + top: 'bbox_outside_weights' + python_param { + module: 'rpn.proposal_target_layer' + layer: 'ProposalTargetLayer' + param_str: "'num_classes': 21" + } +} + +#========= RCNN ============ + +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_w: 7 + pooled_h: 7 + spatial_scale: 0.0625 # 1/16 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 21 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 84 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "loss_cls" + type: "SoftmaxWithLoss" + bottom: "cls_score" + bottom: "labels" + propagate_down: 1 + propagate_down: 0 + top: "loss_cls" + loss_weight: 1 +} +layer { + name: "loss_bbox" + type: "SmoothL1Loss" + bottom: "bbox_pred" + bottom: "bbox_targets" + bottom: "bbox_inside_weights" + bottom: "bbox_outside_weights" + top: "loss_bbox" + loss_weight: 1 +} diff --git a/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/solver.prototxt b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/solver.prototxt new file mode 100644 index 000000000..ab35bff95 --- /dev/null +++ b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/solver.prototxt @@ -0,0 +1,16 @@ +train_net: "models/pascal_voc/VGGx4/faster_rcnn_end2end/train.prototxt" +base_lr: 0.001 +lr_policy: "step" +gamma: 0.1 +stepsize: 50000 +display: 20 +average_loss: 100 +# iter_size: 1 +momentum: 0.9 +weight_decay: 0.0005 +# We disable standard caffe solver snapshotting and implement our own snapshot +# function +snapshot: 0 +# We still use the snapshot prefix, though +snapshot_prefix: "vggx4" +iter_size: 2 diff --git a/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/test.prototxt b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/test.prototxt new file mode 100644 index 000000000..0a34ddd8c --- /dev/null +++ b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/test.prototxt @@ -0,0 +1,608 @@ +name: "VGG_ILSVRC_16_layers" + +input: "data" +input_shape { + dim: 1 + dim: 3 + dim: 224 + dim: 224 +} + +input: "im_info" +input_shape { + dim: 1 + dim: 3 +} + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 12 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 21 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 73 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 58 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 121 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 166 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#========= RPN ============ + +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + param { lr_mult: 1.0 decay_mult: 1.0 } + param { lr_mult: 2.0 decay_mult: 0 } + convolution_param { + num_output: 512 + kernel_size: 3 pad: 1 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} + +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + param { lr_mult: 1.0 decay_mult: 1.0 } + param { lr_mult: 2.0 decay_mult: 0 } + convolution_param { + num_output: 18 # 2(bg/fg) * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { lr_mult: 1.0 decay_mult: 1.0 } + param { lr_mult: 2.0 decay_mult: 0 } + convolution_param { + num_output: 36 # 4 * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + name: "rpn_cls_score_reshape" + type: "Reshape" + reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } +} + +#========= RoI Proposal ============ + +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} +layer { + name: 'rpn_cls_prob_reshape' + type: 'Reshape' + bottom: 'rpn_cls_prob' + top: 'rpn_cls_prob_reshape' + reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } +} +layer { + name: 'proposal' + type: 'Python' + bottom: 'rpn_cls_prob_reshape' + bottom: 'rpn_bbox_pred' + bottom: 'im_info' + top: 'rois' + python_param { + module: 'rpn.proposal_layer' + layer: 'ProposalLayer' + param_str: "'feat_stride': 16" + } +} + +#========= RCNN ============ + +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_w: 7 + pooled_h: 7 + spatial_scale: 0.0625 # 1/16 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 21 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + inner_product_param { + num_output: 84 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "cls_prob" + type: "Softmax" + bottom: "cls_score" + top: "cls_prob" +} diff --git a/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/train.prototxt b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/train.prototxt new file mode 100644 index 000000000..b3c705741 --- /dev/null +++ b/examples/model_optimization/models/Fast-RCNN/pascal_voc/VGG16-X4/faster_rcnn_end2end/train.prototxt @@ -0,0 +1,673 @@ +name: "VGG_ILSVRC_16_layers" +layer { + name: 'input-data' + type: 'Python' + top: 'data' + top: 'im_info' + top: 'gt_boxes' + python_param { + module: 'roi_data_layer.layer' + layer: 'RoIDataLayer' + param_str: "'num_classes': 21" + } +} + +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 12 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 21 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 73 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 58 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 121 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 166 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} + +#========= RPN ============ + +layer { + name: "rpn_conv/3x3" + type: "Convolution" + bottom: "conv5_3" + top: "rpn/output" + param { lr_mult: 1.0 } + param { lr_mult: 2.0 } + convolution_param { + num_output: 512 + kernel_size: 3 pad: 1 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} +layer { + name: "rpn_relu/3x3" + type: "ReLU" + bottom: "rpn/output" + top: "rpn/output" +} + +layer { + name: "rpn_cls_score" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_cls_score" + param { lr_mult: 1.0 } + param { lr_mult: 2.0 } + convolution_param { + num_output: 18 # 2(bg/fg) * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} + +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { lr_mult: 1.0 } + param { lr_mult: 2.0 } + convolution_param { + num_output: 36 # 4 * 9(anchors) + kernel_size: 1 pad: 0 stride: 1 + weight_filler { type: "gaussian" std: 0.01 } + bias_filler { type: "constant" value: 0 } + } +} + +layer { + bottom: "rpn_cls_score" + top: "rpn_cls_score_reshape" + name: "rpn_cls_score_reshape" + type: "Reshape" + reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } +} + +layer { + name: 'rpn-data' + type: 'Python' + bottom: 'rpn_cls_score' + bottom: 'gt_boxes' + bottom: 'im_info' + bottom: 'data' + top: 'rpn_labels' + top: 'rpn_bbox_targets' + top: 'rpn_bbox_inside_weights' + top: 'rpn_bbox_outside_weights' + python_param { + module: 'rpn.anchor_target_layer' + layer: 'AnchorTargetLayer' + param_str: "'feat_stride': 16" + } +} + +layer { + name: "rpn_loss_cls" + type: "SoftmaxWithLoss" + bottom: "rpn_cls_score_reshape" + bottom: "rpn_labels" + propagate_down: 1 + propagate_down: 0 + top: "rpn_cls_loss" + loss_weight: 1 + loss_param { + ignore_label: -1 + normalize: true + } +} + +layer { + name: "rpn_loss_bbox" + type: "SmoothL1Loss" + bottom: "rpn_bbox_pred" + bottom: "rpn_bbox_targets" + bottom: 'rpn_bbox_inside_weights' + bottom: 'rpn_bbox_outside_weights' + top: "rpn_loss_bbox" + loss_weight: 1 + smooth_l1_loss_param { sigma: 3.0 } +} + +#========= RoI Proposal ============ + +layer { + name: "rpn_cls_prob" + type: "Softmax" + bottom: "rpn_cls_score_reshape" + top: "rpn_cls_prob" +} + +layer { + name: 'rpn_cls_prob_reshape' + type: 'Reshape' + bottom: 'rpn_cls_prob' + top: 'rpn_cls_prob_reshape' + reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } +} + +layer { + name: 'proposal' + type: 'Python' + bottom: 'rpn_cls_prob_reshape' + bottom: 'rpn_bbox_pred' + bottom: 'im_info' + top: 'rpn_rois' +# top: 'rpn_scores' + python_param { + module: 'rpn.proposal_layer' + layer: 'ProposalLayer' + param_str: "'feat_stride': 16" + } +} + +#layer { +# name: 'debug-data' +# type: 'Python' +# bottom: 'data' +# bottom: 'rpn_rois' +# bottom: 'rpn_scores' +# python_param { +# module: 'rpn.debug_layer' +# layer: 'RPNDebugLayer' +# } +#} + +layer { + name: 'roi-data' + type: 'Python' + bottom: 'rpn_rois' + bottom: 'gt_boxes' + top: 'rois' + top: 'labels' + top: 'bbox_targets' + top: 'bbox_inside_weights' + top: 'bbox_outside_weights' + python_param { + module: 'rpn.proposal_target_layer' + layer: 'ProposalTargetLayer' + param_str: "'num_classes': 21" + } +} + +#========= RCNN ============ + +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_w: 7 + pooled_h: 7 + spatial_scale: 0.0625 # 1/16 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 21 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1 + } + param { + lr_mult: 2 + } + inner_product_param { + num_output: 84 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "loss_cls" + type: "SoftmaxWithLoss" + bottom: "cls_score" + bottom: "labels" + propagate_down: 1 + propagate_down: 0 + top: "loss_cls" + loss_weight: 1 +} +layer { + name: "loss_bbox" + type: "SmoothL1Loss" + bottom: "bbox_pred" + bottom: "bbox_targets" + bottom: "bbox_inside_weights" + bottom: "bbox_outside_weights" + top: "loss_bbox" + loss_weight: 1 +} diff --git a/examples/model_optimization/tools/copy_params.py b/examples/model_optimization/tools/copy_params.py new file mode 100644 index 000000000..1c911b20b --- /dev/null +++ b/examples/model_optimization/tools/copy_params.py @@ -0,0 +1,286 @@ +#!/usr/bin/env python +#-*-coding=utf-8 + +import sys +sys.path.append('python') + +import os + +os.environ["GLOG_minloglevel"] = "3" +import caffe +import numpy as np +from prune_opts import * + +# show all parameters +np.set_printoptions(threshold='nan') + +if len(sys.argv) < 5: + print "Usage: python copy_params.py .prototxt .caffemodel .prototxt .caffemodel" + exit() + +if len(sys.argv) < 6: + print ("KL") + prune_type = "kl" +else: + if (sys.argv[5] == 'kl'): + print ("KL") + prune_type = "kl" + if (sys.argv[5] == 'random'): + print ("RANDOM") + prune_type = "random" + if (sys.argv[5] == 'l1'): + print ("L1") + prune_type = "l1" + +# original caffe model +net = caffe.Net(sys.argv[1], sys.argv[2], caffe.TRAIN) + +# model to be fileed +net0 = caffe.Net(sys.argv[3],sys.argv[4],caffe.TRAIN) + +#keys0 includes all layers +keys0 = net.params.keys() + +#dic includes sorted index +dic = dict.fromkeys(keys0, []) + +#keys1 includes layers except branch +keys1 = net.params.keys() +keys1.remove('res2a_branch1') +keys1.remove('res3a_branch1') +keys1.remove('res4a_branch1') +keys1.remove('res5a_branch1') +keys1.remove('bn3a_branch1') +keys1.remove('bn4a_branch1') +keys1.remove('bn5a_branch1') +keys1.remove('bn2a_branch1') + +#keys2 includes branch layers +keys2 = ('res2a_branch1','bn2a_branch1','res3a_branch1','bn3a_branch1','res4a_branch1','bn4a_branch1','res5a_branch1','bn5a_branch1') + + +#drop conv weights + +for i in range(0,(len(keys1)-3),2): + + layer_key_1 = keys1[i] + + # drop layer_key_1 weights + weight_shape_layer1 = net.params[layer_key_1][0].shape + + # load weights + if i == 0: + weight_layer1 = net.params[layer_key_1][0].data + + else: + weight_layer1 = weight_layer2 + weight_shape_layer1 = (weight_shape_layer1[0],net0.params[layer_key_1][0].shape[1],weight_shape_layer1[2],weight_shape_layer1[3]) + weight_layer1.shape = weight_shape_layer1 + + # get every kernel kl sortedindex + if (prune_type == 'kl'): + weight_sum_sortedindex = prune_by_kl(weight_layer1,weight_shape_layer1[1],weight_shape_layer1[0],weight_shape_layer1[2]) + elif (prune_type == 'random'): + weight_sum_sortedindex = prune_by_random(weight_layer1,weight_shape_layer1[1],weight_shape_layer1[0],weight_shape_layer1[2]) + elif (prune_type == 'l1'): + weight_sum_sortedindex = prune_by_l1(weight_layer1,weight_shape_layer1[1],weight_shape_layer1[0],weight_shape_layer1[2]) + + # one kernel occupies one column + weight_layer1.shape = (weight_shape_layer1[0],-1) + + # delete parameters accordingly + len_need = net.params[layer_key_1][0].shape[0] - net0.params[layer_key_1][0].shape[0] + if (len_need > 0): + print ("[1]prune %d filters in %s"%(len_need, layer_key_1)) + weight_sum_sortedindex = weight_sum_sortedindex[0:len_need] + weight_layer1 = np.delete(weight_layer1,weight_sum_sortedindex,axis=0) + + # save sortedindex for later use + dic[layer_key_1] = weight_sum_sortedindex + + # write remaining weights + weight_layer1.shape = net0.params[layer_key_1][0].shape + + for m in range(0,weight_layer1.shape[0]): + net0.params[layer_key_1][0].data[m] = weight_layer1[m] + + + # drop bn weights + bn = keys1[i+1] + weight_bn = net.params[bn][0].data + weight_bn = np.delete(weight_bn,weight_sum_sortedindex,axis=0) + for m in range(0,weight_bn.shape[0]): + net0.params[bn][0].data[m] = weight_bn[m] + + + # drop layer_key_2 weights + + # load weights + layer_key_2 = keys1[i+2] + weight_layer2 = net.params[layer_key_2][0].data + weight_shape_layer2 = weight_layer2.shape + weightshape = weight_shape_layer2[2]*weight_shape_layer2[3] + weight_layer2.shape = (-1,weightshape) + + #delete minimum kernel + row_delete=[] + + for n in weight_sum_sortedindex: + for m in range(n,weight_shape_layer2[0]*weight_shape_layer2[1]+1,weight_shape_layer2[1]): + row_delete.append(m) + + weight_layer2 = np.delete(weight_layer2,row_delete,axis=0) + + +#drop branch1 weights + +for i in range(0,len(keys2),2): + layer_key_0 = keys2[i] + + # load weights + weight_layer0 = net.params[layer_key_0][0].data + weight_shape_layer0 = net.params[layer_key_0][0].shape + + weightshape = weight_shape_layer0[2]*weight_shape_layer0[3] + weight_layer0.shape = (-1,weightshape) + + # get sortedindex + layer0_index = keys0.index(layer_key_0) + layer_above = keys0[layer0_index-2] + weight_sum_sortedindex = dic[layer_above] + + # delete minimum kernel + row_delete=[] + for n in weight_sum_sortedindex: + for m in range(n,weight_shape_layer0[0]*weight_shape_layer0[1]+1,weight_shape_layer0[1]): + row_delete.append(m) + + weight_layer0 = np.delete(weight_layer0,row_delete,axis=0) + + # get every kernel kl sortedindex + weight_layer0.shape = (weight_shape_layer0[0],-1,weight_shape_layer0[2],weight_shape_layer0[3]) + if (prune_type == 'kl'): + weight_sum_sortedindex = prune_by_kl(weight_layer0,weight_layer0.shape[1],weight_shape_layer0[0],weight_shape_layer0[2]) + elif (prune_type == 'random'): + weight_sum_sortedindex = prune_by_random(weight_layer0,weight_layer0.shape[1],weight_shape_layer0[0],weight_shape_layer0[2]) + elif (prune_type == 'l1'): + weight_sum_sortedindex = prune_by_l1(weight_layer0,weight_layer0.shape[1],weight_shape_layer0[0],weight_shape_layer0[2]) + + # one kernel occupies one column + weight_layer0.shape = (weight_shape_layer0[0],-1) + + # delete parameters accordingly + len_need = net.params[layer_key_0][0].shape[0] - net0.params[layer_key_0][0].shape[0] + if (len_need > 0): + print ("[2]prune %d filters in %s"%(len_need, layer_key_0)) + weight_sum_sortedindex = weight_sum_sortedindex[0:len_need] + weight_layer0 = np.delete(weight_layer0,weight_sum_sortedindex,axis=0) + + # write remaining weights + weight_layer0.shape = net0.params[layer_key_0][0].shape + + for m in range(0,weight_layer0.shape[0]): + net0.params[layer_key_0][0].data[m] = weight_layer0[m] + + + # drop bn weights + bn = keys2[i+1] + weight_bn = net.params[bn][0].data + weight_bn = np.delete(weight_bn,weight_sum_sortedindex,axis=0) + for m in range(0,weight_bn.shape[0]): + net0.params[bn][0].data[m] = weight_bn[m] + +# drop res5c_branch2c weights + +# drop weights related with deleted filters of above layer + +# load weights +layer_key_2 = 'res5c_branch2c' +layer2_index = keys0.index(layer_key_2) +layer_above = keys0[layer2_index-2] +weight_layer2 = net.params[layer_key_2][0].data +weight_shape_layer2 = weight_layer2.shape +weightshape = weight_shape_layer2[2]*weight_shape_layer2[3] +weight_layer2.shape = (-1,weightshape) + +# delete minimum kernel +row_delete=[] +weight_sum_sortedindex = dic[layer_above] +for n in weight_sum_sortedindex: + for m in range(n,weight_shape_layer2[0]*weight_shape_layer2[1]+1,weight_shape_layer2[1]): + row_delete.append(m) + +weight_layer2 = np.delete(weight_layer2,row_delete,axis=0) + + +# drop res5c_branch2c weights according to its own sortedindex + +layer_key_1 = layer_key_2 +weight_shape_layer1 = net.params[layer_key_1][0].shape + +# load weights +weight_layer1 = weight_layer2 +weight_shape_layer1 = (weight_shape_layer1[0],net0.params[layer_key_1][0].shape[1],weight_shape_layer1[2],weight_shape_layer1[3]) +weight_layer1.shape = weight_shape_layer1 + +# get every kernel kl sortedindex +if (prune_type == 'kl'): + weight_sum_sortedindex = prune_by_kl(weight_layer1,weight_shape_layer1[1],weight_shape_layer1[0],weight_shape_layer1[2]) +elif (prune_type == 'random'): + weight_sum_sortedindex = prune_by_random(weight_layer1,weight_shape_layer1[1],weight_shape_layer1[0],weight_shape_layer1[2]) +elif (prune_type == 'l1'): + weight_sum_sortedindex = prune_by_l1(weight_layer1,weight_shape_layer1[1],weight_shape_layer1[0],weight_shape_layer1[2]) + +# one kernel occupies one column +weight_layer1.shape = (weight_shape_layer1[0],-1) + +# delete 16X parameters +len_need = net.params[layer_key_1][0].shape[0] - net0.params[layer_key_1][0].shape[0] +if (len_need > 0): + print ("[3]prune %d filters in %s"%(len_need, layer_key_1)) +weight_sum_sortedindex = weight_sum_sortedindex[0:len_need] + +# delete minimum kernel +weight_layer1 = np.delete(weight_layer1,weight_sum_sortedindex,axis=0) + +# write remaining weights +weight_layer1.shape = net0.params[layer_key_1][0].shape + +for m in range(0,weight_layer1.shape[0]): + net0.params[layer_key_1][0].data[m] = weight_layer1[m] + + +# drop bn weights +bn = keys0[layer2_index+1] +weight_bn = net.params[bn][0].data +weight_bn = np.delete(weight_bn,weight_sum_sortedindex,axis=0) +for m in range(0,weight_bn.shape[0]): + net0.params[bn][0].data[m] = weight_bn[m] + + +# drop fc1000 weights +layerfc = 'fc1000' +weight_layerfc = net.params[layerfc][0].data +weight_shape_layerfc = weight_layerfc.shape + +# calculate the size of feature map +featuremap = weight_layerfc.shape[1] / net.params[layer_key_1][0].shape[0] +weight_layerfc.shape = (-1,featuremap) + +# delete minimum kernel +row_delete=[] +for n in weight_sum_sortedindex: + for m in range(n,weight_shape_layerfc[0]*weight_shape_layerfc[1]+1,weight_shape_layerfc[1]): + row_delete.append(m) + +weight_layerfc = np.delete(weight_layerfc,row_delete,axis=0) + +# write remaining weights +weight_layerfc.shape = net0.params[layerfc][0].shape + +for m in range(0,weight_layerfc.shape[0]): + net0.params[layerfc][0].data[m] = weight_layerfc[m] + +#save caffemodel +net0.save(sys.argv[4]) + diff --git a/examples/model_optimization/tools/gen_shapes_Facebook_ResNet_50.sh b/examples/model_optimization/tools/gen_shapes_Facebook_ResNet_50.sh new file mode 100755 index 000000000..c99391905 --- /dev/null +++ b/examples/model_optimization/tools/gen_shapes_Facebook_ResNet_50.sh @@ -0,0 +1,16 @@ +mkdir examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned +cp $1 examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/$3.100.prototxt +cp $2 examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/$3.100.caffemodel +echo generate small size Facebook ResNet-50 models in examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/ +for ((i=85; i<86; ++i)) +do + echo Generating 0.$i model + cat $1 |awk -v prec=0.$i -f examples/model_optimization/tools/prune_shape.awk > examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/$3.$i.prototxt + cp examples/model_optimization/models/Facebook_ResNet_50/Baseline/solver_Facebook_ResNet_50_dummy.prototxt examples/model_optimization/tools/Facebook_ResNet_50_dummy.prototxt + echo net: \"examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/$3.$i.prototxt\" >> examples/model_optimization/tools/Facebook_ResNet_50_dummy.prototxt + echo snapshot_prefix: \"examples/model_optimization/tools/Facebook_ResNet_50_dummy\" >> examples/model_optimization/tools/Facebook_ResNet_50_dummy.prototxt + ./build_1/tools/caffe train --solver=examples/model_optimization/tools/Facebook_ResNet_50_dummy.prototxt --engine=MKLDNN >/dev/null 2>/dev/null + mv examples/model_optimization/tools/Facebook_ResNet_50_dummy_iter_1.caffemodel examples/model_optimization/models/Facebook_ResNet_50/Facebook_ResNet_50_pruned/$3.$i.caffemodel + rm examples/model_optimization/tools/Facebook_ResNet_50_dummy_iter_1.solverstate + rm examples/model_optimization/tools/Facebook_ResNet_50_dummy.prototxt +done diff --git a/examples/model_optimization/tools/prune_opts.py b/examples/model_optimization/tools/prune_opts.py new file mode 100644 index 000000000..6cb422460 --- /dev/null +++ b/examples/model_optimization/tools/prune_opts.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python +#-*-coding=utf-8 + +import sys +import numpy as np +import scipy +from sklearn.metrics import normalized_mutual_info_score +from sklearn.metrics import mutual_info_score +import copy +import random + +def prune_by_sum_min(weight, axis = 1): + weight_sum = weight.sum(axis) + sorted_index = np.argsort(weight_sum) + return sorted_index + +def measure_entropy2(src, dst): + kl = scipy.stats.entropy(src, dst) + return kl + +def measure_entropy(src): + kl = -scipy.stats.entropy(src) + return kl + +def measure_l1(src): + l1 = sum(src) + return l1 + +def zero_weight(weight, ic_num, k_size, oc_index = 0): + index_zero = np.zeros((ic_num,k_size,k_size)) + weight[oc_index] = index_zero + return weight + +def prune_by_kl(weight, ic_num, oc_num, k_size): + kls = [] + for index in range(0, oc_num): + weight_copy = np.fabs(copy.deepcopy(weight[index]).flatten()) + weight_copy[weight_copy < 1e-6] = 1e-6 + kl = measure_entropy(weight_copy) + kls.append(kl) + + sorted_index = np.argsort(kls) + return sorted_index + +def prune_by_l1(weight, ic_num, oc_num, k_size): + kls = [] + for index in range(0, oc_num): + weight_copy = np.fabs(copy.deepcopy(weight[index]).flatten()) + kl = measure_l1(weight_copy) + kls.append(kl) + + sorted_index = np.argsort(kls) + return sorted_index + +def prune_by_random(weight, ic_num, oc_num, k_size): + rds = [] + for index in range(0, oc_num): + rds.append(random.random()) + + sorted_index = np.argsort(rds) + return sorted_index + +def measure_mi(src, dst): + mi = mutual_info_score(src, dst) + return mi + +def measure_nmi(src, dst): + nmi = normalized_mutual_info_score(src, dst) + return nmi + +def prune_by_mi(weight, ic_num, oc_num): + mis = [] + weight_flatten = weight.flatten() + for index in range(0, oc_num): + weight_copy = copy.deepcopy(weight) + new_weight = zero_weight(weight_copy, ic_num, index) + new_weight_flatten = new_weight.flatten() + mi = measure_mi(weight_flatten, new_weight_flatten) + mis.append(mi) + + sorted_index = np.argsort(mis) + return sorted_index + +def prune_by_nmi(weight, ic_num, oc_num): + nmis = [] + weight_flatten = weight.flatten() + for index in range(0, oc_num): + weight_copy = copy.deepcopy(weight) + new_weight = zero_weight(weight_copy, ic_num, index) + new_weight_flatten = new_weight.flatten() + nmi = measure_nmi(weight_flatten, new_weight_flatten) + nmis.append(nmi) + + sorted_index = np.argsort(nmis) + return sorted_index diff --git a/examples/model_optimization/tools/prune_shape.awk b/examples/model_optimization/tools/prune_shape.awk new file mode 100644 index 000000000..6f73e0265 --- /dev/null +++ b/examples/model_optimization/tools/prune_shape.awk @@ -0,0 +1,34 @@ +/num_output:/ { + if (conv == 1 && prune == 1) { + old = $2 + new = int($2 * prec) + if (new % 16 != 0) { + new = new + 16 - (new % 16) + } + print " num_output: " new " # was " old + } else { + print $0 + } + num = 1 +} +/layer/ { + conv = 0 +} +/name:/ { + if ($2 == "\"res5c_branch2c\"") { + prune = 1 + } else { + prune = 1 + } +} +/type:/ { + if ($2 == "\"Convolution\"") { + conv = 1 + } +} +// { + if (num == 0) + print $0 + num = 0 +} + diff --git a/examples/pycaffe/tune_model.py b/examples/pycaffe/tune_model.py index cf2bd6334..ae8c87592 100644 --- a/examples/pycaffe/tune_model.py +++ b/examples/pycaffe/tune_model.py @@ -6,6 +6,7 @@ import copy import utils + def genOptimalModel(net, mkldnn_direct_time_map, mkldnn_winograd_time_map, optimal_model): for index in range(0, len(net.layer)): l = net.layer[index] @@ -16,20 +17,37 @@ def genOptimalModel(net, mkldnn_direct_time_map, mkldnn_winograd_time_map, optim stride = 1 if len(l.convolution_param.stride) != 0: stride = l.convolution_param.stride[0] - if mkldnn_winograd_time_map[l.name] < mkldnn_direct_time_map[l.name] and kernel_size == 3 and stride == 1 and l.convolution_param.num_output % 16 ==0: + if mkldnn_winograd_time_map[l.name] < mkldnn_direct_time_map[l.name] and kernel_size == 3 and stride == 1 and l.convolution_param.num_output % 16 == 0: l.convolution_param.conv_algorithm = "winograd" else: l.convolution_param.conv_algorithm = "direct" - + with open(optimal_model, "w") as f: f.write(txtf.MessageToString(net, float_format=".17g")) -def tuneModelDefinition(model_path, iteration): + +def tuneModelDefinition(model_path, iteration, is_test_phase, core_num, socket_num): working_dir = sys.path[0] - caffe_path = os.path.join(working_dir, "..", "..", "build", "tools", "caffe") + caffe_path = os.path.join(working_dir, "..", "..", + "build", "tools", "caffe") if not os.path.exists(caffe_path): print "Caffe binary does not exist; please build Caffe binary first." sys.exit(1) + core_num_per_socket = int([i for i in os.popen('lscpu').readlines( + ) if i.startswith('Core(s) per socket:')][0].strip().split(':')[-1].strip()) + core_num_per_socket *= socket_num + if is_test_phase: + caffe_path += ' time -phase TEST -forward_only' + if core_num != 0: + core_num = core_num_per_socket if core_num > core_num_per_socket else core_num + env_prefix = 'OMP_NUM_THREADS={} KMP_HW_SUBSET=1t KMP_AFFINITY=compact,granularity=fine numactl -C 0-{} -m 0 '.format( + core_num, core_num) + elif socket_num == 1: + env_prefix = 'OMP_NUM_THREADS={} KMP_HW_SUBSET=1t KMP_AFFINITY=compact,granularity=fine numactl -C 0-{} -m 0 '.format( + core_num_per_socket, core_num_per_socket) + else: + env_prefix = 'numactl -l ' + caffe_path = env_prefix + caffe_path base_model_name = os.path.basename(model_path) model_dir = os.path.dirname(model_path) @@ -65,20 +83,29 @@ def tuneModelDefinition(model_path, iteration): mkldnn_winograd_log = "mkldnn_winograd.log" mkldnn_direct_log_path = os.path.join(model_dir, mkldnn_direct_log) mkldnn_winograd_log_path = os.path.join(model_dir, mkldnn_winograd_log) - - mkldnn_direct_command = caffe_path + " time -model " + direct_model_path + " -engine MKLDNN -iterations " + str(iteration) + " >& " + mkldnn_direct_log_path + mkldnn_direct_command = caffe_path + " -model " + direct_model_path + \ + " -engine MKLDNN -iterations " + \ + str(iteration) + " >& " + mkldnn_direct_log_path os.system(mkldnn_direct_command) - mkldnn_winograd_command = caffe_path + " time -model " + winograd_model_path + " -engine MKLDNN -iterations " + str(iteration) + " >& " + mkldnn_winograd_log_path + mkldnn_winograd_command = caffe_path + " -model " + winograd_model_path + \ + " -engine MKLDNN -iterations " + \ + str(iteration) + " >& " + mkldnn_winograd_log_path os.system(mkldnn_winograd_command) (model_str, mkldnn_direct_time_lines) = utils.parseLog(mkldnn_direct_log_path) - mkldnn_direct_layer_time_map = utils.parseTimeLines(mkldnn_direct_time_lines) - (model_str, mkldnn_winograd_time_lines) = utils.parseLog(mkldnn_winograd_log_path) - mkldnn_winograd_layer_time_map = utils.parseTimeLines(mkldnn_winograd_time_lines) + mkldnn_direct_layer_time_map = utils.parseTimeLines( + mkldnn_direct_time_lines) + (model_str, mkldnn_winograd_time_lines) = utils.parseLog( + mkldnn_winograd_log_path) + mkldnn_winograd_layer_time_map = utils.parseTimeLines( + mkldnn_winograd_time_lines) hybrid_model_name = base_model_name.split(".")[0] + "_hybrid.prototxt" hybrid_model_path = os.path.join(model_dir, hybrid_model_name) - genOptimalModel(base_net, mkldnn_direct_layer_time_map, mkldnn_winograd_layer_time_map, hybrid_model_path) + genOptimalModel(base_net, mkldnn_direct_layer_time_map, + mkldnn_winograd_layer_time_map, hybrid_model_path) + print '{} has been generated.'.format(hybrid_model_path) + if __name__ == '__main__': parser = argparse.ArgumentParser() @@ -89,7 +116,17 @@ def tuneModelDefinition(model_path, iteration): parser.add_argument('-i', '--iteration', action='store', dest='iterations', type=int, default=10, help='require iterations number to run the model') - parser.add_argument('-v', '--version', action='version', version='%(prog)s 1.0') + parser.add_argument('-t', '--phase', action='store', dest='is_test_phase', type=bool, default=False, + help='Train or Test phase') + + parser.add_argument('-c', '--core_num', action='store', dest='core_num', type=int, default=0, + help='core number for inference') + + parser.add_argument('-s', '--socket', action='store', dest='socket_num', type=int, default=2, + help='socket number for inference') + + parser.add_argument('-v', '--version', action='version', + version='%(prog)s 1.0') params = parser.parse_args() @@ -98,4 +135,5 @@ def tuneModelDefinition(model_path, iteration): print "[ERROR] Please specify the model definition file with -m" exit(1) - tuneModelDefinition(params.model, params.iterations) + tuneModelDefinition(params.model, params.iterations, + params.is_test_phase, params.core_num, params.socket_num) diff --git a/examples/rfcn/lib/datasets/imdb.py b/examples/rfcn/lib/datasets/imdb.py index 41c9ead06..b56bf0a80 100644 --- a/examples/rfcn/lib/datasets/imdb.py +++ b/examples/rfcn/lib/datasets/imdb.py @@ -106,8 +106,8 @@ def append_flipped_images(self): boxes = self.roidb[i]['boxes'].copy() oldx1 = boxes[:, 0].copy() oldx2 = boxes[:, 2].copy() - boxes[:, 0] = widths[i] - oldx2 - boxes[:, 2] = widths[i] - oldx1 + boxes[:, 0] = widths[i] - oldx2 - 1 + boxes[:, 2] = widths[i] - oldx1 - 1 assert (boxes[:, 2] >= boxes[:, 0]).all() entry = {'boxes' : boxes, 'gt_overlaps' : self.roidb[i]['gt_overlaps'], diff --git a/examples/rfcn/lib/fast_rcnn/config.py b/examples/rfcn/lib/fast_rcnn/config.py index e838ba3b9..d80833f65 100644 --- a/examples/rfcn/lib/fast_rcnn/config.py +++ b/examples/rfcn/lib/fast_rcnn/config.py @@ -41,7 +41,7 @@ __C.TRAIN.FORMAT = '' # 'pickle' # Max pixel size of the longest side of a scaled input image -__C.TRAIN.MAX_SIZE = 800 +__C.TRAIN.MAX_SIZE = 1000 # Images to use per minibatch __C.TRAIN.IMS_PER_BATCH = 2 diff --git a/examples/rfcn/lib/fast_rcnn/net_sample_utils.py b/examples/rfcn/lib/fast_rcnn/net_sample_utils.py new file mode 100755 index 000000000..3933d0661 --- /dev/null +++ b/examples/rfcn/lib/fast_rcnn/net_sample_utils.py @@ -0,0 +1,69 @@ +# +# All modification made by Intel Corporation: Copyright (c) 2016 Intel Corporation +# +# All contributions by the University of California: +# Copyright (c) 2014, 2015, The Regents of the University of California (Regents) +# All rights reserved. +# +# All other contributions: +# Copyright (c) 2014, 2015, the respective contributors +# All rights reserved. +# For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md +# +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of Intel Corporation nor the names of its contributors +# may be used to endorse or promote products derived from this software +# without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +import caffe +import google.protobuf.text_format as txtf +from caffe.proto import caffe_pb2 + +def update_conv_quantized_dict(conv_quantized_dict, tmp_conv_quantized_dict): + for conv in conv_quantized_dict: + if tmp_conv_quantized_dict[conv][0] > conv_quantized_dict[conv][0]: + conv_quantized_dict[conv][0] = tmp_conv_quantized_dict[conv][0] + + if tmp_conv_quantized_dict[conv][1] > conv_quantized_dict[conv][1]: + conv_quantized_dict[conv][1] = tmp_conv_quantized_dict[conv][1] + + +def create_quantized_net(raw_net, quantized_net, conv_quantized_dict): + net_param = caffe_pb2.NetParameter() + with open(raw_net) as f: + txtf.Merge(f.read(), net_param) + #skip first conv layer when quantizing net + first_conv = True + for layer_param in net_param.layer: + if layer_param.type == "Convolution": + if first_conv: + first_conv = False + continue + layer_param.quantization_param.bw_layer_in = 8 + layer_param.quantization_param.bw_layer_out = 8 + layer_param.quantization_param.bw_params = 8 + layer_param.quantization_param.scale_in.append(conv_quantized_dict[layer_param.name][0]) + layer_param.quantization_param.scale_out.append(conv_quantized_dict[layer_param.name][1]) + for param_scale in conv_quantized_dict[layer_param.name][2]: + layer_param.quantization_param.scale_params.append(param_scale) + with open(quantized_net, 'w') as f: + f.write(str(net_param)) diff --git a/examples/rfcn/lib/fast_rcnn/test.py b/examples/rfcn/lib/fast_rcnn/test.py index d12c3a866..e05a8a899 100644 --- a/examples/rfcn/lib/fast_rcnn/test.py +++ b/examples/rfcn/lib/fast_rcnn/test.py @@ -10,15 +10,21 @@ from fast_rcnn.config import cfg, get_output_dir from fast_rcnn.bbox_transform import clip_boxes, bbox_transform_inv import argparse +from fast_rcnn import net_sample_utils from utils.timer import Timer import numpy as np import cv2 import caffe +import os +import sys +utilpath = os.path.split(os.path.realpath(__file__))[0] + "/../../../../scripts/" +print utilpath +sys.path.insert(0, utilpath) +import sampling from fast_rcnn.nms_wrapper import nms import cPickle import gzip from utils.blob import im_list_to_blob -import os def _get_image_blob(im): """Converts an image into a network input. @@ -106,7 +112,7 @@ def _get_blobs(im, rois): blobs['rois'] = _get_rois_blob(rois, im_scale_factors) return blobs, im_scale_factors -def im_detect(net, im, boxes=None): +def im_detect(net, im, boxes=None, sample_blobs = None): """Detect object classes in an image given object proposals. Arguments: @@ -154,6 +160,16 @@ def im_detect(net, im, boxes=None): forward_kwargs['rois'] = blobs['rois'].astype(np.float32, copy=False) blobs_out = net.forward(**forward_kwargs) + if sample_blobs != None: + for k, _ in net.blobs.items(): # top blob + output = np.array(net.blobs[k].data) + if k not in sample_blobs.keys(): + sample_blobs[k] = [output] + else: + new_outputs = sample_blobs[k] + #print "new_outputs type: " + str(type(new_outputs)) + new_outputs.append(output) + sample_blobs[k] = new_outputs if cfg.TEST.HAS_RPN: assert len(im_scales) == 1, "Only single-image batch implemented" rois = net.blobs['rois'].data.copy() @@ -225,6 +241,28 @@ def apply_nms(all_boxes, thresh): nms_boxes[cls_ind][im_ind] = dets[keep, :].copy() return nms_boxes +def sample_net(raw_net_prototxt, net, imdb, sampling_iterations, quant_mode, enable_first_conv = False, winograd_algo = False): + + (conv_layers, test_net_top_names, test_net_bottom_names, conv_top_blob_layer_map, conv_bottom_blob_layer_map) = sampling.get_blob_map(net, enable_first_conv) + #currently out sample_net only supports TEST with HAS_RPN and AGNOSTIC flag + num_images = len(imdb.image_index) + image_index = 0 + box_proposals = None + sample_blobs = {} + for iter_ in xrange(sampling_iterations): + im = cv2.imread(imdb.image_path_at(image_index)) + im_detect(net, im, box_proposals, sample_blobs) + image_index += 1 + + params = {} + for k, _ in net.params.items(): + if k not in conv_layers: + continue + param = np.abs(net.params[k][0].data) # ignore bias + params[k] = [param] + + (winograd_bottoms, winograd_convolutions) = sampling.get_winograd_info(raw_net_prototxt, conv_bottom_blob_layer_map, winograd_algo) + return (sample_blobs, params, test_net_top_names, test_net_bottom_names, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) def test_net(net, imdb, max_per_image=400, thresh=-np.inf, vis=False): """Test a Fast R-CNN network on an image database.""" num_images = len(imdb.image_index) diff --git a/examples/rfcn/tools/test_net.py b/examples/rfcn/tools/test_net.py index ff47037d6..2e6220046 100755 --- a/examples/rfcn/tools/test_net.py +++ b/examples/rfcn/tools/test_net.py @@ -11,12 +11,19 @@ import _init_paths from fast_rcnn.test import test_net +from fast_rcnn.test import sample_net +from fast_rcnn import net_sample_utils from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list from datasets.factory import get_imdb import caffe + +import time, os, sys +utilpath = os.path.split(os.path.realpath(__file__))[0] + "/../../../scripts/" +sys.path.insert(0, utilpath) +import calibrator +import sampling import argparse import pprint -import time, os, sys def parse_args(): """ @@ -51,7 +58,30 @@ def parse_args(): default=400, type=int) parser.add_argument('--rpn_file', dest='rpn_file', default=None, type=str) + parser.add_argument('--quantized_net', action='store', dest='quantized_prototxt', + help='the path of generated quantized net prototxt', + default=None, type=str) + parser.add_argument('--sample_iters', action='store', dest='sample_iters', + help='iterations to run for sampling', + default=70, type=int) + parser.add_argument('--quant_mode', action='store', dest='quant_mode', + help='quantization mode, single or multiple', + default='single', type=str) + + parser.add_argument('-u', '--unsigned_range', dest='unsigned_range', action="store_true", default=False, + help='to quantize using unsigned range for activation') + + parser.add_argument('-t', '--concat_use_fp32', dest='concat_use_fp32', action="store_true", default=False, + help='to use fp32 for concat') + + parser.add_argument('-f', '--unify_concat_scales', dest='unify_concat_scales', action="store_true", default=False, + help='to unify concat scales') + + parser.add_argument('-a', '--calibration_algos', dest='calibration_algos', action='store', default="DIRECT", + help='to choose the calibration alogorithm') + parser.add_argument('-wi', '--conv_algo', dest='conv_algo', action="store_true", default=False, + help='to choose the convolution algorithm') if len(sys.argv) == 1: parser.print_help() sys.exit(1) @@ -91,4 +121,17 @@ def parse_args(): if cfg.TEST.PROPOSAL_METHOD == 'rpn': imdb.config['rpn_file'] = args.rpn_file - test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis) + if args.quantized_prototxt == None: + test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis) + else: + (blobs, params, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) = sample_net(args.prototxt, net, imdb, args.sample_iters, args.quant_mode) + + (inputs_max, outputs_max, inputs_min) = sampling.calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, args.calibration_algos, "SINGLE", args.conv_algo) + params_max = sampling.calibrate_parameters(params, winograd_convolutions, "DIRECT", args.quant_mode.upper(), args.conv_algo) + calibrator.generate_sample_impl(args.prototxt, args.quantized_prototxt, inputs_max, outputs_max, inputs_min, params_max, False) + compiled_net_str = caffe.compile_net(args.prototxt, caffe.TEST, "MKLDNN") + raw_net_basename = os.path.basename(args.prototxt) + compile_net_path = "./compiled_" + raw_net_basename + with open(compile_net_path, "w") as f: + f.write(compiled_net_str) + calibrator.transform_convolutions(args.quantized_prototxt, compile_net_path, top_blobs_map, bottom_blobs_map, args.unsigned_range, args.concat_use_fp32, args.unify_concat_scales, args.conv_algo, False) diff --git a/examples/ssd/ssd_detect.cpp b/examples/ssd/ssd_detect.cpp index cc0ec5ba9..07de166cf 100644 --- a/examples/ssd/ssd_detect.cpp +++ b/examples/ssd/ssd_detect.cpp @@ -279,6 +279,9 @@ DEFINE_double(confidence_threshold, 0.01, "Only store detections with score higher than the threshold."); int main(int argc, char** argv) { +#ifdef USE_MLSL + caffe::mn::init(&argc, &argv); +#endif ::google::InitGoogleLogging(argv[0]); // Print output to stderr (while still logging) FLAGS_alsologtostderr = 1; diff --git a/examples/yolo/LICENSE b/examples/yolo/LICENSE new file mode 100755 index 000000000..4b14258d2 --- /dev/null +++ b/examples/yolo/LICENSE @@ -0,0 +1,48 @@ +Copyright (c) 2016 Wei Liu + +******************************************************************************** +COPYRIGHT + +All contributions by the University of California: +Copyright (c) 2014, 2015, The Regents of the University of California (Regents) +All rights reserved. + +All other contributions: +Copyright (c) 2014, 2015, the respective contributors +All rights reserved. + +Caffe uses a shared copyright model: each contributor holds copyright over +their contributions to Caffe. The project versioning records all such +contribution and copyright details. If a contributor wants to further mark +their specific copyright on a particular contribution, they should indicate +their copyright solely in the commit message of the change when it is +committed. + +LICENSE + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +CONTRIBUTION AGREEMENT + +By contributing to the BVLC/caffe repository through pull-request, comment, +or otherwise, the contributor releases their content to the +license and copyright terms herein. +******************************************************************************** diff --git a/examples/yolo/README.md b/examples/yolo/README.md new file mode 100644 index 000000000..7d9aebe46 --- /dev/null +++ b/examples/yolo/README.md @@ -0,0 +1,48 @@ +# CAFFE for YOLO9000 + +## Reference + +> YOLO9000: Better, Faster, Stronger + +> http://pjreddie.com/yolo9000/ + +> https://github.com/yeahkun/caffe-yolo + +> https://github.com/weiliu89/caffe/tree/ssd + +> https://github.com/choasup/caffe-yolo9000 +## Usage + +### Caffe +```Shell + You can compile Caffe with CMake or make method. +``` + +### Data preparation +Like SSD data setting. +```Shell + cd data/VOC0712 + vim create_data.sh + ./create_data.sh +``` + +### Train +```Shell + cd models/intel_optimized_models/yolo + # download pretrain_model +``` + > https://pan.baidu.com/s/1c71EB-6A1xQb2ImOISZiHA password: 9u5v +``` + # change pretrain model related path in script train.sh + cd examples/yolo/ + vim train_yolo_V2.sh + + ./train_yolo_V2.sh +``` + +### Eval mAP +```Shell + cd examples/yolo/ + + ./test_yolo_V2.sh +``` diff --git a/examples/yolo/converter/create_yolo_caffemodel.py b/examples/yolo/converter/create_yolo_caffemodel.py new file mode 100644 index 000000000..fe6ec2e2d --- /dev/null +++ b/examples/yolo/converter/create_yolo_caffemodel.py @@ -0,0 +1,147 @@ +# -*- coding: utf-8 -*- + +import caffe +import numpy as np +import sys, getopt + +def Print(name, data, sz, dims): + print "=======",name,"======" + print sz + print dims + print data + +def main(argv): + model_filename = '' + yoloweight_filename = '' + caffemodel_filename = '' + try: + opts, args = getopt.getopt(argv, "hm:w:o:") + print opts + except getopt.GetoptError: + print 'create_yolo_caffemodel.py -m -w -o ' + sys.exit(2) + for opt, arg in opts: + if opt == '-h': + print 'create_yolo_caffemodel.py -m -w -o ' + sys.exit() + elif opt == "-m": + model_filename = arg + elif opt == "-w": + yoloweight_filename = arg + elif opt == "-o": + caffemodel_filename = arg + + print 'model file is: ', model_filename + print 'weight file is: ', yoloweight_filename + print 'output caffemodel file is: ', caffemodel_filename + net = caffe.Net(model_filename, caffe.TEST) + + # for each layer, show the output shape + for layer_name, blob in net.blobs.iteritems(): + print "[Debug] Layer name is: " + layer_name + "\t Data blob size is: " + str(blob.data.shape) + count = 0 + for layer_name, param in net.params.iteritems(): + print "[Debug] Layer name is: " + layer_name + "\t Param blob size is: ", + for i in range(len(param)): + count += np.prod(param[i].data.shape) + print str(param[i].data.shape) + '\t', + print + print 'Param total count = ', str(count) + params = net.params.keys() + + # read weights from file and assign to the network + netWeightsInt = np.fromfile(yoloweight_filename, dtype=np.int32) + transFlag = (netWeightsInt[0]>1000 or netWeightsInt[1]>1000) + # transpose flag, the first 4 entries are major, minor, revision and net.seen + start = 4 + if (netWeightsInt[0]*10 + netWeightsInt[1]) >= 2: + start = 5 + + print 'transFlag = %r' % transFlag + + netWeightsFloat = np.fromfile(yoloweight_filename, dtype=np.float32) + netWeights = netWeightsFloat[start:] # start from the 5th entry, the first 4 entries are major, minor, revision and net.seen + print netWeights.shape + count = 0 + + print "#Total Net Layer", len(net.layers) + + layercnt = 0 + for pr in params: + layercnt = layercnt + 1 + lidx = list(net._layer_names).index(pr) + layer = net.layers[lidx] + if count == netWeights.shape[0] and (layer.type != 'BatchNorm' and layer.type != 'Scale'): + print "WARNING: no weights left for %s" % pr + break + if layer.type == 'Convolution': + print pr + "(conv)" + "-" + str(layercnt) + "-" + str(len(net.params[pr]) > 1) + # bias + if len(net.params[pr]) > 1: + bias_dim = net.params[pr][1].data.shape + else: + bias_dim = (net.params[pr][0].data.shape[0], ) + biasSize = np.prod(bias_dim) + conv_bias = np.reshape(netWeights[count:count+biasSize], bias_dim) + if len(net.params[pr]) > 1: + assert(bias_dim == net.params[pr][1].data.shape) + net.params[pr][1].data[...] = conv_bias + conv_bias = None + count = count + biasSize + # batch_norm + if lidx + 1 < len(net.layers) and net.layers[lidx+1].type == 'BatchNorm': + bn_dims = (3, net.params[pr][0].data.shape[0]) + bnSize = np.prod(bn_dims) + batch_norm = np.reshape(netWeights[count:count+bnSize], bn_dims) + count = count + bnSize + # weights + dims = net.params[pr][0].data.shape + weightSize = np.prod(dims) + net.params[pr][0].data[...] = np.reshape(netWeights[count:count+weightSize], dims) + count = count + weightSize + + #print "layercnt: " + str(layercnt) + #if 25 == layercnt: + # print net.params[pr] + # print("conv", net.params[pr][0].data, weightSize, dims) + # Print("bias", conv_bias, biasSize, bias_dim) + # Print("bnorm", batch_norm, bnSize, bn_dims) + + elif layer.type == 'InnerProduct': + print pr + "(fc)" + # bias + biasSize = np.prod(net.params[pr][1].data.shape) + net.params[pr][1].data[...] = np.reshape(netWeights[count:count+biasSize], net.params[pr][1].data.shape) + count = count + biasSize + # weights + dims = net.params[pr][0].data.shape + weightSize = np.prod(dims) + if transFlag: + net.params[pr][0].data[...] = np.reshape(netWeights[count:count+weightSize], (dims[1], dims[0])).transpose() + else: + net.params[pr][0].data[...] = np.reshape(netWeights[count:count+weightSize], dims) + count = count + weightSize + elif layer.type == 'BatchNorm': + print pr+"(batchnorm)" + net.params[pr][0].data[...] = batch_norm[1] # mean + net.params[pr][1].data[...] = batch_norm[2] # variance + net.params[pr][2].data[...] = 1.0 # scale factor + elif layer.type == 'Scale': + print pr + "(scale)" + if batch_norm is not None: + net.params[pr][0].data[...] = batch_norm[0] # scale + batch_norm = None + if len(net.params[pr]) > 1: + net.params[pr][1].data[...] = conv_bias # bias + conv_bias = None + else: + print "WARNING: unsupported layer, " + pr + if np.prod(netWeights.shape) != count: + print "ERROR: size mismatch: %d" % count + print "Anyway, we will save the caffemodel" + else: + print "RIGHT: size matched: %d" % count + net.save(caffemodel_filename) + +if __name__=='__main__': + main(sys.argv[1:]) diff --git a/examples/yolo/converter/create_yolo_prototxt.py b/examples/yolo/converter/create_yolo_prototxt.py new file mode 100644 index 000000000..f27431c1a --- /dev/null +++ b/examples/yolo/converter/create_yolo_prototxt.py @@ -0,0 +1,361 @@ +# -*- coding: utf-8 -*- +from ConfigParser import ConfigParser +from collections import OrderedDict +import argparse +import logging +import os +import sys + +class CaffeLayerGenerator(object): + def __init__(self, name, ltype): + self.name = name + self.bottom = [] + self.top = [] + self.type = ltype + def get_template(self): + return """ +layer {{{{ + name: "{}" + type: "{}" + bottom: "{}" + top: "{}"{{}} +}}}}""".format(self.name, self.type, self.bottom[0], self.top[0]) + +class CaffeInputLayer(CaffeLayerGenerator): + def __init__(self, name, channels, width, height): + super(CaffeInputLayer, self).__init__(name, 'Input') + self.channels = channels + self.width = width + self.height = height + def write(self, f): + f.write(""" +input: "{}" +input_shape {{ + dim: 1 + dim: {} + dim: {} + dim: {} +}}""".format(self.name, self.channels, self.width, self.height)) + +class CaffeConvolutionLayer(CaffeLayerGenerator): + def __init__(self, name, filters, ksize=None, stride=None, pad=None, bias=True): + super(CaffeConvolutionLayer, self).__init__(name, 'Convolution') + self.filters = filters + self.ksize = ksize + self.stride = stride + self.pad = pad + self.bias = bias + def write(self, f): + opts = [''] + if self.ksize is not None: opts.append('kernel_size: {}'.format(self.ksize)) + if self.stride is not None: opts.append('stride: {}'.format(self.stride)) + if self.pad is not None: opts.append('pad: {}'.format(self.pad)) + if not self.bias: opts.append('bias_term: false') + param_str = """ + convolution_param {{ + num_output: {}{} + }}""".format(self.filters, '\n '.join(opts)) + f.write(self.get_template().format(param_str)) + +class CaffePoolingLayer(CaffeLayerGenerator): + def __init__(self, name, pooltype, ksize=None, stride=None, pad=None, global_pooling=None): + super(CaffePoolingLayer, self).__init__(name, 'Pooling') + self.pooltype = pooltype + self.ksize = ksize + self.stride = stride + self.pad = pad + self.global_pooling = global_pooling + def write(self, f): + opts = [''] + if self.ksize is not None: opts.append('kernel_size: {}'.format(self.ksize)) + if self.stride is not None: opts.append('stride: {}'.format(self.stride)) + if self.pad is not None: opts.append('pad: {}'.format(self.pad)) + if self.global_pooling is not None: opts.append('global_pooling: {}'.format('True' if self.global_pooling else 'False')) + param_str = """ + pooling_param {{ + pool: {}{} + }}""".format(self.pooltype, '\n '.join(opts)) + f.write(self.get_template().format(param_str)) + +class CaffeInnerProductLayer(CaffeLayerGenerator): + def __init__(self, name, num_output): + super(CaffeInnerProductLayer, self).__init__(name, 'InnerProduct') + self.num_output = num_output + def write(self, f): + param_str = """ + inner_product_param {{ + num_output: {} + }}""".format(self.num_output) + f.write(self.get_template().format(param_str)) + +class CaffeBatchNormLayer(CaffeLayerGenerator): + def __init__(self, name): + super(CaffeBatchNormLayer, self).__init__(name, 'BatchNorm') + def write(self, f): + param_str = """ + batch_norm_param { + use_global_stats: true + }""" + f.write(self.get_template().format(param_str)) + +class CaffeScaleLayer(CaffeLayerGenerator): + def __init__(self, name): + super(CaffeScaleLayer, self).__init__(name, 'Scale') + def write(self, f): + param_str = """ + scale_param { + bias_term: true + }""" + f.write(self.get_template().format(param_str)) + +class CaffeReluLayer(CaffeLayerGenerator): + def __init__(self, name, negslope=None): + super(CaffeReluLayer, self).__init__(name, 'ReLU') + self.negslope = negslope + def write(self, f): + param_str = "" + if self.negslope is not None: + param_str = """ + relu_param {{ + negative_slope: {} + }}""".format(self.negslope) + f.write(self.get_template().format(param_str)) + +class CaffeDropoutLayer(CaffeLayerGenerator): + def __init__(self, name, prob): + super(CaffeDropoutLayer, self).__init__(name, 'Dropout') + self.prob = prob + def write(self, f): + param_str = """ + dropout_param {{ + dropout_ratio: {} + }}""".format(self.prob) + f.write(self.get_template().format(param_str)) + +class CaffePowerLayer(CaffeLayerGenerator): + def __init__(self,name,scale): + super(CaffePowerLayer, self).__init__(name, 'Power') + self.scale = scale + def write(self, f): + param_str=""" + power_param {{ + scale: {} + }}""".format(self.scale) + f.write(self.get_template().format(param_str)) + +class CaffeEltWiseLayer(CaffeLayerGenerator): + def __init__(self,name,operation): # need 2 blob to do operation + super(CaffeEltWiseLayer, self).__init__(name, 'Eltwise') + self.operation = operation + def write(self, f): + param_str=""" + bottom: "{}" + eltwise_param{{ + operation: {} + }}""".format(self.bottom[1],self.operation) + f.write(self.get_template().format(param_str)) + +class CaffeSoftmaxLayer(CaffeLayerGenerator): + def __init__(self, name): + super(CaffeSoftmaxLayer, self).__init__(name, 'Softmax') + def write(self, f): + f.write(self.get_template().format("")) + +class CaffeProtoGenerator: + def __init__(self, name): + self.name = name + self.sections = [] + self.lnum = 0 + self.layer = None + def add_layer(self, l): + self.sections.append( l ) + def add_input_layer(self, items): + self.lnum = 0 + lname = "data" + self.layer = CaffeInputLayer(lname, items['channels'], items['width'], items['height']) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def update_last_convolution_layer(self): + self.sections[len(self.sections)-1].pad = 0 + def add_convolution_layer(self, items): + self.lnum += 1 + prev_blob = self.layer.top[0] + lname = "conv"+str(self.lnum) + filters = items['filters'] + ksize = items['size'] if 'size' in items else None + stride = items['stride'] if 'stride' in items else None + pad = items['pad'] if 'pad' in items else None + bias = not bool(items['batch_normalize']) if 'batch_normalize' in items else True + self.layer = CaffeConvolutionLayer( lname, filters, ksize=ksize, stride=stride, pad=pad, bias=bias ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_innerproduct_layer(self, items): + self.lnum += 1 + prev_blob = self.layer.top[0] + lname = "fc"+str(self.lnum) + num_output = items['output'] + self.layer = CaffeInnerProductLayer( lname, num_output ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_pooling_layer(self, ltype, items, global_pooling=None): + prev_blob = self.layer.top[0] + lname = "pool"+str(self.lnum) + ksize = items['size'] if 'size' in items else None + stride = items['stride'] if 'stride' in items else None + pad = items['pad'] if 'pad' in items else None + self.layer = CaffePoolingLayer( lname, ltype, ksize=ksize, stride=stride, pad=pad, global_pooling=global_pooling ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_batchnorm_layer(self, items): + prev_blob = self.layer.top[0] + lname = "bn"+str(self.lnum) + self.layer = CaffeBatchNormLayer( lname ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_scale_layer(self, items): + prev_blob = self.layer.top[0] + lname = "scale"+str(self.lnum) + self.layer = CaffeScaleLayer( lname ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_leaky_relu_layer(self,items): + prev_blob = self.layer.top[0] + lname = "relu"+str(self.lnum) + self.layer = CaffeReluLayer( lname , 0.1) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( prev_blob) + self.add_layer( self.layer ) + def add_relu_layer(self, items): + prev_blob = self.layer.top[0] + lname = "relu"+str(self.lnum) + self.layer = CaffeReluLayer( lname ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( prev_blob ) # loopback + self.add_layer( self.layer ) + def add_relu_separate_layer(self, items): + prev_blob = self.layer.top[0] + lname = "relu"+str(self.lnum) + self.layer = CaffeReluLayer( lname ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) # not done in place so create new blob + self.add_layer( self.layer ) + def add_power_layer(self,items,scale=0.08): + prev_blob = self.layer.bottom[0] # not the top since it use same previous blob + prev_relu = self.layer.top[0] # save the same level relu + lname = "power"+str(self.lnum) + self.layer = CaffePowerLayer( lname, scale) + self.layer.bottom.append( prev_blob ) + self.layer.bottom.append (prev_relu ) # save the same level relu, not gonna created since create_layer only use index 0 + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_eltwise_layer(self,items,operation='SUM'): + prev_blob = self.layer.top[0] + prev_relu = self.layer.bottom[1] # get the relu from power layer + lname = "eltwise"+str(self.lnum) + self.layer = CaffeEltWiseLayer( lname, operation) + self.layer.bottom.append( prev_blob ) + self.layer.bottom.append( prev_relu ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def add_dropout_layer(self, items): + prev_blob = self.layer.top[0] + lname = "drop"+str(self.lnum) + self.layer = CaffeDropoutLayer( lname, items['probability'] ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( prev_blob ) # loopback + self.add_layer( self.layer ) + def add_softmax_layer(self, items): + prev_blob = self.layer.top[0] + lname = "prob" + self.layer = CaffeSoftmaxLayer( lname ) + self.layer.bottom.append( prev_blob ) + self.layer.top.append( lname ) + self.add_layer( self.layer ) + def finalize(self, name): + self.layer.top[0] = name # replace + def write(self, fname): + with open(fname, 'w') as f: + f.write('name: "{}"'.format(self.name)) + for sec in self.sections: + sec.write(f) + logging.info('{} is generated'.format(fname)) + +###################################################################33 +class uniqdict(OrderedDict): + _unique = 0 + def __setitem__(self, key, val): + if isinstance(val, OrderedDict): + self._unique += 1 + key += "_"+str(self._unique) + OrderedDict.__setitem__(self, key, val) + +def convert(cfgfile, ptxtfile,approximate): + # + parser = ConfigParser(dict_type=uniqdict) + parser.read(cfgfile) + netname = os.path.basename(cfgfile).split('.')[0] + #print netname + gen = CaffeProtoGenerator(netname) + for section in parser.sections(): + _section = section.split('_')[0] + if _section in ["crop", "cost"]: + continue + # + batchnorm_followed = False + relu_followed = False + items = dict(parser.items(section)) + if 'batch_normalize' in items and items['batch_normalize']: + batchnorm_followed = True + if 'activation' in items and items['activation'] != 'linear': + relu_followed = True + # + if _section == 'net': + gen.add_input_layer(items) + elif _section == 'convolutional': + gen.add_convolution_layer(items) + if batchnorm_followed: + gen.add_batchnorm_layer(items) + gen.add_scale_layer(items) + if relu_followed: + if approximate: + gen.add_relu_separate_layer(items) # relu done not in place, but out different blob + gen.add_power_layer(items) + gen.add_eltwise_layer(items) + else: + gen.add_leaky_relu_layer(items) #assume all pre trained yolo model use leaky + elif _section == 'connected': + gen.add_innerproduct_layer(items) + if relu_followed: + gen.add_leaky_relu_layer(items) #assume all pre trained yolo model use leaky + elif _section == 'maxpool': + gen.add_pooling_layer('MAX', items) + elif _section == 'avgpool': + gen.add_pooling_layer('AVE', items, global_pooling=True) + elif _section == 'dropout': + gen.add_dropout_layer(items) + elif _section == 'softmax': + gen.add_softmax_layer(items) + else: + logging.error("{} layer is not supported".format(_section)) + gen.update_last_convolution_layer() + #gen.finalize('result') + gen.write(ptxtfile) + +def main(): + parser = argparse.ArgumentParser(description='Convert YOLO cfg to Caffe prototxt') + parser.add_argument('cfg', type=str, help='YOLO cfg') + parser.add_argument('prototxt', type=str, help='Caffe prototxt') + parser.add_argument('--approx', help='flag whether to approximate leaky relu or not (for TensorRT implementation',action='store_true') + args = parser.parse_args() + + convert(args.cfg, args.prototxt, args.approx) + +if __name__ == "__main__": + main() + +# vim:sw=4:ts=4:et diff --git a/examples/yolo/data/convert.sh b/examples/yolo/data/convert.sh new file mode 100755 index 000000000..2e977123d --- /dev/null +++ b/examples/yolo/data/convert.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env sh + +CAFFE_ROOT=../../.. +ROOT_DIR=/your/path/to/vocroot/ +LABEL_FILE=$CAFFE_ROOT/examples/yolo/data/label_map.txt + +# 2007 + 2012 trainval +LIST_FILE=$CAFFE_ROOT/examples/yolo/data/trainval.txt +LMDB_DIR=./lmdb/trainval_lmdb +SHUFFLE=true + +# 2007 test +# LIST_FILE=$CAFFE_ROOT/examples/yolo/data/test_2007.txt +# LMDB_DIR=./lmdb/test2007_lmdb +# SHUFFLE=false + +RESIZE_W=448 +RESIZE_H=448 + +#If you want to generate the test lmdb, please comment the trainval part and uncomment the test part +$CAFFE_ROOT/build/tools/convert_box_data --resize_width=$RESIZE_W --resize_height=$RESIZE_H \ + --label_file=$LABEL_FILE $ROOT_DIR $LIST_FILE $LMDB_DIR --encoded=true --encode_type=jpg --shuffle=$SHUFFLE + diff --git a/examples/yolo/data/get_list.py b/examples/yolo/data/get_list.py new file mode 100755 index 000000000..f519f1a1a --- /dev/null +++ b/examples/yolo/data/get_list.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python +import os + +trainval_jpeg_list = [] +trainval_xml_list = [] +test07_jpeg_list = [] +test07_xml_list = [] +test12_jpeg_list = [] + +for name in ["VOC2007", "VOC2012"]: + voc_dir = os.path.join("VOCdevkit", name) + txt_fold = os.path.join(voc_dir, "ImageSets/Main") + jpeg_fold = os.path.join(voc_dir, "JPEGImages") + xml_fold = os.path.join(voc_dir, "Annotations") + for t in ["train.txt", "val.txt"]: + file_path = os.path.join(txt_fold, t) + with open(file_path, 'r') as fp: + for line in fp: + line = line.strip() + trainval_jpeg_list.append(os.path.join(jpeg_fold, "{}.jpg".format(line))) + trainval_xml_list.append(os.path.join(xml_fold, "{}.xml".format(line))) + if not os.path.exists(trainval_jpeg_list[-1]): + print trainval_jpeg_list[-1], "not exist" + if not os.path.exists(trainval_xml_list[-1]): + print trainval_xml_list[-1], "not exist" + if name == "VOC2007": + file_path = os.path.join(txt_fold, "test.txt") + with open(file_path, 'r') as fp: + for line in fp: + line = line.strip() + test07_jpeg_list.append(os.path.join(jpeg_fold, "{}.jpg".format(line))) + test07_xml_list.append(os.path.join(xml_fold, "{}.xml".format(line))) + if not os.path.exists(test07_jpeg_list[-1]): + print test07_jpeg_list[-1], "not exist" + if not os.path.exists(test07_xml_list[-1]): + print test07_xml_list[-1], "not exist" + elif name == "VOC2012": + file_path = os.path.join(txt_fold, "test.txt") + with open(file_path, 'r') as fp: + for line in fp: + line = line.strip() + test12_jpeg_list.append(os.path.join(jpeg_fold, "{}.jpg".format(line))) + if not os.path.exists(test12_jpeg_list[-1]): + print test12_jpeg_list[-1], "not exist" + +with open("trainval.txt", "w") as wr: + for i in range(len(trainval_jpeg_list)): + wr.write("{} {}\n".format(trainval_jpeg_list[i], trainval_xml_list[i])) + +with open("test_2007.txt", "w") as wr: + for i in range(len(test07_jpeg_list)): + wr.write("{} {}\n".format(test07_jpeg_list[i], test07_xml_list[i])) + +with open("test_2012.txt", "w") as wr: + for i in range(len(test12_jpeg_list)): + wr.write("{}\n".format(test12_jpeg_list[i])) + diff --git a/examples/yolo/data/images/dog.jpg b/examples/yolo/data/images/dog.jpg new file mode 100644 index 000000000..77b038122 Binary files /dev/null and b/examples/yolo/data/images/dog.jpg differ diff --git a/examples/yolo/data/images/eagle.jpg b/examples/yolo/data/images/eagle.jpg new file mode 100644 index 000000000..8b7509505 Binary files /dev/null and b/examples/yolo/data/images/eagle.jpg differ diff --git a/examples/yolo/data/images/giraffe.jpg b/examples/yolo/data/images/giraffe.jpg new file mode 100644 index 000000000..a93e8b883 Binary files /dev/null and b/examples/yolo/data/images/giraffe.jpg differ diff --git a/examples/yolo/data/images/horses.jpg b/examples/yolo/data/images/horses.jpg new file mode 100644 index 000000000..3a761f46b Binary files /dev/null and b/examples/yolo/data/images/horses.jpg differ diff --git a/examples/yolo/data/images/kite.jpg b/examples/yolo/data/images/kite.jpg new file mode 100644 index 000000000..9eb325ac5 Binary files /dev/null and b/examples/yolo/data/images/kite.jpg differ diff --git a/examples/yolo/data/images/person.jpg b/examples/yolo/data/images/person.jpg new file mode 100644 index 000000000..61d377fff Binary files /dev/null and b/examples/yolo/data/images/person.jpg differ diff --git a/examples/yolo/data/images/scream.jpg b/examples/yolo/data/images/scream.jpg new file mode 100644 index 000000000..43f2c36a8 Binary files /dev/null and b/examples/yolo/data/images/scream.jpg differ diff --git a/examples/yolo/data/label_map.txt b/examples/yolo/data/label_map.txt new file mode 100644 index 000000000..1fe873a84 --- /dev/null +++ b/examples/yolo/data/label_map.txt @@ -0,0 +1,20 @@ +aeroplane 0 +bicycle 1 +bird 2 +boat 3 +bottle 4 +bus 5 +car 6 +cat 7 +chair 8 +cow 9 +diningtable 10 +dog 11 +horse 12 +motorbike 13 +person 14 +pottedplant 15 +sheep 16 +sofa 17 +train 18 +tvmonitor 19 diff --git a/examples/yolo/test_yolo_V2.sh b/examples/yolo/test_yolo_V2.sh new file mode 100644 index 000000000..da4961e0f --- /dev/null +++ b/examples/yolo/test_yolo_V2.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env sh + +CAFFE_HOME=../.. + +PROTO=$CAFFE_HOME/models/intel_optimized_models/yolo/yolov2_test.prototxt +MODEL=$CAFFE_HOME/models/intel_optimized_models/yolo/yolov2_iter_80000.caffemodel +ITER=619 + +echo $MODEL + +$CAFFE_HOME/build/tools/test_detection \ + --model=$PROTO --iterations=$ITER \ + --weights=$MODEL --objects=5 --classes=20 --side=17 \ No newline at end of file diff --git a/examples/yolo/train_yolo_V2.sh b/examples/yolo/train_yolo_V2.sh new file mode 100644 index 000000000..90050c130 --- /dev/null +++ b/examples/yolo/train_yolo_V2.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env sh + +CAFFE_HOME=../.. + +SOLVER=$CAFFE_HOME/models/intel_optimized_models/yolo/yolov2_solver.prototxt + +#SNAPSHOT +WEIGHTS=$CAFFE_HOME/models/intel_optimized_models/yolo/darknet19_448.caffemodel + +$CAFFE_HOME/build/tools/caffe train \ + --solver=$SOLVER --weights=$WEIGHTS 2>&1 | tee train_YOLO_V2.log diff --git a/external/mkl/prepare_mkl.sh b/external/mkl/prepare_mkl.sh index 708a746c3..18bed0f82 100755 --- a/external/mkl/prepare_mkl.sh +++ b/external/mkl/prepare_mkl.sh @@ -74,11 +74,11 @@ echo $VERSION_LINE # Return Version Line # MKL DST=`dirname $0` OMP=0 -VERSION_MATCH=20171227 -ARCHIVE_BASENAME=mklml_lnx_2018.0.1.20171227.tgz +VERSION_MATCH=20180710 +ARCHIVE_BASENAME=mklml_lnx_2019.0.20180710.tgz MKL_CONTENT_DIR=`echo $ARCHIVE_BASENAME | rev | cut -d "." -f 2- | rev` -MKLURL="https://github.com/01org/mkl-dnn/releases/download/v0.12/$ARCHIVE_BASENAME" +MKLURL="https://github.com/intel/mkl-dnn/releases/download/v0.16/$ARCHIVE_BASENAME" # there are diffrent MKL lib to be used for GCC and for ICC reg='^[0-9]+$' VERSION_LINE=`GetVersionName $MKLROOT` diff --git a/external/mlsl/prepare_mlsl.sh b/external/mlsl/prepare_mlsl.sh index 2b1f5d10d..9e9bdc6c3 100755 --- a/external/mlsl/prepare_mlsl.sh +++ b/external/mlsl/prepare_mlsl.sh @@ -58,7 +58,7 @@ fi } # Clean up the previous MLSL version -# Can be used for l_mlsl_2017.1.016, l_mlsl_2017.2.018 +# Can be used for l_mlsl_2017.1.016, l_mlsl_2017.2.018, l_mlsl_2018.0.003 CleanUpPreviousMLSL() { version_year=$1 @@ -93,17 +93,18 @@ if [ -z $MLSL_ROOT ]; then CleanUpPreviousMLSL2017_0_014 CleanUpPreviousMLSL 2017 1 016 CleanUpPreviousMLSL 2017 2 018 + CleanUpPreviousMLSL 2018 0 003 fi -VERSION_MATCH=20180003 -ARCHIVE_BASENAME=l_mlsl_2018.0.003.tgz -ARCHIVE_INSTALL_FOLDERNAME=l_mlsl_2018.0.003 +VERSION_MATCH=20181005 +ARCHIVE_BASENAME=l_mlsl_2018.1.005.tgz +ARCHIVE_INSTALL_FOLDERNAME=l_mlsl_2018.1.005 #because the l_mlsl_2018.0.003.tgz will unpacked files.tar.gz and install.sh to the ARCHIVE_INSTALL_FOLDERNAME #not unpacked to the DST folder (Different behavior against l_mlsl_p_2017.0.014.tgz) -ARCHIVE_INSTALL_FOLDERNAME_TEMP=l_mlsl_2018.0.003_temp +ARCHIVE_INSTALL_FOLDERNAME_TEMP=l_mlsl_2018.1.005_temp MLSL_CONTENT_DIR=`echo $ARCHIVE_BASENAME | rev | cut -d "." -f 2- | rev` #echo "[Debug] MLSL_CONTENT_DIR value: $MLSL_CONTENT_DIR" -GITHUB_RELEASE_TAG=v2018-Preview +GITHUB_RELEASE_TAG=v2018.1-Preview MLSLURL="https://github.com/01org/MLSL/releases/download/$GITHUB_RELEASE_TAG/$ARCHIVE_BASENAME" #echo "[Debug] MLSL_ROOT value: $MLSL_ROOT" @@ -132,14 +133,14 @@ if [ -z $MLSL_ROOT ] || [ $VERSION_LINE -lt $VERSION_MATCH ]; then wget --no-check-certificate -P $DST $MLSLURL -O $DST/$ARCHIVE_BASENAME if [ ! -d $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP ]; then mkdir $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP - #echo "[Debug] Create l_mlsl_2018.0.003_temp folder for unpacking!" + #echo "[Debug] Create l_mlsl_2018.1.005_temp folder for unpacking!" fi tar -xzf $DST/$ARCHIVE_BASENAME -C $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP #echo "[Debug] PWD value: $PWD" #install.sh did not support the relative path as the parameter bash $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP/$ARCHIVE_INSTALL_FOLDERNAME/install.sh -s -d $ABS_DST/$ARCHIVE_INSTALL_FOLDERNAME rm -rf $DST/$ARCHIVE_INSTALL_FOLDERNAME_TEMP - #echo "[Debug] Remove l_mlsl_2018.0.003_temp folder for unpacking!" + #echo "[Debug] Remove l_mlsl_2018.1.005_temp folder for unpacking!" fi #else: version is just our expected version, no need to donload again, but need to set the MLSL_ROOT #do not change the value of MLSL_ROOT if MLSL_ROOT is set, but version is not given diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp index f1704e37a..6dfcc0e15 100644 --- a/include/caffe/data_transformer.hpp +++ b/include/caffe/data_transformer.hpp @@ -53,6 +53,13 @@ namespace caffe { class DataReader; +class BoxLabel { + public: + float class_label_; + float difficult_; + float box_[4]; +}; + class RandNumbers { public: /** @@ -192,7 +199,7 @@ class DataTransformer { Blob* transformed_blob, vector* transformed_anno_vec, RandNumbers& rand_num); - + void Transform(const Datum& datum, Blob* transformed_blob, vector* box_labels); /** * @brief Transform the annotation according to the transformation applied * to the datum. diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp index fa1f45356..7168cdcf0 100644 --- a/include/caffe/internal_thread.hpp +++ b/include/caffe/internal_thread.hpp @@ -78,11 +78,19 @@ class InternalThread { /* Should be tested when running loops to exit when requested. */ bool must_stop(); - void SetThreadAffinity(); + void SetThreadAffinity( +#ifdef USE_MLSL + int ppn, int rank +#endif + ); private: void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count, - bool root_solver); + bool root_solver +#ifdef USE_MLSL + , int ppn, int rank +#endif + ); shared_ptr thread_; }; diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp index 5a95a7730..7361c23b2 100644 --- a/include/caffe/layer.hpp +++ b/include/caffe/layer.hpp @@ -521,12 +521,7 @@ class Layer { CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be " "unspecified or specified once per top blob."; for (int top_id = 0; top_id < top.size(); ++top_id) { -#ifdef USE_MLSL - const Dtype loss_weight = layer_param_.loss_weight(top_id) / - GetDistribution().get_data_parts(); -#else const Dtype loss_weight = layer_param_.loss_weight(top_id); -#endif if (loss_weight == Dtype(0)) { continue; } this->set_loss(top_id, loss_weight); const int count = top[top_id]->count(); diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp index 0a428232a..c1b925ee0 100644 --- a/include/caffe/layers/base_data_layer.hpp +++ b/include/caffe/layers/base_data_layer.hpp @@ -80,12 +80,14 @@ class BaseDataLayer : public Layer { TransformationParameter transform_param_; shared_ptr > data_transformer_; bool output_labels_; + bool box_label_; }; template class Batch { public: Blob data_, label_; + vector > > multi_label_; }; template diff --git a/include/caffe/layers/box_data_layer.hpp b/include/caffe/layers/box_data_layer.hpp new file mode 100755 index 000000000..7cb7aa966 --- /dev/null +++ b/include/caffe/layers/box_data_layer.hpp @@ -0,0 +1,42 @@ +#ifndef CAFFE_DATA_LAYER_HPP_ +#define CAFFE_DATA_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/data_reader.hpp" +#include "caffe/data_transformer.hpp" +#include "caffe/internal_thread.hpp" +#include "caffe/layer.hpp" +#include "caffe/layers/base_data_layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include "caffe/util/db.hpp" + +namespace caffe { + +template +class BoxDataLayer : public BasePrefetchingDataLayer { + public: + explicit BoxDataLayer(const LayerParameter& param); + virtual ~BoxDataLayer(); + virtual void DataLayerSetUp(const vector*>& bottom, + const vector*>& top); + // DataLayer uses DataReader instead for sharing for parallelism + virtual inline bool ShareInParallel() const { return false; } + virtual inline const char* type() const { return "BoxData"; } + virtual inline int ExactNumBottomBlobs() const { return 0; } + virtual inline int MinTopBlobs() const { return 1; } + virtual inline int MaxTopBlobs() const { return 100; } + + void transform_label(int count, Dtype* top_label, const vector& box_labels, int side); + + protected: + virtual void load_batch(Batch* batch); + + DataReader reader_; + vector sides_; +}; + +} // namespace caffe + +#endif // CAFFE_DATA_LAYER_HPP_ diff --git a/include/caffe/layers/eval_detection_layer.hpp b/include/caffe/layers/eval_detection_layer.hpp new file mode 100644 index 000000000..f0068e6a6 --- /dev/null +++ b/include/caffe/layers/eval_detection_layer.hpp @@ -0,0 +1,48 @@ +#ifndef CAFFE_EVAL_DETECTION_LAYER_HPP_ +#define CAFFE_EVAL_DETECTION_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +template +class EvalDetectionLayer : public Layer { + public: + explicit EvalDetectionLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "EvalDetection"; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + for (int i = 0; i < propagate_down.size(); ++i) { + if (propagate_down[i]) { NOT_IMPLEMENTED; } + } + } + + int side_; + int num_class_; + int num_object_; + float threshold_; + //bool sqrt_; + //bool constriant_; + int score_type_; + float nms_; + vector biases_; +}; + +} // namespace caffe + +#endif // CAFFE_EVAL_DETECTION_LAYER_HPP_ diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp old mode 100644 new mode 100755 index 367ef4246..3b3caa27c --- a/include/caffe/layers/infogain_loss_layer.hpp +++ b/include/caffe/layers/infogain_loss_layer.hpp @@ -1,40 +1,3 @@ -/* -All modification made by Intel Corporation: © 2016 Intel Corporation - -All contributions by the University of California: -Copyright (c) 2014, 2015, The Regents of the University of California (Regents) -All rights reserved. - -All other contributions: -Copyright (c) 2014, 2015, the respective contributors -All rights reserved. -For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md - - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors - may be used to endorse or promote products derived from this software - without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - #ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_ #define CAFFE_INFOGAIN_LOSS_LAYER_HPP_ @@ -45,24 +8,26 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "caffe/proto/caffe.pb.h" #include "caffe/layers/loss_layer.hpp" +#include "caffe/layers/softmax_layer.hpp" namespace caffe { /** - * @brief A generalization of MultinomialLogisticLossLayer that takes an + * @brief A generalization of SoftmaxWithLossLayer that takes an * "information gain" (infogain) matrix specifying the "value" of all label * pairs. * - * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the + * Equivalent to the SoftmaxWithLossLayer if the infogain matrix is the * identity. * * @param bottom input Blob vector (length 2-3) * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{p} @f$, a Blob with values in - * @f$ [0, 1] @f$ indicating the predicted probability of each of the - * @f$ K = CHW @f$ classes. Each prediction vector @f$ \hat{p}_n @f$ - * should sum to 1 as in a probability distribution: @f$ - * \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$. + * the predictions @f$ x @f$, a Blob with values in + * @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of + * the @f$ K = CHW @f$ classes. This layer maps these scores to a + * probability distribution over classes using the softmax function + * @f$ \hat{p}_{nk} = \exp(x_{nk}) / + * \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer). * -# @f$ (N \times 1 \times 1 \times 1) @f$ * the labels @f$ l @f$, an integer-valued Blob with values * @f$ l_n \in [0, 1, 2, ..., K - 1] @f$ @@ -71,7 +36,7 @@ namespace caffe { * (\b optional) the infogain matrix @f$ H @f$. This must be provided as * the third bottom blob input if not provided as the infogain_mat in the * InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the - * MultinomialLogisticLossLayer. + * SoftmaxWithLossLayer. * @param top output Blob vector (length 1) * -# @f$ (1 \times 1 \times 1 \times 1) @f$ * the computed infogain multinomial logistic loss: @f$ E = @@ -97,6 +62,12 @@ class InfogainLossLayer : public LossLayer { virtual inline int MinBottomBlobs() const { return 2; } virtual inline int MaxBottomBlobs() const { return 3; } + // InfogainLossLayer computes softmax prob internally. + // optional second "top" outputs the softmax prob + virtual inline int ExactNumTopBlobs() const { return -1; } + virtual inline int MinTopBlobs() const { return 1; } + virtual inline int MaxTopBlobs() const { return 2; } + virtual inline const char* type() const { return "InfogainLoss"; } protected: @@ -128,8 +99,8 @@ class InfogainLossLayer : public LossLayer { * infogain matrix, if provided as bottom[2]) * @param bottom input Blob vector (length 2-3) * -# @f$ (N \times C \times H \times W) @f$ - * the predictions @f$ \hat{p} @f$; Backward computes diff - * @f$ \frac{\partial E}{\partial \hat{p}} @f$ + * the predictions @f$ x @f$; Backward computes diff + * @f$ \frac{\partial E}{\partial x} @f$ * -# @f$ (N \times 1 \times 1 \times 1) @f$ * the labels -- ignored as we can't compute their error gradients * -# @f$ (1 \times 1 \times K \times K) @f$ @@ -139,7 +110,35 @@ class InfogainLossLayer : public LossLayer { virtual void Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom); + /// Read the normalization mode parameter and compute the normalizer based + /// on the blob size. If normalization_mode is VALID, the count of valid + /// outputs will be read from valid_count, unless it is -1 in which case + /// all outputs are assumed to be valid. + virtual Dtype get_normalizer( + LossParameter_NormalizationMode normalization_mode, int valid_count); + /// fill sum_rows_H_ according to matrix H + virtual void sum_rows_of_H(const Blob* H); + + /// The internal SoftmaxLayer used to map predictions to a distribution. + shared_ptr > softmax_layer_; + /// prob stores the output probability predictions from the SoftmaxLayer. + Blob prob_; + /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward + vector*> softmax_bottom_vec_; + /// top vector holder used in call to the underlying SoftmaxLayer::Forward + vector*> softmax_top_vec_; + Blob infogain_; + Blob sum_rows_H_; // cache the row sums of H. + + /// Whether to ignore instances with a certain label. + bool has_ignore_label_; + /// The label indicating that an instance should be ignored. + int ignore_label_; + /// How to normalize the output loss. + LossParameter_NormalizationMode normalization_; + + int infogain_axis_, outer_num_, inner_num_, num_labels_; }; } // namespace caffe diff --git a/include/caffe/layers/loss_layer.hpp b/include/caffe/layers/loss_layer.hpp index 9fa911d35..880bbe135 100644 --- a/include/caffe/layers/loss_layer.hpp +++ b/include/caffe/layers/loss_layer.hpp @@ -93,6 +93,8 @@ class LossLayer : public Layer { virtual inline bool AllowForceBackward(const int bottom_index) const { return bottom_index != 1; } + protected: + Dtype cached_normalizer_; }; } // namespace caffe diff --git a/include/caffe/layers/mkldnn_layers.hpp b/include/caffe/layers/mkldnn_layers.hpp index 659b8bcf8..aca64c362 100644 --- a/include/caffe/layers/mkldnn_layers.hpp +++ b/include/caffe/layers/mkldnn_layers.hpp @@ -164,10 +164,15 @@ class MKLDNNConvolutionLayer : public MKLDNNLayer , public ConvolutionLay //For test the parameters of kernel/stride/pad int GetKernelWidth() { return kernel_w_; } int GetKernelHeight() { return kernel_h_; } + int GetKernelDepth() { return kernel_d_; } + int GetStrideWidth() { return stride_w_; } int GetStrideHeight() { return stride_h_; } + int GetStrideDepth() { return stride_d_; } + int GetPadWidth() { return pad_w_; } int GetPadHeight() { return pad_h_; } + int GetPadDepth() { return pad_d_; } protected: virtual void Forward_cpu(const vector*>& bottom, const vector*>& top); virtual void Forward_gpu(const vector*>& bottom, const vector*>& top); @@ -199,8 +204,8 @@ class MKLDNNConvolutionLayer : public MKLDNNLayer , public ConvolutionLay shared_ptr fwd_bottom_data_primitive, fwd_weights_data_primitive, fwd_bias_data_primitive , bwdd_top_diff_primitive, bwdd_weights_data_primitive , bwdw_top_diff_primitive, bwdw_bottom_data_primitive; - int32_t width_, height_, width_out_, height_out_, kernel_w_, kernel_h_, stride_w_, stride_h_; - int pad_w_, pad_h_; + int32_t width_, height_, depth_, width_out_, height_out_, depth_out_, kernel_w_, kernel_h_, kernel_d_, stride_w_, stride_h_, stride_d_; + int pad_w_, pad_h_, pad_d_; mkldnn::algorithm conv_algorithm; /* In case of (iter_size > 1) we need additional buffers */ @@ -473,6 +478,7 @@ class MKLDNNConcatLayer : public MKLDNNLayer , public Layer { vector > > bwd_bottom_diff; vector > reorders; vector split_dims; + bool in_place_; int32_t num_, width_, height_, channels_, num_concats_; int concat_dimension; diff --git a/include/caffe/layers/region_loss_layer.hpp b/include/caffe/layers/region_loss_layer.hpp new file mode 100644 index 000000000..d970c8b12 --- /dev/null +++ b/include/caffe/layers/region_loss_layer.hpp @@ -0,0 +1,98 @@ +#ifndef CAFFE_REGION_LOSS_LAYER_HPP_ +#define CAFFE_REGION_LOSS_LAYER_HPP_ + +#include +#include "caffe/util/tree.hpp" +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" +#include +#include "caffe/layers/loss_layer.hpp" +#include + +namespace caffe { +template +Dtype Overlap(Dtype x1, Dtype w1, Dtype x2, Dtype w2); + +template +Dtype Calc_iou(const vector& box, const vector& truth); + +template +void disp(Blob& swap); + +template +Dtype softmax_region(Dtype* input, int classes); +//template +//Dtype softmax_region(Dtype* input, int n, float temp, Dtype* output); + +//template +//Dtype* flatten(Dtype* input_data, int size, int channels, int batch, int forward); +template +void softmax_tree(Dtype* input, tree *t); + +template +Dtype get_hierarchy_prob(Dtype* input_data, tree *t, int c); + +template +vector get_region_box(Dtype* x, vector biases, int n, int index, int i, int j, int w, int h); + +template +Dtype delta_region_box(vector truth, Dtype* x, vector biases, int n, int index, int i, int j, int w, int h, Dtype* delta, float scale); + +template +void delta_region_class(Dtype* input_data, Dtype* &diff, int index, int class_label, int classes, string softmax_tree, tree *t, float scale, Dtype* avg_cat); + +template +class RegionLossLayer : public LossLayer { + public: + explicit RegionLossLayer(const LayerParameter& param) + : LossLayer(param), diff_() {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "RegionLoss"; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + // virtual void Forward_gpu(const vector*>& bottom, + // const vector*>& top); + + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + // virtual void Backward_gpu(const vector*>& top, + // const vector& propagate_down, const vector*>& bottom); + + int side_; + int bias_match_; + int num_class_; + int coords_; + int num_; + int softmax_; + string softmax_tree_; + float jitter_; + int rescore_; + + float object_scale_; + float class_scale_; + float noobject_scale_; + float coord_scale_; + + int absolute_; + float thresh_; + int random_; + vector biases_; + + Blob diff_; + Blob real_diff_; + tree t_; + + string class_map_; + map cls_map_; +}; + +} // namespace caffe + +#endif // CAFFE_REGION_LOSS_LAYER_HPP_ diff --git a/include/caffe/layers/reorg_layer.hpp b/include/caffe/layers/reorg_layer.hpp new file mode 100644 index 000000000..90870cfc3 --- /dev/null +++ b/include/caffe/layers/reorg_layer.hpp @@ -0,0 +1,113 @@ +#ifndef CAFFE_REORG_LAYER_HPP_ +#define CAFFE_REORG_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +/* + * @brief Reshapes the input Blob into an arbitrary-sized output Blob. + * + * Note: similarly to FlattenLayer, this layer does not change the input values + * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff). + */ + template + class ReorgLayer : public Layer { + public: + explicit ReorgLayer(const LayerParameter ¶m) + : Layer(param) {} + + virtual void LayerSetUp(const vector *> &bottom, + const vector *> &top); + + virtual void Reshape(const vector *> &bottom, + const vector *> &top); + + virtual inline const char *type() const { return "Reorg"; } + + virtual inline int ExactNumBottomBlobs() const { return 1; } + + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + + + virtual void Forward_cpu(const vector *> &bottom, + const vector *> &top); + + virtual void Backward_cpu(const vector *> &top, + const vector &propagate_down, const vector *> &bottom); + + virtual void Forward_gpu(const vector *> &bottom, + const vector *> &top); + + virtual void Backward_gpu(const vector *> &top, + const vector &propagate_down, const vector *> &bottom); + + int stride_; + bool reverse_; + int batch_num_; + int channels_; + int reorged_channels_; + int height_, width_; + int reorged_height_, reorged_width_; + Blob diff_; + }; + template + void reorg_cpu(Dtype *x, int w, int h, int c, int batch, int stride, int forward, Dtype *out) + { + int b,i,j,k; + int out_c = c/(stride*stride); + + for(b = 0; b < batch; ++b){ + for(k = 0; k < c; ++k){ + for(j = 0; j < h; ++j){ + for(i = 0; i < w; ++i){ + int in_index = i + w*(j + h*(k + c*b)); + int c2 = k % out_c; + int offset = k / out_c; + int w2 = i*stride + offset % stride; + int h2 = j*stride + offset / stride; + int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); + if(forward) out[out_index] = x[in_index]; + else out[in_index] = x[out_index]; + } + } + } + } + } + + template + void reorg_cpu(const Dtype *bottom_data, const int b_w, const int b_h, + const int b_c, const int b_n, const int stride, + const bool forward, Dtype *top_data) { + int t_c = b_c / (stride * stride); + int t_w = b_w * stride; + int t_h = b_h * stride; + for (int n = 0; n < b_n; n++) { + for (int c = 0; c < b_c; c++) { + for (int h = 0; h < b_h; h++) { + for (int w = 0; w < b_w; w++) { + int bottom_index = w + b_w * (h + b_h * (c + b_c * n)); + int c2 = c % t_c; + int offset = c / t_c; + int w2 = w * stride + offset % stride; + int h2 = h * stride + offset / stride; + int top_index = w2 + t_w * (h2 + t_h * (c2 + t_c * n)); + if (forward) top_data[top_index] = bottom_data[bottom_index]; + else + top_data[bottom_index] = bottom_data[top_index]; + } + } + } + } + } + + +} // namespace caffe + +#endif // CAFFE_REORG_LAYER_HPP_ diff --git a/include/caffe/layers/reshape_layer.hpp b/include/caffe/layers/reshape_layer.hpp index 16ef36f8e..42f96c3a0 100644 --- a/include/caffe/layers/reshape_layer.hpp +++ b/include/caffe/layers/reshape_layer.hpp @@ -68,13 +68,13 @@ class ReshapeLayer : public Layer { protected: virtual void Forward_cpu(const vector*>& bottom, - const vector*>& top) {} + const vector*>& top); virtual void Backward_cpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} - virtual void Forward_gpu(const vector*>& bottom, - const vector*>& top) {} - virtual void Backward_gpu(const vector*>& top, - const vector& propagate_down, const vector*>& bottom) {} + const vector& propagate_down, const vector*>& bottom); +// virtual void Forward_gpu(const vector*>& bottom, +// const vector*>& top) {} +// virtual void Backward_gpu(const vector*>& top, +// const vector& propagate_down, const vector*>& bottom) {} /// @brief vector of axes indices whose dimensions we'll copy from the bottom vector copy_axes_; diff --git a/include/caffe/layers/swish_layer.hpp b/include/caffe/layers/swish_layer.hpp new file mode 100644 index 000000000..6a41e3664 --- /dev/null +++ b/include/caffe/layers/swish_layer.hpp @@ -0,0 +1,133 @@ +/* +All modification made by Intel Corporation: © 2018 Intel Corporation + +All contributions by the University of California: +Copyright (c) 2014, 2015, The Regents of the University of California (Regents) +All rights reserved. + +All other contributions: +Copyright (c) 2014, 2015, the respective contributors +All rights reserved. +For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md + + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of Intel Corporation nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef CAFFE_SWISH_LAYER_HPP_ +#define CAFFE_SWISH_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +#include "caffe/layers/neuron_layer.hpp" +#include "caffe/layers/sigmoid_layer.hpp" + +namespace caffe { + +/** + * @brief Swish non-linearity @f$ y = x \sigma (\beta x) @f$. + * A novel activation function that tends to work better than ReLU [1]. + * + * [1] Prajit Ramachandran, Barret Zoph, Quoc V. Le. "Searching for + * Activation Functions". arXiv preprint arXiv:1710.05941v2 (2017). + */ +template +class SwishLayer : public NeuronLayer { + public: + /** + * @param param provides SwishParameter swish_param, + * with SwishLayer options: + * - beta (\b optional, default 1). + * the value @f$ \beta @f$ in the @f$ y = x \sigma (\beta x) @f$. + */ + explicit SwishLayer(const LayerParameter& param) + : NeuronLayer(param), + sigmoid_layer_(new SigmoidLayer(param)), + sigmoid_input_(new Blob()), + sigmoid_output_(new Blob()) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "Swish"; } + + protected: + /** + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$ + * @param top output Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the computed outputs @f$ + * y = x \sigma (\beta x) + * @f$. + */ + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + + /** + * @brief Computes the error gradient w.r.t. the sigmoid inputs. + * + * @param top output Blob vector (length 1), providing the error gradient with + * respect to the outputs + * -# @f$ (N \times C \times H \times W) @f$ + * containing error gradients @f$ \frac{\partial E}{\partial y} @f$ + * with respect to computed outputs @f$ y @f$ + * @param propagate_down see Layer::Backward. + * @param bottom input Blob vector (length 1) + * -# @f$ (N \times C \times H \times W) @f$ + * the inputs @f$ x @f$; Backward fills their diff with + * gradients @f$ + * \frac{\partial E}{\partial x} + * = \frac{\partial E}{\partial y}(\beta y + + * \sigma (\beta x)(1 - \beta y)) + * @f$ if propagate_down[0] + */ + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + /// The internal SigmoidLayer + shared_ptr > sigmoid_layer_; + /// sigmoid_input_ stores the input of the SigmoidLayer. + shared_ptr > sigmoid_input_; + /// sigmoid_output_ stores the output of the SigmoidLayer. + shared_ptr > sigmoid_output_; + /// bottom vector holder to call the underlying SigmoidLayer::Forward + vector*> sigmoid_bottom_vec_; + /// top vector holder to call the underlying SigmoidLayer::Forward + vector*> sigmoid_top_vec_; +}; + +} // namespace caffe + +#endif // CAFFE_SWISH_LAYER_HPP_ diff --git a/include/caffe/mkldnn_memory.hpp b/include/caffe/mkldnn_memory.hpp index 7bb4a3ae2..671961d1f 100644 --- a/include/caffe/mkldnn_memory.hpp +++ b/include/caffe/mkldnn_memory.hpp @@ -61,15 +61,10 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr MKLDNNMemoryDescriptorBase(shared_ptr usr_memory_pd , shared_ptr prv_memory_pd , Blob* blob, MKLDNNLayer* mkldnn_layer - , std::vectorfl=std::vector(1,0) - , bool is_sum=false); - - MKLDNNMemoryDescriptorBase(shared_ptr usr_memory_pd - , shared_ptr prv_memory_pd - , Blob* blob, MKLDNNLayer* mkldnn_layer - , bool is_float , std::vectorscale=std::vector(1,1.) - , bool is_sum=false); + , int mask=0 + , bool is_sum=false + , bool is_wino=false); ~MKLDNNMemoryDescriptorBase() {} @@ -113,16 +108,9 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr std::vector get_scale() { return _scale; } void set_scale(std::vector scale) { _scale.assign(scale.begin(),scale.end());} - int get_fl(int i) { return _fl[i]; } - std::vector get_fl() { return _fl; } - void set_fl(std::vector fl) { _fl.assign(fl.begin(),fl.end());} - void set_sum(bool is_sum) { _is_sum = is_sum; } bool get_sum() { return _is_sum; } - void set_float(bool is_float) { _is_float = is_float; } - bool get_float() { return _is_float; } - void set_mkldnn_layer(MKLDNNLayer* layer) { _mkldnn_layer = layer; } MKLDNNLayer* mkldnn_layer() const { return _mkldnn_layer; } @@ -154,27 +142,12 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr // TODO: may need initialize memory by 0 } } - void set_prv_memory_pd(shared_ptr memory_pd, std::vector scale) { + void set_prv_memory_pd(shared_ptr memory_pd, std::vector scale, int mask, bool is_wino) { _prv_memory_pd = memory_pd; if (_prv_memory_pd && _usr_memory_pd) { check_usr_with_prv_descriptors(); - this->create_reorder_descriptors(scale); - } - } - - void set_prv_memory_pd(shared_ptr memory_pd, std::vector fl) { - _prv_memory_pd = memory_pd; - if (_prv_memory_pd && _usr_memory_pd) { - check_usr_with_prv_descriptors(); - this->create_reorder_descriptors(fl); - } - } - - void set_extprv_memory_pd(shared_ptr memory_pd, std::vector fl, std::vector fl_ext, bool is_sum) { - _extprv_memory_pd = memory_pd; - if (_prv_memory_pd && _usr_memory_pd) { - check_usr_with_prv_descriptors(); - this->create_reorder_descriptors(fl, fl_ext, is_sum); + std::vectorscale_ext = std::vector(1,1.); + this->create_reorder_descriptors(scale, mask, scale_ext, false, is_wino); } } @@ -182,7 +155,7 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr _extprv_memory_pd = memory_pd; if (_prv_memory_pd && _usr_memory_pd) { check_usr_with_prv_descriptors(); - this->create_reorder_descriptors(scale, scale_ext, is_sum); + this->create_reorder_descriptors(scale, 0, scale_ext, is_sum); } } @@ -190,13 +163,7 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr _usr_memory_pd = memory_pd; } - void set_usr_memory_pd(shared_ptr memory_pd, std::vector fl) { - _usr_memory_pd = memory_pd; - } - - void create_reorder_descriptors(std::vector scale, std::vectorscale_ext=std::vector(1,1.), bool is_sum=false); - - void create_reorder_descriptors(std::vector fl, std::vectorfl_ext=std::vector(1,0), bool is_sum=false); + void create_reorder_descriptors(std::vector scale, int mask=0, std::vectorscale_ext=std::vector(1,1.), bool is_sum=false, bool is_wino=false); shared_ptr _usr_memory_pd; shared_ptr _prv_memory_pd; @@ -223,9 +190,7 @@ class MKLDNNMemoryDescriptorBase : public PrvMemDescr MKLDNNLayer* _mkldnn_layer; Blob* _blob; std::vector _scale = std::vector(1,1.); - std::vector _fl = std::vector(1,0); bool _is_sum = false; - bool _is_float = false; #ifdef USE_MLSL shared_ptr _mlsl_memory; #endif @@ -237,15 +202,10 @@ class MKLDNNMemoryDescriptor : public MKLDNNMemoryDescriptorBase { MKLDNNMemoryDescriptor(shared_ptr usr_memory_pd , shared_ptr prv_memory_pd , Blob* blob, MKLDNNLayer* mkldnn_layer - , std::vector fl=std::vector(1,0) - , bool is_sum=false); - - MKLDNNMemoryDescriptor(shared_ptr usr_memory_pd - , shared_ptr prv_memory_pd - , Blob* blob, MKLDNNLayer* mkldnn_layer - , bool is_float , std::vector scale=std::vector(1,1.) - , bool is_sum=false); + , int mask=0 + , bool is_sum=false + , bool is_wino=false); virtual void convert_from_prv(void* cpu_ptr); virtual void convert_to_prv(void* cpu_ptr); @@ -290,17 +250,11 @@ class MKLDNNData : public MKLDNNMemoryDescriptor MKLDNNData(shared_ptr usr_memory_pd , shared_ptr prv_memory_pd , Blob* blob, MKLDNNLayer* mkldnn_layer - , std::vector fl=std::vector(1,0) - , bool is_sum=false) - : MKLDNNMemoryDescriptor(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, fl, is_sum) {} - - MKLDNNData(shared_ptr usr_memory_pd - , shared_ptr prv_memory_pd - , Blob* blob, MKLDNNLayer* mkldnn_layer - , bool is_float , std::vector scale=std::vector(1,1.) - , bool is_sum=false) - : MKLDNNMemoryDescriptor(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, is_float, scale, is_sum) {} + , int mask=0 + , bool is_sum=false + , bool is_wino=false) + : MKLDNNMemoryDescriptor(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, scale, mask, is_sum, is_wino) {} }; template diff --git a/include/caffe/multinode/mlsl.hpp b/include/caffe/multinode/mlsl.hpp index 02360f8c0..6a03c64a0 100644 --- a/include/caffe/multinode/mlsl.hpp +++ b/include/caffe/multinode/mlsl.hpp @@ -49,8 +49,14 @@ namespace caffe { #define MLSL_DEFAULT_COLOR -1 + extern MLSL::Distribution *global_distrib; + void init(int* argc, char** argv[]); + inline void barrier() { + global_distrib->Barrier(MLSL::GT_DATA); + } + inline void free(void *addr) { return MLSL::Environment::GetEnv().Free(addr); } @@ -85,15 +91,29 @@ namespace caffe { } inline int get_world_size() { - int size = 0; - MPI_Comm_size(MPI_COMM_WORLD, &size); - return size; + return global_distrib->GetProcessCount(MLSL::GT_DATA); } inline int get_node_rank() { - int rank = -1; + return global_distrib->GetProcessIdx(MLSL::GT_DATA); + } + + inline int get_ppn() { + int is_mpi_inited = 0; + MPI_Initialized(&is_mpi_inited); + assert(is_mpi_inited); + + int ppn = 1; + int rank; MPI_Comm_rank(MPI_COMM_WORLD, &rank); - return rank; + MPI_Comm node_comm; + MPI_Comm_split_type(MPI_COMM_WORLD, MPI_COMM_TYPE_SHARED, rank, MPI_INFO_NULL, &node_comm); + MPI_Comm_size(node_comm, &ppn); + MPI_Comm_free(&node_comm); + + DLOG(INFO) << "ppn " << ppn; + + return ppn; } inline bool is_multinode() { diff --git a/include/caffe/multinode/multi_sync.hpp b/include/caffe/multinode/multi_sync.hpp index 79d88b328..b26ce4a5d 100644 --- a/include/caffe/multinode/multi_sync.hpp +++ b/include/caffe/multinode/multi_sync.hpp @@ -437,6 +437,7 @@ namespace caffe { // communication for (int i = layers.size() - 1; i >= 0; i--) { for (int j = layer_param_ids[i].size() - 1; j >= 0; j--) { + if (!layers[i]->ParamNeedReduce(j)) continue; int param_id = layer_param_ids[i][j]; if (!broadcast_launched[param_id]) { if (irecv_done[param_id]) { @@ -461,6 +462,7 @@ namespace caffe { } else { std::vector ¶m_ids = layer_param_ids[layer_id]; for (int i=param_ids.size() - 1; i >= 0; --i) { + if (!layer->ParamNeedReduce(i)) continue; launch_param_broadcast(param_ids[i]); } } @@ -486,6 +488,7 @@ namespace caffe { check_and_launch_broadcast(); bcast_launched = true; for (int i = param_ids.size() - 1; i >= 0; i--) { + if (!layers[layer_id]->ParamNeedReduce(i)) continue; bcast_launched = bcast_launched & broadcast_launched[param_ids[i]]; } } @@ -496,6 +499,8 @@ namespace caffe { #endif for (int i = param_ids.size() - 1; i >= 0; i--) { + if (!layers[layer_id]->ParamNeedReduce(i)) continue; + int param_id = param_ids[i]; DLOG(INFO) << " Wait reduce layer id " << layer_id << " param id " << param_id; // wait for reduce diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp index 0d69f671b..4892d46b7 100644 --- a/include/caffe/net.hpp +++ b/include/caffe/net.hpp @@ -327,7 +327,8 @@ class Net { static void CompilationRuleFuseBnRelu(const NetParameter& param, NetParameter* param_compiled); - /** + + /** * @brief If find "Conv--BN--Scale" in current network, merge BN and Scale layer into Convolution * layers, this optimization only works in caffe TEST phase now. */ @@ -452,6 +453,9 @@ class Net { bool bn_scale_remove_; bool bn_scale_merge_; vector kept_bn_layers_; + + vector first_conv_quantized_shift_; + vector first_conv_names_; /// @brief The network name string name_; /// @brief The engine name diff --git a/include/caffe/quant/base_quant_layer.hpp b/include/caffe/quant/base_quant_layer.hpp index e9deacdd1..a878507be 100644 --- a/include/caffe/quant/base_quant_layer.hpp +++ b/include/caffe/quant/base_quant_layer.hpp @@ -51,10 +51,9 @@ class BaseQuantLayer{ protected: int bw_params_, bw_layer_in_, bw_layer_out_; vector fl_params_, fl_layer_in_, fl_layer_out_; - vector scale_params_, scale_in_, scale_out_; + vector scale_params_, scale_in_, scale_out_; // min_in_; int rounding_, precision_; bool need_quantize_; - bool is_float_; }; } // namespace caffe diff --git a/include/caffe/quant/quantization.hpp b/include/caffe/quant/quantization.hpp index 0f2420be1..b932f2323 100644 --- a/include/caffe/quant/quantization.hpp +++ b/include/caffe/quant/quantization.hpp @@ -55,7 +55,7 @@ using caffe::Net; class Quantization { public: explicit Quantization(string model, string weights, string model_quantized, - int iterations, string trimming_mode, double error_margin, int score_number, string scaling="single", int detection=0, int power=1); + int iterations, string trimming_mode, double error_margin, int score_number, string scaling="single", int detection=0); void QuantizeNet(); private: void CheckWritePermissions(const string path); @@ -88,19 +88,16 @@ class Quantization { const string layer_quantize, const string network_part, const int bw_conv, const int bw_in, const int bw_out); - vector GetIntegerLengthParams(const string layer_name); vector GetScaleParams(const string layer_name); /** * @brief Find the integer length for dynamic fixed point inputs of a certain * layer. */ - int GetIntegerLengthIn(const string layer_name); float GetScaleIn(const string layer_name); /** * @brief Find the integer length for dynamic fixed point outputs of a certain * layer. */ - int GetIntegerLengthOut(const string layer_name); float GetScaleOut(const string layer_name); string model_; @@ -112,7 +109,6 @@ class Quantization { int score_number; string scaling; int detection; - int power; float test_score_baseline_; // The maximal absolute values of layer inputs, parameters and // layer outputs. diff --git a/include/caffe/util/bbox_util.hpp b/include/caffe/util/bbox_util.hpp index e33e88f6b..a592893c2 100644 --- a/include/caffe/util/bbox_util.hpp +++ b/include/caffe/util/bbox_util.hpp @@ -180,6 +180,80 @@ void MatchBBox(const vector& gt, const bool ignore_cross_boundary_bbox, vector* match_indices, vector* match_overlaps); +template +void disp(Blob& swap) +{ + std::cout<<"#######################################"< +Dtype Overlap(Dtype x1, Dtype w1, Dtype x2, Dtype w2) { + Dtype l1 = x1 - w1/2; + Dtype l2 = x2 - w2/2; + Dtype left = l1 > l2 ? l1 : l2; + Dtype r1 = x1 + w1/2; + Dtype r2 = x2 + w2/2; + Dtype right = r1 < r2 ? r1 : r2; + return right - left; +} + +template +void setNormalizedBBox(NormalizedBBox& bbox, Dtype x, Dtype y, Dtype w, Dtype h) +{ + Dtype xmin = x - w/2.0; + Dtype xmax = x + w/2.0; + Dtype ymin = y - h/2.0; + Dtype ymax = y + h/2.0; + + if (xmin < 0.0){ + xmin = 0.0; + } + if (xmax > 1.0){ + xmax = 1.0; + } + if (ymin < 0.0){ + ymin = 0.0; + } + if (ymax > 1.0){ + ymax = 1.0; + } + bbox.set_xmin(xmin); + bbox.set_ymin(ymin); + bbox.set_xmax(xmax); + bbox.set_ymax(ymax); + float bbox_size = BBoxSize(bbox, true); + bbox.set_size(bbox_size); +} + +template +Dtype Calc_iou(const vector& box, const vector& truth) { + NormalizedBBox Bbox1, Bbox2; + setNormalizedBBox(Bbox1, box[0], box[1], box[2], box[3]); + setNormalizedBBox(Bbox2, truth[0], truth[1], truth[2], truth[3]); + return JaccardOverlap(Bbox1, Bbox2, true); +} + +//Real optimization core of NMS +void cpu_nms_avx512_parallize_inner(int* __restrict keep_out, + int* __restrict num_out, + const float* __restrict x1, + const float* __restrict y1, + const float* __restrict x2, + const float* __restrict y2, + int64_t boxes_num, float thresh_f); + // Find matches between prediction bboxes and ground truth bboxes. // all_loc_preds: stores the location prediction, where each item contains // location prediction for an image. diff --git a/include/caffe/util/tree.hpp b/include/caffe/util/tree.hpp new file mode 100755 index 000000000..ef4cdbb87 --- /dev/null +++ b/include/caffe/util/tree.hpp @@ -0,0 +1,28 @@ +#ifndef TREE_H +#define TREE_H +#include +#include + +using std::vector; +using std::string; + +class tree +{ +public: + tree(); + tree(string filename); + + vector leaf; + int n; + vector parent; + vector child; + vector group; + vector name; + + int groups; + vector group_size; + vector group_offset; +}; + + +#endif // TREE_H diff --git a/mkldnn.commit b/mkldnn.commit index ab7cef75c..48f2006cd 100644 --- a/mkldnn.commit +++ b/mkldnn.commit @@ -1 +1 @@ -464c268e544bae26f9b85a2acb9122c766a4c396 +4e333787e0d66a1dca1218e99a891d493dbc8ef1 diff --git a/models/intel_optimized_models/benchmark/googlenet_v3/solver.prototxt b/models/intel_optimized_models/benchmark/googlenet_v3/solver.prototxt new file mode 100644 index 000000000..603a11e10 --- /dev/null +++ b/models/intel_optimized_models/benchmark/googlenet_v3/solver.prototxt @@ -0,0 +1,22 @@ +net: "models/intel_optimized_models/benchmark/googlenet_v3/train_val.prototxt" + +warmup_iter: 3125 +warmup_start_lr: 0.05 + +test_iter: 1000 +test_interval: 6250 +test_initialization: false + +display: 100 + +max_iter: 62500 # 100 epochs +base_lr: 0.6 + +lr_policy: "poly" +power: 2 +momentum: 0.9 +weight_decay: 0.0002 + +random_seed: 1 + +solver_mode: CPU diff --git a/models/intel_optimized_models/benchmark/googlenet_v3/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet_v3/solver_dummydata.prototxt new file mode 100644 index 000000000..60b5196aa --- /dev/null +++ b/models/intel_optimized_models/benchmark/googlenet_v3/solver_dummydata.prototxt @@ -0,0 +1,22 @@ +net: "models/intel_optimized_models/benchmark/googlenet_v3/train_val_dummydata.prototxt" + +warmup_iter: 3125 +warmup_start_lr: 0.05 + +test_iter: 1000 +test_interval: 6250 +test_initialization: false + +display: 100 + +max_iter: 62500 # 100 epochs +base_lr: 0.6 + +lr_policy: "poly" +power: 2 +momentum: 0.9 +weight_decay: 0.0002 + +random_seed: 1 + +solver_mode: CPU diff --git a/models/intel_optimized_models/benchmark/googlenet_v3/train_val.prototxt b/models/intel_optimized_models/benchmark/googlenet_v3/train_val.prototxt new file mode 100644 index 000000000..6ee6ebc5a --- /dev/null +++ b/models/intel_optimized_models/benchmark/googlenet_v3/train_val.prototxt @@ -0,0 +1,3794 @@ +name: "InceptionV3" +bn_stats_batch_size: 32 +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + data_param { + source: "examples/imagenet/ilsvrc12_train_lmdb" + shuffle: true + backend: LMDB + batch_size: 64 + } + transform_param { + crop_size: 299 + mean_value: 104 + mean_value: 117 + mean_value: 123 + + mirror: true + + random_aspect_ratio_param { + min_area_ratio: 0.05 + max_area_ratio: 1 + aspect_ratio_change: 0.75 + max_attempt: 100 + resize_param { + interp_mode: CUBIC + } + } + } + include: { phase: TRAIN } +} +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + data_param { + source: "examples/imagenet/ilsvrc12_val_lmdb" + shuffle: false + backend: LMDB + batch_size: 50 + } + transform_param { + crop_size: 299 + mean_value: 104 + mean_value: 117 + mean_value: 123 + + mirror: false + + random_resize_param { + min_size: 320 + max_size: 320 + resize_param { + interp_mode: CUBIC + } + } + } + include: { phase: TEST } +} +layer { + name: "conv_conv2d" + type: "Convolution" + bottom: "data" + top: "conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_batchnorm" + type: "BatchNorm" + bottom: "conv_conv2d" + top: "conv_conv2d_bn" + + batch_norm_param { + } +} +layer { + name: "conv_relu" + type: "ReLU" + bottom: "conv_conv2d_bn" + top: "conv_conv2d_relu" +} +layer { + name: "conv_1_1_conv2d" + type: "Convolution" + bottom: "conv_conv2d_relu" + top: "conv_1_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_1_1_batchnorm" + type: "BatchNorm" + bottom: "conv_1_1_conv2d" + top: "conv_1_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_1_1_relu" + type: "ReLU" + bottom: "conv_1_1_conv2d_bn" + top: "conv_1_1_conv2d_relu" +} +layer { + name: "conv_2_2_conv2d" + type: "Convolution" + bottom: "conv_1_1_conv2d_relu" + top: "conv_2_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_2_2_batchnorm" + type: "BatchNorm" + bottom: "conv_2_2_conv2d" + top: "conv_2_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_2_2_relu" + type: "ReLU" + bottom: "conv_2_2_conv2d_bn" + top: "conv_2_2_conv2d_relu" +} +layer { + name: "pool" + type: "Pooling" + bottom: "conv_2_2_conv2d_relu" + top: "pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "conv_3_3_conv2d" + type: "Convolution" + bottom: "pool" + top: "conv_3_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 80 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_3_3_batchnorm" + type: "BatchNorm" + bottom: "conv_3_3_conv2d" + top: "conv_3_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_3_3_relu" + type: "ReLU" + bottom: "conv_3_3_conv2d_bn" + top: "conv_3_3_conv2d_relu" +} +layer { + name: "conv_4_4_conv2d" + type: "Convolution" + bottom: "conv_3_3_conv2d_relu" + top: "conv_4_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_4_4_batchnorm" + type: "BatchNorm" + bottom: "conv_4_4_conv2d" + top: "conv_4_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_4_4_relu" + type: "ReLU" + bottom: "conv_4_4_conv2d_bn" + top: "conv_4_4_conv2d_relu" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv_4_4_conv2d_relu" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "mixed_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_conv_conv2d" + top: "mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_conv_relu" + type: "ReLU" + bottom: "mixed_conv_conv2d_bn" + top: "mixed_conv_conv2d_relu" +} +layer { + name: "mixed_tower_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_conv_conv2d" + top: "mixed_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_conv_relu" + type: "ReLU" + bottom: "mixed_tower_conv_conv2d_bn" + top: "mixed_tower_conv_conv2d_relu" +} +layer { + name: "mixed_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_tower_conv_conv2d_relu" + top: "mixed_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_conv_1_conv2d" + top: "mixed_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_tower_conv_1_conv2d_bn" + top: "mixed_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_conv2d" + top: "mixed_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_conv2d_bn" + top: "mixed_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_tower_1_conv_conv2d_relu" + top: "mixed_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_1_conv2d" + top: "mixed_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_1_conv2d_bn" + top: "mixed_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_tower_1_conv_1_conv2d_relu" + top: "mixed_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_2_conv2d" + top: "mixed_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_2_conv2d_bn" + top: "mixed_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_pool" + type: "Pooling" + bottom: "pool1" + top: "AVE_pool_mixed_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_pool" + top: "mixed_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_2_conv_conv2d" + top: "mixed_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_tower_2_conv_conv2d_bn" + top: "mixed_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_chconcat" + type: "Concat" + bottom: "mixed_conv_conv2d_relu" + bottom: "mixed_tower_conv_1_conv2d_relu" + bottom: "mixed_tower_1_conv_2_conv2d_relu" + bottom: "mixed_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_conv_conv2d" + top: "mixed_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_conv_relu" + type: "ReLU" + bottom: "mixed_1_conv_conv2d_bn" + top: "mixed_1_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_conv_conv2d" + top: "mixed_1_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_conv_conv2d_bn" + top: "mixed_1_tower_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_conv_conv2d_relu" + top: "mixed_1_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_conv_1_conv2d" + top: "mixed_1_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_1_tower_conv_1_conv2d_bn" + top: "mixed_1_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_conv2d" + top: "mixed_1_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_conv2d_bn" + top: "mixed_1_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_1_conv_conv2d_relu" + top: "mixed_1_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_1_conv2d" + top: "mixed_1_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_1_conv2d_bn" + top: "mixed_1_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_1_conv_1_conv2d_relu" + top: "mixed_1_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_2_conv2d" + top: "mixed_1_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_2_conv2d_bn" + top: "mixed_1_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_1_pool" + type: "Pooling" + bottom: "ch_concat_mixed_chconcat" + top: "AVE_pool_mixed_1_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_1_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_1_pool" + top: "mixed_1_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_2_conv_conv2d" + top: "mixed_1_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_2_conv_conv2d_bn" + top: "mixed_1_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_1_chconcat" + type: "Concat" + bottom: "mixed_1_conv_conv2d_relu" + bottom: "mixed_1_tower_conv_1_conv2d_relu" + bottom: "mixed_1_tower_1_conv_2_conv2d_relu" + bottom: "mixed_1_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_1_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_2_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_conv_conv2d" + top: "mixed_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_conv_relu" + type: "ReLU" + bottom: "mixed_2_conv_conv2d_bn" + top: "mixed_2_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_conv_conv2d" + top: "mixed_2_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_conv_conv2d_bn" + top: "mixed_2_tower_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_conv_conv2d_relu" + top: "mixed_2_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_conv_1_conv2d" + top: "mixed_2_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_2_tower_conv_1_conv2d_bn" + top: "mixed_2_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_conv2d" + top: "mixed_2_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_conv2d_bn" + top: "mixed_2_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_1_conv_conv2d_relu" + top: "mixed_2_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_1_conv2d" + top: "mixed_2_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_1_conv2d_bn" + top: "mixed_2_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_1_conv_1_conv2d_relu" + top: "mixed_2_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_2_conv2d" + top: "mixed_2_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_2_conv2d_bn" + top: "mixed_2_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_2_pool" + type: "Pooling" + bottom: "ch_concat_mixed_1_chconcat" + top: "AVE_pool_mixed_2_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_2_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_2_pool" + top: "mixed_2_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_2_conv_conv2d" + top: "mixed_2_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_2_conv_conv2d_bn" + top: "mixed_2_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_2_chconcat" + type: "Concat" + bottom: "mixed_2_conv_conv2d_relu" + bottom: "mixed_2_tower_conv_1_conv2d_relu" + bottom: "mixed_2_tower_1_conv_2_conv2d_relu" + bottom: "mixed_2_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_2_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_3_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_2_chconcat" + top: "mixed_3_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_conv_conv2d" + top: "mixed_3_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_conv_relu" + type: "ReLU" + bottom: "mixed_3_conv_conv2d_bn" + top: "mixed_3_conv_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_2_chconcat" + top: "mixed_3_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_conv2d" + top: "mixed_3_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_tower_conv_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_conv2d_bn" + top: "mixed_3_tower_conv_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_3_tower_conv_conv2d_relu" + top: "mixed_3_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_1_conv2d" + top: "mixed_3_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_1_conv2d_bn" + top: "mixed_3_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_3_tower_conv_1_conv2d_relu" + top: "mixed_3_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_2_conv2d" + top: "mixed_3_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_2_conv2d_bn" + top: "mixed_3_tower_conv_2_conv2d_relu" +} +layer { + name: "max_pool_mixed_3_pool" + type: "Pooling" + bottom: "ch_concat_mixed_2_chconcat" + top: "max_pool_mixed_3_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "ch_concat_mixed_3_chconcat" + type: "Concat" + bottom: "max_pool_mixed_3_pool" + bottom: "mixed_3_conv_conv2d_relu" + bottom: "mixed_3_tower_conv_2_conv2d_relu" + top: "ch_concat_mixed_3_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_4_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_conv_conv2d" + top: "mixed_4_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_conv_relu" + type: "ReLU" + bottom: "mixed_4_conv_conv2d_bn" + top: "mixed_4_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_conv2d" + top: "mixed_4_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_conv2d_bn" + top: "mixed_4_tower_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_conv_conv2d_relu" + top: "mixed_4_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_1_conv2d" + top: "mixed_4_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_1_conv2d_bn" + top: "mixed_4_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_conv_1_conv2d_relu" + top: "mixed_4_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_2_conv2d" + top: "mixed_4_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_2_conv2d_bn" + top: "mixed_4_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_conv2d" + top: "mixed_4_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_conv2d_bn" + top: "mixed_4_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_conv2d_relu" + top: "mixed_4_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_1_conv2d" + top: "mixed_4_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_1_conv2d_bn" + top: "mixed_4_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_1_conv2d_relu" + top: "mixed_4_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_2_conv2d" + top: "mixed_4_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_2_conv2d_bn" + top: "mixed_4_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_2_conv2d_relu" + top: "mixed_4_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_3_conv2d" + top: "mixed_4_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_3_conv2d_bn" + top: "mixed_4_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_3_conv2d_relu" + top: "mixed_4_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_4_conv2d" + top: "mixed_4_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_4_conv2d_bn" + top: "mixed_4_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_4_pool" + type: "Pooling" + bottom: "ch_concat_mixed_3_chconcat" + top: "AVE_pool_mixed_4_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_4_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_4_pool" + top: "mixed_4_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_2_conv_conv2d" + top: "mixed_4_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_2_conv_conv2d_bn" + top: "mixed_4_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_4_chconcat" + type: "Concat" + bottom: "mixed_4_conv_conv2d_relu" + bottom: "mixed_4_tower_conv_2_conv2d_relu" + bottom: "mixed_4_tower_1_conv_4_conv2d_relu" + bottom: "mixed_4_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_4_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_5_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_conv_conv2d" + top: "mixed_5_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_conv_relu" + type: "ReLU" + bottom: "mixed_5_conv_conv2d_bn" + top: "mixed_5_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_conv2d" + top: "mixed_5_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_conv2d_bn" + top: "mixed_5_tower_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_conv_conv2d_relu" + top: "mixed_5_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_1_conv2d" + top: "mixed_5_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_1_conv2d_bn" + top: "mixed_5_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_conv_1_conv2d_relu" + top: "mixed_5_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_2_conv2d" + top: "mixed_5_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_2_conv2d_bn" + top: "mixed_5_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_conv2d" + top: "mixed_5_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_conv2d_bn" + top: "mixed_5_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_conv2d_relu" + top: "mixed_5_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_1_conv2d" + top: "mixed_5_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_1_conv2d_bn" + top: "mixed_5_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_1_conv2d_relu" + top: "mixed_5_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_2_conv2d" + top: "mixed_5_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_2_conv2d_bn" + top: "mixed_5_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_2_conv2d_relu" + top: "mixed_5_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_3_conv2d" + top: "mixed_5_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_3_conv2d_bn" + top: "mixed_5_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_3_conv2d_relu" + top: "mixed_5_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_4_conv2d" + top: "mixed_5_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_4_conv2d_bn" + top: "mixed_5_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_5_pool" + type: "Pooling" + bottom: "ch_concat_mixed_4_chconcat" + top: "AVE_pool_mixed_5_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_5_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_5_pool" + top: "mixed_5_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_2_conv_conv2d" + top: "mixed_5_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_2_conv_conv2d_bn" + top: "mixed_5_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_5_chconcat" + type: "Concat" + bottom: "mixed_5_conv_conv2d_relu" + bottom: "mixed_5_tower_conv_2_conv2d_relu" + bottom: "mixed_5_tower_1_conv_4_conv2d_relu" + bottom: "mixed_5_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_5_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_6_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_conv_conv2d" + top: "mixed_6_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_conv_relu" + type: "ReLU" + bottom: "mixed_6_conv_conv2d_bn" + top: "mixed_6_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_conv2d" + top: "mixed_6_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_conv2d_bn" + top: "mixed_6_tower_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_conv_conv2d_relu" + top: "mixed_6_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_1_conv2d" + top: "mixed_6_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_1_conv2d_bn" + top: "mixed_6_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_conv_1_conv2d_relu" + top: "mixed_6_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_2_conv2d" + top: "mixed_6_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_2_conv2d_bn" + top: "mixed_6_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_conv2d" + top: "mixed_6_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_conv2d_bn" + top: "mixed_6_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_conv2d_relu" + top: "mixed_6_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_1_conv2d" + top: "mixed_6_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_1_conv2d_bn" + top: "mixed_6_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_1_conv2d_relu" + top: "mixed_6_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_2_conv2d" + top: "mixed_6_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_2_conv2d_bn" + top: "mixed_6_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_2_conv2d_relu" + top: "mixed_6_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_3_conv2d" + top: "mixed_6_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_3_conv2d_bn" + top: "mixed_6_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_3_conv2d_relu" + top: "mixed_6_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_4_conv2d" + top: "mixed_6_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_4_conv2d_bn" + top: "mixed_6_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_6_pool" + type: "Pooling" + bottom: "ch_concat_mixed_5_chconcat" + top: "AVE_pool_mixed_6_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_6_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_6_pool" + top: "mixed_6_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_2_conv_conv2d" + top: "mixed_6_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_2_conv_conv2d_bn" + top: "mixed_6_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_6_chconcat" + type: "Concat" + bottom: "mixed_6_conv_conv2d_relu" + bottom: "mixed_6_tower_conv_2_conv2d_relu" + bottom: "mixed_6_tower_1_conv_4_conv2d_relu" + bottom: "mixed_6_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_6_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_7_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_conv_conv2d" + top: "mixed_7_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_conv_relu" + type: "ReLU" + bottom: "mixed_7_conv_conv2d_bn" + top: "mixed_7_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_conv2d" + top: "mixed_7_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_conv2d_bn" + top: "mixed_7_tower_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_conv_conv2d_relu" + top: "mixed_7_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_1_conv2d" + top: "mixed_7_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_1_conv2d_bn" + top: "mixed_7_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_conv_1_conv2d_relu" + top: "mixed_7_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_2_conv2d" + top: "mixed_7_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_2_conv2d_bn" + top: "mixed_7_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_conv2d" + top: "mixed_7_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_conv2d_bn" + top: "mixed_7_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_conv2d_relu" + top: "mixed_7_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_1_conv2d" + top: "mixed_7_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_1_conv2d_bn" + top: "mixed_7_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_1_conv2d_relu" + top: "mixed_7_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_2_conv2d" + top: "mixed_7_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_2_conv2d_bn" + top: "mixed_7_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_2_conv2d_relu" + top: "mixed_7_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_3_conv2d" + top: "mixed_7_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_3_conv2d_bn" + top: "mixed_7_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_3_conv2d_relu" + top: "mixed_7_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_4_conv2d" + top: "mixed_7_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_4_conv2d_bn" + top: "mixed_7_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_7_pool" + type: "Pooling" + bottom: "ch_concat_mixed_6_chconcat" + top: "AVE_pool_mixed_7_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_7_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_7_pool" + top: "mixed_7_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_2_conv_conv2d" + top: "mixed_7_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_2_conv_conv2d_bn" + top: "mixed_7_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_7_chconcat" + type: "Concat" + bottom: "mixed_7_conv_conv2d_relu" + bottom: "mixed_7_tower_conv_2_conv2d_relu" + bottom: "mixed_7_tower_1_conv_4_conv2d_relu" + bottom: "mixed_7_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_7_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_8_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_7_chconcat" + top: "mixed_8_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_conv_conv2d" + top: "mixed_8_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_conv_relu" + type: "ReLU" + bottom: "mixed_8_tower_conv_conv2d_bn" + top: "mixed_8_tower_conv_conv2d_relu" +} +layer { + name: "mixed_8_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_conv_conv2d_relu" + top: "mixed_8_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_conv_1_conv2d" + top: "mixed_8_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_8_tower_conv_1_conv2d_bn" + top: "mixed_8_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_7_chconcat" + top: "mixed_8_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_conv2d" + top: "mixed_8_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_conv2d_bn" + top: "mixed_8_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_conv2d_relu" + top: "mixed_8_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_8_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_1_conv2d" + top: "mixed_8_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_1_conv2d_bn" + top: "mixed_8_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_1_conv2d_relu" + top: "mixed_8_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_8_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_2_conv2d" + top: "mixed_8_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_2_conv2d_bn" + top: "mixed_8_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_2_conv2d_relu" + top: "mixed_8_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_3_conv2d" + top: "mixed_8_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_3_conv2d_bn" + top: "mixed_8_tower_1_conv_3_conv2d_relu" +} +layer { + name: "MAX_pool_mixed_8_pool" + type: "Pooling" + bottom: "ch_concat_mixed_7_chconcat" + top: "MAX_pool_mixed_8_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "ch_concat_mixed_8_chconcat" + type: "Concat" + bottom: "mixed_8_tower_conv_1_conv2d_relu" + bottom: "mixed_8_tower_1_conv_3_conv2d_relu" + bottom: "MAX_pool_mixed_8_pool" + top: "ch_concat_mixed_8_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_9_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + kernel_h: 1 + kernel_w: 1 + } +} +layer { + name: "mixed_9_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_conv_conv2d" + top: "mixed_9_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_conv_relu" + type: "ReLU" + bottom: "mixed_9_conv_conv2d_bn" + top: "mixed_9_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_conv_conv2d" + top: "mixed_9_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_conv_conv2d_bn" + top: "mixed_9_tower_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_conv_conv2d_relu" + top: "mixed_9_tower_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_9_tower_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_mixed_conv_conv2d" + top: "mixed_9_tower_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_mixed_conv_conv2d_bn" + top: "mixed_9_tower_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_conv_conv2d_relu" + top: "mixed_9_tower_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_9_tower_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_mixed_conv_1_conv2d" + top: "mixed_9_tower_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_mixed_conv_1_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 448 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_conv_conv2d" + top: "mixed_9_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_conv_conv2d_bn" + top: "mixed_9_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_conv2d_relu" + top: "mixed_9_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_conv_1_conv2d" + top: "mixed_9_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_conv_1_conv2d_bn" + top: "mixed_9_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_1_conv2d_relu" + top: "mixed_9_tower_1_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_mixed_conv_conv2d" + top: "mixed_9_tower_1_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_mixed_conv_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_1_conv2d_relu" + top: "mixed_9_tower_1_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_9_pool" + type: "Pooling" + bottom: "ch_concat_mixed_8_chconcat" + top: "AVE_pool_mixed_9_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_9_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_9_pool" + top: "mixed_9_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_2_conv_conv2d" + top: "mixed_9_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_2_conv_conv2d_bn" + top: "mixed_9_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_9_chconcat" + type: "Concat" + bottom: "mixed_9_conv_conv2d_relu" + bottom: "mixed_9_tower_mixed_conv_conv2d_relu" + bottom: "mixed_9_tower_mixed_conv_1_conv2d_relu" + bottom: "mixed_9_tower_1_mixed_conv_conv2d_relu" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_relu" + bottom: "mixed_9_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_9_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_10_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + kernel_h: 1 + kernel_w: 1 + } +} +layer { + name: "mixed_10_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_conv_conv2d" + top: "mixed_10_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_conv_relu" + type: "ReLU" + bottom: "mixed_10_conv_conv2d_bn" + top: "mixed_10_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_conv_conv2d" + top: "mixed_10_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_conv_conv2d_bn" + top: "mixed_10_tower_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_conv_conv2d_relu" + top: "mixed_10_tower_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_10_tower_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_mixed_conv_conv2d" + top: "mixed_10_tower_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_mixed_conv_conv2d_bn" + top: "mixed_10_tower_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_conv_conv2d_relu" + top: "mixed_10_tower_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_10_tower_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_mixed_conv_1_conv2d" + top: "mixed_10_tower_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_mixed_conv_1_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 448 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_conv_conv2d" + top: "mixed_10_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_conv_conv2d_bn" + top: "mixed_10_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_conv2d_relu" + top: "mixed_10_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_conv_1_conv2d" + top: "mixed_10_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_conv_1_conv2d_bn" + top: "mixed_10_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_1_conv2d_relu" + top: "mixed_10_tower_1_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_mixed_conv_conv2d" + top: "mixed_10_tower_1_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_mixed_conv_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_1_conv2d_relu" + top: "mixed_10_tower_1_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_relu" +} +layer { + name: "MAX_pool_mixed_10_pool" + type: "Pooling" + bottom: "ch_concat_mixed_9_chconcat" + top: "MAX_pool_mixed_10_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_10_tower_2_conv_conv2d" + type: "Convolution" + bottom: "MAX_pool_mixed_10_pool" + top: "mixed_10_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_2_conv_conv2d" + top: "mixed_10_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_2_conv_conv2d_bn" + top: "mixed_10_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_10_chconcat" + type: "Concat" + bottom: "mixed_10_conv_conv2d_relu" + bottom: "mixed_10_tower_mixed_conv_conv2d_relu" + bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu" + bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu" + bottom: "mixed_10_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_10_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "global_pool" + type: "Pooling" + bottom: "ch_concat_mixed_10_chconcat" + top: "global_pool" + pooling_param { + pool: AVE + kernel_size: 8 + stride: 1 + pad: 0 + } +} +layer { + name: "drop" + type: "Dropout" + bottom: "global_pool" + top: "global_pool" + dropout_param { + dropout_ratio: 0.8 + } +} +layer { + name: "flatten" + type: "Flatten" + bottom: "global_pool" + top: "flatten" +} +layer { + name: "fc1" + type: "InnerProduct" + bottom: "flatten" + top: "fc1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc1" + bottom: "label" + top: "loss" +} +layer { + name: "acc/top-1" + type: "Accuracy" + bottom: "fc1" + bottom: "label" + top: "acc/top-1" + include { + phase: TEST + } +} +layer { + name: "acc/top-5" + type: "Accuracy" + bottom: "fc1" + bottom: "label" + top: "acc/top-5" + include { + phase: TEST + } + accuracy_param { + top_k: 5 + } +} diff --git a/models/intel_optimized_models/benchmark/googlenet_v3/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/googlenet_v3/train_val_dummydata.prototxt new file mode 100644 index 000000000..b5e1d08fb --- /dev/null +++ b/models/intel_optimized_models/benchmark/googlenet_v3/train_val_dummydata.prototxt @@ -0,0 +1,3769 @@ +name: "InceptionV3" +bn_stats_batch_size: 32 +layer { + name: "data" + type: "DummyData" + top: "data" + top: "label" + include { + phase: TRAIN + } + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape: { dim: 64 dim: 3 dim: 299 dim: 299 } + shape: { dim: 64 dim: 1 dim: 1 dim: 1 } + } +} + +layer { + name: "data" + type: "DummyData" + top: "data" + top: "label" + include { + phase: TEST + } + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape: { dim: 32 dim: 3 dim: 299 dim: 299 } + shape: { dim: 32 dim: 1 dim: 1 dim: 1 } + } +} +layer { + name: "conv_conv2d" + type: "Convolution" + bottom: "data" + top: "conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_batchnorm" + type: "BatchNorm" + bottom: "conv_conv2d" + top: "conv_conv2d_bn" + + batch_norm_param { + } +} +layer { + name: "conv_relu" + type: "ReLU" + bottom: "conv_conv2d_bn" + top: "conv_conv2d_relu" +} +layer { + name: "conv_1_1_conv2d" + type: "Convolution" + bottom: "conv_conv2d_relu" + top: "conv_1_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_1_1_batchnorm" + type: "BatchNorm" + bottom: "conv_1_1_conv2d" + top: "conv_1_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_1_1_relu" + type: "ReLU" + bottom: "conv_1_1_conv2d_bn" + top: "conv_1_1_conv2d_relu" +} +layer { + name: "conv_2_2_conv2d" + type: "Convolution" + bottom: "conv_1_1_conv2d_relu" + top: "conv_2_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_2_2_batchnorm" + type: "BatchNorm" + bottom: "conv_2_2_conv2d" + top: "conv_2_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_2_2_relu" + type: "ReLU" + bottom: "conv_2_2_conv2d_bn" + top: "conv_2_2_conv2d_relu" +} +layer { + name: "pool" + type: "Pooling" + bottom: "conv_2_2_conv2d_relu" + top: "pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "conv_3_3_conv2d" + type: "Convolution" + bottom: "pool" + top: "conv_3_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 80 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_3_3_batchnorm" + type: "BatchNorm" + bottom: "conv_3_3_conv2d" + top: "conv_3_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_3_3_relu" + type: "ReLU" + bottom: "conv_3_3_conv2d_bn" + top: "conv_3_3_conv2d_relu" +} +layer { + name: "conv_4_4_conv2d" + type: "Convolution" + bottom: "conv_3_3_conv2d_relu" + top: "conv_4_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "conv_4_4_batchnorm" + type: "BatchNorm" + bottom: "conv_4_4_conv2d" + top: "conv_4_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "conv_4_4_relu" + type: "ReLU" + bottom: "conv_4_4_conv2d_bn" + top: "conv_4_4_conv2d_relu" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv_4_4_conv2d_relu" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "mixed_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_conv_conv2d" + top: "mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_conv_relu" + type: "ReLU" + bottom: "mixed_conv_conv2d_bn" + top: "mixed_conv_conv2d_relu" +} +layer { + name: "mixed_tower_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_conv_conv2d" + top: "mixed_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_conv_relu" + type: "ReLU" + bottom: "mixed_tower_conv_conv2d_bn" + top: "mixed_tower_conv_conv2d_relu" +} +layer { + name: "mixed_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_tower_conv_conv2d_relu" + top: "mixed_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_conv_1_conv2d" + top: "mixed_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_tower_conv_1_conv2d_bn" + top: "mixed_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_conv2d" + top: "mixed_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_conv2d_bn" + top: "mixed_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_tower_1_conv_conv2d_relu" + top: "mixed_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_1_conv2d" + top: "mixed_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_1_conv2d_bn" + top: "mixed_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_tower_1_conv_1_conv2d_relu" + top: "mixed_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_2_conv2d" + top: "mixed_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_2_conv2d_bn" + top: "mixed_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_pool" + type: "Pooling" + bottom: "pool1" + top: "AVE_pool_mixed_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_pool" + top: "mixed_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_2_conv_conv2d" + top: "mixed_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_tower_2_conv_conv2d_bn" + top: "mixed_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_chconcat" + type: "Concat" + bottom: "mixed_conv_conv2d_relu" + bottom: "mixed_tower_conv_1_conv2d_relu" + bottom: "mixed_tower_1_conv_2_conv2d_relu" + bottom: "mixed_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_conv_conv2d" + top: "mixed_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_conv_relu" + type: "ReLU" + bottom: "mixed_1_conv_conv2d_bn" + top: "mixed_1_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_conv_conv2d" + top: "mixed_1_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_conv_conv2d_bn" + top: "mixed_1_tower_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_conv_conv2d_relu" + top: "mixed_1_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_conv_1_conv2d" + top: "mixed_1_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_1_tower_conv_1_conv2d_bn" + top: "mixed_1_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_conv2d" + top: "mixed_1_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_conv2d_bn" + top: "mixed_1_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_1_conv_conv2d_relu" + top: "mixed_1_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_1_conv2d" + top: "mixed_1_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_1_conv2d_bn" + top: "mixed_1_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_1_conv_1_conv2d_relu" + top: "mixed_1_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_2_conv2d" + top: "mixed_1_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_2_conv2d_bn" + top: "mixed_1_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_1_pool" + type: "Pooling" + bottom: "ch_concat_mixed_chconcat" + top: "AVE_pool_mixed_1_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_1_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_1_pool" + top: "mixed_1_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_1_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_2_conv_conv2d" + top: "mixed_1_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_1_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_2_conv_conv2d_bn" + top: "mixed_1_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_1_chconcat" + type: "Concat" + bottom: "mixed_1_conv_conv2d_relu" + bottom: "mixed_1_tower_conv_1_conv2d_relu" + bottom: "mixed_1_tower_1_conv_2_conv2d_relu" + bottom: "mixed_1_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_1_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_2_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_conv_conv2d" + top: "mixed_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_conv_relu" + type: "ReLU" + bottom: "mixed_2_conv_conv2d_bn" + top: "mixed_2_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_conv_conv2d" + top: "mixed_2_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_conv_conv2d_bn" + top: "mixed_2_tower_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_conv_conv2d_relu" + top: "mixed_2_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_conv_1_conv2d" + top: "mixed_2_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_2_tower_conv_1_conv2d_bn" + top: "mixed_2_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_conv2d" + top: "mixed_2_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_conv2d_bn" + top: "mixed_2_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_1_conv_conv2d_relu" + top: "mixed_2_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_1_conv2d" + top: "mixed_2_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_1_conv2d_bn" + top: "mixed_2_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_1_conv_1_conv2d_relu" + top: "mixed_2_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_2_conv2d" + top: "mixed_2_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_2_conv2d_bn" + top: "mixed_2_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_2_pool" + type: "Pooling" + bottom: "ch_concat_mixed_1_chconcat" + top: "AVE_pool_mixed_2_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_2_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_2_pool" + top: "mixed_2_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_2_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_2_conv_conv2d" + top: "mixed_2_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_2_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_2_conv_conv2d_bn" + top: "mixed_2_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_2_chconcat" + type: "Concat" + bottom: "mixed_2_conv_conv2d_relu" + bottom: "mixed_2_tower_conv_1_conv2d_relu" + bottom: "mixed_2_tower_1_conv_2_conv2d_relu" + bottom: "mixed_2_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_2_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_3_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_2_chconcat" + top: "mixed_3_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_conv_conv2d" + top: "mixed_3_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_conv_relu" + type: "ReLU" + bottom: "mixed_3_conv_conv2d_bn" + top: "mixed_3_conv_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_2_chconcat" + top: "mixed_3_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_conv2d" + top: "mixed_3_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_tower_conv_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_conv2d_bn" + top: "mixed_3_tower_conv_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_3_tower_conv_conv2d_relu" + top: "mixed_3_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_1_conv2d" + top: "mixed_3_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_1_conv2d_bn" + top: "mixed_3_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_3_tower_conv_1_conv2d_relu" + top: "mixed_3_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_3_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_2_conv2d" + top: "mixed_3_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_3_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_2_conv2d_bn" + top: "mixed_3_tower_conv_2_conv2d_relu" +} +layer { + name: "max_pool_mixed_3_pool" + type: "Pooling" + bottom: "ch_concat_mixed_2_chconcat" + top: "max_pool_mixed_3_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "ch_concat_mixed_3_chconcat" + type: "Concat" + bottom: "max_pool_mixed_3_pool" + bottom: "mixed_3_conv_conv2d_relu" + bottom: "mixed_3_tower_conv_2_conv2d_relu" + top: "ch_concat_mixed_3_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_4_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_conv_conv2d" + top: "mixed_4_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_conv_relu" + type: "ReLU" + bottom: "mixed_4_conv_conv2d_bn" + top: "mixed_4_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_conv2d" + top: "mixed_4_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_conv2d_bn" + top: "mixed_4_tower_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_conv_conv2d_relu" + top: "mixed_4_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_1_conv2d" + top: "mixed_4_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_1_conv2d_bn" + top: "mixed_4_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_conv_1_conv2d_relu" + top: "mixed_4_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_2_conv2d" + top: "mixed_4_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_2_conv2d_bn" + top: "mixed_4_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_conv2d" + top: "mixed_4_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_conv2d_bn" + top: "mixed_4_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_conv2d_relu" + top: "mixed_4_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_1_conv2d" + top: "mixed_4_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_1_conv2d_bn" + top: "mixed_4_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_1_conv2d_relu" + top: "mixed_4_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_2_conv2d" + top: "mixed_4_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_2_conv2d_bn" + top: "mixed_4_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_2_conv2d_relu" + top: "mixed_4_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_3_conv2d" + top: "mixed_4_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_3_conv2d_bn" + top: "mixed_4_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_3_conv2d_relu" + top: "mixed_4_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_4_conv2d" + top: "mixed_4_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_4_conv2d_bn" + top: "mixed_4_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_4_pool" + type: "Pooling" + bottom: "ch_concat_mixed_3_chconcat" + top: "AVE_pool_mixed_4_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_4_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_4_pool" + top: "mixed_4_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_4_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_2_conv_conv2d" + top: "mixed_4_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_4_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_2_conv_conv2d_bn" + top: "mixed_4_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_4_chconcat" + type: "Concat" + bottom: "mixed_4_conv_conv2d_relu" + bottom: "mixed_4_tower_conv_2_conv2d_relu" + bottom: "mixed_4_tower_1_conv_4_conv2d_relu" + bottom: "mixed_4_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_4_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_5_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_conv_conv2d" + top: "mixed_5_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_conv_relu" + type: "ReLU" + bottom: "mixed_5_conv_conv2d_bn" + top: "mixed_5_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_conv2d" + top: "mixed_5_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_conv2d_bn" + top: "mixed_5_tower_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_conv_conv2d_relu" + top: "mixed_5_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_1_conv2d" + top: "mixed_5_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_1_conv2d_bn" + top: "mixed_5_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_conv_1_conv2d_relu" + top: "mixed_5_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_2_conv2d" + top: "mixed_5_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_2_conv2d_bn" + top: "mixed_5_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_conv2d" + top: "mixed_5_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_conv2d_bn" + top: "mixed_5_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_conv2d_relu" + top: "mixed_5_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_1_conv2d" + top: "mixed_5_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_1_conv2d_bn" + top: "mixed_5_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_1_conv2d_relu" + top: "mixed_5_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_2_conv2d" + top: "mixed_5_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_2_conv2d_bn" + top: "mixed_5_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_2_conv2d_relu" + top: "mixed_5_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_3_conv2d" + top: "mixed_5_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_3_conv2d_bn" + top: "mixed_5_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_3_conv2d_relu" + top: "mixed_5_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_4_conv2d" + top: "mixed_5_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_4_conv2d_bn" + top: "mixed_5_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_5_pool" + type: "Pooling" + bottom: "ch_concat_mixed_4_chconcat" + top: "AVE_pool_mixed_5_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_5_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_5_pool" + top: "mixed_5_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_5_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_2_conv_conv2d" + top: "mixed_5_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_5_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_2_conv_conv2d_bn" + top: "mixed_5_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_5_chconcat" + type: "Concat" + bottom: "mixed_5_conv_conv2d_relu" + bottom: "mixed_5_tower_conv_2_conv2d_relu" + bottom: "mixed_5_tower_1_conv_4_conv2d_relu" + bottom: "mixed_5_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_5_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_6_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_conv_conv2d" + top: "mixed_6_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_conv_relu" + type: "ReLU" + bottom: "mixed_6_conv_conv2d_bn" + top: "mixed_6_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_conv2d" + top: "mixed_6_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_conv2d_bn" + top: "mixed_6_tower_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_conv_conv2d_relu" + top: "mixed_6_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_1_conv2d" + top: "mixed_6_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_1_conv2d_bn" + top: "mixed_6_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_conv_1_conv2d_relu" + top: "mixed_6_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_2_conv2d" + top: "mixed_6_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_2_conv2d_bn" + top: "mixed_6_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_conv2d" + top: "mixed_6_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_conv2d_bn" + top: "mixed_6_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_conv2d_relu" + top: "mixed_6_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_1_conv2d" + top: "mixed_6_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_1_conv2d_bn" + top: "mixed_6_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_1_conv2d_relu" + top: "mixed_6_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_2_conv2d" + top: "mixed_6_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_2_conv2d_bn" + top: "mixed_6_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_2_conv2d_relu" + top: "mixed_6_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_3_conv2d" + top: "mixed_6_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_3_conv2d_bn" + top: "mixed_6_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_3_conv2d_relu" + top: "mixed_6_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_4_conv2d" + top: "mixed_6_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_4_conv2d_bn" + top: "mixed_6_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_6_pool" + type: "Pooling" + bottom: "ch_concat_mixed_5_chconcat" + top: "AVE_pool_mixed_6_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_6_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_6_pool" + top: "mixed_6_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_6_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_2_conv_conv2d" + top: "mixed_6_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_6_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_2_conv_conv2d_bn" + top: "mixed_6_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_6_chconcat" + type: "Concat" + bottom: "mixed_6_conv_conv2d_relu" + bottom: "mixed_6_tower_conv_2_conv2d_relu" + bottom: "mixed_6_tower_1_conv_4_conv2d_relu" + bottom: "mixed_6_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_6_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_7_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_conv_conv2d" + top: "mixed_7_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_conv_relu" + type: "ReLU" + bottom: "mixed_7_conv_conv2d_bn" + top: "mixed_7_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_conv2d" + top: "mixed_7_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_conv2d_bn" + top: "mixed_7_tower_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_conv_conv2d_relu" + top: "mixed_7_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_1_conv2d" + top: "mixed_7_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_1_conv2d_bn" + top: "mixed_7_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_conv_1_conv2d_relu" + top: "mixed_7_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_2_conv2d" + top: "mixed_7_tower_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_2_conv2d_bn" + top: "mixed_7_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_conv2d" + top: "mixed_7_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_conv2d_bn" + top: "mixed_7_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_conv2d_relu" + top: "mixed_7_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_1_conv2d" + top: "mixed_7_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_1_conv2d_bn" + top: "mixed_7_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_1_conv2d_relu" + top: "mixed_7_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_2_conv2d" + top: "mixed_7_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_2_conv2d_bn" + top: "mixed_7_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_2_conv2d_relu" + top: "mixed_7_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_3_conv2d" + top: "mixed_7_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_3_conv2d_bn" + top: "mixed_7_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_3_conv2d_relu" + top: "mixed_7_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_4_conv2d" + top: "mixed_7_tower_1_conv_4_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_4_conv2d_bn" + top: "mixed_7_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_7_pool" + type: "Pooling" + bottom: "ch_concat_mixed_6_chconcat" + top: "AVE_pool_mixed_7_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_7_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_7_pool" + top: "mixed_7_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_7_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_2_conv_conv2d" + top: "mixed_7_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_7_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_2_conv_conv2d_bn" + top: "mixed_7_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_7_chconcat" + type: "Concat" + bottom: "mixed_7_conv_conv2d_relu" + bottom: "mixed_7_tower_conv_2_conv2d_relu" + bottom: "mixed_7_tower_1_conv_4_conv2d_relu" + bottom: "mixed_7_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_7_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_8_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_7_chconcat" + top: "mixed_8_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_conv_conv2d" + top: "mixed_8_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_conv_relu" + type: "ReLU" + bottom: "mixed_8_tower_conv_conv2d_bn" + top: "mixed_8_tower_conv_conv2d_relu" +} +layer { + name: "mixed_8_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_conv_conv2d_relu" + top: "mixed_8_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_conv_1_conv2d" + top: "mixed_8_tower_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_8_tower_conv_1_conv2d_bn" + top: "mixed_8_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_7_chconcat" + top: "mixed_8_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_conv2d" + top: "mixed_8_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_conv2d_bn" + top: "mixed_8_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_conv2d_relu" + top: "mixed_8_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_8_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_1_conv2d" + top: "mixed_8_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_1_conv2d_bn" + top: "mixed_8_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_1_conv2d_relu" + top: "mixed_8_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_8_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_2_conv2d" + top: "mixed_8_tower_1_conv_2_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_2_conv2d_bn" + top: "mixed_8_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_2_conv2d_relu" + top: "mixed_8_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_8_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_3_conv2d" + top: "mixed_8_tower_1_conv_3_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_8_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_3_conv2d_bn" + top: "mixed_8_tower_1_conv_3_conv2d_relu" +} +layer { + name: "MAX_pool_mixed_8_pool" + type: "Pooling" + bottom: "ch_concat_mixed_7_chconcat" + top: "MAX_pool_mixed_8_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "ch_concat_mixed_8_chconcat" + type: "Concat" + bottom: "mixed_8_tower_conv_1_conv2d_relu" + bottom: "mixed_8_tower_1_conv_3_conv2d_relu" + bottom: "MAX_pool_mixed_8_pool" + top: "ch_concat_mixed_8_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_9_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + kernel_h: 1 + kernel_w: 1 + } +} +layer { + name: "mixed_9_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_conv_conv2d" + top: "mixed_9_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_conv_relu" + type: "ReLU" + bottom: "mixed_9_conv_conv2d_bn" + top: "mixed_9_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_conv_conv2d" + top: "mixed_9_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_conv_conv2d_bn" + top: "mixed_9_tower_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_conv_conv2d_relu" + top: "mixed_9_tower_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_9_tower_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_mixed_conv_conv2d" + top: "mixed_9_tower_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_mixed_conv_conv2d_bn" + top: "mixed_9_tower_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_conv_conv2d_relu" + top: "mixed_9_tower_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_9_tower_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_mixed_conv_1_conv2d" + top: "mixed_9_tower_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_mixed_conv_1_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 448 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_conv_conv2d" + top: "mixed_9_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_conv_conv2d_bn" + top: "mixed_9_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_conv2d_relu" + top: "mixed_9_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_conv_1_conv2d" + top: "mixed_9_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_conv_1_conv2d_bn" + top: "mixed_9_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_1_conv2d_relu" + top: "mixed_9_tower_1_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_mixed_conv_conv2d" + top: "mixed_9_tower_1_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_mixed_conv_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_1_conv2d_relu" + top: "mixed_9_tower_1_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_9_pool" + type: "Pooling" + bottom: "ch_concat_mixed_8_chconcat" + top: "AVE_pool_mixed_9_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_9_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_9_pool" + top: "mixed_9_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_9_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_2_conv_conv2d" + top: "mixed_9_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_9_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_2_conv_conv2d_bn" + top: "mixed_9_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_9_chconcat" + type: "Concat" + bottom: "mixed_9_conv_conv2d_relu" + bottom: "mixed_9_tower_mixed_conv_conv2d_relu" + bottom: "mixed_9_tower_mixed_conv_1_conv2d_relu" + bottom: "mixed_9_tower_1_mixed_conv_conv2d_relu" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_relu" + bottom: "mixed_9_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_9_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_10_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + kernel_h: 1 + kernel_w: 1 + } +} +layer { + name: "mixed_10_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_conv_conv2d" + top: "mixed_10_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_conv_relu" + type: "ReLU" + bottom: "mixed_10_conv_conv2d_bn" + top: "mixed_10_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_conv_conv2d" + top: "mixed_10_tower_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_conv_conv2d_bn" + top: "mixed_10_tower_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_conv_conv2d_relu" + top: "mixed_10_tower_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_10_tower_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_mixed_conv_conv2d" + top: "mixed_10_tower_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_mixed_conv_conv2d_bn" + top: "mixed_10_tower_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_conv_conv2d_relu" + top: "mixed_10_tower_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_10_tower_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_mixed_conv_1_conv2d" + top: "mixed_10_tower_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_mixed_conv_1_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 448 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_conv_conv2d" + top: "mixed_10_tower_1_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_conv_conv2d_bn" + top: "mixed_10_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_conv2d_relu" + top: "mixed_10_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_conv_1_conv2d" + top: "mixed_10_tower_1_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_conv_1_conv2d_bn" + top: "mixed_10_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_1_conv2d_relu" + top: "mixed_10_tower_1_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_mixed_conv_conv2d" + top: "mixed_10_tower_1_mixed_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_mixed_conv_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_1_conv2d_relu" + top: "mixed_10_tower_1_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_relu" +} +layer { + name: "MAX_pool_mixed_10_pool" + type: "Pooling" + bottom: "ch_concat_mixed_9_chconcat" + top: "MAX_pool_mixed_10_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_10_tower_2_conv_conv2d" + type: "Convolution" + bottom: "MAX_pool_mixed_10_pool" + top: "mixed_10_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + } +} +layer { + name: "mixed_10_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_2_conv_conv2d" + top: "mixed_10_tower_2_conv_conv2d_bn" + batch_norm_param { + } +} +layer { + name: "mixed_10_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_2_conv_conv2d_bn" + top: "mixed_10_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_10_chconcat" + type: "Concat" + bottom: "mixed_10_conv_conv2d_relu" + bottom: "mixed_10_tower_mixed_conv_conv2d_relu" + bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu" + bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu" + bottom: "mixed_10_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_10_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "global_pool" + type: "Pooling" + bottom: "ch_concat_mixed_10_chconcat" + top: "global_pool" + pooling_param { + pool: AVE + kernel_size: 8 + stride: 1 + pad: 0 + } +} +layer { + name: "drop" + type: "Dropout" + bottom: "global_pool" + top: "global_pool" + dropout_param { + dropout_ratio: 0.8 + } +} +layer { + name: "flatten" + type: "Flatten" + bottom: "global_pool" + top: "flatten" +} +layer { + name: "fc1" + type: "InnerProduct" + bottom: "flatten" + top: "fc1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc1" + bottom: "label" + top: "loss" +} +layer { + name: "acc/top-1" + type: "Accuracy" + bottom: "fc1" + bottom: "label" + top: "acc/top-1" + include { + phase: TEST + } +} +layer { + name: "acc/top-5" + type: "Accuracy" + bottom: "fc1" + bottom: "label" + top: "acc/top-5" + include { + phase: TEST + } + accuracy_param { + top_k: 5 + } +} diff --git a/models/intel_optimized_models/benchmark/vgg16/solver.prototxt b/models/intel_optimized_models/benchmark/vgg16/solver.prototxt new file mode 100644 index 000000000..f3a97601b --- /dev/null +++ b/models/intel_optimized_models/benchmark/vgg16/solver.prototxt @@ -0,0 +1,18 @@ +net: "models/intel_optimized_models/benchmark/vgg16/train_val.prototxt" +test_iter: 1000 +test_interval: 1000 +momentum: 0.9 +weight_decay: 0.0005 +power: 2 +base_lr: 7 +warmup_start_lr: 1 +warmup_iter: 3127 # 5 epochs +local_lr_auto: true +local_gw_ratio: 0.001 + +lr_policy: "poly" +gamma: 0.1 +max_iter: 40000 +display: 20 +snapshot: 5000 +solver_mode: CPU diff --git a/models/intel_optimized_models/benchmark/vgg16/solver_dummydata.prototxt b/models/intel_optimized_models/benchmark/vgg16/solver_dummydata.prototxt new file mode 100644 index 000000000..f80b961b1 --- /dev/null +++ b/models/intel_optimized_models/benchmark/vgg16/solver_dummydata.prototxt @@ -0,0 +1,18 @@ +net: "models/intel_optimized_models/benchmark/vgg16/train_val_dummydata.prototxt" +test_iter: 1000 +test_interval: 1000 +momentum: 0.9 +weight_decay: 0.0005 +power: 2 +base_lr: 7 +warmup_start_lr: 1 +warmup_iter: 3127 # 5 epochs +local_lr_auto: true +local_gw_ratio: 0.001 + +lr_policy: "poly" +gamma: 0.1 +max_iter: 40000 +display: 20 +snapshot: 5000 +solver_mode: CPU diff --git a/models/intel_optimized_models/benchmark/vgg16/train_val.prototxt b/models/intel_optimized_models/benchmark/vgg16/train_val.prototxt new file mode 100644 index 000000000..046166772 --- /dev/null +++ b/models/intel_optimized_models/benchmark/vgg16/train_val.prototxt @@ -0,0 +1,613 @@ +name: "VGG_ILSVRC_16_layer" +layer { + name: "data" + type: "Data" + include { + phase: TRAIN + } + transform_param { + crop_size: 224 + mean_value: 104 + mean_value: 117 + mean_value: 124 + mirror: true + } + data_param { + source: "examples/imagenet/ilsvrc12_train_lmdb" + batch_size: 64 + backend: LMDB + shuffle: true + } + top: "data" + top: "label" +} +layer { + name: "data" + type: "Data" + include { + phase: TEST + } + transform_param { + crop_size: 224 + mean_value: 104 + mean_value: 117 + mean_value: 124 + mirror: false + } + data_param { + source: "examples/imagenet/ilsvrc12_val_lmdb" + batch_size: 50 + backend: LMDB + } + top: "data" + top: "label" +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + convolution_param { + + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv1_1" + top: "conv1_1" + name: "relu1_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv1_1" + top: "conv1_2" + name: "conv1_2" + type: "Convolution" + convolution_param { + + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv1_2" + top: "conv1_2" + name: "relu1_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv1_2" + top: "pool1" + name: "pool1" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool1" + top: "conv2_1" + name: "conv2_1" + type: "Convolution" + convolution_param { + + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv2_1" + top: "conv2_1" + name: "relu2_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv2_1" + top: "conv2_2" + name: "conv2_2" + type: "Convolution" + convolution_param { + + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv2_2" + top: "conv2_2" + name: "relu2_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv2_2" + top: "pool2" + name: "pool2" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool2" + top: "conv3_1" + name: "conv3_1" + type: "Convolution" + convolution_param { + + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv3_1" + top: "conv3_1" + name: "relu3_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv3_1" + top: "conv3_2" + name: "conv3_2" + type: "Convolution" + convolution_param { + + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv3_2" + top: "conv3_2" + name: "relu3_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv3_2" + top: "conv3_3" + name: "conv3_3" + type: "Convolution" + convolution_param { + + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv3_3" + top: "conv3_3" + name: "relu3_3" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv3_3" + top: "pool3" + name: "pool3" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool3" + top: "conv4_1" + name: "conv4_1" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv4_1" + top: "conv4_1" + name: "relu4_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv4_1" + top: "conv4_2" + name: "conv4_2" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv4_2" + top: "conv4_2" + name: "relu4_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv4_2" + top: "conv4_3" + name: "conv4_3" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv4_3" + top: "conv4_3" + name: "relu4_3" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv4_3" + top: "pool4" + name: "pool4" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool4" + top: "conv5_1" + name: "conv5_1" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv5_1" + top: "conv5_1" + name: "relu5_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv5_1" + top: "conv5_2" + name: "conv5_2" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv5_2" + top: "conv5_2" + name: "relu5_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv5_2" + top: "conv5_3" + name: "conv5_3" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + + +} +layer { + bottom: "conv5_3" + top: "conv5_3" + name: "relu5_3" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv5_3" + top: "pool5" + name: "pool5" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool5" + top: "fc6" + name: "fc6" + type: "InnerProduct" + inner_product_param { + num_output: 4096 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.1 + } + } + + +} +layer { + bottom: "fc6" + top: "fc6" + name: "relu6" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "fc6" + top: "fc6" + name: "drop6" + type: "Dropout" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + bottom: "fc6" + top: "fc7" + name: "fc7" + type: "InnerProduct" + inner_product_param { + num_output: 4096 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.1 + } + } + + +} +layer { + bottom: "fc7" + top: "fc7" + name: "relu7" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "fc7" + top: "fc7" + name: "drop7" + type: "Dropout" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc8" + bottom: "fc7" + top: "fc8" + type: "InnerProduct" + inner_product_param { + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.1 + } + } + + +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc8" + bottom: "label" + top: "loss/loss" +} +layer { + name: "accuracy/top1" + type: "Accuracy" + bottom: "fc8" + bottom: "label" + top: "accuracy@1" + include: { phase: TEST } + accuracy_param { + top_k: 1 + } +} +layer { + name: "accuracy/top5" + type: "Accuracy" + bottom: "fc8" + bottom: "label" + top: "accuracy@5" + include: { phase: TEST } + accuracy_param { + top_k: 5 + } +} diff --git a/models/intel_optimized_models/benchmark/vgg16/train_val_dummydata.prototxt b/models/intel_optimized_models/benchmark/vgg16/train_val_dummydata.prototxt new file mode 100644 index 000000000..faa3832dd --- /dev/null +++ b/models/intel_optimized_models/benchmark/vgg16/train_val_dummydata.prototxt @@ -0,0 +1,602 @@ +name: "VGG_ILSVRC_16_layer" +layer { + name: "data" + type: "DummyData" + top: "data" + top: "label" + include { + phase: TRAIN + } + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape: { dim: 64 dim: 3 dim: 224 dim: 224 } + shape: { dim: 64 dim: 1 dim: 1 dim: 1 } + } +} +layer { + name: "data" + type: "DummyData" + top: "data" + top: "label" + include { + phase: TEST + } + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape: { dim: 32 dim: 3 dim: 224 dim: 224 } + shape: { dim: 32 dim: 1 dim: 1 dim: 1 } + } +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + convolution_param { + + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + bottom: "conv1_1" + top: "conv1_1" + name: "relu1_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv1_1" + top: "conv1_2" + name: "conv1_2" + type: "Convolution" + convolution_param { + + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv1_2" + top: "conv1_2" + name: "relu1_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv1_2" + top: "pool1" + name: "pool1" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool1" + top: "conv2_1" + name: "conv2_1" + type: "Convolution" + convolution_param { + + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv2_1" + top: "conv2_1" + name: "relu2_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv2_1" + top: "conv2_2" + name: "conv2_2" + type: "Convolution" + convolution_param { + + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv2_2" + top: "conv2_2" + name: "relu2_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv2_2" + top: "pool2" + name: "pool2" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool2" + top: "conv3_1" + name: "conv3_1" + type: "Convolution" + convolution_param { + + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv3_1" + top: "conv3_1" + name: "relu3_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv3_1" + top: "conv3_2" + name: "conv3_2" + type: "Convolution" + convolution_param { + + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv3_2" + top: "conv3_2" + name: "relu3_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv3_2" + top: "conv3_3" + name: "conv3_3" + type: "Convolution" + convolution_param { + + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv3_3" + top: "conv3_3" + name: "relu3_3" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv3_3" + top: "pool3" + name: "pool3" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool3" + top: "conv4_1" + name: "conv4_1" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv4_1" + top: "conv4_1" + name: "relu4_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv4_1" + top: "conv4_2" + name: "conv4_2" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv4_2" + top: "conv4_2" + name: "relu4_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv4_2" + top: "conv4_3" + name: "conv4_3" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv4_3" + top: "conv4_3" + name: "relu4_3" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv4_3" + top: "pool4" + name: "pool4" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool4" + top: "conv5_1" + name: "conv5_1" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv5_1" + top: "conv5_1" + name: "relu5_1" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv5_1" + top: "conv5_2" + name: "conv5_2" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } + + +} +layer { + bottom: "conv5_2" + top: "conv5_2" + name: "relu5_2" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv5_2" + top: "conv5_3" + name: "conv5_3" + type: "Convolution" + convolution_param { + + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + + +} +layer { + bottom: "conv5_3" + top: "conv5_3" + name: "relu5_3" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "conv5_3" + top: "pool5" + name: "pool5" + type: "Pooling" + pooling_param { + + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + bottom: "pool5" + top: "fc6" + name: "fc6" + type: "InnerProduct" + inner_product_param { + num_output: 4096 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.1 + } + } + + +} +layer { + bottom: "fc6" + top: "fc6" + name: "relu6" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "fc6" + top: "fc6" + name: "drop6" + type: "Dropout" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + bottom: "fc6" + top: "fc7" + name: "fc7" + type: "InnerProduct" + inner_product_param { + num_output: 4096 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.1 + } + } + + +} +layer { + bottom: "fc7" + top: "fc7" + name: "relu7" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "fc7" + top: "fc7" + name: "drop7" + type: "Dropout" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc8" + bottom: "fc7" + top: "fc8" + type: "InnerProduct" + inner_product_param { + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.1 + } + } + + +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc8" + bottom: "label" + top: "loss/loss" +} +layer { + name: "accuracy/top1" + type: "Accuracy" + bottom: "fc8" + bottom: "label" + top: "accuracy@1" + include: { phase: TEST } + accuracy_param { + top_k: 1 + } +} +layer { + name: "accuracy/top5" + type: "Accuracy" + bottom: "fc8" + bottom: "label" + top: "accuracy@5" + include: { phase: TEST } + accuracy_param { + top_k: 5 + } +} diff --git a/models/intel_optimized_models/faster-rcnn/pascal_voc/VGG16/faster_rcnn_end2end/test_int8.prototxt b/models/intel_optimized_models/faster-rcnn/pascal_voc/VGG16/faster_rcnn_end2end/test_int8.prototxt new file mode 100644 index 000000000..0df9dbcb7 --- /dev/null +++ b/models/intel_optimized_models/faster-rcnn/pascal_voc/VGG16/faster_rcnn_end2end/test_int8.prototxt @@ -0,0 +1,5482 @@ +name: "VGG_ILSVRC_16_layers" +input: "data" +input: "im_info" +input_shape { + dim: 1 + dim: 3 + dim: 600 + dim: 800 +} +input_shape { + dim: 1 + dim: 3 +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 0.30863244031360554 + scale_out: 0.06049258646116979 + scale_params: 572.3605468367787 + scale_params: 758.419512184704 + scale_params: 788.4878355478455 + scale_params: 617.03599529876 + scale_params: 546.7713130818305 + scale_params: 513.2287043971045 + scale_params: 706.5540364061421 + scale_params: 1045.4495553221927 + scale_params: 1410.027002163889 + scale_params: 1591.4322927690303 + scale_params: 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{ + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 18 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 15.990577893992576 + scale_out: 12.551948557957266 + scale_params: 929.0570611633563 + scale_params: 1015.4903547294842 + scale_params: 1192.908540408836 + scale_params: 1008.9448737615223 + scale_params: 1004.4462330963964 + scale_params: 1163.0432520243992 + scale_params: 955.2716513894426 + scale_params: 950.4941910268079 + scale_params: 1810.8008475647598 + scale_params: 980.2927260171306 + scale_params: 1078.635602220468 + scale_params: 1372.8357285428017 + scale_params: 953.9213683203433 + scale_params: 1056.8704435480695 + scale_params: 1205.589922289986 + scale_params: 935.6979900800591 + scale_params: 1083.3887955503246 + scale_params: 1680.5525702006876 + } +} +layer { + name: "rpn_bbox_pred" + type: "Convolution" + bottom: "rpn/output" + top: "rpn_bbox_pred" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 36 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 15.990577893992576 + scale_out: 123.62781762383905 + scale_params: 5626.949783579508 + scale_params: 5791.193562355045 + scale_params: 3989.585918341482 + scale_params: 3309.157981259758 + scale_params: 9033.958638425915 + scale_params: 7424.397286115187 + scale_params: 3928.6919433202115 + scale_params: 3393.234345584674 + scale_params: 16692.138053492734 + scale_params: 5624.359278060483 + scale_params: 6600.9174541220855 + scale_params: 3110.029714472963 + scale_params: 6911.482660525164 + scale_params: 5894.608957958901 + scale_params: 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"rpn_cls_score_reshape" + top: "rpn_cls_prob" +} +layer { + name: "rpn_cls_prob_reshape" + type: "Reshape" + bottom: "rpn_cls_prob" + top: "rpn_cls_prob_reshape" + reshape_param { + shape { + dim: 0 + dim: 18 + dim: -1 + dim: 0 + } + } +} +layer { + name: "proposal" + type: "Python" + bottom: "rpn_cls_prob_reshape" + bottom: "rpn_bbox_pred" + bottom: "im_info" + top: "rois" + python_param { + module: "rpn.proposal_layer" + layer: "ProposalLayer" + param_str: "\'feat_stride\': 16" + } +} +layer { + name: "roi_pool5" + type: "ROIPooling" + bottom: "conv5_3" + bottom: "rois" + top: "pool5" + roi_pooling_param { + pooled_h: 7 + pooled_w: 7 + spatial_scale: 0.0625 + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "drop6" + type: "Dropout" + bottom: "fc6" + top: "fc6" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "fc7" + type: "InnerProduct" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 4096 + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "drop7" + type: "Dropout" + bottom: "fc7" + top: "fc7" + dropout_param { + dropout_ratio: 0.5 + } +} +layer { + name: "cls_score" + type: "InnerProduct" + bottom: "fc7" + top: "cls_score" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 21 + weight_filler { + type: "gaussian" + std: 0.01 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "bbox_pred" + type: "InnerProduct" + bottom: "fc7" + top: "bbox_pred" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 84 + weight_filler { + type: "gaussian" + std: 0.001 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "cls_prob" + type: "Softmax" + bottom: "cls_score" + top: "cls_prob" +} diff --git a/models/intel_optimized_models/int8/inceptionv3_int8.prototxt b/models/intel_optimized_models/int8/inceptionv3_int8.prototxt index 40a810628..ec2e3b753 100644 --- a/models/intel_optimized_models/int8/inceptionv3_int8.prototxt +++ b/models/intel_optimized_models/int8/inceptionv3_int8.prototxt @@ -1,4 +1,3 @@ -# For INT8 reference name: "InceptionV3" layer { name: "data" @@ -7,27 +6,14 @@ layer { dummy_data_param { data_filler { type: "constant" - value: 0.0099999997764825821 - } + value: 0.01 + } shape { dim: 64 dim: 3 dim: 299 - dim: 299 - } - } -} -layer { - name: "data" - type: "DummyData" - top: "label" - dummy_data_param { - data_filler { - type: "constant" - } - shape { - dim: 64 - } + dim: 299 + } } } layer { @@ -86,13 +72,43 @@ layer { } } quantization_param { - precision: DYNAMIC_FIXED_POINT bw_layer_in: 8 bw_layer_out: 8 bw_params: 8 - scale_in: 75.3399963379 - scale_out: 48.9399986267 - scale_params: 125.419998169 + scale_in: 64.0800018311 + scale_out: 45.8899993896 + scale_params: 252.449996948 + scale_params: 167.039993286 + scale_params: 1207.32995605 + scale_params: 1072.41003418 + scale_params: 1075.7199707 + scale_params: 91.3499984741 + scale_params: 1389.04003906 + scale_params: 94.5500030518 + scale_params: 284.700012207 + scale_params: 197.190002441 + scale_params: 1210.29003906 + scale_params: 2963.06005859 + scale_params: 212.5 + scale_params: 198.770004272 + scale_params: 1247.43005371 + scale_params: 235.289993286 + scale_params: 110.050003052 + scale_params: 191.259994507 + scale_params: 104.209999084 + scale_params: 128.839996338 + scale_params: 61.5200004578 + scale_params: 114.190002441 + scale_params: 1347.42004395 + scale_params: 372.609985352 + scale_params: 1810.06994629 + 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bw_layer_in: 8 bw_layer_out: 8 bw_params: 8 - scale_in: 160.86000061 - scale_out: 126.050003052 - scale_params: 141.570007324 + scale_in: 100.629997253 + scale_out: 104.940002441 + scale_params: 636.099975586 + scale_params: 923.049987793 + scale_params: 421.109985352 + scale_params: 589.070007324 + scale_params: 604.0 + scale_params: 1002.23999023 + scale_params: 256.739990234 + scale_params: 843.66998291 + scale_params: 519.729980469 + scale_params: 990.739990234 + scale_params: 551.900024414 + scale_params: 457.510009766 + scale_params: 372.600006104 + scale_params: 701.630004883 + scale_params: 585.219970703 + scale_params: 796.390014648 + scale_params: 752.83001709 + scale_params: 465.279998779 + scale_params: 807.539978027 + scale_params: 592.640014648 + scale_params: 629.520019531 + scale_params: 585.66998291 + scale_params: 1102.81005859 + scale_params: 936.349975586 + scale_params: 765.059997559 + scale_params: 695.010009766 + scale_params: 1067.72998047 + scale_params: 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+ scale_params: 836.309997559 + scale_params: 1047.55004883 + scale_params: 942.75 + scale_params: 302.350006104 + scale_params: 168.320007324 } } layer { @@ -1799,13 +5201,139 @@ layer { kernel_w: 1 } quantization_param { - precision: DYNAMIC_FIXED_POINT bw_layer_in: 8 bw_layer_out: 8 bw_params: 8 - scale_in: 139.050003052 - scale_out: 159.149993896 - scale_params: 185.009994507 + scale_in: 98.8700027466 + scale_out: 106.970001221 + scale_params: 529.590026855 + scale_params: 315.040008545 + scale_params: 759.640014648 + scale_params: 1050.79003906 + scale_params: 500.119995117 + scale_params: 872.840026855 + scale_params: 634.349975586 + scale_params: 490.25 + scale_params: 625.150024414 + scale_params: 1189.97998047 + scale_params: 1071.57995605 + scale_params: 603.429992676 + scale_params: 671.409973145 + scale_params: 575.859985352 + scale_params: 1144.70996094 + scale_params: 596.390014648 + scale_params: 592.659973145 + scale_params: 372.760009766 + scale_params: 545.539978027 + 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scale_params: 140.5 + scale_params: 158.830001831 } } layer { @@ -4568,4 +21552,3 @@ layer { } } } - diff --git a/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt b/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt index f6366fd27..ec2e3b753 100644 --- a/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt +++ b/models/intel_optimized_models/int8/inceptionv3_int8_mc.prototxt @@ -1,4 +1,3 @@ -# For INT8 reference name: "InceptionV3" layer { name: "data" @@ -7,27 +6,14 @@ layer { dummy_data_param { data_filler { type: "constant" - value: 0.0099999997764825821 - } + value: 0.01 + } shape { dim: 64 dim: 3 dim: 299 - dim: 299 - } - } -} -layer { - name: "data" - type: "DummyData" - top: "label" - dummy_data_param { - data_filler { - type: "constant" - } - shape { - dim: 64 - } + dim: 299 + } } } layer { @@ -86,44 +72,43 @@ layer { } } quantization_param { - precision: DYNAMIC_FIXED_POINT bw_layer_in: 8 bw_layer_out: 8 bw_params: 8 - scale_in: 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+6,13 @@ layer { dummy_data_param { data_filler { type: "constant" - value: 0.01 + value: 0.01 } shape { dim: 64 dim: 3 dim: 224 - dim: 224 - } - } -} -layer { - name: "data" - type: "DummyData" - top: "label" - dummy_data_param { - data_filler { - type: "constant" - } - shape { - dim: 64 + dim: 224 } } } @@ -105,13 +91,267 @@ layer { } } quantization_param { - precision: DYNAMIC_FIXED_POINT bw_layer_in: 8 bw_layer_out: 8 bw_params: 8 - scale_in: 8.38000011444 - scale_out: 5.73999977112 + scale_in: 7.36999988556 + scale_out: 6.21999979019 + scale_params: 3828.66992188 + scale_params: 618.320007324 + scale_params: 587.33001709 + scale_params: 1369.47998047 + scale_params: 814.809997559 + scale_params: 1789.85998535 + scale_params: 920.719970703 + scale_params: 199.800003052 + scale_params: 1450.02001953 + scale_params: 575.429992676 + scale_params: 384.109985352 + scale_params: 3843.97998047 + scale_params: 577.200012207 + scale_params: 420.649993896 + scale_params: 234.669998169 + 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+ scale_params: 9547.58984375 + scale_params: 469.320007324 + scale_params: 619.890014648 + scale_params: 357.589996338 + scale_params: 410.890014648 + scale_params: 324.609985352 + scale_params: 398.429992676 + scale_params: 278.899993896 + scale_params: 456.299987793 + scale_params: 316.279998779 + scale_params: 268.130004883 + scale_params: 754.450012207 + scale_params: 255.759994507 + scale_params: 1370.48999023 + scale_params: 993.679992676 + scale_params: 319.820007324 + scale_params: 849.070007324 + scale_params: 3807.34008789 + scale_params: 2119.58007812 + scale_params: 313.339996338 + scale_params: 793.539978027 + scale_params: 527.809997559 + scale_params: 349.459991455 + scale_params: 549.559997559 + scale_params: 478.790008545 + scale_params: 1185.48999023 + scale_params: 586.460021973 + scale_params: 569.400024414 + scale_params: 524.880004883 scale_params: 75.9199981689 + scale_params: 407.049987793 + scale_params: 347.980010986 + scale_params: 930.729980469 + 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type: "DummyData" + top: "data" + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim: 32 + dim: 3 + dim: 300 + dim: 300 + } + } +} +layer { + name: "data" + type: "DummyData" + top: "label" + dummy_data_param { + data_filler { + type: "constant" + value: 1 + } + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 8 + } + } +} +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv0/bn" + type: "BatchNorm" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } +} +layer { + name: "conv0/scale" + type: "Scale" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1.0 + } + bias_term: true + bias_filler { + value: 0.0 + } + } +} +layer { + name: "conv0/relu" + type: "ReLU" + bottom: "conv0" + top: "conv0" +} +layer { + name: "conv1/dw" + type: "Convolution" + bottom: "conv0" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + weight_filler { + type: "msra" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 18.1787223816 + scale_out: 11.1095628738 + scale_params: 42.0692214966 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 35.9848022461 + scale_params: 96.1156158447 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 48.4883956909 + scale_params: 35.077381134 + scale_params: 35.6163787842 + scale_params: 0.0 + scale_params: 52.9423866272 + scale_params: 75.9592132568 + scale_params: 37.7726249695 + scale_params: 0.0 + scale_params: 23.2944393158 + scale_params: 41.6536369324 + scale_params: 74.3933258057 + scale_params: 114.592155457 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 58.0522079468 + scale_params: 32.906917572 + scale_params: 0.0 + scale_params: 36.6667823792 + scale_params: 76.1828308105 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 23.9832286835 + } +} +layer { + name: "conv1/dw/bn" + type: "BatchNorm" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } +} +layer { + name: "conv1/dw/scale" + type: "Scale" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1.0 + } + bias_term: true + bias_filler { + value: 0.0 + } + } +} +layer { + name: "conv1/dw/relu" + type: "ReLU" + bottom: "conv1/dw" + top: "conv1/dw" +} +layer { + name: "conv1" + type: "Convolution" + bottom: "conv1/dw" + top: "conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 11.1095628738 + scale_out: 3.00517463684 + scale_params: 62.2967948914 + scale_params: 340.009216309 + scale_params: 244.764648438 + scale_params: 114.332649231 + scale_params: 111.084205627 + scale_params: 48.3250808716 + scale_params: 276.6902771 + scale_params: 649.946838379 + scale_params: 136.880599976 + scale_params: 230.259521484 + scale_params: 226.491470337 + scale_params: 199.632385254 + scale_params: 58.7150344849 + scale_params: 312.996856689 + scale_params: 215.028823853 + scale_params: 233.231369019 + scale_params: 37.1700286865 + scale_params: 236.888717651 + scale_params: 602.868347168 + scale_params: 111.41103363 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lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + group: 64 + stride: 2 + weight_filler { + type: "msra" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 3.00517463684 + scale_out: 16.3315830231 + scale_params: 439.582366943 + scale_params: 60.190322876 + scale_params: 83.5455932617 + scale_params: 485.688720703 + scale_params: 369.882568359 + scale_params: 995.153137207 + scale_params: 77.8068389893 + scale_params: 19.6928310394 + scale_params: 453.722686768 + scale_params: 76.307472229 + scale_params: 401.455871582 + scale_params: 94.9558563232 + scale_params: 244.565155029 + scale_params: 226.900314331 + scale_params: 375.830871582 + scale_params: 169.798782349 + scale_params: 1268.0333252 + scale_params: 266.880340576 + scale_params: 28.2426528931 + scale_params: 508.968933105 + scale_params: 344.379272461 + scale_params: 101.716270447 + scale_params: 460.506439209 + 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+ weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 32 + bw_params: 8 + scale_in: 43.8704795837 + scale_out: 1.0 + scale_params: 208.038955688 + scale_params: 132.439605713 + scale_params: 84.2204971313 + scale_params: 111.774223328 + scale_params: 141.969482422 + scale_params: 163.904327393 + scale_params: 78.3555450439 + scale_params: 142.426132202 + scale_params: 197.591598511 + scale_params: 171.335952759 + scale_params: 135.308731079 + scale_params: 143.217559814 + } +} +layer { + name: "conv11_mbox_conf" + type: "Convolution" + bottom: "conv11" + top: "conv11_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 63 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 32 + bw_params: 8 + 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num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 32 + bw_params: 8 + scale_in: 60.7414512634 + scale_out: 1.0 + scale_params: 92.4733581543 + scale_params: 70.2865753174 + scale_params: 74.4145431519 + scale_params: 85.9849243164 + scale_params: 65.5593338013 + scale_params: 60.4277992249 + scale_params: 45.0546951294 + scale_params: 93.1596679688 + scale_params: 69.0841827393 + scale_params: 110.154876709 + scale_params: 60.6069030762 + scale_params: 61.0965690613 + scale_params: 85.1604766846 + scale_params: 47.833278656 + scale_params: 75.4739990234 + scale_params: 96.224647522 + scale_params: 72.1066741943 + scale_params: 54.6109199524 + scale_params: 85.2343978882 + scale_params: 71.0934677124 + scale_params: 55.4594650269 + scale_params: 103.131530762 + scale_params: 69.060760498 + scale_params: 79.2966842651 + scale_params: 84.1527252197 + 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+ scale_params: 63.7748031616 + } +} +layer { + name: "conv17_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv17_2" + bottom: "data" + top: "conv17_2_mbox_priorbox" + prior_box_param { + min_size: 285.0 + max_size: 300.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv11_mbox_loc" + bottom: "conv13_mbox_loc" + bottom: "conv14_2_mbox_loc" + bottom: "conv15_2_mbox_loc" + bottom: "conv16_2_mbox_loc" + bottom: "conv17_2_mbox_loc" + top: "mbox_loc" + concat_param { + axis: 1 + per_fla_fuse : true + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv11_mbox_conf" + bottom: "conv13_mbox_conf" + bottom: "conv14_2_mbox_conf" + bottom: "conv15_2_mbox_conf" + bottom: "conv16_2_mbox_conf" + bottom: "conv17_2_mbox_conf" + top: "mbox_conf" + concat_param { + axis: 1 + per_fla_fuse : true + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv11_mbox_priorbox" + bottom: "conv13_mbox_priorbox" + bottom: "conv14_2_mbox_priorbox" + bottom: "conv15_2_mbox_priorbox" + bottom: "conv16_2_mbox_priorbox" + bottom: "conv17_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 21 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.449999988079 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.00999999977648 + } +} diff --git a/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt new file mode 100644 index 000000000..87947abf5 --- /dev/null +++ b/models/intel_optimized_models/int8/ssd_mobilenet_int8_acc.prototxt @@ -0,0 +1,16667 @@ +name: "MobileNet-SSD" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + scale: 0.007842999883 + mean_value: 127.5 + mean_value: 127.5 + mean_value: 127.5 + resize_param { + prob: 1.0 + resize_mode: WARP + height: 300 + width: 300 + interp_mode: LINEAR + } + } + data_param { + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv0/bn" + type: "BatchNorm" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } +} +layer { + name: "conv0/scale" + type: "Scale" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1.0 + } + bias_term: true + bias_filler { + value: 0.0 + } + } +} +layer { + name: "conv0/relu" + type: "ReLU" + bottom: "conv0" + top: "conv0" +} +layer { + name: "conv1/dw" + type: "Convolution" + bottom: "conv0" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + weight_filler { + type: "msra" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 18.1787223816 + scale_out: 11.1095628738 + scale_params: 42.0692214966 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 35.9848022461 + scale_params: 96.1156158447 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 48.4883956909 + scale_params: 35.077381134 + scale_params: 35.6163787842 + scale_params: 0.0 + scale_params: 52.9423866272 + scale_params: 75.9592132568 + scale_params: 37.7726249695 + scale_params: 0.0 + scale_params: 23.2944393158 + scale_params: 41.6536369324 + scale_params: 74.3933258057 + scale_params: 114.592155457 + scale_params: 0.0 + scale_params: 0.0 + scale_params: 58.0522079468 + 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weight_filler { + type: "msra" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 11.1095628738 + scale_out: 3.00517463684 + scale_params: 62.2967948914 + scale_params: 340.009216309 + scale_params: 244.764648438 + scale_params: 114.332649231 + scale_params: 111.084205627 + scale_params: 48.3250808716 + scale_params: 276.6902771 + scale_params: 649.946838379 + scale_params: 136.880599976 + scale_params: 230.259521484 + scale_params: 226.491470337 + scale_params: 199.632385254 + scale_params: 58.7150344849 + scale_params: 312.996856689 + scale_params: 215.028823853 + scale_params: 233.231369019 + scale_params: 37.1700286865 + scale_params: 236.888717651 + scale_params: 602.868347168 + scale_params: 111.41103363 + scale_params: 297.324462891 + scale_params: 157.185012817 + scale_params: 21.9425697327 + scale_params: 251.113037109 + scale_params: 250.054946899 + scale_params: 95.9932327271 + scale_params: 101.70880127 + scale_params: 257.062194824 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type: "PriorBox" + bottom: "conv16_2" + bottom: "data" + top: "conv16_2_mbox_priorbox" + prior_box_param { + min_size: 240.0 + max_size: 285.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + offset: 0.5 + } +} +layer { + name: "conv17_2_mbox_loc" + type: "Convolution" + bottom: "conv17_2" + top: "conv17_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 32 + bw_params: 8 + scale_in: 60.7414512634 + scale_out: 1.0 + scale_params: 262.461090088 + scale_params: 304.732971191 + scale_params: 429.353149414 + scale_params: 397.977111816 + scale_params: 268.701080322 + scale_params: 321.984375 + scale_params: 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aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv11_mbox_loc" + bottom: "conv13_mbox_loc" + bottom: "conv14_2_mbox_loc" + bottom: "conv15_2_mbox_loc" + bottom: "conv16_2_mbox_loc" + bottom: "conv17_2_mbox_loc" + top: "mbox_loc" + concat_param { + axis: 1 + per_fla_fuse : true + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv11_mbox_conf" + bottom: "conv13_mbox_conf" + bottom: "conv14_2_mbox_conf" + bottom: "conv15_2_mbox_conf" + bottom: "conv16_2_mbox_conf" + bottom: "conv17_2_mbox_conf" + top: "mbox_conf" + concat_param { + axis: 1 + per_fla_fuse : true + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv11_mbox_priorbox" + bottom: "conv13_mbox_priorbox" + bottom: "conv14_2_mbox_priorbox" + bottom: "conv15_2_mbox_priorbox" + bottom: "conv16_2_mbox_priorbox" + bottom: "conv17_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 21 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.449999988079 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.00999999977648 + } +} +layer { + name: "detection_eval" + type: "DetectionEvaluate" + bottom: "detection_out" + bottom: "label" + top: "detection_eval" + include { + phase: TEST + } + detection_evaluate_param { + num_classes: 21 + background_label_id: 0 + overlap_threshold: 0.5 + evaluate_difficult_gt: false + } +} diff --git a/models/intel_optimized_models/int8/ssd_vgg16_int8.prototxt b/models/intel_optimized_models/int8/ssd_vgg16_int8.prototxt new file mode 100644 index 000000000..4f0c77f4e --- /dev/null +++ b/models/intel_optimized_models/int8/ssd_vgg16_int8.prototxt @@ -0,0 +1,10513 @@ +name: "VGG_VOC0712_SSD_300x300_test" +layer { + name: "data" + type: "DummyData" + top: "data" + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim: 32 + dim: 3 + dim: 300 + dim: 300 + } + } +} +layer { + name: "data" + type: "DummyData" + top: "label" + dummy_data_param { + data_filler { + type: "constant" + value: 1 + } + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 8 + } + } +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 0.310000002384 + scale_out: 0.0799999982119 + scale_params: 619.210021973 + scale_params: 919.309997559 + scale_params: 750.010009766 + scale_params: 683.83001709 + 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name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 8.0 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 21.4099998474 + scale_out: 9.82999992 + scale_params: 2240.48999023 + scale_params: 1874.27001953 + scale_params: 2609.90991211 + scale_params: 2807.5 + scale_params: 2143.11010742 + 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2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 16.0 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 24.9899997711 + scale_out: 9.82999992 + scale_params: 1858.48999023 + scale_params: 1961.79003906 + scale_params: 1624.2199707 + scale_params: 1448.85998535 + scale_params: 1735.55004883 + scale_params: 1758.54003906 + scale_params: 1704.69995117 + scale_params: 1714.52001953 + scale_params: 1839.34997559 + scale_params: 1787.63000488 + scale_params: 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+ param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 19.9599990845 + scale_out: 9.82999992 + scale_params: 2108.98999023 + scale_params: 2730.60009766 + scale_params: 1795.97998047 + scale_params: 2472.20996094 + scale_params: 2778.52001953 + scale_params: 3050.41992188 + scale_params: 2355.82006836 + scale_params: 2605.15991211 + scale_params: 2932.04003906 + scale_params: 2543.04003906 + scale_params: 2496.73999023 + scale_params: 2253.38989258 + scale_params: 1473.22998047 + scale_params: 3179.5 + scale_params: 1359.68994141 + scale_params: 2487.13989258 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1.0 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+ prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 100.0 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 9.5 + scale_out: 9.82999992 + scale_params: 1395.75 + scale_params: 1995.0300293 + scale_params: 1638.64001465 + scale_params: 1182.09997559 + scale_params: 1228.89001465 + scale_params: 1826.86999512 + scale_params: 1477.43005371 + scale_params: 1117.32995605 + scale_params: 2557.14990234 + 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scale_params: 1405.4699707 + scale_params: 1065.10998535 + scale_params: 1888.45996094 + scale_params: 1524.33996582 + scale_params: 1435.15002441 + scale_params: 1506.07995605 + scale_params: 1646.11999512 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2.0 + flip: true + clip: false + variance: 0.10000000149 + variance: 0.10000000149 + variance: 0.20000000298 + variance: 0.20000000298 + step: 300.0 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc" + bottom: "fc7_mbox_loc" + bottom: "conv6_2_mbox_loc" + bottom: "conv7_2_mbox_loc" + bottom: "conv8_2_mbox_loc" + bottom: "conv9_2_mbox_loc" + top: "mbox_loc" + concat_param { + axis: 1 + per_fla_fuse : true + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf" + bottom: "fc7_mbox_conf" + bottom: "conv6_2_mbox_conf" + bottom: "conv7_2_mbox_conf" + bottom: "conv8_2_mbox_conf" + bottom: "conv9_2_mbox_conf" + top: "mbox_conf" + concat_param { + axis: 1 + per_fla_fuse : true + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 21 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + } +} diff --git a/models/intel_optimized_models/int8/yolov2_int8.prototxt b/models/intel_optimized_models/int8/yolov2_int8.prototxt new file mode 100644 index 000000000..14714bfb1 --- /dev/null +++ b/models/intel_optimized_models/int8/yolov2_int8.prototxt @@ -0,0 +1,11611 @@ +name: "yolov2" +layer { + name: "data" + type: "DummyData" + top: "data" + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim: 32 + dim: 3 + dim: 544 + dim: 544 + } + } +} +layer { + name: "data" + type: "DummyData" + top: "label" + dummy_data_param { + data_filler { + type: "constant" + value: 1 + } + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 8 + } + } +} +layer { + name: "layer1-conv" + type: "Convolution" + bottom: "data" + top: "layer1-conv" + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + } +} +layer { + name: "layer1-bn" + type: "BatchNorm" + bottom: "layer1-conv" + top: "layer1-conv" + batch_norm_param { + use_global_stats: true + } +} +layer { + name: "layer1-scale" + type: "Scale" + bottom: "layer1-conv" + top: "layer1-conv" + scale_param { + bias_term: true + } +} +layer { + name: "layer1-act" + type: "ReLU" + bottom: "layer1-conv" + top: "layer1-conv" + relu_param { + negative_slope: 0.0 + } +} +layer { + name: "layer2-maxpool" + type: "Pooling" + bottom: "layer1-conv" + top: "layer2-maxpool" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "layer3-conv" + type: "Convolution" + bottom: "layer2-maxpool" + top: "layer3-conv" + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 14.1342363358 + scale_out: 5.11548805237 + scale_params: 1435.140625 + scale_params: 878.553771973 + scale_params: 141.516220093 + scale_params: 525.462585449 + scale_params: 434.804199219 + scale_params: 477.665710449 + scale_params: 796.670227051 + scale_params: 301.627716064 + scale_params: 927.535888672 + scale_params: 348.136993408 + scale_params: 122.350845337 + scale_params: 307.39553833 + scale_params: 344.012573242 + scale_params: 2157.28857422 + scale_params: 541.550231934 + scale_params: 289.741668701 + scale_params: 283.050323486 + scale_params: 692.754516602 + scale_params: 177.813339233 + scale_params: 318.43347168 + scale_params: 250.770645142 + scale_params: 844.971435547 + scale_params: 548.127380371 + scale_params: 564.895446777 + scale_params: 159.45425415 + scale_params: 603.991699219 + scale_params: 413.319549561 + scale_params: 1194.97192383 + 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000000000..1812d5382 --- /dev/null +++ b/models/intel_optimized_models/int8/yolov2_int8_acc.prototxt @@ -0,0 +1,11751 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_value: 104.0 + mean_value: 117.0 + mean_value: 123.0 + resize_param { + prob: 1.0 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + } + } + data_param { + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + name: "layer1-conv" + type: "Convolution" + bottom: "data" + top: "layer1-conv" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + } +} +layer { + name: "layer1-bn" + type: "BatchNorm" + bottom: "layer1-conv" + top: "layer1-conv" + batch_norm_param { + use_global_stats: true + } +} +layer { + name: "layer1-scale" + type: "Scale" + bottom: "layer1-conv" + top: "layer1-conv" + scale_param { + bias_term: true + } +} +layer { + name: "layer1-act" + type: "ReLU" + bottom: "layer1-conv" + top: "layer1-conv" + relu_param { + } +} +layer { + name: "layer2-maxpool" + type: "Pooling" + bottom: "layer1-conv" + top: "layer2-maxpool" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "layer3-conv" + type: "Convolution" + bottom: "layer2-maxpool" + top: "layer3-conv" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 14.449379921 + scale_out: 10.8545017242 + scale_params: 1268.80871582 + scale_params: 797.671447754 + scale_params: 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name: "layer3-scale" + type: "Scale" + bottom: "layer3-conv" + top: "layer3-conv" + scale_param { + bias_term: true + } +} +layer { + name: "layer3-act" + type: "ReLU" + bottom: "layer3-conv" + top: "layer3-conv" + relu_param { + } +} +layer { + name: "layer4-maxpool" + type: "Pooling" + bottom: "layer3-conv" + top: "layer4-maxpool" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "layer5-conv" + type: "Convolution" + bottom: "layer4-maxpool" + top: "layer5-conv" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 10.8545017242 + scale_out: 14.7164936066 + scale_params: 742.768676758 + scale_params: 961.595397949 + scale_params: 861.889038086 + scale_params: 804.749267578 + scale_params: 882.783996582 + scale_params: 767.02722168 + 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4739.31152344 + scale_params: 4432.20654297 + scale_params: 3128.40209961 + scale_params: 4926.04736328 + scale_params: 4259.61914062 + scale_params: 4596.91357422 + scale_params: 3902.61157227 + scale_params: 4148.70214844 + scale_params: 4583.68798828 + scale_params: 4689.63476562 + scale_params: 5931.07714844 + scale_params: 4526.89892578 + scale_params: 4905.04101562 + scale_params: 3963.45800781 + scale_params: 4398.41162109 + scale_params: 3875.99951172 + scale_params: 4849.73242188 + scale_params: 4767.63916016 + scale_params: 4021.96655273 + scale_params: 4102.96044922 + scale_params: 4559.88574219 + scale_params: 4151.56689453 + scale_params: 3967.45507812 + scale_params: 4756.29248047 + scale_params: 5015.47753906 + } +} +layer { + name: "extra_layer27-bn" + type: "BatchNorm" + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + batch_norm_param { + use_global_stats: true + } +} +layer { + name: "extra_layer27-scale" + type: "Scale" + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + scale_param { + bias_term: true + } +} +layer { + name: "extra_layer27-act" + type: "ReLU" + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + relu_param { + } +} +layer { + name: "conv_reg" + type: "Convolution" + bottom: "extra_layer27-conv" + top: "conv_reg" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 125 + bias_term: true + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 32 + bw_params: 8 + scale_in: 39.3150100708 + scale_out: 1.0 + scale_params: 1761.96813965 + scale_params: 1572.40234375 + scale_params: 2506.81323242 + scale_params: 1738.84130859 + scale_params: 842.13458252 + scale_params: 1247.6328125 + scale_params: 1077.25939941 + scale_params: 1134.3392334 + scale_params: 1257.1986084 + scale_params: 817.802612305 + scale_params: 1002.8303833 + scale_params: 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2599.35717773 + scale_params: 1733.34838867 + scale_params: 1327.27966309 + scale_params: 1173.95849609 + scale_params: 1452.47216797 + scale_params: 1182.44848633 + scale_params: 1400.58569336 + scale_params: 1295.34594727 + scale_params: 1138.9855957 + scale_params: 1335.28515625 + scale_params: 989.639282227 + scale_params: 1318.20996094 + scale_params: 939.912780762 + scale_params: 1164.5390625 + scale_params: 1387.2454834 + scale_params: 1158.49560547 + scale_params: 953.006164551 + scale_params: 1314.00598145 + scale_params: 1357.06237793 + scale_params: 1182.73413086 + scale_params: 1306.48291016 + scale_params: 1372.66577148 + scale_params: 1300.48144531 + scale_params: 1617.54016113 + scale_params: 2323.30932617 + scale_params: 3357.89257812 + scale_params: 373.388793945 + scale_params: 1199.1003418 + scale_params: 1095.17163086 + scale_params: 1139.55529785 + scale_params: 1062.84338379 + scale_params: 986.456787109 + scale_params: 1185.50036621 + scale_params: 996.506591797 + scale_params: 901.011352539 + scale_params: 1012.07073975 + scale_params: 1076.61535645 + scale_params: 939.787719727 + scale_params: 1087.89160156 + scale_params: 1068.81884766 + scale_params: 1195.94897461 + scale_params: 722.515014648 + scale_params: 976.797912598 + scale_params: 1242.41711426 + scale_params: 991.435852051 + scale_params: 1204.57287598 + scale_params: 1072.55957031 + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + score_type: MULTIPLY + nms: 0.449999988079 + biases: 1.32000005245 + biases: 1.73000001907 + biases: 3.19000005722 + biases: 4.01000022888 + biases: 5.05999994278 + biases: 8.10000038147 + biases: 9.47000026703 + biases: 4.84000015259 + biases: 11.2399997711 + biases: 10.0100002289 + } +} diff --git a/models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt b/models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt index 1729d9320..947e2bacc 100644 --- a/models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt +++ b/models/intel_optimized_models/multinode/default_resnet50_64nodes/solver.prototxt @@ -14,6 +14,6 @@ warmup_iter: 780 # 1281167 / 8192 * 5 epochs warmup_start_lr: 0.1 momentum: 0.9 weight_decay: 0.0001 -snapshot: 1408 +snapshot: 1560 snapshot_prefix: "default_resnet_50_64_nodes" solver_mode: CPU diff --git a/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt b/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt index 6cab44e72..85a637a2c 100644 --- a/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt +++ b/models/intel_optimized_models/multinode/default_resnet50_8nodes/solver.prototxt @@ -1,6 +1,6 @@ net: "models/intel_optimized_models/multinode/default_resnet50_8nodes/train_val.prototxt" test_iter: 1000 -test_interval: 1126 +test_interval: 1248 test_initialization: false display: 40 base_lr: 0.4 @@ -12,6 +12,6 @@ gamma: 0.1 max_iter: 112600 momentum: 0.9 weight_decay: 0.0001 -snapshot: 11260 +snapshot: 12480 snapshot_prefix: "default_resnet_50_8_nodes" solver_mode: CPU diff --git a/models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/solver.prototxt b/models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/solver.prototxt new file mode 100644 index 000000000..6b0e1556c --- /dev/null +++ b/models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/solver.prototxt @@ -0,0 +1,26 @@ +net: "models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/train_val.prototxt" +# global batch size: 2048 +# local batch size: 64 +warmup_iter: 3125 +warmup_start_lr: 0.05 + +test_iter: 1000 +test_interval: 6250 +test_initialization: false + +display: 100 + +max_iter: 62500 # 100 epoch +base_lr: 0.6 + +lr_policy: "poly" +power: 3 +momentum: 0.9 +weight_decay: 0.0002 + +snapshot: 62500 +snapshot_prefix: "models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/snapshots/googlenet_v3-2k" + +random_seed: 1 + +solver_mode: CPU diff --git a/models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/train_val.prototxt b/models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/train_val.prototxt new file mode 100644 index 000000000..933ca3ee3 --- /dev/null +++ b/models/intel_optimized_models/multinode/googlenet_v3_aux_32nodes_2k_batch/train_val.prototxt @@ -0,0 +1,5375 @@ +name: "InceptionV3" +bn_stats_batch_size: 32 +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + data_param { + +source: "examples/imagenet/ilsvrc12_train_lmdb" + + shuffle: true + backend: LMDB + batch_size: 64 + } + transform_param { + crop_size: 299 + #scale: 0.0078125 + mean_value: 104 + mean_value: 117 + mean_value: 123 + mirror: true + + random_aspect_ratio_param { + min_area_ratio: 0.05 + max_area_ratio: 1 + aspect_ratio_change: 0.75 + resize_param { + interp_mode: CUBIC + } + } + } + include: { phase: TRAIN } +} +layer { + name: "data" + type: "Data" + top: "data" + top: "label" + data_param { +source: "examples/imagenet/ilsvrc12_val_lmdb" + shuffle: false + backend: LMDB + batch_size: 50 + } + transform_param { + crop_size: 299 + #scale: 0.0078125 + mean_value: 104 + mean_value: 117 + mean_value: 123 + + mirror: false + + random_resize_param { + min_size: 320 + max_size: 320 + resize_param { + interp_mode: CUBIC + } + } + } + include: { phase: TEST } +} +layer { + name: "conv_conv2d" + type: "Convolution" + bottom: "data" + top: "conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv_batchnorm" + type: "BatchNorm" + bottom: "conv_conv2d" + top: "conv_conv2d_bn" + + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + } +} +layer { + bottom: "conv_conv2d_bn" + top: "conv_conv2d_bn" + name: "conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "conv_relu" + type: "ReLU" + bottom: "conv_conv2d_bn" + top: "conv_conv2d_relu" +} +layer { + name: "conv_1_1_conv2d" + type: "Convolution" + bottom: "conv_conv2d_relu" + top: "conv_1_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv_1_1_batchnorm" + type: "BatchNorm" + bottom: "conv_1_1_conv2d" + top: "conv_1_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "conv_1_1_conv2d_bn" + top: "conv_1_1_conv2d_bn" + name: "conv_1_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "conv_1_1_relu" + type: "ReLU" + bottom: "conv_1_1_conv2d_bn" + top: "conv_1_1_conv2d_relu" +} +layer { + name: "conv_2_2_conv2d" + type: "Convolution" + bottom: "conv_1_1_conv2d_relu" + top: "conv_2_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv_2_2_batchnorm" + type: "BatchNorm" + bottom: "conv_2_2_conv2d" + top: "conv_2_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "conv_2_2_conv2d_bn" + top: "conv_2_2_conv2d_bn" + name: "conv_2_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "conv_2_2_relu" + type: "ReLU" + bottom: "conv_2_2_conv2d_bn" + top: "conv_2_2_conv2d_relu" +} +layer { + name: "pool" + type: "Pooling" + bottom: "conv_2_2_conv2d_relu" + top: "pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "conv_3_3_conv2d" + type: "Convolution" + bottom: "pool" + top: "conv_3_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 80 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "conv_3_3_batchnorm" + type: "BatchNorm" + bottom: "conv_3_3_conv2d" + top: "conv_3_3_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "conv_3_3_conv2d_bn" + top: "conv_3_3_conv2d_bn" + name: "conv_3_3_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "conv_3_3_relu" + type: "ReLU" + bottom: "conv_3_3_conv2d_bn" + top: "conv_3_3_conv2d_relu" +} +layer { + name: "conv_4_4_conv2d" + type: "Convolution" + bottom: "conv_3_3_conv2d_relu" + top: "conv_4_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "conv_4_4_batchnorm" + type: "BatchNorm" + bottom: "conv_4_4_conv2d" + top: "conv_4_4_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "conv_4_4_conv2d_bn" + top: "conv_4_4_conv2d_bn" + name: "conv_4_4_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "conv_4_4_relu" + type: "ReLU" + bottom: "conv_4_4_conv2d_bn" + top: "conv_4_4_conv2d_relu" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv_4_4_conv2d_relu" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "mixed_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_conv_conv2d" + top: "mixed_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_conv_conv2d_bn" + top: "mixed_conv_conv2d_bn" + name: "mixed_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_conv_relu" + type: "ReLU" + bottom: "mixed_conv_conv2d_bn" + top: "mixed_conv_conv2d_relu" +} +layer { + name: "mixed_tower_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_conv_conv2d" + top: "mixed_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_tower_conv_conv2d_bn" + top: "mixed_tower_conv_conv2d_bn" + name: "mixed_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_tower_conv_relu" + type: "ReLU" + bottom: "mixed_tower_conv_conv2d_bn" + top: "mixed_tower_conv_conv2d_relu" +} +layer { + name: "mixed_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_tower_conv_conv2d_relu" + top: "mixed_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_conv_1_conv2d" + top: "mixed_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_tower_conv_1_conv2d_bn" + top: "mixed_tower_conv_1_conv2d_bn" + name: "mixed_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_tower_conv_1_conv2d_bn" + top: "mixed_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_conv2d" + type: "Convolution" + bottom: "pool1" + top: "mixed_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + + } +} +layer { + name: "mixed_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_conv2d" + top: "mixed_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_tower_1_conv_conv2d_bn" + top: "mixed_tower_1_conv_conv2d_bn" + name: "mixed_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_conv2d_bn" + top: "mixed_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_tower_1_conv_conv2d_relu" + top: "mixed_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + + } +} +layer { + name: "mixed_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_1_conv2d" + top: "mixed_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_tower_1_conv_1_conv2d_bn" + top: "mixed_tower_1_conv_1_conv2d_bn" + name: "mixed_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_1_conv2d_bn" + top: "mixed_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_tower_1_conv_1_conv2d_relu" + top: "mixed_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + + } +} +layer { + name: "mixed_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_1_conv_2_conv2d" + top: "mixed_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_tower_1_conv_2_conv2d_bn" + top: "mixed_tower_1_conv_2_conv2d_bn" + name: "mixed_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_tower_1_conv_2_conv2d_bn" + top: "mixed_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_pool" + type: "Pooling" + bottom: "pool1" + top: "AVE_pool_mixed_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_pool" + top: "mixed_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + + } +} +layer { + name: "mixed_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_tower_2_conv_conv2d" + top: "mixed_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_tower_2_conv_conv2d_bn" + top: "mixed_tower_2_conv_conv2d_bn" + name: "mixed_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_tower_2_conv_conv2d_bn" + top: "mixed_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_chconcat" + type: "Concat" + bottom: "mixed_conv_conv2d_relu" + bottom: "mixed_tower_conv_1_conv2d_relu" + bottom: "mixed_tower_1_conv_2_conv2d_relu" + bottom: "mixed_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_conv_conv2d" + top: "mixed_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_conv_conv2d_bn" + top: "mixed_1_conv_conv2d_bn" + name: "mixed_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_conv_relu" + type: "ReLU" + bottom: "mixed_1_conv_conv2d_bn" + top: "mixed_1_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_1_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_conv_conv2d" + top: "mixed_1_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_tower_conv_conv2d_bn" + top: "mixed_1_tower_conv_conv2d_bn" + name: "mixed_1_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_tower_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_conv_conv2d_bn" + top: "mixed_1_tower_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_conv_conv2d_relu" + top: "mixed_1_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_1_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_conv_1_conv2d" + top: "mixed_1_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_tower_conv_1_conv2d_bn" + top: "mixed_1_tower_conv_1_conv2d_bn" + name: "mixed_1_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_1_tower_conv_1_conv2d_bn" + top: "mixed_1_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_chconcat" + top: "mixed_1_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_1_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_conv2d" + top: "mixed_1_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_tower_1_conv_conv2d_bn" + top: "mixed_1_tower_1_conv_conv2d_bn" + name: "mixed_1_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_conv2d_bn" + top: "mixed_1_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_1_conv_conv2d_relu" + top: "mixed_1_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_1_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_1_conv2d" + top: "mixed_1_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_tower_1_conv_1_conv2d_bn" + top: "mixed_1_tower_1_conv_1_conv2d_bn" + name: "mixed_1_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_1_conv2d_bn" + top: "mixed_1_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_1_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_1_tower_1_conv_1_conv2d_relu" + top: "mixed_1_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_1_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_1_conv_2_conv2d" + top: "mixed_1_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_tower_1_conv_2_conv2d_bn" + top: "mixed_1_tower_1_conv_2_conv2d_bn" + name: "mixed_1_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_1_tower_1_conv_2_conv2d_bn" + top: "mixed_1_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_1_pool" + type: "Pooling" + bottom: "ch_concat_mixed_chconcat" + top: "AVE_pool_mixed_1_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_1_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_1_pool" + top: "mixed_1_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_1_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_1_tower_2_conv_conv2d" + top: "mixed_1_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_1_tower_2_conv_conv2d_bn" + top: "mixed_1_tower_2_conv_conv2d_bn" + name: "mixed_1_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_1_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_1_tower_2_conv_conv2d_bn" + top: "mixed_1_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_1_chconcat" + type: "Concat" + bottom: "mixed_1_conv_conv2d_relu" + bottom: "mixed_1_tower_conv_1_conv2d_relu" + bottom: "mixed_1_tower_1_conv_2_conv2d_relu" + bottom: "mixed_1_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_1_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_2_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_conv_conv2d" + top: "mixed_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_conv_conv2d_bn" + top: "mixed_2_conv_conv2d_bn" + name: "mixed_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_conv_relu" + type: "ReLU" + bottom: "mixed_2_conv_conv2d_bn" + top: "mixed_2_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 48 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_2_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_conv_conv2d" + top: "mixed_2_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_tower_conv_conv2d_bn" + top: "mixed_2_tower_conv_conv2d_bn" + name: "mixed_2_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_tower_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_conv_conv2d_bn" + top: "mixed_2_tower_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_conv_conv2d_relu" + top: "mixed_2_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_2_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_conv_1_conv2d" + top: "mixed_2_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_tower_conv_1_conv2d_bn" + top: "mixed_2_tower_conv_1_conv2d_bn" + name: "mixed_2_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_2_tower_conv_1_conv2d_bn" + top: "mixed_2_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_1_chconcat" + top: "mixed_2_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_2_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_conv2d" + top: "mixed_2_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_tower_1_conv_conv2d_bn" + top: "mixed_2_tower_1_conv_conv2d_bn" + name: "mixed_2_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_conv2d_bn" + top: "mixed_2_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_1_conv_conv2d_relu" + top: "mixed_2_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_2_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_1_conv2d" + top: "mixed_2_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_tower_1_conv_1_conv2d_bn" + top: "mixed_2_tower_1_conv_1_conv2d_bn" + name: "mixed_2_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_1_conv2d_bn" + top: "mixed_2_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_2_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_2_tower_1_conv_1_conv2d_relu" + top: "mixed_2_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + } +} +layer { + name: "mixed_2_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_1_conv_2_conv2d" + top: "mixed_2_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_tower_1_conv_2_conv2d_bn" + top: "mixed_2_tower_1_conv_2_conv2d_bn" + name: "mixed_2_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_2_tower_1_conv_2_conv2d_bn" + top: "mixed_2_tower_1_conv_2_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_2_pool" + type: "Pooling" + bottom: "ch_concat_mixed_1_chconcat" + top: "AVE_pool_mixed_2_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_2_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_2_pool" + top: "mixed_2_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_2_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_2_tower_2_conv_conv2d" + top: "mixed_2_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_2_tower_2_conv_conv2d_bn" + top: "mixed_2_tower_2_conv_conv2d_bn" + name: "mixed_2_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_2_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_2_tower_2_conv_conv2d_bn" + top: "mixed_2_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_2_chconcat" + type: "Concat" + bottom: "mixed_2_conv_conv2d_relu" + bottom: "mixed_2_tower_conv_1_conv2d_relu" + bottom: "mixed_2_tower_1_conv_2_conv2d_relu" + bottom: "mixed_2_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_2_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_3_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_2_chconcat" + top: "mixed_3_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_3_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_conv_conv2d" + top: "mixed_3_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_3_conv_conv2d_bn" + top: "mixed_3_conv_conv2d_bn" + name: "mixed_3_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_3_conv_relu" + type: "ReLU" + bottom: "mixed_3_conv_conv2d_bn" + top: "mixed_3_conv_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_2_chconcat" + top: "mixed_3_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_3_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_conv2d" + top: "mixed_3_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_3_tower_conv_conv2d_bn" + top: "mixed_3_tower_conv_conv2d_bn" + name: "mixed_3_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_3_tower_conv_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_conv2d_bn" + top: "mixed_3_tower_conv_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_3_tower_conv_conv2d_relu" + top: "mixed_3_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_3_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_1_conv2d" + top: "mixed_3_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_3_tower_conv_1_conv2d_bn" + top: "mixed_3_tower_conv_1_conv2d_bn" + name: "mixed_3_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_3_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_1_conv2d_bn" + top: "mixed_3_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_3_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_3_tower_conv_1_conv2d_relu" + top: "mixed_3_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 96 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_3_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_3_tower_conv_2_conv2d" + top: "mixed_3_tower_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_3_tower_conv_2_conv2d_bn" + top: "mixed_3_tower_conv_2_conv2d_bn" + name: "mixed_3_tower_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_3_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_3_tower_conv_2_conv2d_bn" + top: "mixed_3_tower_conv_2_conv2d_relu" +} +layer { + name: "max_pool_mixed_3_pool" + type: "Pooling" + bottom: "ch_concat_mixed_2_chconcat" + top: "max_pool_mixed_3_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "ch_concat_mixed_3_chconcat" + type: "Concat" + bottom: "max_pool_mixed_3_pool" + bottom: "mixed_3_conv_conv2d_relu" + bottom: "mixed_3_tower_conv_2_conv2d_relu" + top: "ch_concat_mixed_3_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_4_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_4_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_conv_conv2d" + top: "mixed_4_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_conv_conv2d_bn" + top: "mixed_4_conv_conv2d_bn" + name: "mixed_4_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_conv_relu" + type: "ReLU" + bottom: "mixed_4_conv_conv2d_bn" + top: "mixed_4_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_4_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_conv2d" + top: "mixed_4_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_conv_conv2d_bn" + top: "mixed_4_tower_conv_conv2d_bn" + name: "mixed_4_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_conv2d_bn" + top: "mixed_4_tower_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_conv_conv2d_relu" + top: "mixed_4_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_1_conv2d" + top: "mixed_4_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_conv_1_conv2d_bn" + top: "mixed_4_tower_conv_1_conv2d_bn" + name: "mixed_4_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_1_conv2d_bn" + top: "mixed_4_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_4_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_conv_1_conv2d_relu" + top: "mixed_4_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_conv_2_conv2d" + top: "mixed_4_tower_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_conv_2_conv2d_bn" + top: "mixed_4_tower_conv_2_conv2d_bn" + name: "mixed_4_tower_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_4_tower_conv_2_conv2d_bn" + top: "mixed_4_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_3_chconcat" + top: "mixed_4_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + + + } +} +layer { + name: "mixed_4_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_conv2d" + top: "mixed_4_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_1_conv_conv2d_bn" + top: "mixed_4_tower_1_conv_conv2d_bn" + name: "mixed_4_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_conv2d_bn" + top: "mixed_4_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_conv2d_relu" + top: "mixed_4_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_1_conv2d" + top: "mixed_4_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_1_conv_1_conv2d_bn" + top: "mixed_4_tower_1_conv_1_conv2d_bn" + name: "mixed_4_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_1_conv2d_bn" + top: "mixed_4_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_1_conv2d_relu" + top: "mixed_4_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_2_conv2d" + top: "mixed_4_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_1_conv_2_conv2d_bn" + top: "mixed_4_tower_1_conv_2_conv2d_bn" + name: "mixed_4_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_2_conv2d_bn" + top: "mixed_4_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_2_conv2d_relu" + top: "mixed_4_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_4_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_3_conv2d" + top: "mixed_4_tower_1_conv_3_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_1_conv_3_conv2d_bn" + top: "mixed_4_tower_1_conv_3_conv2d_bn" + name: "mixed_4_tower_1_conv_3_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_3_conv2d_bn" + top: "mixed_4_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_4_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_4_tower_1_conv_3_conv2d_relu" + top: "mixed_4_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_4_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_1_conv_4_conv2d" + top: "mixed_4_tower_1_conv_4_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_1_conv_4_conv2d_bn" + top: "mixed_4_tower_1_conv_4_conv2d_bn" + name: "mixed_4_tower_1_conv_4_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_4_tower_1_conv_4_conv2d_bn" + top: "mixed_4_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_4_pool" + type: "Pooling" + bottom: "ch_concat_mixed_3_chconcat" + top: "AVE_pool_mixed_4_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_4_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_4_pool" + top: "mixed_4_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_4_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_4_tower_2_conv_conv2d" + top: "mixed_4_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_4_tower_2_conv_conv2d_bn" + top: "mixed_4_tower_2_conv_conv2d_bn" + name: "mixed_4_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_4_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_4_tower_2_conv_conv2d_bn" + top: "mixed_4_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_4_chconcat" + type: "Concat" + bottom: "mixed_4_conv_conv2d_relu" + bottom: "mixed_4_tower_conv_2_conv2d_relu" + bottom: "mixed_4_tower_1_conv_4_conv2d_relu" + bottom: "mixed_4_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_4_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_5_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_5_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_conv_conv2d" + top: "mixed_5_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_conv_conv2d_bn" + top: "mixed_5_conv_conv2d_bn" + name: "mixed_5_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_conv_relu" + type: "ReLU" + bottom: "mixed_5_conv_conv2d_bn" + top: "mixed_5_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_5_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_conv2d" + top: "mixed_5_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_conv_conv2d_bn" + top: "mixed_5_tower_conv_conv2d_bn" + name: "mixed_5_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_conv2d_bn" + top: "mixed_5_tower_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_conv_conv2d_relu" + top: "mixed_5_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_1_conv2d" + top: "mixed_5_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_conv_1_conv2d_bn" + top: "mixed_5_tower_conv_1_conv2d_bn" + name: "mixed_5_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_1_conv2d_bn" + top: "mixed_5_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_5_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_conv_1_conv2d_relu" + top: "mixed_5_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_conv_2_conv2d" + top: "mixed_5_tower_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_conv_2_conv2d_bn" + top: "mixed_5_tower_conv_2_conv2d_bn" + name: "mixed_5_tower_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_5_tower_conv_2_conv2d_bn" + top: "mixed_5_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_4_chconcat" + top: "mixed_5_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_5_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_conv2d" + top: "mixed_5_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_1_conv_conv2d_bn" + top: "mixed_5_tower_1_conv_conv2d_bn" + name: "mixed_5_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_conv2d_bn" + top: "mixed_5_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_conv2d_relu" + top: "mixed_5_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_1_conv2d" + top: "mixed_5_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_1_conv_1_conv2d_bn" + top: "mixed_5_tower_1_conv_1_conv2d_bn" + name: "mixed_5_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_1_conv2d_bn" + top: "mixed_5_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_1_conv2d_relu" + top: "mixed_5_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_2_conv2d" + top: "mixed_5_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_1_conv_2_conv2d_bn" + top: "mixed_5_tower_1_conv_2_conv2d_bn" + name: "mixed_5_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_2_conv2d_bn" + top: "mixed_5_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_2_conv2d_relu" + top: "mixed_5_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_5_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_3_conv2d" + top: "mixed_5_tower_1_conv_3_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_1_conv_3_conv2d_bn" + top: "mixed_5_tower_1_conv_3_conv2d_bn" + name: "mixed_5_tower_1_conv_3_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_3_conv2d_bn" + top: "mixed_5_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_5_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_5_tower_1_conv_3_conv2d_relu" + top: "mixed_5_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_5_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_1_conv_4_conv2d" + top: "mixed_5_tower_1_conv_4_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_1_conv_4_conv2d_bn" + top: "mixed_5_tower_1_conv_4_conv2d_bn" + name: "mixed_5_tower_1_conv_4_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_5_tower_1_conv_4_conv2d_bn" + top: "mixed_5_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_5_pool" + type: "Pooling" + bottom: "ch_concat_mixed_4_chconcat" + top: "AVE_pool_mixed_5_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_5_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_5_pool" + top: "mixed_5_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_5_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_5_tower_2_conv_conv2d" + top: "mixed_5_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_5_tower_2_conv_conv2d_bn" + top: "mixed_5_tower_2_conv_conv2d_bn" + name: "mixed_5_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_5_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_5_tower_2_conv_conv2d_bn" + top: "mixed_5_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_5_chconcat" + type: "Concat" + bottom: "mixed_5_conv_conv2d_relu" + bottom: "mixed_5_tower_conv_2_conv2d_relu" + bottom: "mixed_5_tower_1_conv_4_conv2d_relu" + bottom: "mixed_5_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_5_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_6_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_6_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_conv_conv2d" + top: "mixed_6_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_conv_conv2d_bn" + top: "mixed_6_conv_conv2d_bn" + name: "mixed_6_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_conv_relu" + type: "ReLU" + bottom: "mixed_6_conv_conv2d_bn" + top: "mixed_6_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_6_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_conv2d" + top: "mixed_6_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_conv_conv2d_bn" + top: "mixed_6_tower_conv_conv2d_bn" + name: "mixed_6_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_conv2d_bn" + top: "mixed_6_tower_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_conv_conv2d_relu" + top: "mixed_6_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_1_conv2d" + top: "mixed_6_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_conv_1_conv2d_bn" + top: "mixed_6_tower_conv_1_conv2d_bn" + name: "mixed_6_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_1_conv2d_bn" + top: "mixed_6_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_6_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_conv_1_conv2d_relu" + top: "mixed_6_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_conv_2_conv2d" + top: "mixed_6_tower_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_conv_2_conv2d_bn" + top: "mixed_6_tower_conv_2_conv2d_bn" + name: "mixed_6_tower_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_6_tower_conv_2_conv2d_bn" + top: "mixed_6_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_5_chconcat" + top: "mixed_6_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_6_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_conv2d" + top: "mixed_6_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_1_conv_conv2d_bn" + top: "mixed_6_tower_1_conv_conv2d_bn" + name: "mixed_6_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_conv2d_bn" + top: "mixed_6_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_conv2d_relu" + top: "mixed_6_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_1_conv2d" + top: "mixed_6_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_1_conv_1_conv2d_bn" + top: "mixed_6_tower_1_conv_1_conv2d_bn" + name: "mixed_6_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_1_conv2d_bn" + top: "mixed_6_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_1_conv2d_relu" + top: "mixed_6_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_2_conv2d" + top: "mixed_6_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_1_conv_2_conv2d_bn" + top: "mixed_6_tower_1_conv_2_conv2d_bn" + name: "mixed_6_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_2_conv2d_bn" + top: "mixed_6_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_2_conv2d_relu" + top: "mixed_6_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 160 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_6_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_3_conv2d" + top: "mixed_6_tower_1_conv_3_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_1_conv_3_conv2d_bn" + top: "mixed_6_tower_1_conv_3_conv2d_bn" + name: "mixed_6_tower_1_conv_3_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_3_conv2d_bn" + top: "mixed_6_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_6_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_6_tower_1_conv_3_conv2d_relu" + top: "mixed_6_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_6_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_1_conv_4_conv2d" + top: "mixed_6_tower_1_conv_4_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_1_conv_4_conv2d_bn" + top: "mixed_6_tower_1_conv_4_conv2d_bn" + name: "mixed_6_tower_1_conv_4_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_6_tower_1_conv_4_conv2d_bn" + top: "mixed_6_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_6_pool" + type: "Pooling" + bottom: "ch_concat_mixed_5_chconcat" + top: "AVE_pool_mixed_6_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_6_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_6_pool" + top: "mixed_6_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_6_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_6_tower_2_conv_conv2d" + top: "mixed_6_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_6_tower_2_conv_conv2d_bn" + top: "mixed_6_tower_2_conv_conv2d_bn" + name: "mixed_6_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_6_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_6_tower_2_conv_conv2d_bn" + top: "mixed_6_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_6_chconcat" + type: "Concat" + bottom: "mixed_6_conv_conv2d_relu" + bottom: "mixed_6_tower_conv_2_conv2d_relu" + bottom: "mixed_6_tower_1_conv_4_conv2d_relu" + bottom: "mixed_6_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_6_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_7_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_7_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_conv_conv2d" + top: "mixed_7_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_conv_conv2d_bn" + top: "mixed_7_conv_conv2d_bn" + name: "mixed_7_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_conv_relu" + type: "ReLU" + bottom: "mixed_7_conv_conv2d_bn" + top: "mixed_7_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_7_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_conv2d" + top: "mixed_7_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_conv_conv2d_bn" + top: "mixed_7_tower_conv_conv2d_bn" + name: "mixed_7_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_conv2d_bn" + top: "mixed_7_tower_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_conv_conv2d_relu" + top: "mixed_7_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_1_conv2d" + top: "mixed_7_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_conv_1_conv2d_bn" + top: "mixed_7_tower_conv_1_conv2d_bn" + name: "mixed_7_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_1_conv2d_bn" + top: "mixed_7_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_7_tower_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_conv_1_conv2d_relu" + top: "mixed_7_tower_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_conv_2_conv2d" + top: "mixed_7_tower_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_conv_2_conv2d_bn" + top: "mixed_7_tower_conv_2_conv2d_bn" + name: "mixed_7_tower_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_conv_2_relu" + type: "ReLU" + bottom: "mixed_7_tower_conv_2_conv2d_bn" + top: "mixed_7_tower_conv_2_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_6_chconcat" + top: "mixed_7_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_7_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_conv2d" + top: "mixed_7_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_1_conv_conv2d_bn" + top: "mixed_7_tower_1_conv_conv2d_bn" + name: "mixed_7_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_conv2d_bn" + top: "mixed_7_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_conv2d_relu" + top: "mixed_7_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_1_conv2d" + top: "mixed_7_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_1_conv_1_conv2d_bn" + top: "mixed_7_tower_1_conv_1_conv2d_bn" + name: "mixed_7_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_1_conv2d_bn" + top: "mixed_7_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_1_conv2d_relu" + top: "mixed_7_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_2_conv2d" + top: "mixed_7_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_1_conv_2_conv2d_bn" + top: "mixed_7_tower_1_conv_2_conv2d_bn" + name: "mixed_7_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_2_conv2d_bn" + top: "mixed_7_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_2_conv2d_relu" + top: "mixed_7_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_7_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_3_conv2d" + top: "mixed_7_tower_1_conv_3_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_1_conv_3_conv2d_bn" + top: "mixed_7_tower_1_conv_3_conv2d_bn" + name: "mixed_7_tower_1_conv_3_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_3_conv2d_bn" + top: "mixed_7_tower_1_conv_3_conv2d_relu" +} +layer { + name: "mixed_7_tower_1_conv_4_conv2d" + type: "Convolution" + bottom: "mixed_7_tower_1_conv_3_conv2d_relu" + top: "mixed_7_tower_1_conv_4_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_7_tower_1_conv_4_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_1_conv_4_conv2d" + top: "mixed_7_tower_1_conv_4_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_1_conv_4_conv2d_bn" + top: "mixed_7_tower_1_conv_4_conv2d_bn" + name: "mixed_7_tower_1_conv_4_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_1_conv_4_relu" + type: "ReLU" + bottom: "mixed_7_tower_1_conv_4_conv2d_bn" + top: "mixed_7_tower_1_conv_4_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_7_pool" + type: "Pooling" + bottom: "ch_concat_mixed_6_chconcat" + top: "AVE_pool_mixed_7_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_7_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_7_pool" + top: "mixed_7_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_7_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_7_tower_2_conv_conv2d" + top: "mixed_7_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_7_tower_2_conv_conv2d_bn" + top: "mixed_7_tower_2_conv_conv2d_bn" + name: "mixed_7_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_7_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_7_tower_2_conv_conv2d_bn" + top: "mixed_7_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_7_chconcat" + type: "Concat" + bottom: "mixed_7_conv_conv2d_relu" + bottom: "mixed_7_tower_conv_2_conv2d_relu" + bottom: "mixed_7_tower_1_conv_4_conv2d_relu" + bottom: "mixed_7_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_7_chconcat" + concat_param { + axis: 1 + } +} + +layer { + name: "AVE_pool_aux_logits_pool" + type: "Pooling" + bottom: "ch_concat_mixed_7_chconcat" + top: "AVE_pool_mixed_8_pool" + pooling_param { + pool: AVE + kernel_size: 5 + stride: 3 + pad: 0 + } +} + + +layer { + name: "aux_logits_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_8_pool" + top: "aux_logits_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + +layer { + name: "aux_logits_conv_batchnorm" + type: "BatchNorm" + bottom: "aux_logits_conv_conv2d" + top: "aux_logits_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + name: "aux_logits_conv_relu" + type: "ReLU" + bottom: "aux_logits_conv_conv2d_bn" + top: "aux_logits_conv_conv2d_relu" +} + + +layer { + name: "aux_logits_conv1_conv2d" + type: "Convolution" + bottom: "aux_logits_conv_conv2d_relu" + top: "aux_logits_conv1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 768 + bias_term: false + pad: 0 + kernel_size: 5 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} + + +layer { + name: "aux_logits_conv1_batchnorm" + type: "BatchNorm" + bottom: "aux_logits_conv1_conv2d" + top: "aux_logits_conv1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + name: "aux_logits_conv1_relu" + type: "ReLU" + bottom: "aux_logits_conv1_conv2d_bn" + top: "aux_logits_conv1_conv2d_relu" +} + + +layer { + name: "aux_logits_flatten" + type: "Flatten" + bottom: "aux_logits_conv1_conv2d_relu" + top: "aux_logits_flatten" +} + + +layer { + name: "aux_logits_fc" + type: "InnerProduct" + bottom: "aux_logits_flatten" + top: "aux_logits_fc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + inner_product_param{ + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} + +layer { + name: "aux_logits_loss" + type: "SoftmaxWithLoss" + bottom: "aux_logits_fc" + bottom: "label" + top: "aux_logits_loss" + loss_param { + #label_smoothing: 0.1 + } + loss_weight: 0.4 +} +layer { + name: "mixed_8_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_7_chconcat" + top: "mixed_8_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_8_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_conv_conv2d" + top: "mixed_8_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_8_tower_conv_conv2d_bn" + top: "mixed_8_tower_conv_conv2d_bn" + name: "mixed_8_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_8_tower_conv_relu" + type: "ReLU" + bottom: "mixed_8_tower_conv_conv2d_bn" + top: "mixed_8_tower_conv_conv2d_relu" +} +layer { + name: "mixed_8_tower_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_conv_conv2d_relu" + top: "mixed_8_tower_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_8_tower_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_conv_1_conv2d" + top: "mixed_8_tower_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_8_tower_conv_1_conv2d_bn" + top: "mixed_8_tower_conv_1_conv2d_bn" + name: "mixed_8_tower_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_8_tower_conv_1_relu" + type: "ReLU" + bottom: "mixed_8_tower_conv_1_conv2d_bn" + top: "mixed_8_tower_conv_1_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_7_chconcat" + top: "mixed_8_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_8_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_conv2d" + top: "mixed_8_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_8_tower_1_conv_conv2d_bn" + top: "mixed_8_tower_1_conv_conv2d_bn" + name: "mixed_8_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_8_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_conv2d_bn" + top: "mixed_8_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_conv2d_relu" + top: "mixed_8_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 3 + kernel_h: 1 + kernel_w: 7 + } +} +layer { + name: "mixed_8_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_1_conv2d" + top: "mixed_8_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_8_tower_1_conv_1_conv2d_bn" + top: "mixed_8_tower_1_conv_1_conv2d_bn" + name: "mixed_8_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_8_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_1_conv2d_bn" + top: "mixed_8_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_2_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_1_conv2d_relu" + top: "mixed_8_tower_1_conv_2_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 3 + pad_w: 0 + kernel_h: 7 + kernel_w: 1 + } +} +layer { + name: "mixed_8_tower_1_conv_2_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_2_conv2d" + top: "mixed_8_tower_1_conv_2_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_8_tower_1_conv_2_conv2d_bn" + top: "mixed_8_tower_1_conv_2_conv2d_bn" + name: "mixed_8_tower_1_conv_2_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_8_tower_1_conv_2_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_2_conv2d_bn" + top: "mixed_8_tower_1_conv_2_conv2d_relu" +} +layer { + name: "mixed_8_tower_1_conv_3_conv2d" + type: "Convolution" + bottom: "mixed_8_tower_1_conv_2_conv2d_relu" + top: "mixed_8_tower_1_conv_3_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_8_tower_1_conv_3_batchnorm" + type: "BatchNorm" + bottom: "mixed_8_tower_1_conv_3_conv2d" + top: "mixed_8_tower_1_conv_3_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_8_tower_1_conv_3_conv2d_bn" + top: "mixed_8_tower_1_conv_3_conv2d_bn" + name: "mixed_8_tower_1_conv_3_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_8_tower_1_conv_3_relu" + type: "ReLU" + bottom: "mixed_8_tower_1_conv_3_conv2d_bn" + top: "mixed_8_tower_1_conv_3_conv2d_relu" +} +layer { + name: "MAX_pool_mixed_8_pool" + type: "Pooling" + bottom: "ch_concat_mixed_7_chconcat" + top: "MAX_pool_mixed_8_pool" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + pad: 0 + } +} +layer { + name: "ch_concat_mixed_8_chconcat" + type: "Concat" + bottom: "mixed_8_tower_conv_1_conv2d_relu" + bottom: "mixed_8_tower_1_conv_3_conv2d_relu" + bottom: "MAX_pool_mixed_8_pool" + top: "ch_concat_mixed_8_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_9_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + kernel_h: 1 + kernel_w: 1 + } +} +layer { + name: "mixed_9_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_conv_conv2d" + top: "mixed_9_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_conv_conv2d_bn" + top: "mixed_9_conv_conv2d_bn" + name: "mixed_9_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_conv_relu" + type: "ReLU" + bottom: "mixed_9_conv_conv2d_bn" + top: "mixed_9_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_9_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_conv_conv2d" + top: "mixed_9_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_conv_conv2d_bn" + top: "mixed_9_tower_conv_conv2d_bn" + name: "mixed_9_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_conv_conv2d_bn" + top: "mixed_9_tower_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_conv_conv2d_relu" + top: "mixed_9_tower_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_9_tower_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_mixed_conv_conv2d" + top: "mixed_9_tower_mixed_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_mixed_conv_conv2d_bn" + top: "mixed_9_tower_mixed_conv_conv2d_bn" + name: "mixed_9_tower_mixed_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_mixed_conv_conv2d_bn" + top: "mixed_9_tower_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_conv_conv2d_relu" + top: "mixed_9_tower_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_9_tower_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_mixed_conv_1_conv2d" + top: "mixed_9_tower_mixed_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_mixed_conv_1_conv2d_bn" + name: "mixed_9_tower_mixed_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_mixed_conv_1_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_8_chconcat" + top: "mixed_9_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 448 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_9_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_conv_conv2d" + top: "mixed_9_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_1_conv_conv2d_bn" + top: "mixed_9_tower_1_conv_conv2d_bn" + name: "mixed_9_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_conv_conv2d_bn" + top: "mixed_9_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_conv2d_relu" + top: "mixed_9_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_9_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_conv_1_conv2d" + top: "mixed_9_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_1_conv_1_conv2d_bn" + top: "mixed_9_tower_1_conv_1_conv2d_bn" + name: "mixed_9_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_conv_1_conv2d_bn" + top: "mixed_9_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_1_conv2d_relu" + top: "mixed_9_tower_1_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_mixed_conv_conv2d" + top: "mixed_9_tower_1_mixed_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_1_mixed_conv_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_conv2d_bn" + name: "mixed_9_tower_1_mixed_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_mixed_conv_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_9_tower_1_conv_1_conv2d_relu" + top: "mixed_9_tower_1_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + name: "mixed_9_tower_1_mixed_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_1_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_9_tower_1_mixed_conv_1_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_9_pool" + type: "Pooling" + bottom: "ch_concat_mixed_8_chconcat" + top: "AVE_pool_mixed_9_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_9_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_9_pool" + top: "mixed_9_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_9_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_9_tower_2_conv_conv2d" + top: "mixed_9_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_9_tower_2_conv_conv2d_bn" + top: "mixed_9_tower_2_conv_conv2d_bn" + name: "mixed_9_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_9_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_9_tower_2_conv_conv2d_bn" + top: "mixed_9_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_9_chconcat" + type: "Concat" + bottom: "mixed_9_conv_conv2d_relu" + bottom: "mixed_9_tower_mixed_conv_conv2d_relu" + bottom: "mixed_9_tower_mixed_conv_1_conv2d_relu" + bottom: "mixed_9_tower_1_mixed_conv_conv2d_relu" + bottom: "mixed_9_tower_1_mixed_conv_1_conv2d_relu" + bottom: "mixed_9_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_9_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "mixed_10_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 320 + bias_term: false + pad: 0 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + kernel_h: 1 + kernel_w: 1 + } +} +layer { + name: "mixed_10_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_conv_conv2d" + top: "mixed_10_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_conv_conv2d_bn" + top: "mixed_10_conv_conv2d_bn" + name: "mixed_10_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_conv_relu" + type: "ReLU" + bottom: "mixed_10_conv_conv2d_bn" + top: "mixed_10_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_tower_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_10_tower_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_conv_conv2d" + top: "mixed_10_tower_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_conv_conv2d_bn" + top: "mixed_10_tower_conv_conv2d_bn" + name: "mixed_10_tower_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_conv_conv2d_bn" + top: "mixed_10_tower_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_conv_conv2d_relu" + top: "mixed_10_tower_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_10_tower_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_mixed_conv_conv2d" + top: "mixed_10_tower_mixed_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_mixed_conv_conv2d_bn" + top: "mixed_10_tower_mixed_conv_conv2d_bn" + name: "mixed_10_tower_mixed_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_mixed_conv_conv2d_bn" + top: "mixed_10_tower_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_conv_conv2d_relu" + top: "mixed_10_tower_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_10_tower_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_mixed_conv_1_conv2d" + top: "mixed_10_tower_mixed_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_mixed_conv_1_conv2d_bn" + name: "mixed_10_tower_mixed_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_mixed_conv_1_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_conv_conv2d" + type: "Convolution" + bottom: "ch_concat_mixed_9_chconcat" + top: "mixed_10_tower_1_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 448 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_10_tower_1_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_conv_conv2d" + top: "mixed_10_tower_1_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_1_conv_conv2d_bn" + top: "mixed_10_tower_1_conv_conv2d_bn" + name: "mixed_10_tower_1_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_1_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_conv_conv2d_bn" + top: "mixed_10_tower_1_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_conv2d_relu" + top: "mixed_10_tower_1_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_10_tower_1_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_conv_1_conv2d" + top: "mixed_10_tower_1_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_1_conv_1_conv2d_bn" + top: "mixed_10_tower_1_conv_1_conv2d_bn" + name: "mixed_10_tower_1_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_1_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_conv_1_conv2d_bn" + top: "mixed_10_tower_1_conv_1_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_mixed_conv_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_1_conv2d_relu" + top: "mixed_10_tower_1_mixed_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 0 + pad_w: 1 + kernel_h: 1 + kernel_w: 3 + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_mixed_conv_conv2d" + top: "mixed_10_tower_1_mixed_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_1_mixed_conv_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_conv2d_bn" + name: "mixed_10_tower_1_mixed_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_mixed_conv_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_conv2d_relu" +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_conv2d" + type: "Convolution" + bottom: "mixed_10_tower_1_conv_1_conv2d_relu" + top: "mixed_10_tower_1_mixed_conv_1_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 384 + bias_term: false + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + pad_h: 1 + pad_w: 0 + kernel_h: 3 + kernel_w: 1 + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + name: "mixed_10_tower_1_mixed_conv_1_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_1_mixed_conv_1_relu" + type: "ReLU" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_bn" + top: "mixed_10_tower_1_mixed_conv_1_conv2d_relu" +} +layer { + name: "AVE_pool_mixed_10_pool" + type: "Pooling" + bottom: "ch_concat_mixed_9_chconcat" + top: "AVE_pool_mixed_10_pool" + pooling_param { + pool: AVE + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "mixed_10_tower_2_conv_conv2d" + type: "Convolution" + bottom: "AVE_pool_mixed_10_pool" + top: "mixed_10_tower_2_conv_conv2d" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 192 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "mixed_10_tower_2_conv_batchnorm" + type: "BatchNorm" + bottom: "mixed_10_tower_2_conv_conv2d" + top: "mixed_10_tower_2_conv_conv2d_bn" + batch_norm_param { + moving_average_fraction: 0.9997 + eps: 0.001 + + } +} +layer { + bottom: "mixed_10_tower_2_conv_conv2d_bn" + top: "mixed_10_tower_2_conv_conv2d_bn" + name: "mixed_10_tower_2_conv_batchnorm/sc" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + name: "mixed_10_tower_2_conv_relu" + type: "ReLU" + bottom: "mixed_10_tower_2_conv_conv2d_bn" + top: "mixed_10_tower_2_conv_conv2d_relu" +} +layer { + name: "ch_concat_mixed_10_chconcat" + type: "Concat" + bottom: "mixed_10_conv_conv2d_relu" + bottom: "mixed_10_tower_mixed_conv_conv2d_relu" + bottom: "mixed_10_tower_mixed_conv_1_conv2d_relu" + bottom: "mixed_10_tower_1_mixed_conv_conv2d_relu" + bottom: "mixed_10_tower_1_mixed_conv_1_conv2d_relu" + bottom: "mixed_10_tower_2_conv_conv2d_relu" + top: "ch_concat_mixed_10_chconcat" + concat_param { + axis: 1 + } +} +layer { + name: "global_pool" + type: "Pooling" + bottom: "ch_concat_mixed_10_chconcat" + top: "global_pool" + pooling_param { + pool: AVE + kernel_size: 8 + stride: 1 + pad: 0 + } +} +layer { + name: "drop" + type: "Dropout" + bottom: "global_pool" + top: "global_pool" + dropout_param { + dropout_ratio: 0.8 + } +} +layer { + name: "flatten" + type: "Flatten" + bottom: "global_pool" + top: "flatten" +} +layer { + name: "fc1" + type: "InnerProduct" + bottom: "flatten" + top: "fc1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 1000 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc1" + bottom: "label" + top: "loss" + loss_param { + #label_smoothing: 0.1 + } + +} +layer { + name: "acc/top-1" + type: "Accuracy" + bottom: "fc1" + bottom: "label" + top: "acc/top-1" + include { + phase: TEST + } +} +layer { + name: "acc/top-5" + type: "Accuracy" + bottom: "fc1" + bottom: "label" + top: "acc/top-5" + include { + phase: TEST + } + accuracy_param { + top_k: 5 + } +} diff --git a/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/solver.prototxt b/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/solver.prototxt new file mode 100644 index 000000000..884464f85 --- /dev/null +++ b/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/solver.prototxt @@ -0,0 +1,28 @@ +train_net: "models/intel_optimized_models/multinode/ssd_16nodes_512_batch/train.prototxt" +test_net: "models/intel_optimized_models/multinode/ssd_16nodes_512_batch/test.prototxt" +test_iter: 619 +test_interval: 1250 +base_lr: 0.016 +display: 10 +max_iter: 7500 +warmup_iter: 5660 +warmup_start_lr: 0.001 +lr_policy: "multistep" +gamma: 0.1 +momentum: 0.9 +weight_decay: 0.0005 +snapshot: 5000 +snapshot_prefix: "models/intel_optimized_models/multinode/ssd_16nodes_512_batch/VGG_VOC0712_SSD_300x300_16nodes" +solver_mode: CPU +device_id: 0 +debug_info: false +snapshot_after_train: true +test_initialization: false +average_loss: 10 +stepvalue: 5000 +stepvalue: 6250 +stepvalue: 7500 +iter_size: 1 +type: "SGD" +eval_type: "detection" +ap_version: "11point" diff --git a/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/test.prototxt b/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/test.prototxt new file mode 100644 index 000000000..9781bcc05 --- /dev/null +++ b/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/test.prototxt @@ -0,0 +1,1671 @@ +name: "VGG_VOC0712_SSD_300x300_test" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 300 + width: 300 + interp_mode: LINEAR + } + } + data_param { + source: "examples/VOC0712/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} +layer { + name: "pool5" + type: "Pooling" + bottom: "conv5_3" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "fc6" + type: "Convolution" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1024 + pad: 6 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 6 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "fc7" + type: "Convolution" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1024 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "conv6_1" + type: "Convolution" + bottom: "fc7" + top: "conv6_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1" + top: "conv6_1" +} +layer { + name: "conv6_2" + type: "Convolution" + bottom: "conv6_1" + top: "conv6_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2" + top: "conv6_2" +} +layer { + name: "conv7_1" + type: "Convolution" + bottom: "conv6_2" + top: "conv7_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1" + top: "conv7_1" +} +layer { + name: "conv7_2" + type: "Convolution" + bottom: "conv7_1" + top: "conv7_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2" + top: "conv7_2" +} +layer { + name: "conv8_1" + type: "Convolution" + bottom: "conv7_2" + top: "conv8_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1" + top: "conv8_1" +} +layer { + name: "conv8_2" + type: "Convolution" + bottom: "conv8_1" + top: "conv8_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2" + top: "conv8_2" +} +layer { + name: "conv9_1" + type: "Convolution" + bottom: "conv8_2" + top: "conv9_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1" + top: "conv9_1" +} +layer { + name: "conv9_2" + type: "Convolution" + bottom: "conv9_1" + top: "conv9_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2" + top: "conv9_2" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "conv4_3" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 21 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + #Only for output to disk + #save_output_param { + # output_directory: "data/ssd_out/VOC2007/SSD_300x300" + # output_name_prefix: "comp4_det_test_" + # output_format: "VOC" + # label_map_file: "data/VOC0712/labelmap_voc.prototxt" + # name_size_file: "data/VOC0712/test_name_size.txt" + # num_test_image: 4952 + #} + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + } +} +layer { + name: "detection_eval" + type: "DetectionEvaluate" + bottom: "detection_out" + bottom: "label" + top: "detection_eval" + include { + phase: TEST + } + detection_evaluate_param { + num_classes: 21 + background_label_id: 0 + overlap_threshold: 0.5 + evaluate_difficult_gt: false + name_size_file: "data/VOC0712/test_name_size.txt" + } +} + diff --git a/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/train.prototxt b/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/train.prototxt new file mode 100644 index 000000000..ee6685520 --- /dev/null +++ b/models/intel_optimized_models/multinode/ssd_16nodes_512_batch/train.prototxt @@ -0,0 +1,1732 @@ +name: "VGG_VOC0712_SSD_300x300_train" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mirror: true + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 300 + width: 300 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: NEAREST + interp_mode: CUBIC + interp_mode: LANCZOS4 + } + emit_constraint { + emit_type: CENTER + } + distort_param { + brightness_prob: 0.5 + brightness_delta: 32 + contrast_prob: 0.5 + contrast_lower: 0.5 + contrast_upper: 1.5 + hue_prob: 0.5 + hue_delta: 18 + saturation_prob: 0.5 + saturation_lower: 0.5 + saturation_upper: 1.5 + random_order_prob: 0.0 + } + expand_param { + prob: 0.5 + max_expand_ratio: 4.0 + } + } + data_param { + source: "examples/VOC0712/VOC0712_trainval_lmdb" + batch_size: 32 + backend: LMDB + } + annotated_data_param { + batch_sampler { + max_sample: 1 + max_trials: 1 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.1 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.3 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.5 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.7 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.9 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + max_jaccard_overlap: 1.0 + } + max_sample: 1 + max_trials: 50 + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + name: "conv1_1" + type: "Convolution" + bottom: "data" + top: "conv1_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1_1" + type: "ReLU" + bottom: "conv1_1" + top: "conv1_1" +} +layer { + name: "conv1_2" + type: "Convolution" + bottom: "conv1_1" + top: "conv1_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu1_2" + type: "ReLU" + bottom: "conv1_2" + top: "conv1_2" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1_2" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv2_1" + type: "Convolution" + bottom: "pool1" + top: "conv2_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu2_1" + type: "ReLU" + bottom: "conv2_1" + top: "conv2_1" +} +layer { + name: "conv2_2" + type: "Convolution" + bottom: "conv2_1" + top: "conv2_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu2_2" + type: "ReLU" + bottom: "conv2_2" + top: "conv2_2" +} +layer { + name: "pool2" + type: "Pooling" + bottom: "conv2_2" + top: "pool2" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv3_1" + type: "Convolution" + bottom: "pool2" + top: "conv3_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_1" + type: "ReLU" + bottom: "conv3_1" + top: "conv3_1" +} +layer { + name: "conv3_2" + type: "Convolution" + bottom: "conv3_1" + top: "conv3_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_2" + type: "ReLU" + bottom: "conv3_2" + top: "conv3_2" +} +layer { + name: "conv3_3" + type: "Convolution" + bottom: "conv3_2" + top: "conv3_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu3_3" + type: "ReLU" + bottom: "conv3_3" + top: "conv3_3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "conv3_3" + top: "pool3" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv4_1" + type: "Convolution" + bottom: "pool3" + top: "conv4_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_1" + type: "ReLU" + bottom: "conv4_1" + top: "conv4_1" +} +layer { + name: "conv4_2" + type: "Convolution" + bottom: "conv4_1" + top: "conv4_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_2" + type: "ReLU" + bottom: "conv4_2" + top: "conv4_2" +} +layer { + name: "conv4_3" + type: "Convolution" + bottom: "conv4_2" + top: "conv4_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu4_3" + type: "ReLU" + bottom: "conv4_3" + top: "conv4_3" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "conv4_3" + top: "pool4" + pooling_param { + pool: MAX + kernel_size: 2 + stride: 2 + } +} +layer { + name: "conv5_1" + type: "Convolution" + bottom: "pool4" + top: "conv5_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_1" + type: "ReLU" + bottom: "conv5_1" + top: "conv5_1" +} +layer { + name: "conv5_2" + type: "Convolution" + bottom: "conv5_1" + top: "conv5_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_2" + type: "ReLU" + bottom: "conv5_2" + top: "conv5_2" +} +layer { + name: "conv5_3" + type: "Convolution" + bottom: "conv5_2" + top: "conv5_3" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 1 + } +} +layer { + name: "relu5_3" + type: "ReLU" + bottom: "conv5_3" + top: "conv5_3" +} +layer { + name: "pool5" + type: "Pooling" + bottom: "conv5_3" + top: "pool5" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 1 + pad: 1 + } +} +layer { + name: "fc6" + type: "Convolution" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1024 + pad: 6 + kernel_size: 3 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + dilation: 6 + } +} +layer { + name: "relu6" + type: "ReLU" + bottom: "fc6" + top: "fc6" +} +layer { + name: "fc7" + type: "Convolution" + bottom: "fc6" + top: "fc7" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 1024 + kernel_size: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "relu7" + type: "ReLU" + bottom: "fc7" + top: "fc7" +} +layer { + name: "conv6_1" + type: "Convolution" + bottom: "fc7" + top: "conv6_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1" + top: "conv6_1" +} +layer { + name: "conv6_2" + type: "Convolution" + bottom: "conv6_1" + top: "conv6_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 512 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2" + top: "conv6_2" +} +layer { + name: "conv7_1" + type: "Convolution" + bottom: "conv6_2" + top: "conv7_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1" + top: "conv7_1" +} +layer { + name: "conv7_2" + type: "Convolution" + bottom: "conv7_1" + top: "conv7_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2" + top: "conv7_2" +} +layer { + name: "conv8_1" + type: "Convolution" + bottom: "conv7_2" + top: "conv8_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1" + top: "conv8_1" +} +layer { + name: "conv8_2" + type: "Convolution" + bottom: "conv8_1" + top: "conv8_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2" + top: "conv8_2" +} +layer { + name: "conv9_1" + type: "Convolution" + bottom: "conv8_2" + top: "conv9_1" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1" + top: "conv9_1" +} +layer { + name: "conv9_2" + type: "Convolution" + bottom: "conv9_1" + top: "conv9_2" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2" + top: "conv9_2" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "conv4_3" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_loss" + type: "MultiBoxLoss" + bottom: "mbox_loc" + bottom: "mbox_conf" + bottom: "mbox_priorbox" + bottom: "label" + top: "mbox_loss" + include { + phase: TRAIN + } + propagate_down: true + propagate_down: true + propagate_down: false + propagate_down: false + loss_param { + normalization: VALID + } + multibox_loss_param { + loc_loss_type: SMOOTH_L1 + conf_loss_type: SOFTMAX + loc_weight: 1.0 + num_classes: 21 + share_location: true + match_type: PER_PREDICTION + overlap_threshold: 0.5 + use_prior_for_matching: true + background_label_id: 0 + use_difficult_gt: true + neg_pos_ratio: 3.0 + neg_overlap: 0.5 + code_type: CENTER_SIZE + ignore_cross_boundary_bbox: false + mining_type: MAX_NEGATIVE + } +} + diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt new file mode 100755 index 000000000..7a2428e88 --- /dev/null +++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/test_agnostic_int8.prototxt @@ -0,0 +1,63150 @@ +name: "ResNet-101" +input: "data" +input: "im_info" +input_shape { + dim: 1 + dim: 3 + dim: 224 + dim: 224 +} +input_shape { + dim: 1 + dim: 3 +} +layer { + name: "conv1" + type: "Convolution" + bottom: "data" + top: "conv1" + param { + lr_mult: 0.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 3 + kernel_size: 7 + stride: 2 + } +} +layer { + name: "bn_conv1" + type: "BatchNorm" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + batch_norm_param { + use_global_stats: true + } +} +layer { + name: "scale_conv1" + type: "Scale" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_relu" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "pool1" + type: "Pooling" + bottom: "conv1" + top: "pool1" + pooling_param { + pool: MAX + kernel_size: 3 + stride: 2 + } +} +layer { + name: "res2a_branch1" + type: "Convolution" + bottom: "pool1" + top: "res2a_branch1" + param { + lr_mult: 0.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 15.4708053777 + scale_out: 7.54857328392 + scale_params: 2349.61507567 + scale_params: 305.8539456 + scale_params: 615.947425937 + scale_params: 1657.17443124 + scale_params: 208.186402822 + scale_params: 145.215197341 + scale_params: 506.483196395 + scale_params: 452.713148859 + scale_params: 1837.15105094 + scale_params: 587.150525518 + scale_params: 816.141149439 + scale_params: 555.704335775 + scale_params: 369.827183422 + scale_params: 492.996092045 + scale_params: 385.143597736 + 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"res2c_branch2b" + type: "Convolution" + bottom: "res2c_branch2a" + top: "res2c_branch2b" + param { + lr_mult: 0.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 24.1707555123 + scale_out: 19.9718900901 + scale_params: 641.012871287 + scale_params: 590.206058214 + scale_params: 530.017905681 + scale_params: 517.264957231 + scale_params: 685.027988664 + scale_params: 902.425006078 + scale_params: 595.863292196 + scale_params: 592.652154053 + scale_params: 598.715866105 + scale_params: 553.221885784 + scale_params: 371.268571236 + scale_params: 551.378755072 + scale_params: 788.957089782 + scale_params: 464.820537208 + scale_params: 446.862150248 + scale_params: 550.520277685 + scale_params: 461.003625251 + scale_params: 895.508687602 + scale_params: 710.12363286 + scale_params: 690.748484039 + scale_params: 522.308458654 + scale_params: 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decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "res3b1" + type: "Eltwise" + bottom: "res3a" + bottom: "res3b1_branch2c" + top: "res3b1" +} +layer { + name: "res3b1_relu" + type: "ReLU" + bottom: "res3b1" + top: "res3b1" +} +layer { + name: "res3b2_branch2a" + type: "Convolution" + bottom: "res3b1" + top: "res3b2_branch2a" + param { + lr_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 + } + quantization_param { + bw_layer_in: 8 + bw_layer_out: 8 + bw_params: 8 + scale_in: 9.64005776818 + scale_out: 20.5439733287 + scale_params: 941.661088328 + scale_params: 1401.08760815 + scale_params: 1393.03046023 + scale_params: 1120.60963673 + scale_params: 3063.05390275 + scale_params: 1722.35048114 + scale_params: 2191.34900254 + scale_params: 1492.93830437 + scale_params: 994.175587618 + scale_params: 1193.5492028 + scale_params: 1961.93535725 + 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bottom: "rfcn_bbox" + bottom: "rois" + top: "psroipooled_loc_rois" + psroi_pooling_param { + spatial_scale: 0.0625 + output_dim: 8 + group_size: 7 + } +} +layer { + name: "ave_bbox_pred_rois" + type: "Pooling" + bottom: "psroipooled_loc_rois" + top: "bbox_pred_pre" + pooling_param { + pool: AVE + kernel_size: 7 + stride: 7 + } +} +layer { + name: "cls_prob" + type: "Softmax" + bottom: "cls_score" + top: "cls_prob_pre" +} +layer { + name: "cls_prob_reshape" + type: "Reshape" + bottom: "cls_prob_pre" + top: "cls_prob" + reshape_param { + shape { + dim: -1 + dim: 21 + } + } +} +layer { + name: "bbox_pred_reshape" + type: "Reshape" + bottom: "bbox_pred_pre" + top: "bbox_pred" + reshape_param { + shape { + dim: -1 + dim: 8 + } + } +} diff --git a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt index ebc0ab814..73a01ad4b 100644 --- a/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt +++ b/models/intel_optimized_models/rfcn/pascal_voc/ResNet-101/rfcn_end2end/train_agnostic.prototxt @@ -7129,7 +7129,6 @@ layer { top: "rfcn_cls" name: "rfcn_cls" type: "Convolution" - engine: "CAFFE" convolution_param { num_output: 1029 #21*(7^2) cls_num*(score_maps_size^2) kernel_size: 1 diff --git a/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt b/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt index 9781bcc05..689387fdf 100644 --- a/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt +++ b/models/intel_optimized_models/ssd/VGGNet/VOC0712/SSD_300x300/test.prototxt @@ -20,7 +20,7 @@ layer { } } data_param { - source: "examples/VOC0712/VOC0712_test_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" batch_size: 8 backend: LMDB } diff --git a/models/intel_optimized_models/ssd_mobilenet/fp32_acc.prototxt b/models/intel_optimized_models/ssd_mobilenet/fp32_acc.prototxt new file mode 100644 index 000000000..0a3408376 --- /dev/null +++ b/models/intel_optimized_models/ssd_mobilenet/fp32_acc.prototxt @@ -0,0 +1,3130 @@ +name: "MobileNet-SSD" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + scale: 0.007843 + mean_value: 127.5 + mean_value: 127.5 + mean_value: 127.5 + resize_param { + prob: 1.0 + resize_mode: WARP + height: 300 + width: 300 + interp_mode: LINEAR + } + } + data_param { + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv0/bn" + type: "BatchNorm" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv0/scale" + type: "Scale" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv0/relu" + type: "ReLU" + bottom: "conv0" + top: "conv0" +} +layer { + name: "conv1/dw" + type: "Convolution" + bottom: "conv0" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/dw/bn" + type: "BatchNorm" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/dw/scale" + type: "Scale" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/dw/relu" + type: "ReLU" + bottom: "conv1/dw" + top: "conv1/dw" +} +layer { + name: "conv1" + type: "Convolution" + bottom: "conv1/dw" + top: "conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/bn" + type: "BatchNorm" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/scale" + type: "Scale" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/relu" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "conv2/dw" + type: "Convolution" + bottom: "conv1" + top: "conv2/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 64 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/dw/bn" + type: "BatchNorm" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/dw/scale" + type: "Scale" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/dw/relu" + type: "ReLU" + bottom: "conv2/dw" + top: "conv2/dw" +} +layer { + name: "conv2" + type: "Convolution" + bottom: "conv2/dw" + top: "conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/bn" + type: "BatchNorm" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/scale" + type: "Scale" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/relu" + type: "ReLU" + bottom: "conv2" + top: "conv2" +} +layer { + name: "conv3/dw" + type: "Convolution" + bottom: "conv2" + top: "conv3/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + group: 128 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/dw/bn" + type: "BatchNorm" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/dw/scale" + type: "Scale" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/dw/relu" + type: "ReLU" + bottom: "conv3/dw" + top: "conv3/dw" +} +layer { + name: "conv3" + type: "Convolution" + bottom: "conv3/dw" + top: "conv3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/bn" + type: "BatchNorm" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/scale" + type: "Scale" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/relu" + type: "ReLU" + bottom: "conv3" + top: "conv3" +} +layer { + name: "conv4/dw" + type: "Convolution" + bottom: "conv3" + top: "conv4/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 128 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/dw/bn" + type: "BatchNorm" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/dw/scale" + type: "Scale" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/dw/relu" + type: "ReLU" + bottom: "conv4/dw" + top: "conv4/dw" +} +layer { + name: "conv4" + type: "Convolution" + bottom: "conv4/dw" + top: "conv4" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/bn" + type: "BatchNorm" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/scale" + type: "Scale" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/relu" + type: "ReLU" + bottom: "conv4" + top: "conv4" +} +layer { + name: "conv5/dw" + type: "Convolution" + bottom: "conv4" + top: "conv5/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/dw/bn" + type: "BatchNorm" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/dw/scale" + type: "Scale" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/dw/relu" + type: "ReLU" + bottom: "conv5/dw" + top: "conv5/dw" +} +layer { + name: "conv5" + type: "Convolution" + bottom: "conv5/dw" + top: "conv5" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/bn" + type: "BatchNorm" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/scale" + type: "Scale" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/relu" + type: "ReLU" + bottom: "conv5" + top: "conv5" +} +layer { + name: "conv6/dw" + type: "Convolution" + bottom: "conv5" + top: "conv6/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 256 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/dw/bn" + type: "BatchNorm" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/dw/scale" + type: "Scale" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/dw/relu" + type: "ReLU" + bottom: "conv6/dw" + top: "conv6/dw" +} +layer { + name: "conv6" + type: "Convolution" + bottom: "conv6/dw" + top: "conv6" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/bn" + type: "BatchNorm" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/scale" + type: "Scale" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/relu" + type: "ReLU" + bottom: "conv6" + top: "conv6" +} +layer { + name: "conv7/dw" + type: "Convolution" + bottom: "conv6" + top: "conv7/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/dw/bn" + type: "BatchNorm" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/dw/scale" + type: "Scale" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/dw/relu" + type: "ReLU" + bottom: "conv7/dw" + top: "conv7/dw" +} +layer { + name: "conv7" + type: "Convolution" + bottom: "conv7/dw" + top: "conv7" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/bn" + type: "BatchNorm" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/scale" + type: "Scale" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/relu" + type: "ReLU" + bottom: "conv7" + top: "conv7" +} +layer { + name: "conv8/dw" + type: "Convolution" + bottom: "conv7" + top: "conv8/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/dw/bn" + type: "BatchNorm" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/dw/scale" + type: "Scale" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/dw/relu" + type: "ReLU" + bottom: "conv8/dw" + top: "conv8/dw" +} +layer { + name: "conv8" + type: "Convolution" + bottom: "conv8/dw" + top: "conv8" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/bn" + type: "BatchNorm" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/scale" + type: "Scale" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/relu" + type: "ReLU" + bottom: "conv8" + top: "conv8" +} +layer { + name: "conv9/dw" + type: "Convolution" + bottom: "conv8" + top: "conv9/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/dw/bn" + type: "BatchNorm" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/dw/scale" + type: "Scale" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/dw/relu" + type: "ReLU" + bottom: "conv9/dw" + top: "conv9/dw" +} +layer { + name: "conv9" + type: "Convolution" + bottom: "conv9/dw" + top: "conv9" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/bn" + type: "BatchNorm" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/scale" + type: "Scale" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/relu" + type: "ReLU" + bottom: "conv9" + top: "conv9" +} +layer { + name: "conv10/dw" + type: "Convolution" + bottom: "conv9" + top: "conv10/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/dw/bn" + type: "BatchNorm" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/dw/scale" + type: "Scale" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/dw/relu" + type: "ReLU" + bottom: "conv10/dw" + top: "conv10/dw" +} +layer { + name: "conv10" + type: "Convolution" + bottom: "conv10/dw" + top: "conv10" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/bn" + type: "BatchNorm" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/scale" + type: "Scale" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/relu" + type: "ReLU" + bottom: "conv10" + top: "conv10" +} +layer { + name: "conv11/dw" + type: "Convolution" + bottom: "conv10" + top: "conv11/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/dw/bn" + type: "BatchNorm" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/dw/scale" + type: "Scale" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/dw/relu" + type: "ReLU" + bottom: "conv11/dw" + top: "conv11/dw" +} +layer { + name: "conv11" + type: "Convolution" + bottom: "conv11/dw" + top: "conv11" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/bn" + type: "BatchNorm" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/scale" + type: "Scale" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/relu" + type: "ReLU" + bottom: "conv11" + top: "conv11" +} +layer { + name: "conv12/dw" + type: "Convolution" + bottom: "conv11" + top: "conv12/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/dw/bn" + type: "BatchNorm" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/dw/scale" + type: "Scale" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/dw/relu" + type: "ReLU" + bottom: "conv12/dw" + top: "conv12/dw" +} +layer { + name: "conv12" + type: "Convolution" + bottom: "conv12/dw" + top: "conv12" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/bn" + type: "BatchNorm" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/scale" + type: "Scale" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/relu" + type: "ReLU" + bottom: "conv12" + top: "conv12" +} +layer { + name: "conv13/dw" + type: "Convolution" + bottom: "conv12" + top: "conv13/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/dw/bn" + type: "BatchNorm" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/dw/scale" + type: "Scale" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/dw/relu" + type: "ReLU" + bottom: "conv13/dw" + top: "conv13/dw" +} +layer { + name: "conv13" + type: "Convolution" + bottom: "conv13/dw" + top: "conv13" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/bn" + type: "BatchNorm" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/scale" + type: "Scale" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/relu" + type: "ReLU" + bottom: "conv13" + top: "conv13" +} +layer { + name: "conv14_1" + type: "Convolution" + bottom: "conv13" + top: "conv14_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv14_1/bn" + type: "BatchNorm" + bottom: "conv14_1" + top: "conv14_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv14_1/scale" + type: "Scale" + bottom: "conv14_1" + top: "conv14_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv14_1/relu" + type: "ReLU" + bottom: "conv14_1" + top: "conv14_1" +} +layer { + name: "conv14_2" + type: "Convolution" + bottom: "conv14_1" + top: "conv14_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv14_2/bn" + type: "BatchNorm" + bottom: "conv14_2" + top: "conv14_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv14_2/scale" + type: "Scale" + bottom: "conv14_2" + top: "conv14_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv14_2/relu" + type: "ReLU" + bottom: "conv14_2" + top: "conv14_2" +} +layer { + name: "conv15_1" + type: "Convolution" + bottom: "conv14_2" + top: "conv15_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv15_1/bn" + type: "BatchNorm" + bottom: "conv15_1" + top: "conv15_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv15_1/scale" + type: "Scale" + bottom: "conv15_1" + top: "conv15_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv15_1/relu" + type: "ReLU" + bottom: "conv15_1" + top: "conv15_1" +} +layer { + name: "conv15_2" + type: "Convolution" + bottom: "conv15_1" + top: "conv15_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv15_2/bn" + type: "BatchNorm" + bottom: "conv15_2" + top: "conv15_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv15_2/scale" + type: "Scale" + bottom: "conv15_2" + top: "conv15_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv15_2/relu" + type: "ReLU" + bottom: "conv15_2" + top: "conv15_2" +} +layer { + name: "conv16_1" + type: "Convolution" + bottom: "conv15_2" + top: "conv16_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16_1/bn" + type: "BatchNorm" + bottom: "conv16_1" + top: "conv16_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16_1/scale" + type: "Scale" + bottom: "conv16_1" + top: "conv16_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16_1/relu" + type: "ReLU" + bottom: "conv16_1" + top: "conv16_1" +} +layer { + name: "conv16_2" + type: "Convolution" + bottom: "conv16_1" + top: "conv16_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16_2/bn" + type: "BatchNorm" + bottom: "conv16_2" + top: "conv16_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16_2/scale" + type: "Scale" + bottom: "conv16_2" + top: "conv16_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16_2/relu" + type: "ReLU" + bottom: "conv16_2" + top: "conv16_2" +} +layer { + name: "conv17_1" + type: "Convolution" + bottom: "conv16_2" + top: "conv17_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17_1/bn" + type: "BatchNorm" + bottom: "conv17_1" + top: "conv17_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17_1/scale" + type: "Scale" + bottom: "conv17_1" + top: "conv17_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17_1/relu" + type: "ReLU" + bottom: "conv17_1" + top: "conv17_1" +} +layer { + name: "conv17_2" + type: "Convolution" + bottom: "conv17_1" + top: "conv17_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17_2/bn" + type: "BatchNorm" + bottom: "conv17_2" + top: "conv17_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17_2/scale" + type: "Scale" + bottom: "conv17_2" + top: "conv17_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17_2/relu" + type: "ReLU" + bottom: "conv17_2" + top: "conv17_2" +} +layer { + name: "conv11_mbox_loc" + type: "Convolution" + bottom: "conv11" + top: "conv11_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 12 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv11_mbox_loc_perm" + type: "Permute" + bottom: "conv11_mbox_loc" + top: "conv11_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv11_mbox_loc_flat" + type: "Flatten" + bottom: "conv11_mbox_loc_perm" + top: "conv11_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv11_mbox_conf" + type: "Convolution" + bottom: "conv11" + top: "conv11_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 63 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv11_mbox_conf_perm" + type: "Permute" + bottom: "conv11_mbox_conf" + top: "conv11_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv11_mbox_conf_flat" + type: "Flatten" + bottom: "conv11_mbox_conf_perm" + top: "conv11_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv11_mbox_priorbox" + type: "PriorBox" + bottom: "conv11" + bottom: "data" + top: "conv11_mbox_priorbox" + prior_box_param { + min_size: 60.0 + aspect_ratio: 2.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv13_mbox_loc" + type: "Convolution" + bottom: "conv13" + top: "conv13_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv13_mbox_loc_perm" + type: "Permute" + bottom: "conv13_mbox_loc" + top: "conv13_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv13_mbox_loc_flat" + type: "Flatten" + bottom: "conv13_mbox_loc_perm" + top: "conv13_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv13_mbox_conf" + type: "Convolution" + bottom: "conv13" + top: "conv13_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv13_mbox_conf_perm" + type: "Permute" + bottom: "conv13_mbox_conf" + top: "conv13_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv13_mbox_conf_flat" + type: "Flatten" + bottom: "conv13_mbox_conf_perm" + top: "conv13_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv13_mbox_priorbox" + type: "PriorBox" + bottom: "conv13" + bottom: "data" + top: "conv13_mbox_priorbox" + prior_box_param { + min_size: 105.0 + max_size: 150.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv14_2_mbox_loc" + type: "Convolution" + bottom: "conv14_2" + top: "conv14_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv14_2_mbox_loc_perm" + type: "Permute" + bottom: "conv14_2_mbox_loc" + top: "conv14_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv14_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv14_2_mbox_loc_perm" + top: "conv14_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv14_2_mbox_conf" + type: "Convolution" + bottom: "conv14_2" + top: "conv14_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv14_2_mbox_conf_perm" + type: "Permute" + bottom: "conv14_2_mbox_conf" + top: "conv14_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv14_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv14_2_mbox_conf_perm" + top: "conv14_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv14_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv14_2" + bottom: "data" + top: "conv14_2_mbox_priorbox" + prior_box_param { + min_size: 150.0 + max_size: 195.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv15_2_mbox_loc" + type: "Convolution" + bottom: "conv15_2" + top: "conv15_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv15_2_mbox_loc_perm" + type: "Permute" + bottom: "conv15_2_mbox_loc" + top: "conv15_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv15_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv15_2_mbox_loc_perm" + top: "conv15_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv15_2_mbox_conf" + type: "Convolution" + bottom: "conv15_2" + top: "conv15_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv15_2_mbox_conf_perm" + type: "Permute" + bottom: "conv15_2_mbox_conf" + top: "conv15_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv15_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv15_2_mbox_conf_perm" + top: "conv15_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv15_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv15_2" + bottom: "data" + top: "conv15_2_mbox_priorbox" + prior_box_param { + min_size: 195.0 + max_size: 240.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv16_2_mbox_loc" + type: "Convolution" + bottom: "conv16_2" + top: "conv16_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv16_2_mbox_loc_perm" + type: "Permute" + bottom: "conv16_2_mbox_loc" + top: "conv16_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv16_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv16_2_mbox_loc_perm" + top: "conv16_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv16_2_mbox_conf" + type: "Convolution" + bottom: "conv16_2" + top: "conv16_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv16_2_mbox_conf_perm" + type: "Permute" + bottom: "conv16_2_mbox_conf" + top: "conv16_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv16_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv16_2_mbox_conf_perm" + top: "conv16_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv16_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv16_2" + bottom: "data" + top: "conv16_2_mbox_priorbox" + prior_box_param { + min_size: 240.0 + max_size: 285.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv17_2_mbox_loc" + type: "Convolution" + bottom: "conv17_2" + top: "conv17_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv17_2_mbox_loc_perm" + type: "Permute" + bottom: "conv17_2_mbox_loc" + top: "conv17_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv17_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv17_2_mbox_loc_perm" + top: "conv17_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv17_2_mbox_conf" + type: "Convolution" + bottom: "conv17_2" + top: "conv17_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv17_2_mbox_conf_perm" + type: "Permute" + bottom: "conv17_2_mbox_conf" + top: "conv17_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv17_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv17_2_mbox_conf_perm" + top: "conv17_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv17_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv17_2" + bottom: "data" + top: "conv17_2_mbox_priorbox" + prior_box_param { + min_size: 285.0 + max_size: 300.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv11_mbox_loc_flat" + bottom: "conv13_mbox_loc_flat" + bottom: "conv14_2_mbox_loc_flat" + bottom: "conv15_2_mbox_loc_flat" + bottom: "conv16_2_mbox_loc_flat" + bottom: "conv17_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv11_mbox_conf_flat" + bottom: "conv13_mbox_conf_flat" + bottom: "conv14_2_mbox_conf_flat" + bottom: "conv15_2_mbox_conf_flat" + bottom: "conv16_2_mbox_conf_flat" + bottom: "conv17_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv11_mbox_priorbox" + bottom: "conv13_mbox_priorbox" + bottom: "conv14_2_mbox_priorbox" + bottom: "conv15_2_mbox_priorbox" + bottom: "conv16_2_mbox_priorbox" + bottom: "conv17_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 21 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + } +} +layer { + name: "detection_eval" + type: "DetectionEvaluate" + bottom: "detection_out" + bottom: "label" + top: "detection_eval" + include { + phase: TEST + } + detection_evaluate_param { + num_classes: 21 + background_label_id: 0 + overlap_threshold: 0.5 + evaluate_difficult_gt: false + } +} diff --git a/models/intel_optimized_models/ssd_mobilenet/fp32_perf.prototxt b/models/intel_optimized_models/ssd_mobilenet/fp32_perf.prototxt new file mode 100644 index 000000000..2f1f8087e --- /dev/null +++ b/models/intel_optimized_models/ssd_mobilenet/fp32_perf.prototxt @@ -0,0 +1,3116 @@ +name: "MobileNet-SSD" +layer { + name: "data" + type: "DummyData" + top: "data" + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim: 32 + dim: 3 + dim: 300 + dim: 300 + } + } +} +layer { + name: "data" + type: "DummyData" + top: "label" + dummy_data_param { + data_filler { + type: "constant" + value: 1 + } + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 8 + } + } +} +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv0/bn" + type: "BatchNorm" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv0/scale" + type: "Scale" + bottom: "conv0" + top: "conv0" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv0/relu" + type: "ReLU" + bottom: "conv0" + top: "conv0" +} +layer { + name: "conv1/dw" + type: "Convolution" + bottom: "conv0" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + group: 32 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/dw/bn" + type: "BatchNorm" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/dw/scale" + type: "Scale" + bottom: "conv1/dw" + top: "conv1/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/dw/relu" + type: "ReLU" + bottom: "conv1/dw" + top: "conv1/dw" +} +layer { + name: "conv1" + type: "Convolution" + bottom: "conv1/dw" + top: "conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv1/bn" + type: "BatchNorm" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv1/scale" + type: "Scale" + bottom: "conv1" + top: "conv1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv1/relu" + type: "ReLU" + bottom: "conv1" + top: "conv1" +} +layer { + name: "conv2/dw" + type: "Convolution" + bottom: "conv1" + top: "conv2/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 64 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/dw/bn" + type: "BatchNorm" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/dw/scale" + type: "Scale" + bottom: "conv2/dw" + top: "conv2/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/dw/relu" + type: "ReLU" + bottom: "conv2/dw" + top: "conv2/dw" +} +layer { + name: "conv2" + type: "Convolution" + bottom: "conv2/dw" + top: "conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv2/bn" + type: "BatchNorm" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv2/scale" + type: "Scale" + bottom: "conv2" + top: "conv2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv2/relu" + type: "ReLU" + bottom: "conv2" + top: "conv2" +} +layer { + name: "conv3/dw" + type: "Convolution" + bottom: "conv2" + top: "conv3/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + group: 128 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/dw/bn" + type: "BatchNorm" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/dw/scale" + type: "Scale" + bottom: "conv3/dw" + top: "conv3/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/dw/relu" + type: "ReLU" + bottom: "conv3/dw" + top: "conv3/dw" +} +layer { + name: "conv3" + type: "Convolution" + bottom: "conv3/dw" + top: "conv3" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv3/bn" + type: "BatchNorm" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv3/scale" + type: "Scale" + bottom: "conv3" + top: "conv3" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv3/relu" + type: "ReLU" + bottom: "conv3" + top: "conv3" +} +layer { + name: "conv4/dw" + type: "Convolution" + bottom: "conv3" + top: "conv4/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 128 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/dw/bn" + type: "BatchNorm" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/dw/scale" + type: "Scale" + bottom: "conv4/dw" + top: "conv4/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/dw/relu" + type: "ReLU" + bottom: "conv4/dw" + top: "conv4/dw" +} +layer { + name: "conv4" + type: "Convolution" + bottom: "conv4/dw" + top: "conv4" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv4/bn" + type: "BatchNorm" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv4/scale" + type: "Scale" + bottom: "conv4" + top: "conv4" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv4/relu" + type: "ReLU" + bottom: "conv4" + top: "conv4" +} +layer { + name: "conv5/dw" + type: "Convolution" + bottom: "conv4" + top: "conv5/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + group: 256 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/dw/bn" + type: "BatchNorm" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/dw/scale" + type: "Scale" + bottom: "conv5/dw" + top: "conv5/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/dw/relu" + type: "ReLU" + bottom: "conv5/dw" + top: "conv5/dw" +} +layer { + name: "conv5" + type: "Convolution" + bottom: "conv5/dw" + top: "conv5" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv5/bn" + type: "BatchNorm" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv5/scale" + type: "Scale" + bottom: "conv5" + top: "conv5" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv5/relu" + type: "ReLU" + bottom: "conv5" + top: "conv5" +} +layer { + name: "conv6/dw" + type: "Convolution" + bottom: "conv5" + top: "conv6/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 256 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/dw/bn" + type: "BatchNorm" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/dw/scale" + type: "Scale" + bottom: "conv6/dw" + top: "conv6/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/dw/relu" + type: "ReLU" + bottom: "conv6/dw" + top: "conv6/dw" +} +layer { + name: "conv6" + type: "Convolution" + bottom: "conv6/dw" + top: "conv6" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv6/bn" + type: "BatchNorm" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv6/scale" + type: "Scale" + bottom: "conv6" + top: "conv6" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv6/relu" + type: "ReLU" + bottom: "conv6" + top: "conv6" +} +layer { + name: "conv7/dw" + type: "Convolution" + bottom: "conv6" + top: "conv7/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/dw/bn" + type: "BatchNorm" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/dw/scale" + type: "Scale" + bottom: "conv7/dw" + top: "conv7/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/dw/relu" + type: "ReLU" + bottom: "conv7/dw" + top: "conv7/dw" +} +layer { + name: "conv7" + type: "Convolution" + bottom: "conv7/dw" + top: "conv7" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv7/bn" + type: "BatchNorm" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv7/scale" + type: "Scale" + bottom: "conv7" + top: "conv7" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv7/relu" + type: "ReLU" + bottom: "conv7" + top: "conv7" +} +layer { + name: "conv8/dw" + type: "Convolution" + bottom: "conv7" + top: "conv8/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/dw/bn" + type: "BatchNorm" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/dw/scale" + type: "Scale" + bottom: "conv8/dw" + top: "conv8/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/dw/relu" + type: "ReLU" + bottom: "conv8/dw" + top: "conv8/dw" +} +layer { + name: "conv8" + type: "Convolution" + bottom: "conv8/dw" + top: "conv8" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv8/bn" + type: "BatchNorm" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv8/scale" + type: "Scale" + bottom: "conv8" + top: "conv8" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv8/relu" + type: "ReLU" + bottom: "conv8" + top: "conv8" +} +layer { + name: "conv9/dw" + type: "Convolution" + bottom: "conv8" + top: "conv9/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/dw/bn" + type: "BatchNorm" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/dw/scale" + type: "Scale" + bottom: "conv9/dw" + top: "conv9/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/dw/relu" + type: "ReLU" + bottom: "conv9/dw" + top: "conv9/dw" +} +layer { + name: "conv9" + type: "Convolution" + bottom: "conv9/dw" + top: "conv9" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv9/bn" + type: "BatchNorm" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv9/scale" + type: "Scale" + bottom: "conv9" + top: "conv9" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv9/relu" + type: "ReLU" + bottom: "conv9" + top: "conv9" +} +layer { + name: "conv10/dw" + type: "Convolution" + bottom: "conv9" + top: "conv10/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/dw/bn" + type: "BatchNorm" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/dw/scale" + type: "Scale" + bottom: "conv10/dw" + top: "conv10/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/dw/relu" + type: "ReLU" + bottom: "conv10/dw" + top: "conv10/dw" +} +layer { + name: "conv10" + type: "Convolution" + bottom: "conv10/dw" + top: "conv10" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv10/bn" + type: "BatchNorm" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv10/scale" + type: "Scale" + bottom: "conv10" + top: "conv10" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv10/relu" + type: "ReLU" + bottom: "conv10" + top: "conv10" +} +layer { + name: "conv11/dw" + type: "Convolution" + bottom: "conv10" + top: "conv11/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/dw/bn" + type: "BatchNorm" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/dw/scale" + type: "Scale" + bottom: "conv11/dw" + top: "conv11/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/dw/relu" + type: "ReLU" + bottom: "conv11/dw" + top: "conv11/dw" +} +layer { + name: "conv11" + type: "Convolution" + bottom: "conv11/dw" + top: "conv11" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv11/bn" + type: "BatchNorm" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv11/scale" + type: "Scale" + bottom: "conv11" + top: "conv11" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv11/relu" + type: "ReLU" + bottom: "conv11" + top: "conv11" +} +layer { + name: "conv12/dw" + type: "Convolution" + bottom: "conv11" + top: "conv12/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + group: 512 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/dw/bn" + type: "BatchNorm" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/dw/scale" + type: "Scale" + bottom: "conv12/dw" + top: "conv12/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/dw/relu" + type: "ReLU" + bottom: "conv12/dw" + top: "conv12/dw" +} +layer { + name: "conv12" + type: "Convolution" + bottom: "conv12/dw" + top: "conv12" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv12/bn" + type: "BatchNorm" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv12/scale" + type: "Scale" + bottom: "conv12" + top: "conv12" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv12/relu" + type: "ReLU" + bottom: "conv12" + top: "conv12" +} +layer { + name: "conv13/dw" + type: "Convolution" + bottom: "conv12" + top: "conv13/dw" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 1024 + bias_term: false + pad: 1 + kernel_size: 3 + group: 1024 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/dw/bn" + type: "BatchNorm" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/dw/scale" + type: "Scale" + bottom: "conv13/dw" + top: "conv13/dw" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/dw/relu" + type: "ReLU" + bottom: "conv13/dw" + top: "conv13/dw" +} +layer { + name: "conv13" + type: "Convolution" + bottom: "conv13/dw" + top: "conv13" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 1024 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv13/bn" + type: "BatchNorm" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv13/scale" + type: "Scale" + bottom: "conv13" + top: "conv13" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv13/relu" + type: "ReLU" + bottom: "conv13" + top: "conv13" +} +layer { + name: "conv14_1" + type: "Convolution" + bottom: "conv13" + top: "conv14_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv14_1/bn" + type: "BatchNorm" + bottom: "conv14_1" + top: "conv14_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv14_1/scale" + type: "Scale" + bottom: "conv14_1" + top: "conv14_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv14_1/relu" + type: "ReLU" + bottom: "conv14_1" + top: "conv14_1" +} +layer { + name: "conv14_2" + type: "Convolution" + bottom: "conv14_1" + top: "conv14_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 512 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv14_2/bn" + type: "BatchNorm" + bottom: "conv14_2" + top: "conv14_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv14_2/scale" + type: "Scale" + bottom: "conv14_2" + top: "conv14_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv14_2/relu" + type: "ReLU" + bottom: "conv14_2" + top: "conv14_2" +} +layer { + name: "conv15_1" + type: "Convolution" + bottom: "conv14_2" + top: "conv15_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv15_1/bn" + type: "BatchNorm" + bottom: "conv15_1" + top: "conv15_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv15_1/scale" + type: "Scale" + bottom: "conv15_1" + top: "conv15_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv15_1/relu" + type: "ReLU" + bottom: "conv15_1" + top: "conv15_1" +} +layer { + name: "conv15_2" + type: "Convolution" + bottom: "conv15_1" + top: "conv15_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv15_2/bn" + type: "BatchNorm" + bottom: "conv15_2" + top: "conv15_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv15_2/scale" + type: "Scale" + bottom: "conv15_2" + top: "conv15_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv15_2/relu" + type: "ReLU" + bottom: "conv15_2" + top: "conv15_2" +} +layer { + name: "conv16_1" + type: "Convolution" + bottom: "conv15_2" + top: "conv16_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16_1/bn" + type: "BatchNorm" + bottom: "conv16_1" + top: "conv16_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16_1/scale" + type: "Scale" + bottom: "conv16_1" + top: "conv16_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16_1/relu" + type: "ReLU" + bottom: "conv16_1" + top: "conv16_1" +} +layer { + name: "conv16_2" + type: "Convolution" + bottom: "conv16_1" + top: "conv16_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv16_2/bn" + type: "BatchNorm" + bottom: "conv16_2" + top: "conv16_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv16_2/scale" + type: "Scale" + bottom: "conv16_2" + top: "conv16_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv16_2/relu" + type: "ReLU" + bottom: "conv16_2" + top: "conv16_2" +} +layer { + name: "conv17_1" + type: "Convolution" + bottom: "conv16_2" + top: "conv17_1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 64 + bias_term: false + kernel_size: 1 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17_1/bn" + type: "BatchNorm" + bottom: "conv17_1" + top: "conv17_1" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17_1/scale" + type: "Scale" + bottom: "conv17_1" + top: "conv17_1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17_1/relu" + type: "ReLU" + bottom: "conv17_1" + top: "conv17_1" +} +layer { + name: "conv17_2" + type: "Convolution" + bottom: "conv17_1" + top: "conv17_2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + } +} +layer { + name: "conv17_2/bn" + type: "BatchNorm" + bottom: "conv17_2" + top: "conv17_2" + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } + param { + lr_mult: 0 + decay_mult: 0 + } +} +layer { + name: "conv17_2/scale" + type: "Scale" + bottom: "conv17_2" + top: "conv17_2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + scale_param { + filler { + value: 1 + } + bias_term: true + bias_filler { + value: 0 + } + } +} +layer { + name: "conv17_2/relu" + type: "ReLU" + bottom: "conv17_2" + top: "conv17_2" +} +layer { + name: "conv11_mbox_loc" + type: "Convolution" + bottom: "conv11" + top: "conv11_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 12 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv11_mbox_loc_perm" + type: "Permute" + bottom: "conv11_mbox_loc" + top: "conv11_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv11_mbox_loc_flat" + type: "Flatten" + bottom: "conv11_mbox_loc_perm" + top: "conv11_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv11_mbox_conf" + type: "Convolution" + bottom: "conv11" + top: "conv11_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 63 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv11_mbox_conf_perm" + type: "Permute" + bottom: "conv11_mbox_conf" + top: "conv11_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv11_mbox_conf_flat" + type: "Flatten" + bottom: "conv11_mbox_conf_perm" + top: "conv11_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv11_mbox_priorbox" + type: "PriorBox" + bottom: "conv11" + bottom: "data" + top: "conv11_mbox_priorbox" + prior_box_param { + min_size: 60.0 + aspect_ratio: 2.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv13_mbox_loc" + type: "Convolution" + bottom: "conv13" + top: "conv13_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv13_mbox_loc_perm" + type: "Permute" + bottom: "conv13_mbox_loc" + top: "conv13_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv13_mbox_loc_flat" + type: "Flatten" + bottom: "conv13_mbox_loc_perm" + top: "conv13_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv13_mbox_conf" + type: "Convolution" + bottom: "conv13" + top: "conv13_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv13_mbox_conf_perm" + type: "Permute" + bottom: "conv13_mbox_conf" + top: "conv13_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv13_mbox_conf_flat" + type: "Flatten" + bottom: "conv13_mbox_conf_perm" + top: "conv13_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv13_mbox_priorbox" + type: "PriorBox" + bottom: "conv13" + bottom: "data" + top: "conv13_mbox_priorbox" + prior_box_param { + min_size: 105.0 + max_size: 150.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv14_2_mbox_loc" + type: "Convolution" + bottom: "conv14_2" + top: "conv14_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv14_2_mbox_loc_perm" + type: "Permute" + bottom: "conv14_2_mbox_loc" + top: "conv14_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv14_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv14_2_mbox_loc_perm" + top: "conv14_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv14_2_mbox_conf" + type: "Convolution" + bottom: "conv14_2" + top: "conv14_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv14_2_mbox_conf_perm" + type: "Permute" + bottom: "conv14_2_mbox_conf" + top: "conv14_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv14_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv14_2_mbox_conf_perm" + top: "conv14_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv14_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv14_2" + bottom: "data" + top: "conv14_2_mbox_priorbox" + prior_box_param { + min_size: 150.0 + max_size: 195.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv15_2_mbox_loc" + type: "Convolution" + bottom: "conv15_2" + top: "conv15_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv15_2_mbox_loc_perm" + type: "Permute" + bottom: "conv15_2_mbox_loc" + top: "conv15_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv15_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv15_2_mbox_loc_perm" + top: "conv15_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv15_2_mbox_conf" + type: "Convolution" + bottom: "conv15_2" + top: "conv15_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv15_2_mbox_conf_perm" + type: "Permute" + bottom: "conv15_2_mbox_conf" + top: "conv15_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv15_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv15_2_mbox_conf_perm" + top: "conv15_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv15_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv15_2" + bottom: "data" + top: "conv15_2_mbox_priorbox" + prior_box_param { + min_size: 195.0 + max_size: 240.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv16_2_mbox_loc" + type: "Convolution" + bottom: "conv16_2" + top: "conv16_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv16_2_mbox_loc_perm" + type: "Permute" + bottom: "conv16_2_mbox_loc" + top: "conv16_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv16_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv16_2_mbox_loc_perm" + top: "conv16_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv16_2_mbox_conf" + type: "Convolution" + bottom: "conv16_2" + top: "conv16_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv16_2_mbox_conf_perm" + type: "Permute" + bottom: "conv16_2_mbox_conf" + top: "conv16_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv16_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv16_2_mbox_conf_perm" + top: "conv16_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv16_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv16_2" + bottom: "data" + top: "conv16_2_mbox_priorbox" + prior_box_param { + min_size: 240.0 + max_size: 285.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "conv17_2_mbox_loc" + type: "Convolution" + bottom: "conv17_2" + top: "conv17_2_mbox_loc" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 24 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv17_2_mbox_loc_perm" + type: "Permute" + bottom: "conv17_2_mbox_loc" + top: "conv17_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv17_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv17_2_mbox_loc_perm" + top: "conv17_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv17_2_mbox_conf" + type: "Convolution" + bottom: "conv17_2" + top: "conv17_2_mbox_conf" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 126 + kernel_size: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv17_2_mbox_conf_perm" + type: "Permute" + bottom: "conv17_2_mbox_conf" + top: "conv17_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv17_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv17_2_mbox_conf_perm" + top: "conv17_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv17_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv17_2" + bottom: "data" + top: "conv17_2_mbox_priorbox" + prior_box_param { + min_size: 285.0 + max_size: 300.0 + aspect_ratio: 2.0 + aspect_ratio: 3.0 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv11_mbox_loc_flat" + bottom: "conv13_mbox_loc_flat" + bottom: "conv14_2_mbox_loc_flat" + bottom: "conv15_2_mbox_loc_flat" + bottom: "conv16_2_mbox_loc_flat" + bottom: "conv17_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv11_mbox_conf_flat" + bottom: "conv13_mbox_conf_flat" + bottom: "conv14_2_mbox_conf_flat" + bottom: "conv15_2_mbox_conf_flat" + bottom: "conv16_2_mbox_conf_flat" + bottom: "conv17_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv11_mbox_priorbox" + bottom: "conv13_mbox_priorbox" + bottom: "conv14_2_mbox_priorbox" + bottom: "conv15_2_mbox_priorbox" + bottom: "conv16_2_mbox_priorbox" + bottom: "conv17_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 21 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 21 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_solver.prototxt b/models/intel_optimized_models/yolo/yolov2_solver.prototxt new file mode 100644 index 000000000..23dbeab58 --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_solver.prototxt @@ -0,0 +1,26 @@ +net: "models/intel_optimized_models/yolo/yolov2_train.prototxt" +#test_iter: 1 +#test_interval: 10000 +#test_initialization: false +display: 25 +average_loss: 25 +#base_lr: 0.001 +#gamma: 0.1 +iter_size: 8 + +lr_policy: "multifixed" +stagelr: 0.0001 +stagelr: 0.001 +stagelr: 0.0001 +stagelr: 0.00001 +stageiter: 1000 +stageiter: 40000 +stageiter: 60000 +stageiter: 80000 + +max_iter: 80000 +momentum: 0.9 +weight_decay: 0.0005 +snapshot: 500 +snapshot_prefix: "models/intel_optimized_models/yolo/yolov2" +solver_mode: CPU diff --git a/models/intel_optimized_models/yolo/yolov2_test.prototxt b/models/intel_optimized_models/yolo/yolov2_test.prototxt new file mode 100644 index 000000000..c27a6afa4 --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_test.prototxt @@ -0,0 +1,1069 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + } + } + data_param { + #source: "examples/yolo/data/lmdb/test2007_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + #label_map_file: "data/VOC07/labelmap_voc.prototxt" + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + } +} +layer { + name: "det_loss" + type: "RegionLoss" + bottom: "conv_reg" + bottom: "label" + top: "det_loss" + loss_weight: 1 + region_loss_param { + side: 17 + num_class: 20 + coords: 4 + num: 5 + softmax: 1 + jitter: 0.2 + rescore: 1 + bias_match: 1 + + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + + absolute: 1 + thresh: 0.5 + random: 0 + + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + nms: 0.45 + score_type: MULTIPLY + #score_type: OBJ + #score_type: PROB + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_test_RELU.prototxt b/models/intel_optimized_models/yolo/yolov2_test_RELU.prototxt new file mode 100644 index 000000000..3ebfff290 --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_test_RELU.prototxt @@ -0,0 +1,1069 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + } + } + data_param { + #source: "examples/yolo/data/lmdb/test2007_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + #label_map_file: "data/VOC07/labelmap_voc.prototxt" + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + } +} +layer { + name: "det_loss" + type: "RegionLoss" + bottom: "conv_reg" + bottom: "label" + top: "det_loss" + loss_weight: 1 + region_loss_param { + side: 17 + num_class: 20 + coords: 4 + num: 5 + softmax: 1 + jitter: 0.2 + rescore: 1 + bias_match: 1 + + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + + absolute: 1 + thresh: 0.5 + random: 0 + + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + nms: 0.45 + score_type: MULTIPLY + #score_type: OBJ + #score_type: PROB + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_test_performance.prototxt b/models/intel_optimized_models/yolo/yolov2_test_performance.prototxt new file mode 100644 index 000000000..fd96edc9b --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_test_performance.prototxt @@ -0,0 +1,1031 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + } + } + data_param { + #source: "examples/yolo/data/lmdb/test2007_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + #label_map_file: "data/VOC07/labelmap_voc.prototxt" + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + nms: 0.45 + score_type: MULTIPLY + #score_type: OBJ + #score_type: PROB + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_test_performance_RELU.prototxt b/models/intel_optimized_models/yolo/yolov2_test_performance_RELU.prototxt new file mode 100644 index 000000000..73e05c4da --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_test_performance_RELU.prototxt @@ -0,0 +1,1196 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TEST + } + transform_param { + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + } + } + data_param { + #source: "examples/yolo/data/lmdb/test2007_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_test_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + #bottom: "extra_layer25-conv" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + nms: 0.45 + score_type: MULTIPLY + #score_type: OBJ + #score_type: PROB + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_test_performance_RELU_dummy.prototxt b/models/intel_optimized_models/yolo/yolov2_test_performance_RELU_dummy.prototxt new file mode 100644 index 000000000..82929712a --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_test_performance_RELU_dummy.prototxt @@ -0,0 +1,1208 @@ +name: "yolov2_darknet448_RELU_dummy" +layer { + name: "data" + type: "DummyData" + top: "data" + include { + phase: TEST + } + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim: 8 + dim: 3 + dim: 544 + dim: 544 + } + } +} +layer { + name: "data" + type: "DummyData" + top: "label" + include { + phase: TEST + } + dummy_data_param { + data_filler { + type: "constant" + value: 1 + } + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 8 + } + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + #bottom: "extra_layer25-conv" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + include { + phase: TEST + } + + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + nms: 0.45 + score_type: MULTIPLY + #score_type: OBJ + #score_type: PROB + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_test_performance_dummy.prototxt b/models/intel_optimized_models/yolo/yolov2_test_performance_dummy.prototxt new file mode 100644 index 000000000..506645591 --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_test_performance_dummy.prototxt @@ -0,0 +1,1038 @@ +name: "yolov2_darknet448_dummy" +layer { + name: "data" + type: "DummyData" + top: "data" + include { + phase: TEST + } + dummy_data_param { + data_filler { + type: "constant" + value: 0.01 + } + shape { + dim: 8 + dim: 3 + dim: 544 + dim: 544 + } + } +} +layer { + name: "data" + type: "DummyData" + top: "label" + include { + phase: TEST + } + dummy_data_param { + data_filler { + type: "constant" + value: 1 + } + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 8 + } + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + } +} +layer { + name: "eval_det" + type: "EvalDetection" + bottom: "conv_reg" + bottom: "label" + bottom: "data" + top: "eval_det" + eval_detection_param { + side: 17 + num_class: 20 + num_object: 5 + threshold: 0.5 + nms: 0.45 + score_type: MULTIPLY + #score_type: OBJ + #score_type: PROB + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_train.prototxt b/models/intel_optimized_models/yolo/yolov2_train.prototxt new file mode 100644 index 000000000..4cbc4bc01 --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_train.prototxt @@ -0,0 +1,1294 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mirror: true + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: NEAREST + interp_mode: CUBIC + interp_mode: LANCZOS4 + } + emit_constraint { + emit_type: CENTER + } + } + data_param { + #source: "examples/VOC0712/VOC0712_trainval_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_trainval_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + max_sample: 1 + max_trials: 1 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.1 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.3 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.5 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.7 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.9 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + max_jaccard_overlap: 1.0 + } + max_sample: 1 + max_trials: 50 + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + #bottom: "extra_layer25-conv" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } +} +layer { + name: "det_loss" + type: "RegionLoss" + bottom: "conv_reg" + bottom: "label" + top: "det_loss" + loss_weight: 1 + region_loss_param { + side: 17 + num_class: 20 + coords: 4 + num: 5 + softmax: 1 + jitter: 0.2 + rescore: 1 + bias_match: 1 + + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + + absolute: 1 + thresh: 0.6 + random: 0 + + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/models/intel_optimized_models/yolo/yolov2_train_RELU.prototxt b/models/intel_optimized_models/yolo/yolov2_train_RELU.prototxt new file mode 100644 index 000000000..442291989 --- /dev/null +++ b/models/intel_optimized_models/yolo/yolov2_train_RELU.prototxt @@ -0,0 +1,1294 @@ +name: "yolov2_darknet448" +layer { + name: "data" + type: "AnnotatedData" + top: "data" + top: "label" + include { + phase: TRAIN + } + transform_param { + mirror: true + mean_value: 104 + mean_value: 117 + mean_value: 123 + resize_param { + prob: 1 + resize_mode: WARP + height: 544 + width: 544 + interp_mode: LINEAR + interp_mode: AREA + interp_mode: NEAREST + interp_mode: CUBIC + interp_mode: LANCZOS4 + } + emit_constraint { + emit_type: CENTER + } + } + data_param { + #source: "examples/VOC0712/VOC0712_trainval_lmdb" + source: "/lustre/dataset/lmdb/voc/VOC0712_trainval_lmdb" + batch_size: 8 + backend: LMDB + } + annotated_data_param { + batch_sampler { + max_sample: 1 + max_trials: 1 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.1 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.3 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.5 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.7 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + min_jaccard_overlap: 0.9 + } + max_sample: 1 + max_trials: 50 + } + batch_sampler { + sampler { + min_scale: 0.3 + max_scale: 1.0 + min_aspect_ratio: 0.5 + max_aspect_ratio: 2.0 + } + sample_constraint { + max_jaccard_overlap: 1.0 + } + max_sample: 1 + max_trials: 50 + } + label_map_file: "data/VOC0712/labelmap_voc.prototxt" + } +} +layer { + bottom: "data" + top: "layer1-conv" + name: "layer1-conv" + type: "Convolution" + convolution_param { + num_output: 32 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer1-conv" + top: "layer1-conv" + name: "layer1-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer1-conv" + top: "layer2-maxpool" + name: "layer2-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer2-maxpool" + top: "layer3-conv" + name: "layer3-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer3-conv" + top: "layer3-conv" + name: "layer3-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer3-conv" + top: "layer4-maxpool" + name: "layer4-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer4-maxpool" + top: "layer5-conv" + name: "layer5-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer5-conv" + top: "layer5-conv" + name: "layer5-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer5-conv" + top: "layer6-conv" + name: "layer6-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer6-conv" + top: "layer6-conv" + name: "layer6-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer6-conv" + top: "layer7-conv" + name: "layer7-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer7-conv" + top: "layer7-conv" + name: "layer7-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer7-conv" + top: "layer8-maxpool" + name: "layer8-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer8-maxpool" + top: "layer9-conv" + name: "layer9-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer9-conv" + top: "layer9-conv" + name: "layer9-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer9-conv" + top: "layer10-conv" + name: "layer10-conv" + type: "Convolution" + convolution_param { + num_output: 128 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer10-conv" + top: "layer10-conv" + name: "layer10-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer10-conv" + top: "layer11-conv" + name: "layer11-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer11-conv" + top: "layer11-conv" + name: "layer11-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer11-conv" + top: "layer12-maxpool" + name: "layer12-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer12-maxpool" + top: "layer13-conv" + name: "layer13-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer13-conv" + top: "layer13-conv" + name: "layer13-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer13-conv" + top: "layer14-conv" + name: "layer14-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer14-conv" + top: "layer14-conv" + name: "layer14-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer14-conv" + top: "layer15-conv" + name: "layer15-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer15-conv" + top: "layer15-conv" + name: "layer15-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer15-conv" + top: "layer16-conv" + name: "layer16-conv" + type: "Convolution" + convolution_param { + num_output: 256 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer16-conv" + top: "layer16-conv" + name: "layer16-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer16-conv" + top: "layer17-conv" + name: "layer17-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer17-conv" + top: "layer17-conv" + name: "layer17-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer17-conv" + top: "layer18-maxpool" + name: "layer18-maxpool" + type: "Pooling" + pooling_param { + kernel_size: 2 + stride: 2 + pool: MAX + } +} +layer { + bottom: "layer18-maxpool" + top: "layer19-conv" + name: "layer19-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer19-conv" + top: "layer19-conv" + name: "layer19-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer19-conv" + top: "layer20-conv" + name: "layer20-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer20-conv" + top: "layer20-conv" + name: "layer20-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer20-conv" + top: "layer21-conv" + name: "layer21-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer21-conv" + top: "layer21-conv" + name: "layer21-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer21-conv" + top: "layer22-conv" + name: "layer22-conv" + type: "Convolution" + convolution_param { + num_output: 512 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer22-conv" + top: "layer22-conv" + name: "layer22-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer22-conv" + top: "layer23-conv" + name: "layer23-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "layer23-conv" + top: "layer23-conv" + name: "layer23-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "layer23-conv" + top: "extra_layer24-conv" + name: "extra_layer24-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer24-conv" + name: "extra_layer24-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer24-conv" + top: "extra_layer25-conv" + name: "extra_layer25-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer25-conv" + top: "extra_layer25-conv" + name: "extra_layer25-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} + + +layer { + bottom: "layer17-conv" + top: "extra_layer26-conv" + name: "extra_layer26-conv" + type: "Convolution" + convolution_param { + num_output: 64 + kernel_size: 1 + pad: 0 #pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-conv" + name: "extra_layer26-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer26-conv" + top: "extra_layer26-reorg" + name: "extra_layer-reorg" + type: "Reorg" + reorg_param { + stride: 2 + } +} +layer { + bottom: "extra_layer26-reorg" + bottom: "extra_layer25-conv" + top: "extra_layer-concat" + name: "extra_layer-concat" + type: "Concat" + concat_param { + axis: 1 + } +} +layer { + bottom: "extra_layer-concat" + #bottom: "extra_layer25-conv" + top: "extra_layer27-conv" + name: "extra_layer27-conv" + type: "Convolution" + convolution_param { + num_output: 1024 + kernel_size: 3 + pad: 1 + stride: 1 + bias_term: false + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-bn" + type: "BatchNorm" + batch_norm_param { + use_global_stats: false + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-scale" + type: "Scale" + scale_param { + bias_term: true + } +} +layer { + bottom: "extra_layer27-conv" + top: "extra_layer27-conv" + name: "extra_layer27-act" + type: "ReLU" + relu_param { + #negative_slope: 0.1 + } +} +layer { + bottom: "extra_layer27-conv" + top: "conv_reg" + name: "conv_reg" + type: "Convolution" + convolution_param { + num_output: 125 + kernel_size: 1 + pad: 0 + stride: 1 + bias_term: true + weight_filler { + type: "xavier" + } + } + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } +} +layer { + name: "det_loss" + type: "RegionLoss" + bottom: "conv_reg" + bottom: "label" + top: "det_loss" + loss_weight: 1 + region_loss_param { + side: 17 + num_class: 20 + coords: 4 + num: 5 + softmax: 1 + jitter: 0.2 + rescore: 1 + bias_match: 1 + + object_scale: 5.0 + noobject_scale: 1.0 + class_scale: 1.0 + coord_scale: 1.0 + + absolute: 1 + thresh: 0.6 + random: 0 + + biases: 1.32 + biases: 1.73 + biases: 3.19 + biases: 4.01 + biases: 5.06 + biases: 8.10 + biases: 9.47 + biases: 4.84 + biases: 11.24 + biases: 10.01 + } +} diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py index 296bfad4f..4b12f41ab 100755 --- a/python/caffe/__init__.py +++ b/python/caffe/__init__.py @@ -35,7 +35,7 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver -from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list, set_random_seed +from ._caffe import init_log, log, set_mode_cpu, set_mode_gpu, set_device, compile_net, Layer, get_solver, layer_type_list, set_random_seed try: from ._caffe import MultiSync, MultiSolver except ImportError: diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp index 2de7611ee..fb9209bd5 100644 --- a/python/caffe/_caffe.cpp +++ b/python/caffe/_caffe.cpp @@ -144,6 +144,19 @@ void Log(const string& s) { void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); } + +//engine string: 1. "MKLDNN"; 2. "MKL2017"; 3. "" +string compile_net(const string& raw_net_prototxt, int phase, const string& engine_str = "MKLDNN") { + NetParameter raw_net_param; + NetParameter compiled_net_param; + ReadNetParamsFromTextFileOrDie(raw_net_prototxt, &raw_net_param); + raw_net_param.mutable_state()->set_phase(static_cast(phase)); + //This is for CompilationRuleConvBNFusion + raw_net_param.mutable_compile_net_state()->set_is_init(true); + raw_net_param.set_engine(engine_str); + Net::CompileNet(raw_net_param, &compiled_net_param); + return compiled_net_param.DebugString(); +} // For convenience, check that input files can be opened, and raise an // exception that boost will send to Python if not (caffe could still crash // later if the input files are disturbed before they are actually used, but @@ -428,6 +441,7 @@ BOOST_PYTHON_MODULE(_caffe) { bp::def("set_random_seed", &set_random_seed); bp::def("set_device", &Caffe::SetDevice); + bp::def("compile_net", &compile_net); bp::def("layer_type_list", &LayerRegistry::LayerTypeList); bp::class_, shared_ptr >, boost::noncopyable >("Net", diff --git a/python/caffe/draw.py b/python/caffe/draw.py index 9b4ccb30b..1f3ab6f7d 100755 --- a/python/caffe/draw.py +++ b/python/caffe/draw.py @@ -140,11 +140,11 @@ def get_layer_label(layer, rankdir): pooling_types_dict[layer.pooling_param.pool], layer.type, separator, - layer.pooling_param.kernel_size[0] if len(layer.pooling_param.kernel_size) else 1, + layer.pooling_param.kernel_size, separator, - layer.pooling_param.stride[0] if len(layer.pooling_param.stride) else 1, + layer.pooling_param.stride, separator, - layer.pooling_param.pad[0] if len(layer.pooling_param.pad) else 0) + layer.pooling_param.pad) else: node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type) return node_label diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py index 9587446cf..12872ea19 100755 --- a/python/caffe/pycaffe.py +++ b/python/caffe/pycaffe.py @@ -169,7 +169,6 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs): # Unpack blobs to extract return {out: self.blobs[out].data for out in outputs} - def _Net_backward(self, diffs=None, start=None, end=None, **kwargs): """ Backward pass: prepare diffs and run the net backward. diff --git a/scripts/benchmark_config_default.json b/scripts/benchmark_config_default.json index 24587e783..c41ebf525 100644 --- a/scripts/benchmark_config_default.json +++ b/scripts/benchmark_config_default.json @@ -10,35 +10,49 @@ "engine" : "MKLDNN" }, "perf_batch_size_table" : { - "resnet_50": { - "knl": "512", - "bdw": "512", - "knm": "512", - "skx": "512" - }, - "googlenet_v2": { - "knl": "192", - "bdw": "192", - "knm": "192", - "skx": "192" - }, - "alexnet": { - "knl": "512", - "bdw": "512", - "knm": "1024", - "skx": "1024" - }, - "googlenet": { - "knl": "144", - "bdw": "96", - "knm": "144", - "skx": "144" - } + "alexnet" : { + "bdw" : "1024", + "skx" : "1024", + "knl" : "512", + "knm" : "1024" + }, + "googlenet" : { + "bdw" : "128", + "skx" : "144", + "knl" : "144", + "knm" : "144" + }, + "googlenet_v2" : { + "bdw" : "128", + "skx" : "192", + "knl" : "192", + "knm" : "128" + }, + "googlenet_v3" : { + "bdw" : "128", + "skx" : "512", + "knl" : "128", + "knm" : "64" + }, + "vgg16" : { + "bdw" : "128", + "skx" : "128", + "knl" : "64", + "knm" : "96" + }, + "resnet_50" : { + "bdw" : "32", + "skx" : "96", + "knl" : "64", + "knm" : "64" + } }, "scal_batch_size_table" : { "alexnet" : "256", "googlenet" : "64", "googlenet_v2" : "64", - "resnet_50" : "128" + "googlenet_v3" : "64", + "vgg16" : "64", + "resnet_50" : "64" } } diff --git a/scripts/benchmark_config_template.json b/scripts/benchmark_config_template.json index a58cd5249..621b55a3a 100644 --- a/scripts/benchmark_config_template.json +++ b/scripts/benchmark_config_template.json @@ -10,35 +10,50 @@ "engine" : "choose CAFFE, MKL2017 or MKLDNN" }, "perf_batch_size_table" : { - "resnet_50": { - "knl": "512", - "bdw": "512", - "knm": "512", - "skx": "512" - }, - "googlenet_v2": { - "knl": "192", - "bdw": "192", - "knm": "192", - "skx": "192" - }, - "alexnet": { - "knl": "512", - "bdw": "512", - "knm": "1024", - "skx": "1024" - }, - "googlenet": { - "knl": "144", - "bdw": "96", - "knm": "144", - "skx": "144" - } + + "alexnet" : { + "bdw" : "1024", + "skx" : "1024", + "knl" : "512", + "knm" : "1024" + }, + "googlenet" : { + "bdw" : "128", + "skx" : "144", + "knl" : "144", + "knm" : "144" + }, + "googlenet_v2" : { + "bdw" : "128", + "skx" : "192", + "knl" : "192", + "knm" : "128" + }, + "googlenet_v3" : { + "bdw" : "128", + "skx" : "512", + "knl" : "128", + "knm" : "64" + }, + "vgg16" : { + "bdw" : "128", + "skx" : "128", + "knl" : "64", + "knm" : "96" + }, + "resnet_50" : { + "bdw" : "32", + "skx" : "96", + "knl" : "64", + "knm" : "64" + } }, "scal_batch_size_table" : { "alexnet" : "256", "googlenet" : "64", "googlenet_v2" : "64", - "resnet_50" : "128" + "googlenet_v3" : "64", + "vgg16" : "64", + "resnet_50" : "64" } } diff --git a/scripts/calibrator.py b/scripts/calibrator.py index b6317fc7b..b14b69bf4 100644 --- a/scripts/calibrator.py +++ b/scripts/calibrator.py @@ -39,8 +39,17 @@ import sys import copy import argparse +pycaffe = os.path.split(os.path.realpath(__file__))[0] + '/../python' +sys.path.insert(0, pycaffe) +import caffe +from caffe.proto import caffe_pb2 import google.protobuf.text_format as txtf +import sampling +int8_layers = ["Convolution", "ReLU", "Split", "Concat", "Pooling", "Eltwise"] + +def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): + return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) def check_existence(path): try: @@ -68,17 +77,7 @@ def read_prototxt(prototxt): print ("Failed to read {} due to {}".format(prototxt, e)) -def get_bottom_layers(top_name, net, start): - bottom_layers = [] - for index, value in enumerate(net.layer[start:]): - for sub_index, sub_value in enumerate(value.bottom): - if sub_value == top_name: - bottom_layers.append((index, value.name, value.type)) - - return bottom_layers - - -def get_top_layers(l, net, end): +def get_input_layers(l, net, end): top_layers = [] for layerIndex in range(0, end): reverse_layer_index = end - layerIndex - 1 @@ -89,162 +88,269 @@ def get_top_layers(l, net, end): return top_layers -def get_all_top_layers(l, net, end, skip_layers, interesting_layers): - all_top_layers = [] - top_layers = get_top_layers(l, net, end) +def get_input_convolutions(l, net, end, interesting_layers): + all_input_layers = [] + input_layers = get_input_layers(l, net, end) while True: - if len(top_layers) == 0: + if len(input_layers) == 0: break - processed_layers = top_layers # sync topLayers change - for (li, ln, lt) in processed_layers: - if lt in skip_layers: - top_layers.remove((li, ln, lt)) + processed_layers = input_layers # sync inputLayers change + for lp in processed_layers: + if lp[2] not in int8_layers: + input_layers.remove(lp) continue - if lt in interesting_layers: - lp = (li, ln, lt) - top_layers.remove(lp) - if lp not in all_top_layers: - all_top_layers.append(lp) + if lp[2] in interesting_layers: + input_layers.remove(lp) + if lp not in all_input_layers: + all_input_layers.append(lp) continue - new_top_layers = get_top_layers(net.layer[li], net, li) - top_layers.remove((li, ln, lt)) - top_layers.extend(new_top_layers) + new_input_layers = get_input_layers(net.layer[lp[0]], net, lp[0]) + input_layers.remove(lp) + input_layers.extend(new_input_layers) - return all_top_layers + return all_input_layers +def analyze_conv_output_with_relu(compiled_net): + convs_output_with_relu = [] + for _, layer in enumerate(compiled_net.layer): + if layer.type == 'Convolution': + if layer.convolution_param.relu: + convs_output_with_relu.append(layer.name) + return convs_output_with_relu -def get_all_bottom_layers(top_name, net, start, skip_layers, interesting_layers): - all_bottom_layers = [] - bottom_layers = get_bottom_layers(top_name, net, start) - while True: - if len(bottom_layers) == 0: - break - processed_layers = bottom_layers # sync bottom_layers change - for (li, ln, lt) in processed_layers: - if lt in skip_layers: - bottom_layers.remove((li, ln, lt)) - continue - if lt in interesting_layers: - lp = (li, ln, lt) - bottom_layers.remove(lp) - if lp not in all_bottom_layers: - all_bottom_layers.append(lp) - continue +def analyze_conv_input_u8(conv_inputs, convs_output_with_relu): + for conv_input in conv_inputs: + if conv_input[1] not in convs_output_with_relu: + return False + return True + - new_bottom_layers = get_bottom_layers(ln, net, li + 1) - bottom_layers.remove((li, ln, lt)) - bottom_layers.extend(new_bottom_layers) +def find_index_by_name(name, layer_infos): + for (l, index) in layer_infos: + if name == l.name: + return index + return -1 - return all_bottom_layers +def is_convolution_input_u8(l, net, end, interesting_layers, convs_output_with_relu): + all_input_layers = [] + input_layers = get_input_layers(l, net, end) + while True: + if len(input_layers) == 0: + break -def get_fusion_conv_names(compiled_model): - compiled_net = caffe_pb2.NetParameter() - with open(compiled_model) as f: - s = f.read() - txtf.Merge(s, compiled_net) - return [(layer.name, layer.bottom[1]) for _, layer in enumerate(compiled_net.layer) - if layer.type == 'Convolution' and len(layer.bottom) > 1] + for input_layer in input_layers: + if input_layer[2] == "Convolution" and (input_layer[1] not in convs_output_with_relu): + return False + processed_layers = input_layers # sync inputLayers change + for lp in processed_layers: + if lp[2] not in int8_layers: + input_layers.remove(lp) + continue + if lp[2] in interesting_layers: + input_layers.remove(lp) + if lp not in all_input_layers: + all_input_layers.append(lp) + continue -def filter_fusion_layers(net, fusion_layer, conv_layer): - if not fusion_layer or not conv_layer: - return [] - interesting_layers = ['ReLU'] - skip_layers = ['Convolution', 'Eltwise', 'Concat'] - output_with_relu_layer = [(l.name, net.layer[index].top[0]) for l, index in conv_layer - if len(get_all_bottom_layers(net.layer[index].top[0], net, index + 1, - skip_layers, interesting_layers)) == 0] - output_without_dict = {v: k for (k, v) in output_with_relu_layer} - for layer_name, top_name in fusion_layer: - if top_name in output_without_dict.keys(): - del output_without_dict[top_name] + new_input_layers = get_input_layers(net.layer[lp[0]], net, lp[0]) + input_layers.remove(lp) + input_layers.extend(new_input_layers) + + return True - return output_without_dict.values() +def analyze_conv_output_with_relu_from_net(convs_output_with_relu, compiled_net, net): + new_convs_output_with_relu = [] + new_convs_output_with_relu.extend(convs_output_with_relu) + compiled_relu_layers = [(value, index) for index, value in enumerate(compiled_net.layer) if value.type == 'ReLU' and value.relu_param.negative_slope != 0] + if len(compiled_relu_layers) != 0: + relu_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'ReLU'] + for (l, index) in relu_layers: + conv_inputs = get_input_convolutions(l, net, index, ["Convolution"]) + new_convs_output_with_relu.append(conv_inputs[0][1]) -def check_relu_existence(net, start, end, exclude_layer): - if net.layer[start].type == 'Convolution' and net.layer[start].name in exclude_layer: - return False + return new_convs_output_with_relu - for i in net.layer[start + 1: end]: - if i.type == 'ReLU': - return True - return False +def find_last_conv_layers(top_blobs_map, bottom_blobs_map, convolution_layers, non_int8_layers): + last_conv_indexes = [] + for (l, index) in convolution_layers: + top_blob = top_blobs_map[l.name][0] + for k, v in bottom_blobs_map.iteritems(): + if len(v) == 1 and v[0] == top_blob: + output_index = find_index_by_name(k, non_int8_layers) + if output_index != -1: + last_conv_indexes.append(index) + return last_conv_indexes -def transform_convolutions(model_path, compiled_model_path): +def transform_convolutions(model_path, compiled_model_path, top_blobs_map, bottom_blobs_map, use_unsigned_range, concat_use_fp32, unify_concat_scales, conv_algo, enable_1st_conv = False): net = caffe_pb2.NetParameter() with open(model_path) as f: s = f.read() txtf.Merge(s, net) - fusion_layer = get_fusion_conv_names(compiled_model_path) + compiled_net = caffe_pb2.NetParameter() + with open(compiled_model_path) as f: + s = f.read() + txtf.Merge(s, compiled_net) + + convs_output_with_relu = analyze_conv_output_with_relu(compiled_net) + # extended convs output with relu is used for convs that cannot fuse with relu due to negative slope + # extended_convs_output_with_relu = analyze_conv_output_with_relu_from_net(convs_output_with_relu, compiled_net, net) new_net = copy.deepcopy(net) convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Convolution'] - - interesting_layers = ['ReLU'] - skip_layers = ['Convolution', 'Eltwise', 'Concat'] + compiled_convolution_layers = [(value, index) for index, value in enumerate(compiled_net.layer) if value.type == 'Convolution'] u8_max = 255 s8_max = 127 - u8_layers = filter_fusion_layers(net, fusion_layer, convolution_layers) + first_conv = True if enable_1st_conv else False for (l, index) in convolution_layers: - outputwith_relu = get_all_bottom_layers(net.layer[index].top[0], net, index + 1, skip_layers, - interesting_layers) - conv_relu_flag = check_relu_existence(net, index, - convolution_layers[convolution_layers.index((l, index)) + 1][1] - if (l, index) != convolution_layers[-1] - else len(net.layer), [i[0] for i in fusion_layer]) - inputwith_relu = get_all_top_layers(l, net, index, skip_layers, interesting_layers) for si in range(0, len(new_net.layer[index].quantization_param.scale_out)): - if len(outputwith_relu) > 0 or l.name in u8_layers or conv_relu_flag: # u8 - new_net.layer[index].quantization_param.scale_out[si] = round(u8_max / new_net.layer[index]. - quantization_param.scale_out[si], 2) + if l.name in convs_output_with_relu: # u8 + new_net.layer[index].quantization_param.scale_out[si] = u8_max / new_net.layer[index].quantization_param.scale_out[si] else: # s8 - new_net.layer[index].quantization_param.scale_out[si] = round(s8_max / new_net.layer[index]. - quantization_param.scale_out[si], 2) + if use_unsigned_range: + new_net.layer[index].quantization_param.scale_out[si] = u8_max / new_net.layer[index].quantization_param.scale_out[si] + else: + new_net.layer[index].quantization_param.scale_out[si] = s8_max / new_net.layer[index].quantization_param.scale_out[si] + + index_in_compiled_net = find_index_by_name(l.name, compiled_convolution_layers) + assert(index_in_compiled_net >= 0) + #conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"]) + #conv_input_u8 = analyze_conv_input_u8(conv_inputs, convs_output_with_relu) + conv_input_u8 = is_convolution_input_u8(l, compiled_net, index_in_compiled_net, ["Convolution"], convs_output_with_relu) # FIXME: extended_convs_output_with_relu for si in range(0, len(new_net.layer[index].quantization_param.scale_in)): - if len(inputwith_relu) > 0 or l.type == 'Convolution': # u8 - new_net.layer[index].quantization_param.scale_in[si] = round(u8_max / new_net.layer[index]. - quantization_param.scale_in[si], 2) + if conv_input_u8: # u8 + if first_conv: # FIXME: remove the condition + new_net.layer[index].quantization_param.scale_in[si] = s8_max / new_net.layer[index].quantization_param.scale_in[si] +# new_net.layer[index].quantization_param.negative = True + first_conv = False + else: + new_net.layer[index].quantization_param.scale_in[si] = u8_max / new_net.layer[index].quantization_param.scale_in[si] + #new_net.layer[index].quantization_param.ClearField('min_in') else: - new_net.layer[index].ClearField('quantization_param') - continue + new_net.layer[index].quantization_param.scale_in[si] = s8_max / new_net.layer[index].quantization_param.scale_in[si] +# new_net.layer[index].quantization_param.negative = True for si in range(0, len(new_net.layer[index].quantization_param.scale_params)): - new_net.layer[index].quantization_param.scale_params[si] = round(s8_max / new_net.layer[index]. - quantization_param.scale_params[si], 2) + if not isclose(new_net.layer[index].quantization_param.scale_params[si], 0.0): + new_net.layer[index].quantization_param.scale_params[si] = s8_max / new_net.layer[index].quantization_param.scale_params[si] + else: + new_net.layer[index].quantization_param.scale_params[si] = 0.0 + + if conv_algo: + for conv_input in conv_inputs: + index_bottom_layer = find_index_by_name(conv_input[1], convolution_layers) + for si in range(0, len(new_net.layer[index_bottom_layer].quantization_param.scale_out)): + new_net.layer[index_bottom_layer].quantization_param.scale_out[si] = new_net.layer[index].quantization_param.scale_in[si] + + concat_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Concat'] + if len(concat_layers) > 0: + compiled_concat_layers = [(value, index) for index, value in enumerate(compiled_net.layer) if value.type == 'Concat'] + concat_layers.reverse() + if unify_concat_scales: + for (l, index) in concat_layers: + index_in_compiled_net = find_index_by_name(l.name, compiled_concat_layers) + assert(index_in_compiled_net >= 0) + conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"]) + min_concat_scale = sys.float_info.max + concat_input_indexes = [] + for conv_input in conv_inputs: + index_in_net = find_index_by_name(conv_input[1], convolution_layers) + assert(index_in_net >= 0) + concat_input_indexes.append(index_in_net) + if new_net.layer[index_in_net].quantization_param.scale_out[0] < min_concat_scale: + min_concat_scale = new_net.layer[index_in_net].quantization_param.scale_out[0] + + for concat_input_index in concat_input_indexes: + new_net.layer[concat_input_index].quantization_param.scale_out[0] = min_concat_scale + else: + if concat_use_fp32: + for (l, index) in concat_layers: + index_in_compiled_net = find_index_by_name(l.name, compiled_concat_layers) + assert(index_in_compiled_net >= 0) + conv_inputs = get_input_convolutions(l, compiled_net, index_in_compiled_net, ["Convolution"]) + for conv_input in conv_inputs: + index_in_net = find_index_by_name(conv_input[1], convolution_layers) + assert(index_in_net >= 0) + new_net.layer[index_in_net].quantization_param.bw_layer_out = 32 + new_net.layer[index_in_net].quantization_param.scale_out[:] = [1.0] + + non_int8_layers = [(value, index) for index, value in enumerate(net.layer) if value.type not in int8_layers] + last_conv_indexes = find_last_conv_layers(top_blobs_map, bottom_blobs_map, convolution_layers, non_int8_layers) + + # TODO: support last convolution without consumer + for index in last_conv_indexes: + new_net.layer[index].quantization_param.bw_layer_out = 32 + new_net.layer[index].quantization_param.scale_out[:] = [1.0] with open(model_path, 'w') as f: f.write(str(new_net)) -def generate_sample(sample_path, input_model, weights, - quantized_model, detection, scaling_mode, iterations=1, error_margin=1, power=0): - cmd = '{0} quantize -model {1} -weights {2} -model_quantized {3} -iterations {4} -error_margin {5} -power {6}' \ - ' -scaling {7} -trimming_mode dynamic_fixed_point'.format(sample_path, input_model, weights, quantized_model, - iterations, error_margin, power, scaling_mode) +def generate_sample_bak(sample_path, input_model, weights, + quantized_model, detection, scaling_mode, iterations=1, error_margin=1): + cmd = '{0} quantize -model {1} -weights {2} -model_quantized {3} -iterations {4} -error_margin {5} ' \ + ' -scaling {6} -trimming_mode dynamic_fixed_point'.format(sample_path, input_model, weights, quantized_model, + iterations, error_margin, scaling_mode) if detection: cmd += ' --detection=1' - + os.system(cmd) +def generate_sample(input_model, weights, quantized_model, scaling_mode, calibration_algo, conv_algo, iterations=10, enable_1st_conv=False): + (blobs, params, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) = sampling.sample(input_model, weights, conv_algo, iterations, enable_1st_conv) + + (inputs_max, outputs_max, inputs_min) = sampling.calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, calibration_algo, "SINGLE", conv_algo) + params_max = sampling.calibrate_parameters(params, winograd_convolutions, "DIRECT", scaling_mode.upper(), conv_algo) + + generate_sample_impl(input_model, quantized_model, inputs_max, outputs_max, inputs_min, params_max, enable_1st_conv) + + return (top_blobs_map, bottom_blobs_map) + + +def generate_sample_impl(input_model, quantized_model, inputs_max, outputs_max, inputs_min, params_max, enable_1st_conv=False): + net = caffe_pb2.NetParameter() + with open(input_model) as f: + s = f.read() + txtf.Merge(s, net) + + new_net = copy.deepcopy(net) + convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Convolution'] + first_conv = False if enable_1st_conv else True + for (l, index) in convolution_layers: + if first_conv: + first_conv = False + continue + new_net.layer[index].quantization_param.bw_layer_in = 8 + new_net.layer[index].quantization_param.bw_layer_out = 8 + new_net.layer[index].quantization_param.bw_params = 8 + new_net.layer[index].quantization_param.scale_in[:] = inputs_max[l.name] + new_net.layer[index].quantization_param.scale_out[:] = outputs_max[l.name] + new_net.layer[index].quantization_param.scale_params[:] = params_max[l.name] + #new_net.layer[index].quantization_param.min_in[:] = inputs_min[l.name] + + with open(quantized_model, 'w') as f: + f.write(str(new_net)) + def get_compiled_net(caffe_bin, model_def, model_weights, detection): output_log_name = '.compiled_net.txt' - cmd = '{} test -model {} -weights {} -iterations 1'.format(caffe_bin, model_def, model_weights) + cmd = '{} test -model {} -weights {} -iterations 1 -sampling'.format(caffe_bin, model_def, model_weights) if detection: cmd += ' -detection' cmd += ' 2>&1 > {}'.format(output_log_name) - + os.environ['GLOG_minloglevel'] = '2' os.system(cmd) + os.environ.pop('GLOG_minloglevel') return os.path.abspath(output_log_name) @@ -263,7 +369,9 @@ def get_the_accuracy(caffe_bin, model_def, model_weights, iterations, detection, for i in data[::-1]: if i.find('{} = '.format(blob_name)) != -1: try: - return float(i.split('=')[-1].strip()) + splits = i.split('=') + last_split = splits[-1].strip().split(' ')[0] # accuracy or loss + return float(last_split) except Exception as e: print 'Failed to generate accuracy due to {}'.format(str(e)) sys.exit(-1) @@ -283,20 +391,22 @@ def remove_top_quantized_parameter(current_quantized_file): f.write(str(net)) -def tuning_quantized_topology(base_top1_accuracy, prototxt, caffe_bin, model_weights, iterations, - is_floating_point, accuracy_loss, detection, blob_name): - if is_floating_point == 0: - print 'Updating quantization parameter...' +def tuning_quantized_topology(base_top1_accuracy, prototxt, caffe_bin, model_weights, top_blob_map, bottom_blobs_map, iterations, + accuracy_loss, detection, blob_name, quantize_only, use_unsigned_range, + concat_use_fp32, unify_concat_scales, conv_algo, enable_1st_conv): + print 'Updating quantization parameter...' - transform_convolutions(prototxt, get_compiled_net(caffe_bin, prototxt, model_weights, detection)) + transform_convolutions(prototxt, get_compiled_net(caffe_bin, prototxt, model_weights, detection), top_blobs_map, bottom_blobs_map, + use_unsigned_range, concat_use_fp32, unify_concat_scales, conv_algo, enable_1st_conv) + if quantize_only: + return current_top1_accuracy = get_the_accuracy(caffe_bin, prototxt, model_weights, iterations, detection, blob_name) - - while abs(current_top1_accuracy - base_top1_accuracy) >= accuracy_loss: - print 'Tuning... ' - print abs(current_top1_accuracy - base_top1_accuracy) - remove_top_quantized_parameter(prototxt) - current_top1_accuracy = get_the_accuracy(caffe_bin, prototxt, model_weights, iterations, detection, blob_name) + #while abs(current_top1_accuracy - base_top1_accuracy) >= accuracy_loss: + # print 'Tuning... ' + # print abs(current_top1_accuracy - base_top1_accuracy) + # remove_top_quantized_parameter(prototxt) + # current_top1_accuracy = get_the_accuracy(caffe_bin, prototxt, model_weights, iterations, detection, blob_name) def accuracy_blob_name_parser(prototxt): @@ -325,6 +435,27 @@ def check_blob_name_existence(prototxt, blob_name): return True return False +def generate_dummy_model(model_path, dummy): + net = caffe_pb2.NetParameter() + with open(model_path) as f: + s = f.read() + txtf.Merge(s, net) + + first_conv = True + convolution_layers = [(value, index) for index, value in enumerate(net.layer) if value.type == 'Convolution'] + for (l, index) in convolution_layers: + if first_conv: + first_conv = False + continue + net.layer[index].quantization_param.bw_layer_in = 8 + net.layer[index].quantization_param.bw_layer_out = 8 + net.layer[index].quantization_param.bw_params = 8 + net.layer[index].quantization_param.scale_in[:] = [1.0] + net.layer[index].quantization_param.scale_out[:] = [1.0] + net.layer[index].quantization_param.scale_params[:] = [1.0] + + with open(dummy, 'w') as f: + f.write(str(net)) if __name__ == '__main__': usage_string = 'Usage: 1.Build the caffe\n ' \ @@ -371,19 +502,54 @@ def check_blob_name_existence(prototxt, blob_name): parser.add_argument('-s', '--sampling_iterations', action='store', dest='sampling_iterations', default=10, help='iteration number of sampling, the default value is 10.') + parser.add_argument('-p', '--performance_model', dest='performance_model', action="store_true", default=False, + help='to generate model to measure performance only') + + parser.add_argument('-q', '--quantize_model', dest='quantize_model', action="store_true", default=False, + help='to quantize the model only') + + parser.add_argument('-u', '--unsigned_range', dest='unsigned_range', action="store_true", default=False, + help='to quantize using unsigned range for activation') + + parser.add_argument('-t', '--concat_use_fp32', dest='concat_use_fp32', action="store_true", default=False, + help='to use fp32 for concat') + + parser.add_argument('-f', '--unify_concat_scales', dest='unify_concat_scales', action="store_true", default=False, + help='to unify concat scales') + + parser.add_argument('-a', '--calibration_algos', dest='calibration_algos', action='store', default="DIRECT", + help='to choose the calibration alogorithm') + + parser.add_argument('-wi', '--conv_algo', dest='conv_algo', action="store_true", default=False, + help='to choose the convolution algorithm') + + parser.add_argument('-1st', '--enable_1st_conv', dest='enable_1st_conv', action="store_true", default=False, + help='to enable 1st conv quantization') + params = parser.parse_args() if not check_existence(params.root): print 'Please check the {} existence.'.format(params.root) sys.exit(-1) - + pycaffe_path = os.path.abspath(os.path.dirname(os.path.abspath(params.root))) + os.path.sep + 'python' if not check_existence(pycaffe_path): print "Please check the pycaffe existence.Suggest to rebuild pycaffe via 'make pycaffe'" sys.path.insert(0, pycaffe_path) import caffe from caffe.proto import caffe_pb2 - + + model = os.path.abspath(params.model) + if not check_existence(model): + print 'Please check model: {} existence.'.format(model) + sys.exit(-1) + + dummy_prototxt = model.rsplit('.')[0] + '_dummy.prototxt' + if params.performance_model: + generate_dummy_model(model, dummy_prototxt) + print 'Updated prototxt {} is generated.'.format(dummy_prototxt) + sys.exit(0) + try: user_input_iterations = int(params.iterations) except: @@ -403,11 +569,21 @@ def check_blob_name_existence(prototxt, blob_name): print 'Invalid sampling iteration!The value should be larger than zero.' sys.exit(-1) - if params.scaling_mode != 'multipe' and params.scaling_mode != 'single': + if params.scaling_mode != 'multiple' and params.scaling_mode != 'single': user_scaling_mode = 'single' else: user_scaling_mode = params.scaling_mode + if params.calibration_algos != 'DIRECT' and params.calibration_algos != "KL" and params.calibration_algos != "MAXP": + user_calibration_algos = 'DIRECT' + else: + user_calibration_alogs = params.calibration_algos + + if params.conv_algo != False and params.conv_algo != True: + user_conv_algo = False + else: + user_conv_algo = params.conv_algo + try: toleration = float(params.loss) if toleration >= 1 or toleration < 0: @@ -422,11 +598,7 @@ def check_blob_name_existence(prototxt, blob_name): print 'Set the test type to classification.' detection_flag = 0 - model = os.path.abspath(params.model) - if not check_existence(model): - print 'Please check model: {} existence.'.format(model) - sys.exit(-1) - + user_enable_1st_conv = params.enable_1st_conv user_input_weights = os.path.abspath(params.weights) if not check_existence(user_input_weights): print 'Please check weights: {} existence.'.format(user_input_weights) @@ -445,25 +617,27 @@ def check_blob_name_existence(prototxt, blob_name): setup_env() target_blob_name = params.blob_name - if not target_blob_name and not detection_flag: - target_blob_name = accuracy_blob_name_parser(model) + if not params.quantize_model: + if not target_blob_name and not detection_flag: + target_blob_name = accuracy_blob_name_parser(model) - if not target_blob_name or not check_blob_name_existence(model, target_blob_name): - print 'Please specify valid blob name and rerun the script.' - sys.exit(-1) + if not target_blob_name or not check_blob_name_existence(model, target_blob_name): + print 'Please specify valid blob name and rerun the script.' + sys.exit(-1) quantized_prototxt = model.rsplit('.')[0] + '_quantized.prototxt' - quantized_weights = user_input_weights.rsplit('.')[0] + '_quantized.caffemodel' - enable_power_of_2 = 0 print 'Sampling...' - generate_sample(sample, model, user_input_weights, quantized_prototxt, detection_flag, user_scaling_mode, - user_sampling_iteration, 100 * toleration, enable_power_of_2) + (top_blobs_map, bottom_blobs_map) = generate_sample(model, user_input_weights, quantized_prototxt, user_scaling_mode, user_calibration_alogs, user_conv_algo, user_sampling_iteration, user_enable_1st_conv) print 'Sampling done' - print 'Generating the FP32 accuracy...' - top_1 = get_the_accuracy(caffe_bin_path, model, user_input_weights, user_input_iterations, detection_flag, + top_1 = None + if not params.quantize_model: + print 'Generating the FP32 accuracy...' + top_1 = get_the_accuracy(caffe_bin_path, model, user_input_weights, user_input_iterations, detection_flag, target_blob_name) - print 'FP32 accuracy is: {}'.format(top_1) - tuning_quantized_topology(top_1, quantized_prototxt, caffe_bin_path, user_input_weights, user_input_iterations, - enable_power_of_2, toleration, detection_flag, target_blob_name) + print 'FP32 accuracy is: {}'.format(top_1) + + tuning_quantized_topology(top_1, quantized_prototxt, caffe_bin_path, user_input_weights, top_blobs_map, bottom_blobs_map, user_input_iterations, + toleration, detection_flag, target_blob_name, params.quantize_model, params.unsigned_range, + params.concat_use_fp32, params.unify_concat_scales, params.conv_algo, user_enable_1st_conv) print 'Updated prototxt {} is generated.'.format(quantized_prototxt) diff --git a/scripts/check_winograd.py b/scripts/check_winograd.py new file mode 100644 index 000000000..f57e83394 --- /dev/null +++ b/scripts/check_winograd.py @@ -0,0 +1,108 @@ +import os +import re +import math +import sys +import numpy as np + +pycaffe = os.path.split(os.path.realpath(__file__))[0] + '/../python' +sys.path.insert(0, pycaffe) +import caffe +from caffe.proto import caffe_pb2 +import google.protobuf.text_format as txtf + +def check(model): + caffe.set_mode_cpu() + test_net = caffe.Net(model, caffe.TEST) + layers = list(test_net._layer_names) + conv_layers = [] + for idx in range(0, len(layers)): + if test_net.layers[idx].type == "Convolution": + conv_layers.append(layers[idx]) + + top_map = test_net.top_names + bottom_map = test_net.bottom_names + channel_map = {} + for k, v in top_map.items(): # layer name : top blobs + if k in conv_layers: + top_blob = v[0] + bottom_blob = bottom_map[k][0] + channel_map[k] = (test_net.blobs[top_blob].shape[1], test_net.blobs[bottom_blob].shape[1]) + + base_net = caffe_pb2.NetParameter() + with open(model) as f: + s = f.read() + txtf.Merge(s, base_net) + + winograd_convolutions = [] + for index in range(0, len(base_net.layer)): + l = base_net.layer[index] + if l.type == "Convolution": + kernel_h = 0 + kernel_w = 0 + if len(l.convolution_param.kernel_size) != 0: + kernel_h = kernel_w = l.convolution_param.kernel_size[0] + if len(l.convolution_param.kernel_size) > 1: + kernel_h = l.convolution_param.kernel_size[0] + kernel_w = l.convolution_param.kernel_size[1] + else: + if l.convolution_param.HasField("kernel_h"): + kernel_h = l.convolution_param.kernel_h + if l.convolution_param.HasField("kernel_w"): + kernel_w = l.convolution_param.kernel_w + + group = l.convolution_param.group + stride_h = 1 + stride_w = 1 + if len(l.convolution_param.stride) != 0: + if len(l.convolution_param.stride) == 1: + stride_h = stride_w = l.convolution_param.stride[0] + else: + stride_h = l.convolution_param.stride[0] + stride_w = l.convolution_param.stride[1] + else: + if l.convolution_param.HasField("stride_h"): + stride_h = l.convolution_param.stride_h + if l.convolution_param.HasField("stride_w"): + stride_w = l.convolution_param.stride_w + + dilate_h = 1 + dilate_w = 1 + if len(l.convolution_param.dilation) != 0: + if len(l.convolution_param.dilation) == 1: + dilate_h = dilate_w = l.convolution_param.dilation[0] + else: + dilate_h = l.convolution_param.dilation[0] + dilate_w = l.convolution_param.dilation[1] + + oc = channel_map[l.name][0] + ic = channel_map[l.name][1] + + pad_h = 0 + pad_w = 0 + if len(l.convolution_param.pad) != 0: + if len(l.convolution_param.pad) == 1: + pad_h = pad_w = l.convolution_param.pad[0] + else: + pad_h = l.convolution_param.pad[0] + pad_w = l.convolution_param.pad[1] + else: + if l.convolution_param.HasField("pad_h"): + pad_h = l.convolution_param.pad_h + if l.convolution_param.HasField("pad_w"): + pad_w = l.convolution_param.pad_w + + if kernel_h == 3 and kernel_w == 3 \ + and group == 1 \ + and oc % 16 == 0 and ic % 16 == 0 \ + and stride_h == 1 and stride_w == 1 \ + and dilate_h == 0 and dilate_w == 0 \ + and pad_h < 2 and pad_w < 2: + winograd_convolutions.append(l.name) + + return winograd_convolutions + +if __name__ == '__main__': + if len(sys.argv) != 2: + print "Usgae: python check_winograd.py $prototxt" + sys.exit(0) + print check(sys.argv[1]) diff --git a/scripts/run_intelcaffe.sh b/scripts/run_intelcaffe.sh index c2d80a9bf..8ffa78c3d 100755 --- a/scripts/run_intelcaffe.sh +++ b/scripts/run_intelcaffe.sh @@ -60,6 +60,8 @@ msg_priority_threshold=10000 mpi_iallreduce_algo="" +ppn=1 + function usage { script_name=$0 @@ -79,6 +81,7 @@ function usage echo " [--cpu cpu_model]" echo " [--msg_priority on/off] [--msg_priority_threshold 10000]" echo " [--mpi_iallreduce_algo mpi_iallreduce_algo]" + echo " [--ppn ppn]" echo "" echo " Parameters:" echo " hostfile: host file includes list of nodes. Only used if you're running with multinode" @@ -107,6 +110,7 @@ function usage echo " msg_priority: off (default). Enable/disable message priority in MLSL." echo " msg_priority_threshold: 10000 (default), if message priority is on." echo " mpi_iallreduce_algo: adjust MPI iallreduce algorithms for synchronizing gradients in nodes." + echo " ppn: process per node. default is 1." echo "" } @@ -212,7 +216,7 @@ function execute_command exec_command="$xeonbin_" fi - if [ ${numnodes} -gt 1 ] || [ $is_local_run -ne 1 ]; then + if [ ${numnodes} -gt 1 ] || [ $is_local_run -ne 1 ] || [ $ppn -gt 1 ]; then # Produce the configuration file for mpiexec. # Each line of the config file contains a # host, environment, binary name. cfile_=$result_dir_/nodeconfig-${cpu_model}-${numnodes}.txt @@ -220,7 +224,7 @@ function execute_command for node in "${nodenames[@]}" do - echo "-host ${node} -n $ppncpu $exec_command" >> $cfile_ + echo "-host ${node} -n $ppn $exec_command" >> $cfile_ done fi log_file=$result_dir_/outputCluster-${cpu_model}-${numnodes}.txt @@ -233,7 +237,7 @@ function execute_command $sensors_bin >$sensor_log_file fi - if [ ${numnodes} -eq 1 ] && [ $is_local_run -eq 1 ]; then + if [ ${numnodes} -eq 1 ] && [ $is_local_run -eq 1 ] && [ $ppn -eq 1 ]; then time GLOG_minloglevel=0 $exec_command 2>&1 | tee ${log_file} else exec_command="-l -configfile $cfile_" @@ -529,10 +533,6 @@ do --help) usage ;; - --engine) - engine=$2 - shift - ;; --caffe_bin) caffe_bin=$2 shift @@ -553,6 +553,10 @@ do mpi_iallreduce_algo=$2 shift ;; + --ppn) + ppn=$2 + shift + ;; *) echo "Unknown option: $key" usage @@ -594,6 +598,7 @@ echo "" echo "Settings:" echo " CPU: $cpu_model" echo " Host file: $host_file" +echo " PPN (Process Per Node): $ppn" echo " Running mode: $mode" echo " Benchmark: $benchmark_mode" echo " Debug option: $debug" @@ -690,6 +695,7 @@ if [ $numanode -eq 0 ]; then else echo " NUMA configuration: Flat mode." fi +echo "" if [ ! -d $result_dir ]; then echo "Create result directory: $result_dir" @@ -697,7 +703,7 @@ if [ ! -d $result_dir ]; then fi env_params="--cpu $cpu_model --debug $debug --network $network --num_mlsl_servers $num_mlsl_servers" -env_params+=" --msg_priority $msg_priority --msg_priority_threshold $msg_priority_threshold" +env_params+=" --msg_priority $msg_priority --msg_priority_threshold $msg_priority_threshold --ppn $ppn" if [ "$network" == "tcp" ]; then env_params+=" --netmask $tcp_netmask" fi diff --git a/scripts/sampling.py b/scripts/sampling.py new file mode 100644 index 000000000..f42f45e68 --- /dev/null +++ b/scripts/sampling.py @@ -0,0 +1,423 @@ +import os +import re +import math +import sys +import numpy as np +import check_winograd as chw + +pycaffe = os.path.split(os.path.realpath(__file__))[0] + '/../python' +sys.path.insert(0, pycaffe) +import caffe + +calibration_algos = ["DIRECT", "KL", "MAXP"] +scaling_modes = ["SINGLE", "MULTIPLE"] + +def get_blob_map(test_net, enable_first_conv = False): + layers = list(test_net._layer_names) + conv_layers = [] + for idx in range(0, len(layers)): + if test_net.layers[idx].type == "Convolution": + conv_layers.append(layers[idx]) + + if not enable_first_conv: + conv_layers.pop(0) + + conv_top_blob_layer_map = {} + for k, v in test_net.top_names.items(): # layer name : top blobs + if k in conv_layers: + conv_top_blob_layer_map[v[0]] = k # top blob : layer name + + conv_bottom_blob_layer_map = {} + for k, v in test_net.bottom_names.items(): # layer name : bottom blobs + if k in conv_layers: + conv_bottom_blob_layer_map[v[0]] = k # bottom blob : layer_name + + return (conv_layers, test_net.top_names, test_net.bottom_names, conv_top_blob_layer_map, conv_bottom_blob_layer_map) + + +def get_winograd_info(model, conv_bottom_blob_layer_map, winograd_algo = False): + winograd_bottoms = [] + winograd_convolutions = [] + if winograd_algo: + winograd_convolutions = chw.check(model) + for c in winograd_convolutions: + for k,v in conv_bottom_blob_layer_map.items(): + if c == v: + winograd_bottoms.append(k) + + return (winograd_bottoms, winograd_convolutions) + + +def sample(model, weight, winograd_algo=False, itern=1, enable_first_conv=False): + caffe.set_mode_cpu() + test_net = caffe.Net(model, weight, caffe.TEST) + (conv_layers, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map) = get_blob_map(test_net, enable_first_conv) + + blobs = {} + for iter_index in range(0, itern): + print "Iteration:", (iter_index + 1) + test_net.forward() + for k, _ in test_net.blobs.items(): # top blob + output = test_net.blobs[k].data + if k not in blobs.keys(): + blobs[k] = [output] + else: + new_outputs = blobs[k] + new_outputs.append(output) + blobs[k] = new_outputs + + params = {} + for k, _ in test_net.params.items(): + if k not in conv_layers: + continue + param = np.abs(test_net.params[k][0].data) # ignore bias + params[k] = [param] + + (winograd_bottoms, winograd_convolutions) = get_winograd_info(model, conv_bottom_blob_layer_map, winograd_algo) + return (blobs, params, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) + + +def expand_quantized_bins(quantized_bins, reference_bins): + expanded_quantized_bins = [0]*len(reference_bins) + num_merged_bins = len(reference_bins)/len(quantized_bins) + j_start = 0 + j_end = num_merged_bins + for idx in xrange(len(quantized_bins)): + zero_count = reference_bins[j_start:j_end].count(0) + num_merged_bins = j_end-j_start + if zero_count == num_merged_bins: + avg_bin_ele = 0 + else: + avg_bin_ele = quantized_bins[idx]/(num_merged_bins - zero_count + 0.0) + for idx1 in xrange(j_start, j_end): + expanded_quantized_bins[idx1] = (0 if reference_bins[idx1] == 0 else avg_bin_ele) + j_start += num_merged_bins + j_end += num_merged_bins + if (idx+1) == len(quantized_bins) - 1: + j_end = len(reference_bins) + return expanded_quantized_bins + +def safe_entropy(reference_distr_P, P_sum, candidate_distr_Q, Q_sum): + assert len(reference_distr_P) == len(candidate_distr_Q) + tmp_sum1 = 0 + tmp_sum2 = 0 + for idx in range(len(reference_distr_P)): + p_idx = reference_distr_P[idx] + q_idx = candidate_distr_Q[idx] + if p_idx == 0: + tmp_sum1 += 0 + tmp_sum2 += 0 + else: + if q_idx == 0: + print "Fatal error!, idx = " + str(idx) + " qindex = 0! p_idx = " + str(p_idx) + tmp_sum1 += p_idx * (math.log(Q_sum*p_idx)) + tmp_sum2 += p_idx * (math.log(P_sum*q_idx)) + return (tmp_sum1 - tmp_sum2)/P_sum + +# Reference: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf +def get_optimal_scaling_factor(activation_blob, num_quantized_bins = 255): + max_val = np.max(activation_blob) + min_val = np.min(activation_blob) + print min_val, max_val + if min_val >= 0: + hist, hist_edeges = np.histogram(activation_blob, bins=2048, range=(min_val, max_val)) + ending_iter = 2047 + starting_iter = int(ending_iter * 0.7) + else: + th = max(abs(max_val), abs(min_val)) + hist, hist_edeges = np.histogram(activation_blob, bins=2048, range=(-th, th)) + starting_iter = 0 + ending_iter = 2047 + if abs(max_val) > abs(min_val): + while starting_iter < ending_iter: + if hist[starting_iter] == 0: + starting_iter += 1 + continue + else: + break + starting_iter += int((ending_iter - starting_iter)*0.6) + else: + while ending_iter > 0: + if hist[ending_iter] == 0: + ending_iter -= 1 + continue + else: + break + starting_iter = int(0.6 * ending_iter) + bin_width = hist_edeges[1]-hist_edeges[0] + P_sum = len(activation_blob) + min_kl_divergence = 0 + min_kl_index = 0 + kl_inited = False + for i in range(starting_iter, ending_iter+1): + reference_distr_P = hist[0:i].tolist() + outliers_count = sum(hist[i:2048]) + if reference_distr_P[i-1] == 0: + continue + reference_distr_P[i-1] += outliers_count + reference_distr_bins = reference_distr_P[:] + candidate_distr_Q = hist[0:i].tolist() + num_merged_bins = i/num_quantized_bins + candidate_distr_Q_quantized = [0]*num_quantized_bins + j_start = 0 + j_end = num_merged_bins + for idx in xrange(num_quantized_bins): + candidate_distr_Q_quantized[idx] = sum(candidate_distr_Q[j_start:j_end]) + j_start += num_merged_bins + j_end += num_merged_bins + if (idx+1) == num_quantized_bins - 1: + j_end = i + candidate_distr_Q = expand_quantized_bins(candidate_distr_Q_quantized, reference_distr_bins) + Q_sum = sum(candidate_distr_Q) + kl_divergence = safe_entropy(reference_distr_P, P_sum, candidate_distr_Q, Q_sum) + if not kl_inited: + min_kl_divergence = kl_divergence + min_kl_index = i + kl_inited = True + elif kl_divergence < min_kl_divergence: + min_kl_divergence = kl_divergence + min_kl_index = i + else: + pass + if min_kl_index == 0: + while starting_iter > 0: + if hist[starting_iter] == 0: + starting_iter -= 1 + continue + else: + break + min_kl_index = starting_iter + return (min_kl_index+0.5)*bin_width + +# calibrator info: data, algo, scaling_mode, max_p +INDEX_DATA = 0 +INDEX_ALGO = 1 +INDEX_SCALE = 2 +INDEX_MAXP = 3 +INDEX_TYPE = 4 +INDEX_CONV = 5 +def calibrate_min(calibrator_info): + iteration_outputs = calibrator_info[INDEX_DATA] + n = iteration_outputs[0].shape[0] + c = iteration_outputs[0].shape[1] + min_map = {} + for ci in range(0, c): + min_map[ci] = 0.0 + + for iteration_output in iteration_outputs: + for ni in range(0, n): + for ci in range(0, c): + min_map[ci] = min_map[ci] + np.min(iteration_output[ni][ci]) + + for ci in range(0, c): + min_map[ci] = min_map[ci] / (len(iteration_outputs) * n + 0.0) + + inputs_min = [] + for ci in range(0, c): + inputs_min.append(min_map[ci]) + + return inputs_min + +def calibrate_max(calibrator_info): + is_activation = calibrator_info[INDEX_TYPE] + calibration_algo = "DIRECT" + if calibrator_info[INDEX_ALGO] in calibration_algos: + calibration_algo = calibrator_info[INDEX_ALGO] + scaling_mode = "SINGLE" + if calibrator_info[INDEX_SCALE] in scaling_modes: + scaling_mode = calibrator_info[INDEX_SCALE] + + is_wino_conv = False + if calibrator_info[INDEX_CONV]: + is_wino_conv = calibrator_info[INDEX_CONV] + + iteration_outputs = calibrator_info[INDEX_DATA] + maxp = calibrator_info[INDEX_MAXP] + if scaling_mode == "SINGLE": + layer_max = sys.float_info.min + if calibration_algo == "DIRECT": + for iteration_output in iteration_outputs: + if is_wino_conv: + if is_activation: + iteration_output_wino = wino_transform_data(iteration_output) + else: + iteration_output_wino = wino_transform_param(iteration_output) + else: + iteration_output_wino = iteration_output + iteration_max = np.max(iteration_output_wino) + if iteration_max > layer_max: + layer_max = iteration_max + elif calibration_algo == "MAXP": + iteration_sum = 0 + for iteration_output in iteration_outputs: + if is_wino_conv: + if is_activation: + iteration_output_wino = wino_transform_data(iteration_output) + else: + iteration_output_wino = wino_transform_param(iteration_output) + else: + iteration_output_wino = iteration_output + iteration_output_sorted = np.sort(iteration_output_wino.flatten()) + iteration_max = iteration_output_sorted[int(len(iteration_output_sorted) * maxp)] + iteration_sum += iteration_max + layer_max = iteration_sum / (len(iteration_outputs) + 0.0) + else: + iteration_blobs = iteration_outputs[0].ravel() + for i in range(1, len(iteration_outputs)): + iteration_blobs = np.append(iteration_blobs, iteration_outputs[i].ravel()) + layer_max = get_optimal_scaling_factor(iteration_blobs, 255) + + layer_max = float(layer_max) + return [layer_max] + else: + layer_max_oc = [] + + if calibration_algo == "DIRECT": + for i in range(0, iteration_outputs[0].shape[0]): + layer_max_oc.append(sys.float_info.min) + for iteration_output in iteration_outputs: + if is_wino_conv: + if is_activation: + iteration_output_wino = wino_transform_data(iteration_output) + else: + iteration_output_wino = wino_transform_param(iteration_output) + else: + iteration_output_wino = iteration_output + for i in range(0, iteration_output_wino.shape[0]): + oc_max = np.max(iteration_output_wino[i]) + if oc_max > layer_max_oc[i]: + layer_max_oc[i] = float(oc_max) + elif calibration_algo == "MAXP": + for i in range(0, iteration_outputs[0].shape[0]): + layer_max_oc.append(0) + for iteration_output in iteration_outputs: + if is_wino_conv: + if is_activation: + iteration_output_wino = wino_transform_data(iteration_output) + else: + iteration_output_wino = wino_transform_param(iteration_output) + else: + iteration_output_wino = iteration_output + for i in range(0, iteration_output_wino.shape[0]): + iteration_output_sorted = np.sort(iteration_output_wino[i].flatten()) + iteration_max = iteration_output_sorted[int(len(iteration_output_sorted) * maxp)] + layer_max_oc[i] += iteration_max + for i in range(0, iteration_outputs[0].shape[0]): + layer_max_oc[i] = layer_max_oc[i] / (len(iteration_outputs) + 0.0) + else: + print "Unsupported..." + + for i in range(len(layer_max_oc)): + layer_max_oc[i] = float(layer_max_oc[i]) + return layer_max_oc + +def np_abs(blobs): + blobs_abs = [] + for blob_index in range(0, len(blobs)): + blobs_abs.append(np.abs(blobs[blob_index])) + return blobs_abs + +def calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, calibration_algo="DIRECT", scaling_mode="SINGLE", is_wino_conv=False, maxp=0.99995): + conv_blobs = {} + for k, _ in conv_top_blob_layer_map.iteritems(): + if k not in conv_blobs.keys(): + conv_blobs[k] = blobs[k] + + for k, _ in conv_bottom_blob_layer_map.iteritems(): + if k not in conv_blobs.keys(): + conv_blobs[k] = blobs[k] + + conv_blobs_max = {} + conv_blobs_min = {} + for k, v in conv_blobs.items(): + print "Calibrate activation for", k + if is_wino_conv: + if k in winograd_bottoms: + v_max = calibrate_max((np_abs(v), calibration_algo, scaling_mode, maxp, 1, True)) + conv_blobs_max[k] = v_max + else: + v_max = calibrate_max((np_abs(v), calibration_algo, scaling_mode, maxp, 0, False)) + conv_blobs_max[k] = v_max + else: + print "-----------------" + v_max = calibrate_max((np_abs(v), calibration_algo, scaling_mode, maxp, 0, False)) + v_min = calibrate_min((v, calibration_algo, scaling_mode, maxp, 0, False)) + conv_blobs_max[k] = v_max + conv_blobs_min[k] = v_min + + outputs_max = {} + for k, v in conv_top_blob_layer_map.iteritems(): + outputs_max[v] = conv_blobs_max[k] + + inputs_max = {} + inputs_min = {} + for k, v in conv_bottom_blob_layer_map.iteritems(): + inputs_max[v] = conv_blobs_max[k] + inputs_min[v] = conv_blobs_min[k] + + return (inputs_max, outputs_max, inputs_min) + +def calibrate_parameters(params, winograd_convolutions, calibration_algo="DIRECT", scaling_mode="SINGLE", is_wino_conv=False, maxp=0.99995): + params_max = {} + for k, v in params.items(): + print "Calibrate parameters for", k + if is_wino_conv: + if k in winograd_convolutions: + v_max = calibrate_max((v, calibration_algo, scaling_mode, maxp, 0, True)) + params_max[k] = v_max + else: + v_max = calibrate_max((v, calibration_algo, scaling_mode, maxp, 0, False)) + params_max[k] = v_max + else: + v_max = calibrate_max((v, calibration_algo, scaling_mode, maxp, 0, False)) + params_max[k] = v_max + + return params_max + +def wino_transform_data(blob): #B^tdB + blob_wino = blob.copy() + blob_cell = ([[1,1,1,1],[1,1,1,1],[1,1,1,1],[1,1,1,1]]) + B_t = np.array([[1,0,-1,0],[0,1,1,0],[0,-1,1,0],[0,1,0,-1]]) + B = B_t.T + for n in range(blob.shape[0]): + for c in range(blob.shape[1]): + for h in range(0,blob.shape[2],2): + for w in range(0,blob.shape[3],2): + if (w+4) <= blob.shape[3] and (h+4) <= blob.shape[2]: + for i in range(4): + for j in range(4): + blob_cell[i][j] = blob[n][c][h+i][w+j] + blob_wino_tmp = np.dot(B_t, blob_cell) + blob_wino_cell = np.dot(blob_wino_tmp, B) + for i in range(4): + for j in range(4): + blob_wino[n][c][h+i][w+j] = blob_wino_cell[i][j] + return blob_wino + +def wino_transform_param(blob): #GgG^t + blob_wino = np.zeros([blob.shape[0],blob.shape[1],4,4]) + blob_cell = ([[1,1,1],[1,1,1],[1,1,1]]) + G = np.array([[1,0,0],[0.5,0.5,0.5],[0.5,-0.5,0.5],[0,0,1]]) + G_t = G.T + for n in range(blob.shape[0]): + for c in range(blob.shape[1]): + for h in range(blob.shape[2]): + for w in range(blob.shape[3]): + blob_cell[h][w] = blob[n][c][h][w] + blob_wino_tmp = np.dot(G, blob_cell) + blob_wino_cell = np.dot(blob_wino_tmp, G_t) + blob_wino[n][c] = blob_wino_cell + return blob_wino + + +if __name__ == '__main__': + if len(sys.argv) != 4: + print "Usgae: python sampling.py $prototxt $weights $whether use winograd(True or False)" + sys.exit(0) + (blobs, params, top_blobs_map, bottom_blobs_map, conv_top_blob_layer_map, conv_bottom_blob_layer_map, winograd_bottoms, winograd_convolutions) = sample(sys.argv[1], sys.argv[2], False, 2, True) + (inputs_max, outputs_max, inputs_min) = calibrate_activations(blobs, conv_top_blob_layer_map, conv_bottom_blob_layer_map,winograd_bottoms, "DIRECT", "SINGLE") +# params_max = calibrate_parameters(params, winograd_convolutions, "DIRECT", "MULTIPLE", True) +# for k, v in inputs_max.items(): +# print "***", k, v[0], outputs_max[k][0] +# for k, v in params_max.items(): +# print "###", k, v[0] diff --git a/scripts/set_env.sh b/scripts/set_env.sh index d38464b0a..a1b053a44 100755 --- a/scripts/set_env.sh +++ b/scripts/set_env.sh @@ -32,15 +32,17 @@ msg_priority_threshold="" mpi_iallreduce_algo="" +ppn=1 + function init_mpi_envs { - if [ ${numnodes} -eq 1 ]; then + if [ ${numnodes} -eq 1 ] && [ $ppn -eq 1 ]; then return fi # IMPI configuration if [ "$network" == "opa" ]; then - export I_MPI_FABRICS=tmi + export I_MPI_FABRICS=shm:tmi export I_MPI_TMI_PROVIDER=psm2 if [ "$cpu_model" == "knl" ] || [ "$cpu_model" == "knm" ]; then # PSM2 configuration @@ -48,11 +50,11 @@ function init_mpi_envs export HFI_NO_CPUAFFINITY=1 export I_MPI_DYNAMIC_CONNECTION=0 export I_MPI_SCALABLE_OPTIMIZATION=0 - export I_MPI_PIN_MODE=lib + export I_MPI_PIN_MODE=lib export I_MPI_PIN_DOMAIN=node fi elif [ "$network" == "tcp" ]; then - export I_MPI_FABRICS=tcp + export I_MPI_FABRICS=shm:tcp export I_MPI_TCP_NETMASK=$tcp_netmask else echo "Invalid network: $network" @@ -65,6 +67,40 @@ function init_mpi_envs if [ "${mpi_iallreduce_algo}" != "" ]; then export I_MPI_ADJUST_IALLREDUCE=${mpi_iallreduce_algo} fi + if [ ${ppn} -gt 1 ]; then + maxcores=64 + domain_str="[" + for ((ppn_index=0; ppn_index/dev/null + if [ $? -ne 0 ]; then + index=$((cpu_index/maxcores)) + offset=$((cpu_index%maxcores)) + masks[$index]=$((masks[$index]+(1<=0; mask_index--)) + do + mask_str+=`printf "%x" "${masks[$mask_index]}"` + done + domain_str+="$mask_str" + if [ $ppn_index -ne $((ppn-1)) ]; then + domain_str+="," + fi + done + domain_str+="]" + echo "I_MPI_PIN_DOMAIN=$domain_str" + export I_MPI_PIN_DOMAIN=$domain_str + fi } @@ -116,7 +152,7 @@ function clear_envs function set_mlsl_vars { - if [ ${numnodes} -eq 1 ]; then + if [ ${numnodes} -eq 1 ] && [ $ppn -eq 1 ]; then return fi @@ -142,10 +178,20 @@ function set_mlsl_vars export MLSL_NUM_SERVERS=${numservers} if [ ${numservers} -gt 0 ]; then - listep="6" - for ((i=7; i<${numservers}+6; i++)) + # first cpu reserved for EP server + first_cpu=6 + for ((ppn_index=0; ppn_index<${ppn}; ppn_index++)) do - listep+=",$i" + # first cpu for current process + first_cpu_proc=$((first_cpu+ppn_index*cores_per_proc)) + for ((srv_index=0; srv_index<${numservers}; srv_index++)) + do + cpu_no=$((first_cpu_proc+srv_index)) + listep+="$cpu_no" + if [ $ppn_index -ne $((ppn-1)) ] || [ $srv_index -ne $((numservers-1)) ]; then + listep+="," + fi + done done export MLSL_SERVER_AFFINITY="${listep}" @@ -172,24 +218,28 @@ function set_mlsl_vars function set_openmp_envs { - ppncpu=1 threadspercore=1 - cpus=`lscpu | grep "^CPU(s):" | awk '{print $2}'` - cores=`lscpu | grep "Core(s) per socket:" | awk '{print $4}'` - sockets=`lscpu | grep "Socket(s)" | awk '{print $2}'` - maxcores=$((cores*sockets)) - if [ "$internal_thread_pin" == "on" ]; then - export INTERNAL_THREADS_PIN=$((cpus-2)),$((cpus-1)) + internal_thread_str="" + for ((ppn_index=0; ppn_index::asum_data() const { if (!data_) { return 0; } switch (data_->head()) { case SyncedMemory::SYNCED_PRV: + case SyncedMemory::HEAD_AT_PRV: { if (prv_data() == NULL) return caffe_cpu_asum(count_, cpu_data()); else return caffe_cpu_asum(prv_data_count(), prv_data()); } - case SyncedMemory::HEAD_AT_PRV: - return caffe_cpu_asum(prv_data_count(), prv_data()); case SyncedMemory::HEAD_AT_CPU: return caffe_cpu_asum(count_, cpu_data()); case SyncedMemory::HEAD_AT_GPU: diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp index 3252ba983..f796bd573 100644 --- a/src/caffe/data_reader.cpp +++ b/src/caffe/data_reader.cpp @@ -148,15 +148,18 @@ void DataReader::Body::read_one(DBWrapper* dbw, QueuePair* qp) { #ifdef USE_MLSL string* data = qp->free_.pop(); - if(first_read_) { /* move each node’s file position to its node ID – this part can be move to the initialization */ - for(int i=0;iNext(); } first_read_ = false; } *data = dbw->value(); qp->full_.push(data); - for(int i=0;iNext(); } #else diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp index f9683215d..e11a10487 100644 --- a/src/caffe/data_transformer.cpp +++ b/src/caffe/data_transformer.cpp @@ -50,6 +50,109 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. namespace caffe { +template +void DataTransformer::Transform(const Datum& datum, + Blob* transformed_blob, + vector* box_labels) { + int float_size = datum.float_data_size(); + CHECK_GT(float_size, 0) << + "Every sample must have label"; + CHECK_EQ(float_size % 6, 0) << + "Every box label has 6 labels (class, difficult, box)"; + //*********************************************************************// + //get ori_labels + vector ori_labels; + for (int j = 0; j < float_size; j += 6) { + BoxLabel box_label; + box_label.class_label_ = datum.float_data(j); + box_label.difficult_ = datum.float_data(j + 1); + for (int k = 2; k < 6; ++k) { + box_label.box_[k-2] = datum.float_data(j+k); + } + ori_labels.push_back(box_label); + } + + // If datum is encoded, decoded and transform the cv::image. + CHECK(datum.encoded()) << "For box data, datum must be encoded"; + CHECK(!(param_.force_color() && param_.force_gray())) + << "cannot set both force_color and force_gray"; + cv::Mat cv_img; + if (param_.force_color() || param_.force_gray()) { + // If force_color then decode in color otherwise decode in gray. + cv_img = DecodeDatumToCVMat(datum, param_.force_color()); + } else { + cv_img = DecodeDatumToCVMatNative(datum); + } + + if (phase_ == TEST) { + *box_labels = ori_labels; + Transform(cv_img, transformed_blob); + return; + } + + int img_width = cv_img.cols; + int img_height = cv_img.rows; + + cv::Mat cv_rand_img; + // bool mirror = Rand(2); + bool mirror = 0; + while (box_labels->size() == 0) { + float rand_scale = (1. - rand_num_(30) / 100.); + int rand_w = static_cast(img_width * rand_scale) - 1; + int rand_h = static_cast(img_height * rand_scale) - 1; + // LOG(INFO) << "rand_w: " << rand_w << " rand_h: " << rand_h; + // LOG(INFO) << "img_width: " << img_width << " img_height: " << img_height; + int rand_x = rand_num_(img_width - rand_w); + int rand_y = rand_num_(img_height - rand_h); + for (int i = 0; i < ori_labels.size(); ++i) { + int ori_x = static_cast(ori_labels[i].box_[0] * img_width); //box center(x,y) + int ori_y = static_cast(ori_labels[i].box_[1] * img_height); + int ori_w = static_cast(ori_labels[i].box_[2] * img_width); //box(w,h) + int ori_h = static_cast(ori_labels[i].box_[3] * img_height); + if (!(ori_x >= rand_x && ori_x < rand_x + rand_w)) { + continue; + } + if (!(ori_y >= rand_y && ori_y < rand_y + rand_h)) { + continue; + } + BoxLabel box_label; + box_label.difficult_ = ori_labels[i].difficult_; + box_label.class_label_ = ori_labels[i].class_label_; + box_label.box_[0] = float(ori_x - rand_x) / float(rand_w); + box_label.box_[1] = float(ori_y - rand_y) / float(rand_h); + box_label.box_[2] = float(ori_w) / float(rand_w); + box_label.box_[3] = float(ori_h) / float(rand_h); + // int xmin = std::max(ori_x - ori_w / 2, rand_x); + // int ymin = std::max(ori_y - ori_h / 2, rand_y); + // int xmax = std::min(ori_x + ori_w / 2, rand_x + rand_w); + // int ymax = std::min(ori_y + ori_h / 2, rand_y + rand_h); + // if (xmin > xmax || ymin > ymax) { + // continue; + // } + // box_label.box_[0] = float(xmin + (xmax - xmin) / 2) / float(rand_w); + // box_label.box_[1] = float(ymin + (ymax - ymin) / 2) / float(rand_h); + // box_label.box_[2] = float(xmax - xmin) / float(rand_w); + // box_label.box_[3] = float(ymax - ymin) / float(rand_h); + if (mirror) { + box_label.box_[0] = std::max(0., 1. - box_label.box_[0]); + box_label.box_[1] = std::max(0., 1. - box_label.box_[1]); + } + box_labels->push_back(box_label); + } + if (box_labels->size() > 0) { + cv::Rect roi(rand_x, rand_y, rand_w, rand_h); + cv_rand_img = cv_img(roi); + if (mirror) { + cv::flip(cv_rand_img, cv_rand_img, 1); // horizen flip + } + } + } + cv::resize(cv_rand_img, cv_rand_img, cv::Size(img_width, img_height)); + // Transform the cv::image into blob. + Transform(cv_rand_img, transformed_blob); + return; +} + template DataTransformer::DataTransformer(const TransformationParameter& param, Phase phase) @@ -110,12 +213,15 @@ void DataTransformer::Transform(const Datum& datum, Dtype* transformed_da mean = data_mean_.mutable_cpu_data(); } if (has_mean_values) { - CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) << - "Specify either 1 mean_value or as many as channels: " << datum_channels; - if (datum_channels > 1 && mean_values_.size() == 1) { - // Replicate the mean_value for simplicity - for (int c = 1; c < datum_channels; ++c) { - mean_values_.push_back(mean_values_[0]); + #pragma omp critical + { + CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) << + "Specify either 1 mean_value or as many as channels: " << datum_channels; + if (datum_channels > 1 && mean_values_.size() == 1) { + // Replicate the mean_value for simplicity + for (int c = 1; c < datum_channels; ++c) { + mean_values_.push_back(mean_values_[0]); + } } } } @@ -307,12 +413,15 @@ void DataTransformer::Transform(const Datum& datum_in, mean = data_mean_.mutable_cpu_data(); } if (has_mean_values) { - CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) << - "Specify either 1 mean_value or as many as channels: " << datum_channels; - if (datum_channels > 1 && mean_values_.size() == 1) { - // Replicate the mean_value for simplicity - for (int c = 1; c < datum_channels; ++c) { - mean_values_.push_back(mean_values_[0]); + #pragma omp critical + { + CHECK(mean_values_.size() == 1 || mean_values_.size() == datum_channels) << + "Specify either 1 mean_value or as many as channels: " << datum_channels; + if (datum_channels > 1 && mean_values_.size() == 1) { + // Replicate the mean_value for simplicity + for (int c = 1; c < datum_channels; ++c) { + mean_values_.push_back(mean_values_[0]); + } } } } @@ -910,12 +1019,15 @@ void DataTransformer::Transform(const cv::Mat& cv_img_in, mean = data_mean_.mutable_cpu_data(); } if (has_mean_values) { - CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) << - "Specify either 1 mean_value or as many as channels: " << img_channels; - if (img_channels > 1 && mean_values_.size() == 1) { - // Replicate the mean_value for simplicity - for (int c = 1; c < img_channels; ++c) { - mean_values_.push_back(mean_values_[0]); + #pragma omp critical + { + CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) << + "Specify either 1 mean_value or as many as channels: " << img_channels; + if (img_channels > 1 && mean_values_.size() == 1) { + // Replicate the mean_value for simplicity + for (int c = 1; c < img_channels; ++c) { + mean_values_.push_back(mean_values_[0]); + } } } } @@ -1010,12 +1122,15 @@ void DataTransformer::TransformInv(const Dtype* data, cv::Mat* cv_img, mean = data_mean_.mutable_cpu_data(); } if (has_mean_values) { - CHECK(mean_values_.size() == 1 || mean_values_.size() == channels) << - "Specify either 1 mean_value or as many as channels: " << channels; - if (channels > 1 && mean_values_.size() == 1) { - // Replicate the mean_value for simplicity - for (int c = 1; c < channels; ++c) { - mean_values_.push_back(mean_values_[0]); + #pragma omp critical + { + CHECK(mean_values_.size() == 1 || mean_values_.size() == channels) << + "Specify either 1 mean_value or as many as channels: " << channels; + if (channels > 1 && mean_values_.size() == 1) { + // Replicate the mean_value for simplicity + for (int c = 1; c < channels; ++c) { + mean_values_.push_back(mean_values_[0]); + } } } } @@ -1142,12 +1257,15 @@ void DataTransformer::ExpandImage(const cv::Mat& img, } } if (has_mean_values) { - CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) << - "Specify either 1 mean_value or as many as channels: " << img_channels; - if (img_channels > 1 && mean_values_.size() == 1) { - // Replicate the mean_value for simplicity - for (int c = 1; c < img_channels; ++c) { - mean_values_.push_back(mean_values_[0]); + #pragma omp critical + { + CHECK(mean_values_.size() == 1 || mean_values_.size() == img_channels) << + "Specify either 1 mean_value or as many as channels: " << img_channels; + if (img_channels > 1 && mean_values_.size() == 1) { + // Replicate the mean_value for simplicity + for (int c = 1; c < img_channels; ++c) { + mean_values_.push_back(mean_values_[0]); + } } } vector channels(img_channels); @@ -1500,6 +1618,7 @@ void DataTransformer::ReinitRand() { } } + INSTANTIATE_CLASS(DataTransformer); } // namespace caffe diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp index 4d1c85715..e97c4d797 100644 --- a/src/caffe/internal_thread.cpp +++ b/src/caffe/internal_thread.cpp @@ -42,6 +42,10 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "caffe/util/cpu_info.hpp" #include "caffe/util/math_functions.hpp" +#ifdef USE_MLSL +#include "caffe/multinode/mlsl.hpp" +#endif + namespace caffe { InternalThread::~InternalThread() { @@ -67,17 +71,28 @@ void InternalThread::StartInternalThread() { int rand_seed = caffe_rng_rand(); int solver_count = Caffe::solver_count(); bool root_solver = Caffe::root_solver(); - +#ifdef USE_MLSL + int ppn = mn::get_ppn(); + int rank = mn::get_node_rank(); +#endif try { thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode, - rand_seed, solver_count, root_solver)); + rand_seed, solver_count, root_solver +#ifdef USE_MLSL + , ppn, rank +#endif + )); } catch (std::exception& e) { LOG(FATAL) << "Thread exception: " << e.what(); } } void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed, - int solver_count, bool root_solver) { + int solver_count, bool root_solver +#ifdef USE_MLSL + , int ppn, int rank +#endif + ) { #ifndef CPU_ONLY CUDA_CHECK(cudaSetDevice(device)); #endif @@ -90,12 +105,20 @@ void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed, caffe::cpu::OpenMpManager::bindCurrentThreadToNonPrimaryCoreIfPossible(); #endif - SetThreadAffinity(); + SetThreadAffinity( +#ifdef USE_MLSL + ppn, rank +#endif + ); InternalThreadEntry(); } -void InternalThread::SetThreadAffinity() { +void InternalThread::SetThreadAffinity( +#ifdef USE_MLSL + int ppn, int rank +#endif + ) { #define MAX_CORES 64 static int count = 0; @@ -117,6 +140,18 @@ void InternalThread::SetThreadAffinity() { break; } } + +#ifdef USE_MLSL + // assign affinity cores for each rank if ppn > 1 and ncores is multiply of ppn + if ((ppn > 1) && (ncores / ppn > 0)) { + ncores /= ppn; + int offset = rank % ppn; + if (offset != 0) { + // overwrite the affinity cores according to rank + memcpy(affinity_cores, affinity_cores + offset * ncores, sizeof(int)); + } + } +#endif } } diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp index 982fe96d8..f4c089c64 100644 --- a/src/caffe/layers/base_data_layer.cpp +++ b/src/caffe/layers/base_data_layer.cpp @@ -62,6 +62,7 @@ void BaseDataLayer::LayerSetUp(const vector*>& bottom, } else { output_labels_ = true; } + box_label_ = false; data_transformer_.reset( new DataTransformer(transform_param_, this->phase_)); data_transformer_->InitRand(); @@ -90,7 +91,13 @@ void BasePrefetchingDataLayer::LayerSetUp( for (int i = 0; i < PREFETCH_COUNT; ++i) { prefetch_[i].data_.mutable_cpu_data(); if (this->output_labels_) { - prefetch_[i].label_.mutable_cpu_data(); + if (this->box_label_) { + for (int j = 0; j < top.size() - 1; ++j) { + prefetch_[i].multi_label_[j]->mutable_cpu_data(); + } + } else { + prefetch_[i].label_.mutable_cpu_data(); + } } } #ifndef CPU_ONLY @@ -98,7 +105,13 @@ void BasePrefetchingDataLayer::LayerSetUp( for (int i = 0; i < PREFETCH_COUNT; ++i) { prefetch_[i].data_.mutable_gpu_data(); if (this->output_labels_) { - prefetch_[i].label_.mutable_gpu_data(); + if (this->box_label_) { + for (int j = 0; j < top.size() - 1; ++j) { + prefetch_[i].multi_label_[j]->mutable_gpu_data(); + } + } else { + prefetch_[i].label_.mutable_gpu_data(); + } } } } @@ -172,11 +185,19 @@ void BasePrefetchingDataLayer::Forward_cpu( top[0]->mutable_cpu_data()); DLOG(INFO) << "Prefetch copied"; if (this->output_labels_) { - // Reshape to loaded labels. - top[1]->ReshapeLike(batch->label_); - // Copy the labels. - caffe_copy(batch->label_.count(), batch->label_.cpu_data(), - top[1]->mutable_cpu_data()); + if (this->box_label_) { + for (int i = 0; i < top.size() - 1; ++i) { + top[i+1]->ReshapeLike(*(batch->multi_label_[i])); + caffe_copy(batch->multi_label_[i]->count(), batch->multi_label_[i]->cpu_data(), + top[i+1]->mutable_cpu_data()); + } + } else { + // Reshape to loaded labels. + top[1]->ReshapeLike(batch->label_); + // Copy the labels. + caffe_copy(batch->label_.count(), batch->label_.cpu_data(), + top[1]->mutable_cpu_data()); + } } // TODO: Consider prefetch_data_array and prefetch_label_array diff --git a/src/caffe/layers/box_data_layer.cpp b/src/caffe/layers/box_data_layer.cpp new file mode 100755 index 000000000..467925c5d --- /dev/null +++ b/src/caffe/layers/box_data_layer.cpp @@ -0,0 +1,202 @@ +#ifdef USE_OPENCV +#include +#endif // USE_OPENCV +#include + +#include + +#include "caffe/data_transformer.hpp" +#include "caffe/layers/box_data_layer.hpp" +#include "caffe/util/benchmark.hpp" + +namespace caffe { + +template +BoxDataLayer::BoxDataLayer(const LayerParameter& param) + : BasePrefetchingDataLayer(param), + reader_(param) { +} + +template +BoxDataLayer::~BoxDataLayer() { + this->StopInternalThread(); +} + +template +void BoxDataLayer::DataLayerSetUp(const vector*>& bottom, + const vector*>& top) { + this->box_label_ = true; + const DataParameter param = this->layer_param_.data_param(); + const int batch_size = param.batch_size(); + // Read a data point, and use it to initialize the top blob. + Datum datum; + string *data = reader_.full().peek(); + datum.ParseFromString(*data); + + // Use data_transformer to infer the expected blob shape from datum. + vector top_shape = this->data_transformer_->InferBlobShape(datum); + this->transformed_data_.Reshape(top_shape); + // Reshape top[0] and prefetch_data according to the batch_size. + top_shape[0] = batch_size; + top[0]->Reshape(top_shape); + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].data_.Reshape(top_shape); + } + LOG(INFO) << "output data size: " << top[0]->num() << "," + << top[0]->channels() << "," << top[0]->height() << "," + << top[0]->width(); + // label + if (this->output_labels_) { + if (param.side_size() > 0) { + for (int i = 0; i < param.side_size(); ++i) { + sides_.push_back(param.side(i)); + } + } + if (sides_.size() == 0) { + sides_.push_back(7); + } + CHECK_EQ(sides_.size(), top.size() - 1) << + "side num not equal to top size"; + for (int i = 0; i < this->PREFETCH_COUNT; ++i) { + this->prefetch_[i].multi_label_.clear(); + } + for (int i = 0; i < sides_.size(); ++i) { + vector label_shape(1, batch_size); + //******************************************************************************// + //int label_size = sides_[i] * sides_[i] * (1 + 1 + 1 + 4); //side_*side*(obj,cls_label_,box_[4]) + int label_size = (30 * 5); //(maxboxes=30)*(4+1) + label_shape.push_back(label_size); + top[i+1]->Reshape(label_shape); + for (int j = 0; j < this->PREFETCH_COUNT; ++j) { + shared_ptr > tmp_blob; + tmp_blob.reset(new Blob(label_shape)); + this->prefetch_[j].multi_label_.push_back(tmp_blob); + } + } + } +} + +// This function is called on prefetch thread +template +void BoxDataLayer::load_batch(Batch* batch) { + CPUTimer batch_timer; + batch_timer.Start(); + double read_time = 0; + double trans_time = 0; + CPUTimer timer; + CHECK(batch->data_.count()); + CHECK(this->transformed_data_.count()); + + // Reshape according to the first datum of each batch + // on single input batches allows for inputs of varying dimension. + const int batch_size = this->layer_param_.data_param().batch_size(); + Datum datum; + string *data = reader_.full().peek(); + datum.ParseFromString(*data); + // Use data_transformer to infer the expected blob shape from datum. + vector top_shape = this->data_transformer_->InferBlobShape(datum); + this->transformed_data_.Reshape(top_shape); + // Reshape batch according to the batch_size. + top_shape[0] = batch_size; + batch->data_.Reshape(top_shape); + + Dtype* top_data = batch->data_.mutable_cpu_data(); + vector top_label; + + if (this->output_labels_) { + for (int i = 0; i < sides_.size(); ++i) { + top_label.push_back(batch->multi_label_[i]->mutable_cpu_data()); + } + } + for (int item_id = 0; item_id < batch_size; ++item_id) { + timer.Start(); + // get a datum + Datum datum; + string *data = reader_.full().pop("Waiting for data"); + datum.ParseFromString(*data); + read_time += timer.MicroSeconds(); + timer.Start(); + // Apply data transformations (mirror, scale, crop...) + int offset = batch->data_.offset(item_id); + vector box_labels; + this->transformed_data_.set_cpu_data(top_data + offset); + if (this->output_labels_) { + //*****************************************************************// + // rand sample a patch, adjust box labels + this->data_transformer_->Transform(datum, &(this->transformed_data_), &box_labels); + // transform label + for (int i = 0; i < sides_.size(); ++i) { + int label_offset = batch->multi_label_[i]->offset(item_id); + int count = batch->multi_label_[i]->count(1); + //LOG(INFO) << "sides_.size: " << sides_.size() << "label_offset:" << label_offset << "count:" << count; + transform_label(count, top_label[i] + label_offset, box_labels, sides_[i]); + } + } else { + this->data_transformer_->Transform(datum, &(this->transformed_data_)); + } + trans_time += timer.MicroSeconds(); + + reader_.free().push(data); + } + timer.Stop(); + batch_timer.Stop(); + DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms."; + DLOG(INFO) << " Read time: " << read_time / 1000 << " ms."; + DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms."; +} + +template +void BoxDataLayer::transform_label(int count, Dtype* top_label, + const vector& box_labels, int side) { + //int locations = pow(side, 2); + CHECK_EQ(count, 30*5) << + "side and count not match"; + // label + caffe_set(30*5, Dtype(0), top_label); + // isobj + //caffe_set(locations, Dtype(0), top_label + locations); + // class label + //caffe_set(locations, Dtype(-1), top_label + locations * 2); + // box + //caffe_set(locations*4, Dtype(0), top_label + locations * 3); + int index = 0; + for (int i = 0; i < box_labels.size(); ++i) { + float difficult = box_labels[i].difficult_; + if (difficult != 0. && difficult != 1.) { + LOG(WARNING) << "Difficult must be 0 or 1"; + } + float class_label = box_labels[i].class_label_; //box_labels[i]: BoxLabel + CHECK_GE(class_label, 0) << "class_label must >= 0"; + //float x = box_labels[i].box_[0]; + //float y = box_labels[i].box_[1]; + + //int x_index = floor(x * side); + //int y_index = floor(y * side); + //x_index = std::min(x_index, side - 1); + //y_index = std::min(y_index, side - 1); + //int dif_index = side * y_index + x_index; // + //int obj_index = locations + dif_index; + //int class_index = locations * 2 + dif_index; + //int cor_index = locations * 3 + dif_index * 4; + top_label[index++] = class_label; + //top_label[index++] = 1; + + //top_label[obj_index] = 1; + // LOG(INFO) << "dif_index: " << dif_index << " class_label: " << class_label; + //top_label[class_index] = class_label; + for (int j = 0; j < 4; ++j) { + top_label[index + j] = box_labels[i].box_[j]; + //top_label[index + j] = top_label[index + j]<1.0?top_label[index+j]:1.0; + //top_label[index + j] = top_label[index + j]>0.0?top_label[index+j]:0.0; + // top_label[index + j] = 1; + //LOG(INFO) << "box_: " << box_labels[i].box_[j]; + } + index += 4; + //LOG(INFO) <<"index: " <::Forward_cpu( } } } - loss = loss / static_cast(bottom[0]->num()) / Dtype(2); + Dtype normalizer = static_cast(bottom[0]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + loss = loss / normalizer / Dtype(2); top[0]->mutable_cpu_data()[0] = loss; } template void ContrastiveLossLayer::Backward_cpu(const vector*>& top, const vector& propagate_down, const vector*>& bottom) { + Dtype normalizer = static_cast(bottom[0]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif Dtype margin = this->layer_param_.contrastive_loss_param().margin(); bool legacy_version = this->layer_param_.contrastive_loss_param().legacy_version(); for (int i = 0; i < 2; ++i) { if (propagate_down[i]) { const Dtype sign = (i == 0) ? 1 : -1; - const Dtype alpha = sign * top[0]->cpu_diff()[0] / - static_cast(bottom[i]->num()); + const Dtype alpha = sign * top[0]->cpu_diff()[0] / normalizer; int num = bottom[i]->num(); int channels = bottom[i]->channels(); for (int j = 0; j < num; ++j) { diff --git a/src/caffe/layers/detection_output_layer.cpp b/src/caffe/layers/detection_output_layer.cpp index 3480fd36e..c6f0bc369 100644 --- a/src/caffe/layers/detection_output_layer.cpp +++ b/src/caffe/layers/detection_output_layer.cpp @@ -180,7 +180,7 @@ void DetectionOutputLayer::Reshape(const vector*>& bottom, // Clean all outputs. if (output_format_ == "VOC") { #ifdef USE_MLSL - MPI_Barrier(MPI_COMM_WORLD); + mn::barrier(); #endif boost::filesystem::path output_directory(output_directory_); for (map::iterator it = label_to_name_.begin(); @@ -246,8 +246,8 @@ void DetectionOutputLayer::Forward_cpu( // Retrieve all prior bboxes. It is same within a batch since we assume all // images in a batch are of same dimension. - vector prior_bboxes; - vector > prior_variances; + vector prior_bboxes(num_priors_); + vector > prior_variances(num_priors_); GetPriorBBoxes(prior_data, num_priors_, &prior_bboxes, &prior_variances); // Decode all loc predictions to bboxes. diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp index a26746b8b..02358d244 100644 --- a/src/caffe/layers/euclidean_loss_layer.cpp +++ b/src/caffe/layers/euclidean_loss_layer.cpp @@ -61,7 +61,16 @@ void EuclideanLossLayer::Forward_cpu(const vector*>& bottom, bottom[1]->cpu_data(), diff_.mutable_cpu_data()); Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data()); - Dtype loss = dot / bottom[0]->num() / Dtype(2); + Dtype normalizer = Dtype(bottom[0]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + Dtype loss = dot / normalizer / Dtype(2); top[0]->mutable_cpu_data()[0] = loss; } @@ -71,7 +80,16 @@ void EuclideanLossLayer::Backward_cpu(const vector*>& top, for (int i = 0; i < 2; ++i) { if (propagate_down[i]) { const Dtype sign = (i == 0) ? 1 : -1; - const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num(); + Dtype normalizer = Dtype(bottom[i]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + const Dtype alpha = sign * top[0]->cpu_diff()[0] / normalizer; caffe_cpu_axpby( bottom[i]->count(), // count alpha, // alpha diff --git a/src/caffe/layers/eval_detection_layer.cpp b/src/caffe/layers/eval_detection_layer.cpp new file mode 100644 index 000000000..cba36d39b --- /dev/null +++ b/src/caffe/layers/eval_detection_layer.cpp @@ -0,0 +1,482 @@ +#include +#include +#include +#include + +#include "caffe/layers/region_loss_layer.hpp" +#include "caffe/layers/eval_detection_layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/util/benchmark.hpp" + +#ifdef ENABLE_NMS_OPTIMIZATION +#include "caffe/util/bbox_util.hpp" +#include +#include "omp.h" +#endif + +namespace caffe { + + class BoxData { + public: + int label_; + float score_; + vector box_; + }; + + inline float sigmoid(float x) + { + return 1. / (1. + exp(-x)); + } + + template + Dtype softmax_region(Dtype* input, int classes) + { + Dtype sum = 0; + Dtype large = input[0]; + + for (int i = 0; i < classes; ++i) { + if (input[i] > large) + large = input[i]; + } + for (int i = 0; i < classes; ++i) { + Dtype e = exp(input[i] - large); + sum += e; + input[i] = e; + } + for (int i = 0; i < classes; ++i) { + input[i] = input[i] / sum; + } + return 0; + } + + bool BoxSortDecendScore(const BoxData& box1, const BoxData& box2) { + return box1.score_ > box2.score_; + } + + void ApplyNms(const vector& boxes, vector* idxes, float threshold) { + map idx_map; + for (int i = 0; i < boxes.size() - 1; ++i) { + if (idx_map.find(i) != idx_map.end()) { + continue; + } + vector box1 = boxes[i].box_; + for (int j = i + 1; j < boxes.size(); ++j) { + if (idx_map.find(j) != idx_map.end()) { + continue; + } + vector box2 = boxes[j].box_; + float iou = Calc_iou(box1, box2); + if (iou >= threshold) { + idx_map[j] = 1; + } + } + } + for (int i = 0; i < boxes.size(); ++i) { + if (idx_map.find(i) == idx_map.end()) { + idxes->push_back(i); + } + } + } + + template + void GetGTBox(int side, vector > boxes, map >* gt_boxes) { + //int locations = pow(side, 2); + //for (int i = 0; i < locations; ++i) { + for (int i = 0; i < boxes.size(); ++i) { + //if (!label_data[locations + i]) { + //if (label_data[i * 5 + 1] == 0) { + //continue; + // break; //maybe problem??? + //} + vector box = boxes[i]; + BoxData gt_box; + //bool difficult = (label_data[i] == 1); + //int label = static_cast(label_data[locations * 2 + i]); + int label = box[0]; + //gt_box.difficult_ = difficult; + gt_box.label_ = label; + gt_box.score_ = i; //not used? + //int box_index = locations * 3 + i * 4; + //int box_index = i * 5 + 1; + //LOG(INFO) << "label:" << label; + //for (int j = 0; j < 4; ++j) { + // gt_box.box_.push_back(label_data[box_index + j]); + //LOG(INFO) << "x,y,w,h:" << label_data[box_index + j]; + //} + gt_box.box_.push_back(box[1]); + gt_box.box_.push_back(box[2]); + gt_box.box_.push_back(box[3]); + gt_box.box_.push_back(box[4]); + + if (gt_boxes->find(label) == gt_boxes->end()) { + (*gt_boxes)[label] = vector(1, gt_box); + } + else { + (*gt_boxes)[label].push_back(gt_box); + } + } + } + + template + void GetPredBox(int side, int num_object, int num_class, Dtype* input_data, map >* pred_boxes, int score_type, float nms_threshold, vector biases) { + vector tmp_boxes; + //int locations = pow(side, 2); + for (int j = 0; j < side; ++j) { + for (int i = 0; i < side; ++i) { + for (int n = 0; n < 5; ++n) + { + int index = (j * side + i) * num_object * (num_class + 1 + 4) + n * (num_class + 1 + 4); + float x = (i + sigmoid(input_data[index + 0])) / side; + float y = (j + sigmoid(input_data[index + 1])) / side; + float w = (exp(input_data[index + 2]) * biases[2 * n]) / side; + float h = (exp(input_data[index + 3]) * biases[2 * n + 1]) / side; + softmax_region(input_data + index + 5, num_class); + + int pred_label = 0; + float max_prob = input_data[index + 5]; + for (int c = 0; c < num_class; ++c) + { + if (max_prob < input_data[index + 5 + c]) + { + max_prob = input_data[index + 5 + c]; + pred_label = c; //0,..,19 + } + } + BoxData pred_box; + pred_box.label_ = pred_label; + + float obj_score = sigmoid(input_data[index + 4]); + if (score_type == 0) { + pred_box.score_ = obj_score; + } + else if (score_type == 1) { + pred_box.score_ = max_prob; + } + else { + pred_box.score_ = obj_score * max_prob; + } + + pred_box.box_.push_back(x); + pred_box.box_.push_back(y); + pred_box.box_.push_back(w); + pred_box.box_.push_back(h); + + tmp_boxes.push_back(pred_box); + //if (w > 1 || h > 1) + // LOG(INFO)<<"Not nms pred_box:" << pred_box.label_ << " " << obj_score << " " << max_prob << " " << pred_box.score_ << " " << pred_box.box_[0] << " " << pred_box.box_[1] << " " << pred_box.box_[2] << " " << pred_box.box_[3]; + } + } + } + /* + for (int i = 0; i < locations; ++i) { + int pred_label = 0; + float max_prob = input_data[i]; + for (int j = 1; j < num_class; ++j) { + int class_index = j * locations + i; + if (input_data[class_index] > max_prob) { + pred_label = j; + max_prob = input_data[class_index]; + } + } + if (nms_threshold < 0) { + if (pred_boxes->find(pred_label) == pred_boxes->end()) { + (*pred_boxes)[pred_label] = vector(); + } + } + // LOG(INFO) << "pred_label: " << pred_label << " max_prob: " << max_prob; + int obj_index = num_class * locations + i; + int coord_index = (num_class + num_object) * locations + i; + for (int k = 0; k < num_object; ++k) { + BoxData pred_box; + float scale = input_data[obj_index + k * locations]; + pred_box.label_ = pred_label; + if (score_type == 0) { + pred_box.score_ = scale; + } else if (score_type == 1) { + pred_box.score_ = max_prob; + } else { + pred_box.score_ = scale * max_prob; + } + int box_index = coord_index + k * 4 * locations; + if (!constriant) { + pred_box.box_.push_back(input_data[box_index + 0 * locations]); + pred_box.box_.push_back(input_data[box_index + 1 * locations]); + } else { + pred_box.box_.push_back((i % side + input_data[box_index + 0 * locations]) / side); + pred_box.box_.push_back((i / side + input_data[box_index + 1 * locations]) / side); + } + float w = input_data[box_index + 2 * locations]; + float h = input_data[box_index + 3 * locations]; + if (use_sqrt) { + pred_box.box_.push_back(pow(w, 2)); + pred_box.box_.push_back(pow(h, 2)); + } else { + pred_box.box_.push_back(w); + pred_box.box_.push_back(h); + } + if (nms_threshold >= 0) { + tmp_boxes.push_back(pred_box); + } else { + (*pred_boxes)[pred_label].push_back(pred_box); + } + } + }*/ + if (nms_threshold >= 0) { + //LOG(INFO) << "The value of nms_threshold is: " << nms_threshold; + std::sort(tmp_boxes.begin(), tmp_boxes.end(), BoxSortDecendScore); + //LOG(INFO) << "The value of tmp_boxes size is: " << tmp_boxes.size(); + vector idxes; + //Timer NMS_timer; + //NMS_timer.Start(); + +#ifdef ENABLE_NMS_OPTIMIZATION + if (_may_i_use_cpu_feature(_FEATURE_AVX512CD | _FEATURE_AVX512F)) { + uint64_t boxes_num = tmp_boxes.size(); + float* x1 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + float* y1 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + float* x2 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + float* y2 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + + uint64_t i = 0; +#pragma omp parallel for + for (i = 0; i < boxes_num; ++i) { + x1[i] = tmp_boxes[i].box_[0] - tmp_boxes[i].box_[2] / 2.0; //xmin + y1[i] = tmp_boxes[i].box_[1] - tmp_boxes[i].box_[3] / 2.0; //ymin + x2[i] = tmp_boxes[i].box_[0] + tmp_boxes[i].box_[2] / 2.0; //xmax + y2[i] = tmp_boxes[i].box_[1] + tmp_boxes[i].box_[3] / 2.0; //ymax + } + int* keep_out = (int*)malloc(sizeof(int) * tmp_boxes.size()); + int num_out = 0; + cpu_nms_avx512_parallize_inner(keep_out, &num_out, x1, y1, x2, y2, boxes_num, nms_threshold); + + for (int i = 0; i < num_out; i++) { + idxes.push_back(keep_out[i]); + } + + if (x1) + free(x1); + if (x2) + free(x2); + if (y1) + free(y1); + if (y2) + free(y2); + if (keep_out) + free(keep_out); + } + else { +#endif + ApplyNms(tmp_boxes, &idxes, nms_threshold); +#ifdef ENABLE_NMS_OPTIMIZATION + } +#endif + + //NMS_timer.Stop(); + //LOG(INFO) << "Total NMS Time: " << NMS_timer.MilliSeconds() << " ms."; //ICC optimization will cause the output is 0. + //LOG(INFO) << "Total NMS Time: " << NMS_timer.MicroSeconds()/1000 << " ms."; + for (int i = 0; i < idxes.size(); ++i) { + BoxData box_data = tmp_boxes[idxes[i]]; + //**************************************************************************************// + if (box_data.score_ < 0.005) // from darknet + continue; + //LOG(INFO)<<"box_data:" << box_data.label_ << " " << box_data.score_ << " " << box_data.box_[0] << " " << box_data.box_[1] << " " << box_data.box_[2] << " " << box_data.box_[3]; + if (pred_boxes->find(box_data.label_) == pred_boxes->end()) { + (*pred_boxes)[box_data.label_] = vector(); + } + (*pred_boxes)[box_data.label_].push_back(box_data); + } + } + else { + for (std::map >::iterator it = pred_boxes->begin(); it != pred_boxes->end(); ++it) { + std::sort(it->second.begin(), it->second.end(), BoxSortDecendScore); + } + } + } + + template + void EvalDetectionLayer::LayerSetUp( + const vector*>& bottom, const vector*>& top) { + EvalDetectionParameter param = this->layer_param_.eval_detection_param(); + side_ = param.side(); + num_class_ = param.num_class(); + num_object_ = param.num_object(); + threshold_ = param.threshold(); + //sqrt_ = param.sqrt(); + //constriant_ = param.constriant(); + + nms_ = param.nms(); + + for (int c = 0; c < param.biases_size(); ++c) { + biases_.push_back(param.biases(c)); + } + + switch (param.score_type()) { + case EvalDetectionParameter_ScoreType_OBJ: + score_type_ = 0; + break; + case EvalDetectionParameter_ScoreType_PROB: + score_type_ = 1; + break; + case EvalDetectionParameter_ScoreType_MULTIPLY: + score_type_ = 2; + break; + default: + LOG(FATAL) << "Unknow score type."; + } + } + + template + void EvalDetectionLayer::Reshape( + const vector*>& bottom, const vector*>& top) { + int input_count = bottom[0]->count(1); //b*13*13*125 + // int label_count = bottom[1]->count(1); //b*30*5 + // outputs: classes, iou, coordinates + int tmp_input_count = side_ * side_ * num_object_ *(num_class_ + 4 + 1); //13*13*5*25 + // label: isobj, class_label, coordinates + //int tmp_label_count = 30 * 5; + + CHECK_EQ(input_count, tmp_input_count); + //CHECK_EQ(label_count, tmp_label_count); + + vector top_shape(2, 1); + top_shape[0] = bottom[0]->num(); + //top_shape[1] = num_class_ + side_ * side_ * num_object_ * 4; + top_shape[1] = num_class_ + side_ * side_ * num_object_ * 4; //num_class_(num of each gt class) + 13 * 13 * 5 * (label + score + tp + fp) + top[0]->Reshape(top_shape); + } + + template + void EvalDetectionLayer::Forward_cpu( + const vector*>& bottom, const vector*>& top) { + //const Dtype* input_data = bottom[0]->cpu_data(); + const Dtype* label_data = bottom[1]->cpu_data(); + //LOG(INFO) << bottom[0]->data_at(0,0,0,0) << " " << bottom[0]->data_at(0,0,0,1); + Blob swap; + swap.Reshape(bottom[0]->num(), bottom[0]->height()*bottom[0]->width(), num_object_, bottom[0]->channels() / num_object_); + + Dtype* swap_data = swap.mutable_cpu_data(); + int index = 0; + + int n_value = bottom[0]->num(); + int h_value = bottom[0]->height(); + int w_value = bottom[0]->width(); + int c_value = bottom[0]->channels(); + const Dtype* input_data = bottom[0]->cpu_data(); + + for (int b = 0; b < n_value; ++b) { + for (int h = 0; h < h_value; ++h) { + for (int w = 0; w < w_value; ++w) { + for (int c = 0; c < c_value; ++c) { + //swap_data[index++] = bottom[0]->data_at(b, c, h, w); //"index" is Not safe for OpenMP + swap_data[index++] = input_data[((b * c_value + c) * h_value + h) * w_value + w]; + } + } + } + } + + //******************************************************** Label ********************************************************// + vector > > labels; + labels.resize(bottom[0]->num() + 1); //Fix: batch_size + 1, the conner case is for dummy data with BS=1, and label=1, viloate access in "labels[item_id].push_back(box);" + int num_boxes = bottom[1]->height(); + //int start = -1; + + for (int i = 0; i < num_boxes; ++i) { + vector box; + int item_id = label_data[i * 8 + 0]; //item_id + if (item_id == -1) continue; + + Dtype xmin = label_data[i * 8 + 3]; + Dtype ymin = label_data[i * 8 + 4]; + Dtype xmax = label_data[i * 8 + 5]; + Dtype ymax = label_data[i * 8 + 6]; + + Dtype cx = (xmin + xmax) / 2.0; + Dtype cy = (ymin + ymax) / 2.0; + Dtype w = xmax - xmin; + Dtype h = ymax - ymin; + + box.push_back(label_data[i * 8 + 1] - 1); //0 label_id 0:bg 1-20:classes + box.push_back(cx); //1 xmin + box.push_back(cy); //2 ymin + box.push_back(w); //3 xmax + box.push_back(h); //4 ymax + + //LOG(INFO) << "item_id: "<< item_id << " label: " << box[0] << " box: [" << box[1] << " "<< box[2] << " " <mutable_cpu_data(); + caffe_set(top[0]->count(), Dtype(0), top_data); + + for (int i = 0; i < bottom[0]->num(); ++i) { + int input_index = i * bottom[0]->count(1); + + //int true_index = i * bottom[1]->count(1); + + int top_index = i * top[0]->count(1); + map > gt_boxes; + GetGTBox(side_, labels[i], >_boxes); + + for (std::map >::iterator it = gt_boxes.begin(); it != gt_boxes.end(); ++it) { + int label = it->first; + vector& g_boxes = it->second; + for (int j = 0; j < g_boxes.size(); ++j) { + top_data[top_index + label] += 1; //class num++; + } + } + + map > pred_boxes; + //GetPredBox(side_, num_object_, num_class_, input_data + input_index, &pred_boxes, sqrt_, constriant_, score_type_, nms_); + GetPredBox(side_, num_object_, num_class_, swap_data + input_index, &pred_boxes, score_type_, nms_, biases_); + + int index = top_index + num_class_; + int pred_count(0); + for (std::map >::iterator it = pred_boxes.begin(); it != pred_boxes.end(); ++it) { + int label = it->first; + vector& p_boxes = it->second; + if (gt_boxes.find(label) == gt_boxes.end()) { + for (int b = 0; b < p_boxes.size(); ++b) { + top_data[index + pred_count * 4 + 0] = p_boxes[b].label_; + top_data[index + pred_count * 4 + 1] = p_boxes[b].score_; + top_data[index + pred_count * 4 + 2] = 0; //tp + top_data[index + pred_count * 4 + 3] = 1; //fp + ++pred_count; + } + continue; + } + vector& g_boxes = gt_boxes[label]; + vector records(g_boxes.size(), false); + for (int k = 0; k < p_boxes.size(); ++k) { + top_data[index + pred_count * 4 + 0] = p_boxes[k].label_; + top_data[index + pred_count * 4 + 1] = p_boxes[k].score_; + float max_iou(-1); + int idx(-1); + for (int g = 0; g < g_boxes.size(); ++g) { + float iou = Calc_iou(p_boxes[k].box_, g_boxes[g].box_); + if (iou > max_iou) { + max_iou = iou; + idx = g; + } + } + if (max_iou >= threshold_) { + if (!records[idx]) { + records[idx] = true; + top_data[index + pred_count * 4 + 2] = 1; + top_data[index + pred_count * 4 + 3] = 0; + } + else { + top_data[index + pred_count * 4 + 2] = 0; + top_data[index + pred_count * 4 + 3] = 1; + } + } + ++pred_count; + } + } + } + } + + INSTANTIATE_CLASS(EvalDetectionLayer); + REGISTER_LAYER_CLASS(EvalDetection); + +} // namespace caffe diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp index ec20c3a16..dd9eb8fed 100644 --- a/src/caffe/layers/hinge_loss_layer.cpp +++ b/src/caffe/layers/hinge_loss_layer.cpp @@ -66,14 +66,23 @@ void HingeLossLayer::Forward_cpu(const vector*>& bottom, Dtype* loss = top[0]->mutable_cpu_data(); switch (this->layer_param_.hinge_loss_param().norm()) { case HingeLossParameter_Norm_L1: - loss[0] = caffe_cpu_asum(count, bottom_diff) / num; + loss[0] = caffe_cpu_asum(count, bottom_diff); break; case HingeLossParameter_Norm_L2: - loss[0] = caffe_cpu_dot(count, bottom_diff, bottom_diff) / num; + loss[0] = caffe_cpu_dot(count, bottom_diff, bottom_diff); break; default: LOG(FATAL) << "Unknown Norm"; } + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + num = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + num *= mn::get_group_size(); + } +#endif + loss[0] /= num; } template @@ -94,6 +103,14 @@ void HingeLossLayer::Backward_cpu(const vector*>& top, bottom_diff[i * dim + static_cast(label[i])] *= -1; } + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + num = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + num *= mn::get_group_size(); + } +#endif const Dtype loss_weight = top[0]->cpu_diff()[0]; switch (this->layer_param_.hinge_loss_param().norm()) { case HingeLossParameter_Norm_L1: diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp index 19c95d344..b4fd48468 100644 --- a/src/caffe/layers/image_data_layer.cpp +++ b/src/caffe/layers/image_data_layer.cpp @@ -37,6 +37,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #ifdef USE_OPENCV #include +#include #include // NOLINT(readability/streams) #include // NOLINT(readability/streams) @@ -116,6 +117,11 @@ void ImageDataLayer::DataLayerSetUp(const vector*>& bottom, new_height, new_width, is_color, min_height, min_width); CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first; + + if (this->layer_param_.transform_param().bgr2rgb()) { + cv::cvtColor(cv_img, cv_img, CV_BGR2RGB); + } + // Use data_transformer to infer the expected blob shape from a cv_image. vector top_shape = this->data_transformer_->InferBlobShape(cv_img); this->transformed_data_.Reshape(top_shape); @@ -171,6 +177,11 @@ void ImageDataLayer::load_batch(Batch* batch) { cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, new_height, new_width, is_color, min_height, min_width); CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first; + + if (this->layer_param_.transform_param().bgr2rgb()) { + cv::cvtColor(cv_img, cv_img, CV_BGR2RGB); + } + // Use data_transformer to infer the expected blob shape from a cv_img. vector top_shape = this->data_transformer_->InferBlobShape(cv_img); this->transformed_data_.Reshape(top_shape); @@ -196,6 +207,10 @@ void ImageDataLayer::load_batch(Batch* batch) { cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first, new_height, new_width, is_color, min_height, min_width); CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first; + if (this->layer_param_.transform_param().bgr2rgb()) { + cv::cvtColor(cv_img, cv_img, CV_BGR2RGB); + } + read_time += timer.MicroSeconds(); timer.Start(); // Apply transformations (mirror, crop...) to the image diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp index 9d2958227..94e3366a4 100644 --- a/src/caffe/layers/infogain_loss_layer.cpp +++ b/src/caffe/layers/infogain_loss_layer.cpp @@ -1,5 +1,5 @@ /* -All modification made by Intel Corporation: © 2016 Intel Corporation +All modification made by Intel Corporation: © 2016 Intel Corporation All contributions by the University of California: Copyright (c) 2014, 2015, The Regents of the University of California (Regents) @@ -40,7 +40,8 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include #include "caffe/layers/infogain_loss_layer.hpp" -#include "caffe/util/io.hpp" +#include "caffe/util/io.hpp" // for blob reading of matrix H +#include "caffe/util/math_functions.hpp" namespace caffe { @@ -48,6 +49,31 @@ template void InfogainLossLayer::LayerSetUp( const vector*>& bottom, const vector*>& top) { LossLayer::LayerSetUp(bottom, top); + // internal softmax layer + LayerParameter softmax_layer_param(this->layer_param_); + SoftmaxParameter* softmax_param = softmax_layer_param.mutable_softmax_param(); + softmax_param->set_axis(this->layer_param_.infogain_loss_param().axis()); + softmax_layer_param.set_type("Softmax"); + softmax_layer_param.clear_loss_weight(); + softmax_layer_param.add_loss_weight(1); + softmax_layer_ = LayerRegistry::CreateLayer(softmax_layer_param); + softmax_bottom_vec_.clear(); + softmax_bottom_vec_.push_back(bottom[0]); + softmax_top_vec_.clear(); + softmax_top_vec_.push_back(&prob_); + softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_); + + // ignore label + has_ignore_label_ = + this->layer_param_.loss_param().has_ignore_label(); + if (has_ignore_label_) { + ignore_label_ = this->layer_param_.loss_param().ignore_label(); + } + // normalization + CHECK(!this->layer_param_.loss_param().has_normalize()) + << "normalize is deprecated. use \"normalization\""; + normalization_ = this->layer_param_.loss_param().normalization(); + // matrix H if (bottom.size() < 3) { CHECK(this->layer_param_.infogain_loss_param().has_source()) << "Infogain matrix source must be specified."; @@ -62,28 +88,104 @@ template void InfogainLossLayer::Reshape( const vector*>& bottom, const vector*>& top) { LossLayer::Reshape(bottom, top); + softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_); + infogain_axis_ = + bottom[0]->CanonicalAxisIndex( + this->layer_param_.infogain_loss_param().axis()); + outer_num_ = bottom[0]->count(0, infogain_axis_); + inner_num_ = bottom[0]->count(infogain_axis_ + 1); + CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count()) + << "Number of labels must match number of predictions; " + << "e.g., if infogain axis == 1 and prediction shape is (N, C, H, W), " + << "label count (number of labels) must be N*H*W, " + << "with integer values in {0, 1, ..., C-1}."; + num_labels_ = bottom[0]->shape(infogain_axis_); Blob* infogain = NULL; if (bottom.size() < 3) { infogain = &infogain_; } else { infogain = bottom[2]; } - CHECK_EQ(bottom[1]->channels(), 1); - CHECK_EQ(bottom[1]->height(), 1); - CHECK_EQ(bottom[1]->width(), 1); - const int num = bottom[0]->num(); - const int dim = bottom[0]->count() / num; - CHECK_EQ(infogain->num(), 1); - CHECK_EQ(infogain->channels(), 1); - CHECK_EQ(infogain->height(), dim); - CHECK_EQ(infogain->width(), dim); + CHECK_EQ(infogain->count(), num_labels_*num_labels_); + sum_rows_H_.Reshape(vector(1, num_labels_)); + if (bottom.size() == 2) { + // H is provided as a parameter and will not change. sum rows once + sum_rows_of_H(infogain); + } + if (top.size() >= 2) { + // softmax output + top[1]->ReshapeLike(*bottom[0]); + } } +template +Dtype InfogainLossLayer::get_normalizer( + LossParameter_NormalizationMode normalization_mode, int valid_count) { + Dtype normalizer; + switch (normalization_mode) { + case LossParameter_NormalizationMode_FULL: + normalizer = Dtype(outer_num_ * inner_num_); + break; + case LossParameter_NormalizationMode_VALID: + if (valid_count == -1) { + normalizer = Dtype(outer_num_ * inner_num_); + } else { + normalizer = Dtype(valid_count); + } + break; + case LossParameter_NormalizationMode_BATCH_SIZE: + normalizer = Dtype(outer_num_); + break; + case LossParameter_NormalizationMode_PRE_FIXED: + normalizer = Dtype(this->layer_param_.loss_param().pre_fixed_normalizer()); + break; + case LossParameter_NormalizationMode_NONE: + normalizer = Dtype(1); + break; + default: + LOG(FATAL) << "Unknown normalization mode: " + << LossParameter_NormalizationMode_Name(normalization_mode); + } +#ifdef USE_MLSL + if (normalization_mode != LossParameter_NormalizationMode_NONE) { + Dtype allnodes_normalizer(normalizer); + DLOG(INFO) << "node id: " << mn::get_node_id() << ", normalizer: " << normalizer; + if (has_ignore_label_) { + mn::allreduce(&allnodes_normalizer, 1); + } else { + // We assume local bs is same across all nodes + allnodes_normalizer *= mn::get_group_size(); + } + normalizer = allnodes_normalizer; + } +#endif + DLOG(INFO) << "Final normalizer: " << normalizer; + // Some users will have no labels for some examples in order to 'turn off' a + // particular loss in a multi-task setup. The max prevents NaNs in that case. + this->cached_normalizer_ = std::max(Dtype(1.0), normalizer); + return this->cached_normalizer_; +} + +template +void InfogainLossLayer::sum_rows_of_H(const Blob* H) { + CHECK_EQ(H->count(), num_labels_*num_labels_) + << "H must be " << num_labels_ << "x" << num_labels_; + const Dtype* infogain_mat = H->cpu_data(); + Dtype* sum = sum_rows_H_.mutable_cpu_data(); + for ( int row = 0; row < num_labels_ ; row++ ) { + sum[row] = 0; + for ( int col = 0; col < num_labels_ ; col++ ) { + sum[row] += infogain_mat[row*num_labels_+col]; + } + } +} template void InfogainLossLayer::Forward_cpu(const vector*>& bottom, const vector*>& top) { - const Dtype* bottom_data = bottom[0]->cpu_data(); + // The forward pass computes the softmax prob values. + softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_); + const Dtype* prob_data = prob_.cpu_data(); const Dtype* bottom_label = bottom[1]->cpu_data(); const Dtype* infogain_mat = NULL; if (bottom.size() < 3) { @@ -91,17 +193,30 @@ void InfogainLossLayer::Forward_cpu(const vector*>& bottom, } else { infogain_mat = bottom[2]->cpu_data(); } - int num = bottom[0]->num(); - int dim = bottom[0]->count() / bottom[0]->num(); + int count = 0; Dtype loss = 0; - for (int i = 0; i < num; ++i) { - int label = static_cast(bottom_label[i]); - for (int j = 0; j < dim; ++j) { - Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD)); - loss -= infogain_mat[label * dim + j] * log(prob); + for (int i = 0; i < outer_num_; ++i) { + for (int j = 0; j < inner_num_; j++) { + const int label_value = + static_cast(bottom_label[i * inner_num_ + j]); + if (has_ignore_label_ && label_value == ignore_label_) { + continue; + } + DCHECK_GE(label_value, 0); + DCHECK_LT(label_value, num_labels_); + for (int l = 0; l < num_labels_; l++) { + loss -= infogain_mat[label_value * num_labels_ + l] * + log(std::max( + prob_data[i * inner_num_*num_labels_ + l * inner_num_ + j], + Dtype(kLOG_THRESHOLD))); + } + ++count; } } - top[0]->mutable_cpu_data()[0] = loss / num; + top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count); + if (top.size() == 2) { + top[1]->ShareData(prob_); + } } template @@ -117,25 +232,43 @@ void InfogainLossLayer::Backward_cpu(const vector*>& top, << " Layer cannot backpropagate to infogain inputs."; } if (propagate_down[0]) { - const Dtype* bottom_data = bottom[0]->cpu_data(); + const Dtype* prob_data = prob_.cpu_data(); const Dtype* bottom_label = bottom[1]->cpu_data(); const Dtype* infogain_mat = NULL; if (bottom.size() < 3) { infogain_mat = infogain_.cpu_data(); } else { infogain_mat = bottom[2]->cpu_data(); + // H is provided as a "bottom" and might change. sum rows every time. + sum_rows_of_H(bottom[2]); } + const Dtype* sum_rows_H = sum_rows_H_.cpu_data(); Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); - int num = bottom[0]->num(); - int dim = bottom[0]->count() / bottom[0]->num(); - const Dtype scale = - top[0]->cpu_diff()[0] / num; - for (int i = 0; i < num; ++i) { - const int label = static_cast(bottom_label[i]); - for (int j = 0; j < dim; ++j) { - Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD)); - bottom_diff[i * dim + j] = scale * infogain_mat[label * dim + j] / prob; + const int dim = bottom[0]->count() / outer_num_; + int count = 0; + for (int i = 0; i < outer_num_; ++i) { + for (int j = 0; j < inner_num_; ++j) { + const int label_value = + static_cast(bottom_label[i * inner_num_ + j]); + DCHECK_GE(label_value, 0); + DCHECK_LT(label_value, num_labels_); + if (has_ignore_label_ && label_value == ignore_label_) { + for (int l = 0; l < num_labels_; ++l) { + bottom_diff[i * dim + l * inner_num_ + j] = 0; + } + } else { + for (int l = 0; l < num_labels_; ++l) { + bottom_diff[i * dim + l * inner_num_ + j] = + prob_data[i*dim + l*inner_num_ + j]*sum_rows_H[label_value] + - infogain_mat[label_value * num_labels_ + l]; + } + ++count; + } } } + // Scale gradient + Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_; + caffe_scal(bottom[0]->count(), loss_weight, bottom_diff); } } diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp index 5881475aa..00f130ce8 100644 --- a/src/caffe/layers/loss_layer.cpp +++ b/src/caffe/layers/loss_layer.cpp @@ -79,6 +79,9 @@ Dtype LossLayer::GetNormalizer( case LossParameter_NormalizationMode_BATCH_SIZE: normalizer = Dtype(outer_num); break; + case LossParameter_NormalizationMode_PRE_FIXED: + normalizer = Dtype(this->layer_param_.loss_param().pre_fixed_normalizer()); + break; case LossParameter_NormalizationMode_NONE: normalizer = Dtype(1); break; @@ -86,9 +89,19 @@ Dtype LossLayer::GetNormalizer( LOG(FATAL) << "Unknown normalization mode: " << LossParameter_NormalizationMode_Name(normalization_mode); } +#ifdef USE_MLSL + if (normalization_mode != LossParameter_NormalizationMode_NONE) { + Dtype allnodes_normalizer(normalizer); + DLOG(INFO) << "node id: " << mn::get_node_id() << ", normalizer: " << normalizer; + mn::allreduce(&allnodes_normalizer, 1); + normalizer = allnodes_normalizer; + } +#endif + DLOG(INFO) << "Final normalizer: " << normalizer; // Some users will have no labels for some examples in order to 'turn off' a // particular loss in a multi-task setup. The max prevents NaNs in that case. - return std::max(Dtype(1.0), normalizer); + this->cached_normalizer_ = std::max(Dtype(1.0), normalizer); + return this->cached_normalizer_; } INSTANTIATE_CLASS(LossLayer); diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp index 280e5da96..75ba4c347 100644 --- a/src/caffe/layers/lrn_layer.cpp +++ b/src/caffe/layers/lrn_layer.cpp @@ -78,8 +78,8 @@ void LRNLayer::LayerSetUp(const vector*>& bottom, LayerParameter pool_param; pool_param.mutable_pooling_param()->set_pool( PoolingParameter_PoolMethod_AVE); - pool_param.mutable_pooling_param()->add_pad(pre_pad_); - pool_param.mutable_pooling_param()->add_kernel_size(size_); + pool_param.mutable_pooling_param()->set_pad(pre_pad_); + pool_param.mutable_pooling_param()->set_kernel_size(size_); pool_layer_.reset(new PoolingLayer(pool_param)); pool_layer_->SetUp(square_top_vec_, pool_top_vec_); // Set up power_layer_ to compute (1 + alpha_/N^2 s)^-beta_, where s is diff --git a/src/caffe/layers/mkl_pooling_layer.cpp b/src/caffe/layers/mkl_pooling_layer.cpp index b3a1b9316..98a2da6a1 100644 --- a/src/caffe/layers/mkl_pooling_layer.cpp +++ b/src/caffe/layers/mkl_pooling_layer.cpp @@ -67,22 +67,22 @@ void MKLPoolingLayer::Init( num_ = bottom[0]->num(); if (pool_param.global_pooling()) { - CHECK(!(pool_param.kernel_size_size() || + CHECK(!(pool_param.has_kernel_size() || pool_param.has_kernel_h() || pool_param.has_kernel_w())) << "With Global_pooling: true Filter size cannot specified"; } else { - CHECK(!pool_param.kernel_size_size() != + CHECK(!pool_param.has_kernel_size() != !(pool_param.has_kernel_h() && pool_param.has_kernel_w())) << "Filter size is kernel_size OR kernel_h and kernel_w; not both"; - CHECK(pool_param.kernel_size_size() || + CHECK(pool_param.has_kernel_size() || (pool_param.has_kernel_h() && pool_param.has_kernel_w())) << "For non-square filters both kernel_h and kernel_w are required."; } - CHECK((!pool_param.pad_size() && pool_param.has_pad_h() + CHECK((!pool_param.has_pad() && pool_param.has_pad_h() && pool_param.has_pad_w()) || (!pool_param.has_pad_h() && !pool_param.has_pad_w())) << "pad is pad OR pad_h and pad_w are required."; - CHECK((!pool_param.stride_size() && pool_param.has_stride_h() + CHECK((!pool_param.has_stride() && pool_param.has_stride_h() && pool_param.has_stride_w()) || (!pool_param.has_stride_h() && !pool_param.has_stride_w())) << "Stride is stride OR stride_h and stride_w are required."; @@ -91,15 +91,8 @@ void MKLPoolingLayer::Init( kernel_h_ = bottom[0]->height(); kernel_w_ = bottom[0]->width(); } else { - if (pool_param.kernel_size_size()) { - CHECK(pool_param.kernel_size_size() == 1 || pool_param.kernel_size_size() == 2) - << "kernel_size must be specified once, or 2 values for Height and Width"; - if (pool_param.kernel_size_size() == 1) { - kernel_h_ = kernel_w_ = pool_param.kernel_size(0); - } else { - kernel_h_ = pool_param.kernel_size(0); - kernel_w_ = pool_param.kernel_size(1); - } + if (pool_param.has_kernel_size()) { + kernel_h_ = kernel_w_ = pool_param.kernel_size(); } else { kernel_h_ = pool_param.kernel_h(); kernel_w_ = pool_param.kernel_w(); @@ -108,33 +101,13 @@ void MKLPoolingLayer::Init( CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero."; CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero."; if (!pool_param.has_pad_h()) { - // CHECK(pool_param.pad_size() == 0 || pool_param.pad_size() == 1 || pool_param.pad_size() == 2) - CHECK(pool_param.pad_size() < 3) - << "pad must be specified no more than 3 dimensions"; - if (pool_param.pad_size() == 0) { - pad_h_ = pad_w_ = 0; - } else if (pool_param.pad_size() == 1) { - pad_h_ = pad_w_ = pool_param.pad(0); - } else { - pad_h_ = pool_param.pad(0); - pad_w_ = pool_param.pad(1); - } + pad_h_ = pad_w_ = pool_param.pad(); } else { pad_h_ = pool_param.pad_h(); pad_w_ = pool_param.pad_w(); } if (!pool_param.has_stride_h()) { - // CHECK(pool_param.stride_size() == 0 || pool_param.stride_size() == 1 || pool_param.stride_size() == 2) - CHECK(pool_param.stride_size() < 3) - << "stride must be specified no more than 3 dimensions"; - if (pool_param.stride_size() == 0) { - stride_h_ = stride_w_ = 1; - } else if (pool_param.stride_size() == 1) { - stride_h_ = stride_w_ = pool_param.stride(0); - } else { - stride_h_ = pool_param.stride(0); - stride_w_ = pool_param.stride(1); - } + stride_h_ = stride_w_ = pool_param.stride(); } else { stride_h_ = pool_param.stride_h(); stride_w_ = pool_param.stride_w(); diff --git a/src/caffe/layers/mkldnn_batch_norm_layer.cpp b/src/caffe/layers/mkldnn_batch_norm_layer.cpp index cdf73bfe5..d3f76bfbd 100644 --- a/src/caffe/layers/mkldnn_batch_norm_layer.cpp +++ b/src/caffe/layers/mkldnn_batch_norm_layer.cpp @@ -229,7 +229,7 @@ void MKLDNNBatchNormLayer::InitBatchNorm(const vector*>& bott batch_normalization_forward::desc BatchNormFwd_desc(propagation, *input_stats_md, eps_, flags); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; @@ -493,7 +493,7 @@ void MKLDNNBatchNormLayer::InitBatchNormBwd( flags); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; diff --git a/src/caffe/layers/mkldnn_concat_layer.cpp b/src/caffe/layers/mkldnn_concat_layer.cpp index 3f86c2f75..9299f9cea 100644 --- a/src/caffe/layers/mkldnn_concat_layer.cpp +++ b/src/caffe/layers/mkldnn_concat_layer.cpp @@ -84,8 +84,10 @@ void MKLDNNConcatLayer::LayerSetUp(const vector*>& bottom, else if (concat_dimension == 1) { CHECK_EQ(bottom[0]->num(), bottom[i]->num()); - CHECK_EQ(bottom[0]->height(), bottom[i]->height()); - CHECK_EQ(bottom[0]->width(), bottom[i]->width()); + if (!concat_param.per_fla_fuse()){ + CHECK_EQ(bottom[0]->height(), bottom[i]->height()); + CHECK_EQ(bottom[0]->width(), bottom[i]->width()); + } break; } else if (concat_dimension == 2) @@ -123,10 +125,20 @@ void MKLDNNConcatLayer::LayerSetUp(const vector*>& bottom, channels_ = 0; height_ = bottom[0]->height(); width_ = bottom[0]->width(); - for (auto i = 0; i < num_concats_; ++i) { - CHECK_EQ(dim_src, bottom[i]->shape().size()); - split_dims[i] = bottom[i]->channels(); - channels_ += split_dims[i]; + if (concat_param.per_fla_fuse()){ + height_ = 1; + width_ = 1; + for (auto i = 0; i < num_concats_; ++i) { + CHECK_EQ(dim_src, bottom[i]->shape().size()); + split_dims[i] = bottom[i]->channels()*bottom[i]->height()*bottom[i]->width(); + channels_ += split_dims[i]; + } + } else{ + for (auto i = 0; i < num_concats_; ++i) { + CHECK_EQ(dim_src, bottom[i]->shape().size()); + split_dims[i] = bottom[i]->channels(); + channels_ += split_dims[i]; + } } } else if (concat_dimension == 2) @@ -159,7 +171,7 @@ template void MKLDNNConcatLayer::Reshape(const vector*>& bottom, const vector*>& top) { VLOG(1) << "MKLDNNConcatLayer::Reshape: " << this->layer_param_.name(); - + const ConcatParameter& concat_param = this->layer_param_.concat_param(); if (concat_dimension == 0) { //Need to re-calculate the shape duo to the change of batch size @@ -187,17 +199,31 @@ void MKLDNNConcatLayer::Reshape(const vector*>& bottom, channels_ = 0; height_ = bottom[0]->height(); width_ = bottom[0]->width(); - for (auto i = 0; i < num_concats_; ++i) { + if (concat_param.per_fla_fuse()){ + height_ = 1; + width_ = 1; + for (auto i = 0; i < num_concats_; ++i) { + split_dims[i] = bottom[i]->channels()*bottom[i]->height()*bottom[i]->width(); + channels_ += split_dims[i]; + } + if (this->num_ == bottom[0]->num()) { + this->reshape = false; + } else { + this->reshape = true; + } + + } else{ + for (auto i = 0; i < num_concats_; ++i) { split_dims[i] = bottom[i]->channels(); channels_ += split_dims[i]; - } - - if (this->num_ == bottom[0]->num() && - this->height_ == bottom[0]->height() && - this->width_ == bottom[0]->width()) { - this->reshape = false; - } else { - this->reshape = true; + } + if (this->num_ == bottom[0]->num() && + this->height_ == bottom[0]->height() && + this->width_ == bottom[0]->width()) { + this->reshape = false; + } else { + this->reshape = true; + } } } else if (concat_dimension == 2) @@ -271,7 +297,6 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, bottom[i]->Reshape(bottom_4D_shape, false); } } - engine cpu_engine = CpuEngine::Instance().get_engine(); memory::data_type usr_dt = memory::data_type::f32; memory::data_type prv_dt = usr_dt; @@ -279,33 +304,46 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, memory::format mfmt_nchw = memory::format::nchw; memory::dims output_tz = {num_, channels_, height_, width_}; - std::vector srcs_mpd; std::vector srcs; fwd_bottom_data.clear(); fwd_input_primitives_.clear(); fwd_input_primitives_at_.clear(); - int fl = 0; - int fl_min = 0; float scale = 1.; float scale_min = 1.; for (auto i = 0; i < num_concats_; i++) { if (const_cast(bottom[i]->prv_data()) != NULL) { shared_ptr > mem_descr = get_mkldnn_prv_descriptor(bottom[i]); - fl = mem_descr->get_fl(0); - if (fl_min == 0) fl_min = fl; - if(fl != 0 && fl < fl_min) fl_min = fl; scale = mem_descr->get_scale(0); if (scale_min == 1.) scale_min = scale; if(scale != 1. && scale < scale_min) scale_min = scale; } } - - bool bottom_is_float = false; + std::vector src_mfmts; + std::vector bottom_scales(num_concats_, 1.); + std::vector >> mem_descr; + + std::vector prv_dt_tmp(num_concats_, memory::data_type::f32); + bool different_input_dt = false; + for(auto i = 0; i < num_concats_; i++) { + if (const_cast(bottom[i]->prv_data()) != NULL) { + shared_ptr > mem_descr_tmp = get_mkldnn_prv_descriptor(bottom[i]); + prv_dt_tmp[i] = static_cast(mem_descr_tmp->prv_memory_pd()->desc().data.data_type); + } + } + + memory::data_type first_prv_dt = prv_dt_tmp[0]; + for (auto i = 0; i < prv_dt_tmp.size(); i++){ + if (prv_dt_tmp[i] != first_prv_dt){ + different_input_dt = true; + } + } + for (auto i = 0; i < num_concats_; i++) { fwd_bottom_data.push_back(boost::shared_ptr >()); + mem_descr.push_back(boost::shared_ptr>()); memory::dims input_tz = {0, 0, 0, 0}; if (concat_dimension == 0) @@ -331,36 +369,28 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, new memory::primitive_desc({input_tz, usr_dt, mfmt_nchw}, cpu_engine)); if (const_cast(bottom[i]->prv_data()) != NULL) { - fl = 0; scale = 1.; - shared_ptr > mem_descr - = get_mkldnn_prv_descriptor(bottom[i]); - bottom_is_float = mem_descr->get_float(); - src_mfmt = static_cast( - mem_descr->prv_memory_pd()->desc().data.format); - prv_dt = static_cast(mem_descr->prv_memory_pd()->desc().data.data_type); + mem_descr[i] = get_mkldnn_prv_descriptor(bottom[i]); + if(!different_input_dt){ + src_mfmt = static_cast( + mem_descr[i]->prv_memory_pd()->desc().data.format); + prv_dt = static_cast(mem_descr[i]->prv_memory_pd()->desc().data.data_type); + scale = mem_descr[i]->get_scale(0); + bottom_scales[i] = scale; + if(scale != 1.) scale = scale_min; + } prv_src_mpd.reset(new memory::primitive_desc( {input_tz, prv_dt, src_mfmt}, cpu_engine)); - fl = mem_descr->get_fl(0); - if(fl != 0) fl = fl_min; - scale = mem_descr->get_scale(0); - if(scale != 1.) scale = scale_min; } - std::vector fl_bottom; - fl_bottom.push_back(fl); std::vector scale_bottom; scale_bottom.push_back(scale); + src_mfmts.push_back(src_mfmt); srcs_mpd.push_back(memory::primitive_desc( {input_tz, prv_dt, src_mfmt}, cpu_engine)); - if(bottom_is_float){ - fwd_bottom_data[i].reset(new MKLDNNData( - usr_src_mpd, prv_src_mpd, bottom[i], this, true, scale_bottom)); - } else{ - fwd_bottom_data[i].reset(new MKLDNNData( - usr_src_mpd, prv_src_mpd, bottom[i], this, fl_bottom)); - } + fwd_bottom_data[i].reset(new MKLDNNData( + usr_src_mpd, prv_src_mpd, bottom[i], this, scale_bottom)); fwd_input_primitives_.push_back(fwd_bottom_data[i]->create_input(false)); fwd_input_primitives_at_.push_back(*fwd_input_primitives_[i]); } @@ -373,20 +403,51 @@ void MKLDNNConcatLayer::InitConcatFwd(const vector*>& bottom, shared_ptr prv_dst_mpd(new memory::primitive_desc( concatFwd_pd->dst_primitive_desc())); - std::vector fl_top; - fl_top.push_back(fl_min); std::vector scale_top; - scale_top.push_back(scale_min); - if(bottom_is_float){ - fwd_top_data.reset(new MKLDNNData(usr_dst_mpd, prv_dst_mpd, top[0], - this, true, scale_top)); + if(!different_input_dt){ + scale_top.push_back(scale_min); } else{ - fwd_top_data.reset(new MKLDNNData(usr_dst_mpd, prv_dst_mpd, top[0], - this, fl_top)); + scale_top.push_back(1.); } - + fwd_top_data.reset(new MKLDNNData(usr_dst_mpd, prv_dst_mpd, top[0], + this, scale_top)); + fwd_output_memory = fwd_top_data->create_output_memory(); + memory::format base_mfmt = mfmt_nchw; + float base_scale = 1.; + this->in_place_ = true; + + for(auto i = 0; i < num_concats_; i++){ + if(i == 0) { + base_mfmt = src_mfmts[i]; + base_scale = bottom_scales[i]; + } + else if((concat_dimension != 0 && bottom[i]->shape()[concat_dimension - 1] != 1) || base_mfmt != src_mfmts[i] || fabs(base_scale-bottom_scales[i]) > FLT_MIN || different_input_dt) { + this->in_place_ = false; + break; + } + } + + if(this->in_place_ && this->phase_ == TEST) { + size_t offset = 0; + for(auto i = 0; i < num_concats_; i++){ + if(bottom[i]->prv_data()){ + if (scale_top[0] != 1.){ + memcpy(static_cast(fwd_output_memory->get_data_handle()) + offset, static_cast(mem_descr[i]->get_prv_memory()->get_data_handle()), sizeof(char) * bottom[i]->count()); + mem_descr[i]->get_prv_memory()->set_data_handle(static_cast(fwd_output_memory->get_data_handle())+offset); + }else{ + caffe_copy(bottom[i]->count(), static_cast(mem_descr[i]->get_prv_memory()->get_data_handle()), static_cast(fwd_output_memory->get_data_handle()) + offset); + mem_descr[i]->get_prv_memory()->set_data_handle(static_cast(fwd_output_memory->get_data_handle()) + offset); + } + } else{ + caffe_copy(bottom[i]->count(), bottom[i]->cpu_data(), static_cast(fwd_output_memory->get_data_handle()) + offset); + bottom[i]->set_cpu_data(static_cast(fwd_output_memory->get_data_handle()) + offset); + } + offset += bottom[i]->count(); + } + } + concatFwd.reset(new concat(*concatFwd_pd, fwd_input_primitives_at_, *fwd_output_memory)); for (auto i = 0; i < num_concats_; i++) { @@ -498,20 +559,22 @@ void MKLDNNConcatLayer::Forward_cpu(const vector*>& bottom, #ifdef DEBUG LOG(INFO) << "MKLDNNConcatLayer::Forward_cpu: " << this->layer_param_.name(); #endif - if ((NULL == concatFwd_pd) || (true == this->reshape)) InitConcatFwd(bottom, top); + for (auto i = 0; i < num_concats_; i++) { // making reorders if needed. fwd_bottom_data[i]->sync_before_read(); } - // update top that head at prv - fwd_top_data->sync_before_write(); + // update top that head at prv + fwd_top_data->sync_before_write(); - PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); - PERFORMANCE_MEASUREMENT_BEGIN(); - concatFwd.submit(); - PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); + if(!this->in_place_ || this->phase_ != TEST) { + PERFORMANCE_EVENT_ID_INIT(perf_id_fw_, PERFORMANCE_MKLDNN_NAME("FW")); + PERFORMANCE_MEASUREMENT_BEGIN(); + concatFwd.submit(); + PERFORMANCE_MEASUREMENT_END_ID(perf_id_fw_); + } } template diff --git a/src/caffe/layers/mkldnn_convolution_layer.cpp b/src/caffe/layers/mkldnn_convolution_layer.cpp index 71cef5865..3e27b552b 100644 --- a/src/caffe/layers/mkldnn_convolution_layer.cpp +++ b/src/caffe/layers/mkldnn_convolution_layer.cpp @@ -63,8 +63,8 @@ MKLDNNConvolutionLayer::MKLDNNConvolutionLayer(const LayerParameter& para , fwd_bottom_data_primitive(NULL), fwd_weights_data_primitive(NULL), fwd_bias_data_primitive(NULL) , bwdd_top_diff_primitive(NULL), bwdd_weights_data_primitive(NULL) , bwdw_top_diff_primitive(NULL), bwdw_bottom_data_primitive(NULL) - , width_(0), height_(0), width_out_(0), height_out_(0), kernel_w_(0), kernel_h_(0) - , stride_w_(0), stride_h_(0), pad_w_(0), pad_h_(0), + , width_(0), height_(0), depth_(0), width_out_(0), height_out_(0), depth_out_(0), kernel_w_(0), kernel_h_(0), kernel_d_(0) + , stride_w_(0), stride_h_(0), stride_d_(0), pad_w_(0), pad_h_(0), pad_d_(0), bwdw_weights_diff_iter(NULL), bwdw_bias_diff_iter(NULL), bwdw_weights_diff_memory_iter(NULL), @@ -79,24 +79,57 @@ template void MKLDNNConvolutionLayer::compute_output_shape() { ConvolutionLayer::compute_output_shape(); - this->height_out_ = this->output_shape_[0]; - this->width_out_ = this->output_shape_[1]; + CHECK_GT(this->output_shape_.size(), 1) << "MKLDNN Convolution layer expects at least 2D spatial output dimension!"; + CHECK_LT(this->output_shape_.size(), 4) << "MKLDNN Convolution layer expects at most 3D spatial output dimension!"; + if (this->output_shape_.size() == 2) { + this->height_out_ = this->output_shape_[0]; + this->width_out_ = this->output_shape_[1]; + } else { + this->depth_out_ = this->output_shape_[0]; + this->height_out_ = this->output_shape_[1]; + this->width_out_ = this->output_shape_[2]; + } } template void MKLDNNConvolutionLayer::init_properties(const vector*>& bottom , const vector*>& top) { - this->stride_w_ = this->stride_.cpu_data()[1]; - this->stride_h_ = this->stride_.cpu_data()[0]; - this->width_ = bottom[0]->width(); - this->height_ = bottom[0]->height(); - this->channels_ = bottom[0]->channels(); - this->num_ = bottom[0]->num(); - this->pad_w_ = this->pad_.cpu_data()[1]; - this->pad_h_ = this->pad_.cpu_data()[0]; - this->kernel_w_ = this->kernel_shape_.cpu_data()[1]; - this->kernel_h_ = this->kernel_shape_.cpu_data()[0]; + CHECK_GT(this->num_spatial_axes_, 1) << "MKLDNN Convolution layer expects at least 2D spatial input dimension!"; + CHECK_LT(this->num_spatial_axes_, 4) << "MKLDNN Convolution layer expects at most 3D spatial input dimension!"; + + this->num_ = bottom[0]->shape(0); + this->channels_ = bottom[0]->shape(1); + + if (this->num_spatial_axes_ == 2) { + this->height_ = bottom[0]->shape(2); + this->width_ = bottom[0]->shape(3); + + this->stride_h_ = this->stride_.cpu_data()[0]; + this->stride_w_ = this->stride_.cpu_data()[1]; + + this->pad_h_ = this->pad_.cpu_data()[0]; + this->pad_w_ = this->pad_.cpu_data()[1]; + + this->kernel_h_ = this->kernel_shape_.cpu_data()[0]; + this->kernel_w_ = this->kernel_shape_.cpu_data()[1]; + } else { + this->depth_ = bottom[0]->shape(2); + this->height_ = bottom[0]->shape(3); + this->width_ = bottom[0]->shape(4); + + this->stride_d_ = this->stride_.cpu_data()[0]; + this->stride_h_ = this->stride_.cpu_data()[1]; + this->stride_w_ = this->stride_.cpu_data()[2]; + + this->pad_d_ = this->pad_.cpu_data()[0]; + this->pad_h_ = this->pad_.cpu_data()[1]; + this->pad_w_ = this->pad_.cpu_data()[2]; + + this->kernel_d_ = this->kernel_shape_.cpu_data()[0]; + this->kernel_h_ = this->kernel_shape_.cpu_data()[1]; + this->kernel_w_ = this->kernel_shape_.cpu_data()[2]; + } string _conv_algorithm = this->layer_param_.convolution_param().conv_algorithm(); if(_conv_algorithm == "direct") @@ -140,17 +173,26 @@ void MKLDNNConvolutionLayer::Reshape(const vector*>& bottom , const vector*>& top) { VLOG(1) << " MKLDNNConvolutionLayer::Reshape: " << this->layer_param_.name(); - this->reshape = (this->width_ == bottom[0]->width() && - this->height_ == bottom[0]->height() && - this->channels_ == bottom[0]->channels() && - this->num_ == bottom[0]->num()) ? false : true; + if (this->num_spatial_axes_ == 2) { + this->reshape = (this->width_ == bottom[0]->shape(3) && + this->height_ == bottom[0]->shape(2) && + this->channels_ == bottom[0]->shape(1) && + this->num_ == bottom[0]->shape(0)) ? false : true; + } else { + this->reshape = (this->depth_ == bottom[0]->shape(2) && + this->width_ == bottom[0]->shape(4) && + this->height_ == bottom[0]->shape(3) && + this->channels_ == bottom[0]->shape(1) && + this->num_ == bottom[0]->shape(0)) ? false : true; + } init_properties(bottom, top); BaseConvolutionLayer::ReshapeForMKL(bottom, top); -#ifndef DISABLE_CONV_SUM_FUSION - if (bottom.size() > 1) { - top[0]->ShareData(*bottom[1]); + // if (bottom.size() > 1) { + if (this->layer_param_.convolution_param().fusion_type() == + ConvolutionParameter::SUM_FUSION && + bottom.size() > 1) { + top[0]->ShareData(*bottom[1]); } -#endif } template @@ -163,7 +205,6 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* Dtype negative_slope = 0; if(relu) { - propagation = prop_kind::forward_inference; negative_slope = this->layer_param_.relu_param().negative_slope(); } @@ -172,21 +213,27 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* int32_t iw = this->width_; int32_t ih = this->height_; int32_t ic = this->channels_; + int32_t id = this->depth_; int32_t ow = this->width_out_; int32_t oh = this->height_out_; int32_t oc = this->num_output_; + int32_t od = this->depth_out_; int32_t kw = this->kernel_w_; int32_t kh = this->kernel_h_; + int32_t kd = this->kernel_d_; int32_t sw = this->stride_w_; int32_t sh = this->stride_h_; + int32_t sd = this->stride_d_; int32_t pw = this->pad_w_; int32_t ph = this->pad_h_; - memory::dims convolutionStrides {sh, sw}; - memory::dims padding {ph, pw}; + int32_t pd = this->pad_d_; + + memory::dims convolutionStrides; + memory::dims padding; memory::dims padding_r; memory::dims dilation; bool dilated_conv = false; @@ -195,8 +242,19 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* dilation.push_back(dilation_data[i] - 1); if (dilation_data[i] != 1) dilated_conv = true; } - padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[0]) + 1) - ph); - padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[1]) + 1) - pw); + + if (this->num_spatial_axes_ == 2) { + convolutionStrides = {sh, sw}; + padding = {ph, pw}; + padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[0]) + 1) - ph); + padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[1]) + 1) - pw); + } else { + convolutionStrides = {sd, sh, sw}; + padding = {pd, ph, pw}; + padding_r.push_back((od - 1) * sd - id + ((kd - 1) * (dilation_data[0]) + 1) - pd); + padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[1]) + 1) - ph); + padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[2]) + 1) - pw); + } // ---- Initialize memory descriptors (fromat = any) to create convolution descriptor ------------- memory::data_type mpcsn = memory::data_type::f32; @@ -213,12 +271,13 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* } } else { - top_dt = memory::data_type::s32; + top_dt = memory::data_type::f32; } } - - bool is_sum; - if (bottom.size() > 1) { + bool is_sum = false; + if (this->layer_param_.convolution_param().fusion_type() == + ConvolutionParameter::SUM_FUSION && + bottom.size() > 1) { is_sum = true; memory::data_type bottom_1_dt = memory::data_type::f32; @@ -231,6 +290,11 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* if (top_dt != bottom_1_dt) { top_dt = bottom_1_dt; + // FIXME: to simplify the calibration tool to handle different data types of conv sum in residual block + if(top_dt == memory::data_type::f32){ + this->need_quantize_ = false; + bottom_dt = memory::data_type::f32; + } } } @@ -238,10 +302,21 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* memory::data_type bias_dt = this->need_quantize_ ? memory::data_type::s32 : memory::data_type::f32; memory::format mfmt_any = memory::format::any; - memory::dims bottom_tz = {n, ic, ih, iw}; - memory::dims bias_tz = {oc}; - memory::dims top_tz = {n, oc, oh, ow}; - memory::dims weights_tz = (g!= 1) ? memory::dims{g, oc/g, ic/g, kh, kw} : memory::dims{oc, ic, kh, kw}; + memory::dims bottom_tz; + memory::dims bias_tz; + memory::dims top_tz; + memory::dims weights_tz; + if (this->num_spatial_axes_ == 2) { + bottom_tz = {n, ic, ih, iw}; + bias_tz = {oc}; + top_tz = {n, oc, oh, ow}; + weights_tz = (g!= 1) ? memory::dims{g, oc/g, ic/g, kh, kw} : memory::dims{oc, ic, kh, kw}; + } else { + bottom_tz = {n, ic, id, ih, iw}; + bias_tz = {oc}; + top_tz = {n, oc, od, oh, ow}; + weights_tz = (g!= 1) ? memory::dims{g, oc/g, ic/g, kd, kh, kw} : memory::dims{oc, ic, kd, kh, kw}; + } // ---- Memory descriptors for initializing of convolution primitive descriptor ------------- memory::desc init_bottom_md({bottom_tz}, bottom_dt, mfmt_any); @@ -249,9 +324,17 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* memory::desc init_top_md({top_tz}, top_dt, mfmt_any); memory::desc init_weights_md({weights_tz}, weights_dt, mfmt_any); + size_t coeff_size = this->layer_param_.convolution_param().coeff_size(); + float coeff0 = 1; + float coeff1 = 1; + if (coeff_size == 2) + { + coeff0 = this->layer_param_.convolution_param().coeff(0); + coeff1 = this->layer_param_.convolution_param().coeff(1); + } + primitive_attr attr; if (this->need_quantize_) { - if(this->scale_in_.size() > 0) this->is_float_ = true; int mask = 0; int count = 1; //single channel if(this->fl_params_.size() > 1 || this->scale_params_.size() > 1){ @@ -261,20 +344,13 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* } std::vector scales(count); float scale; - if(this->is_float_){ - #pragma omp parallel for if (count > 1) - for(int i=0; iscale_out_[0] / (this->scale_in_[0] * this->scale_params_[i]); - scales[i] = scale; - } - } else { - int output_shift; - #pragma omp parallel for if (count > 1) - for(int i=0; ifl_layer_out_[0] - this->fl_layer_in_[0] - this->fl_params_[i]; - scale = pow(2. ,output_shift); - scales[i] = scale; - } + #pragma omp parallel for if (count > 1) + for(int i=0; iscale_params_[i] == 0.0) + scale = this->scale_out_[0] * coeff1; + else + scale = this->scale_out_[0] / (this->scale_in_[0] * this->scale_params_[i]) * coeff1; + scales[i] = scale; } attr.set_output_scales(mask, scales); attr.set_int_output_round_mode(round_nearest); @@ -282,45 +358,34 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; mkldnn::algorithm eligibleAlgorithms[2] = {conv_algorithm, algorithm::convolution_direct}; convFwd_pd = NULL; mkldnn::post_ops ops; - -#ifndef DISABLE_CONV_SUM_FUSION - if(relu || bottom.size() > 1) { -#else - if(relu) { -#endif - float scale = 1.0f; - Dtype alpha = negative_slope; // negative slope for mkldnn_eltwise_relu. - float beta = 1.0f; //ignored for mkldnn_eltwise_relu. -#ifndef DISABLE_CONV_SUM_FUSION - if (bottom.size() > 1) { - if (this->need_quantize_) { - float sum_scale; - if(this->is_float_){ - sum_scale = this->scale_out_[0] / - get_mkldnn_prv_descriptor(bottom[1])->get_scale(0); - } else{ - int sum_shift = - this->fl_layer_out_[0] - - get_mkldnn_prv_descriptor(bottom[1])->get_fl(0); - sum_scale = pow(2., sum_shift); - } - ops.append_sum(sum_scale); - } else { - ops.append_sum(1.0f); - } - } -#endif - ops.append_eltwise(scale, eltwise_relu, alpha, beta); - attr.set_post_ops(ops); + float scale = 1.0f; + Dtype alpha = negative_slope; // negative slope for mkldnn_eltwise_relu. + float beta = 1.0f; // ignored for mkldnn_eltwise_relu. + + if (this->layer_param_.convolution_param().fusion_type() == + ConvolutionParameter::SUM_FUSION && + bottom.size() > 1) { + if (this->need_quantize_) { + float sum_scale; + sum_scale = + this->scale_out_[0] / + get_mkldnn_prv_descriptor(bottom[1])->get_scale(0) * coeff0; + ops.append_sum(sum_scale); + } else { + ops.append_sum(1.0f); + } } + if (relu) ops.append_eltwise(scale, eltwise_relu, alpha, beta); + attr.set_post_ops(ops); + for (auto& convAlgorithm : eligibleAlgorithms) { // ---- Initialize convolution primitive descriptor ------------- shared_ptr convFwd_desc; @@ -351,12 +416,11 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* for (subEngineIndex = 0; subEngineIndex < ep.getNumberOfSubEngines(); subEngineIndex++) { try { -#ifndef DISABLE_CONV_SUM_FUSION - if(this->need_quantize_ || relu || bottom.size() > 1) { -#else - if(relu) { -#endif - convFwd_pd.reset(new convolution_forward::primitive_desc( + if (this->need_quantize_ || relu || + (this->layer_param_.convolution_param().fusion_type() == + ConvolutionParameter::SUM_FUSION && + bottom.size() > 1)) { + convFwd_pd.reset(new convolution_forward::primitive_desc( *convFwd_desc, attr, ep.getMKLDNNSubEngine(subEngineIndex))); } else { convFwd_pd.reset(new convolution_forward::primitive_desc( @@ -387,86 +451,61 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* info_mem_pd(prv_fwd_top_data_memory_pd, "conv_dst:" + this->layer_param_.name()); // ---- Create usr memory primitive descriptors ------------- - memory::format mfmt_nchw = memory::format::nchw; - memory::format weights_mfmt = (g!= 1) ? memory::format::goihw : memory::format::oihw; + memory::format data_mfmt; + memory::format weights_mfmt; + if (this->num_spatial_axes_ == 2) { + data_mfmt = memory::format::nchw; + weights_mfmt = (g!= 1) ? memory::format::goihw : memory::format::oihw; + } else { + data_mfmt = memory::format::ncdhw; + weights_mfmt = (g!= 1) ? memory::format::goidhw : memory::format::oidhw; + } // TODO: There should not be a problem to use this for Backward as well - shared_ptr usr_bottom_data_memory_pd(new MemPD({{bottom_tz}, mpcsn, mfmt_nchw}, cpu_engine)); + shared_ptr usr_bottom_data_memory_pd(new MemPD({{bottom_tz}, mpcsn, data_mfmt}, cpu_engine)); shared_ptr usr_bias_data_memory_pd(new MemPD({{bias_tz}, mpcsn, memory::format::x}, cpu_engine)); - shared_ptr usr_top_data_memory_pd(new MemPD({{top_tz}, mpcsn, mfmt_nchw}, cpu_engine)); + shared_ptr usr_top_data_memory_pd(new MemPD({{top_tz}, mpcsn, data_mfmt}, cpu_engine)); shared_ptr usr_weights_data_memory_pd(new MemPD({{weights_tz}, mpcsn, weights_mfmt}, cpu_engine)); // --- init primitive and prv_memory descriptors ---------------------- - bool bottom_is_float = false; - if (const_cast(bottom[0]->prv_data()) != NULL) { - shared_ptr > blob_prv_mkldnn_mem_descr = get_mkldnn_prv_descriptor(bottom[0]); - bottom_is_float = blob_prv_mkldnn_mem_descr->get_float(); - } if (this->need_quantize_){ - if(this->is_float_ || bottom_is_float){ - std::vector scale_bottom; - scale_bottom.push_back(this->scale_in_[0]); - fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this, true, scale_bottom)); - } else{ - std::vector fl_bottom; - fl_bottom.push_back(this->fl_layer_in_[0]); - fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this, fl_bottom)); - } - } else if(bottom_is_float){ - fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this, false)); - } else{ + std::vector scale_bottom; + scale_bottom.push_back(this->scale_in_[0]); + fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this, scale_bottom)); + } else { fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_memory_pd, prv_fwd_bottom_data_memory_pd, bottom[0], this)); } fwd_bottom_data->name = "fwd_bottom_data @ " + this->layer_param_.name(); fwd_bottom_data_primitive = fwd_bottom_data->create_input(false); if (this->need_quantize_){ - if(this->is_float_ || bottom_is_float){ - std::vector scale_top; - scale_top.push_back(this->scale_out_[0]); - fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this, true, scale_top, is_sum)); - } else{ - std::vector fl_top; - fl_top.push_back(this->fl_layer_out_[0]); - fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this, fl_top, is_sum)); - } - } else if(bottom_is_float){ - fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this, false)); + std::vector scale_top; + scale_top.push_back(this->scale_out_[0]); + fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this, scale_top, 0, is_sum)); } else{ fwd_top_data.reset(new MKLDNNData(usr_top_data_memory_pd, prv_fwd_top_data_memory_pd, top[0], this)); } fwd_top_data->name = "fwd_top_data @ " + this->layer_param_.name(); fwd_top_data_memory = fwd_top_data->create_output_memory(); + bool is_wino = conv_algorithm == algorithm::convolution_winograd ? true : false; if (fwd_weights_data == NULL) { if (this->need_quantize_){ int count = 1; //single channel - if(this->is_float_ || bottom_is_float){ - if(this->scale_params_.size() > 1){ - count = oc; //multi channel - } - std::vector scale_weight(count); - #pragma omp parallel for if (count > 1) - for(int i=0; iscale_params_[i]; - } - fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, true, scale_weight)); - } else{ - if(this->fl_params_.size() > 1){ - count = oc; //multi channel - } - std::vector fl_weight(count); - #pragma omp parallel for if (count > 1) - for(int i=0; ifl_params_[i]; - } - fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, fl_weight)); + int reorder_mask = 0; + if(this->scale_params_.size() > 1){ + count = oc; //multi channel + reorder_mask = (g!= 1) ? (1<<1)+(1<<0) : 1<<0; } - } else if(bottom_is_float){ - fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, false)); + std::vector scale_weight(count); + #pragma omp parallel for if (count > 1) + for(int i=0; iscale_params_[i]; + } + fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, scale_weight, reorder_mask)); } else{ - fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this)); + fwd_weights_data.reset(new MKLDNNData(usr_weights_data_memory_pd, prv_fwd_weights_data_memory_pd, this->blobs_[0].get(), this, {1.}, 0, is_sum, is_wino)); } fwd_weights_data->name = "fwd_weights_data @ " + this->layer_param_.name(); fwd_weights_data_primitive = fwd_weights_data->create_input(true); @@ -476,30 +515,21 @@ void MKLDNNConvolutionLayer::InitConvolutionFwd(const vector* if (fwd_bias_data == NULL) { shared_ptr prv_fwd_bias_data_memory_pd(new MemPD(convFwd_pd->bias_primitive_desc())); if (this->need_quantize_){ - int count = 1; //single channel - if(this->is_float_ || bottom_is_float){ - if(this->scale_params_.size() > 1){ - count = oc; //multi channel - } - std::vector scale_bias(count); - #pragma omp parallel for if (count > 1) - for(int i=0; iscale_in_[0] * this->scale_params_[i]; - } - fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, true, scale_bias)); - } else{ - if(this->fl_params_.size() > 1){ - count = oc; //multi channel - } - std::vector fl_bias(count); - #pragma omp parallel for if (count > 1) - for(int i=0; ifl_layer_in_[0] + this->fl_params_[i]; - } - fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, fl_bias)); + int count = 1; //single channel + int reorder_mask = 0; + if(this->scale_params_.size() > 1){ + count = oc; //multi channel + reorder_mask = 1<<0; + } + std::vector scale_bias(count); + #pragma omp parallel for if (count > 1) + for(int i=0; iscale_params_[i] == 0.0) + scale_bias[i] = 1.0; + else + scale_bias[i] = this->scale_in_[0] * this->scale_params_[i]; } - } else if(bottom_is_float){ - fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, false)); + fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this, scale_bias, reorder_mask)); } else{ fwd_bias_data.reset(new MKLDNNData(usr_bias_data_memory_pd, prv_fwd_bias_data_memory_pd, this->blobs_[1].get(), this)); } @@ -540,8 +570,8 @@ void MKLDNNConvolutionLayer::Forward_cpu(const vector*>& bott if( convFwd_pd == NULL || this->reshape) InitConvolutionFwd(bottom, top); - // making reorders if needed. - fwd_bottom_data->sync_before_read(); + + fwd_bottom_data->sync_before_read(); fwd_weights_data->sync_before_read(); if (this->bias_term_) fwd_bias_data->sync_before_read(); @@ -567,21 +597,27 @@ void MKLDNNConvolutionLayer::InitConvolutionBwd(const vector* int32_t iw = this->width_; int32_t ih = this->height_; int32_t ic = this->channels_; + int32_t id = this->depth_; int32_t ow = this->width_out_; int32_t oh = this->height_out_; + int32_t od = this->depth_out_; int32_t oc = this->num_output_; int32_t kw = this->kernel_w_; int32_t kh = this->kernel_h_; + int32_t kd = this->kernel_d_; int32_t sw = this->stride_w_; int32_t sh = this->stride_h_; + int32_t sd = this->stride_d_; int32_t pw = this->pad_w_; int32_t ph = this->pad_h_; - memory::dims convolutionStrides {sh, sw}; - memory::dims padding {ph, pw}; + int32_t pd = this->pad_d_; + + memory::dims convolutionStrides; + memory::dims padding; memory::dims padding_r; memory::dims dilation; bool dilated_conv = false; @@ -590,17 +626,38 @@ void MKLDNNConvolutionLayer::InitConvolutionBwd(const vector* dilation.push_back(dilation_data[i] - 1); if (dilation_data[i] != 1) dilated_conv = true; } - padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[0]) + 1) - ph); - padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[1]) + 1) - pw); + if (this->num_spatial_axes_ == 2) { + convolutionStrides = {sh, sw}; + padding = {ph, pw}; + padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[0]) + 1) - ph); + padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[1]) + 1) - pw); + } else { + convolutionStrides = {sd, sh, sw}; + padding = {pd, ph, pw}; + padding_r.push_back((od - 1) * sd - id + ((kd - 1) * (dilation_data[0]) + 1) - pd); + padding_r.push_back((oh - 1) * sh - ih + ((kh - 1) * (dilation_data[1]) + 1) - ph); + padding_r.push_back((ow - 1) * sw - iw + ((kw - 1) * (dilation_data[2]) + 1) - pw); + } // ---- Initialize memory descriptors (fromat = any) to create convolution descriptor ------------- memory::data_type mpcsn = memory::data_type::f32; memory::format mfmt_any = memory::format::any; - memory::dims bottom_tz = {n, ic, ih, iw}; - memory::dims bias_tz = {oc}; - memory::dims top_tz = {n, oc, oh, ow}; - memory::dims weights_tz = ( g!= 1) ? memory::dims{g, oc/g, ic/g, kh, kw} : memory::dims{oc, ic, kh, kw}; + memory::dims bottom_tz; + memory::dims bias_tz; + memory::dims top_tz; + memory::dims weights_tz; + if (this->num_spatial_axes_ == 2) { + bottom_tz = {n, ic, ih, iw}; + bias_tz = {oc}; + top_tz = {n, oc, oh, ow}; + weights_tz = ( g!= 1) ? memory::dims{g, oc/g, ic/g, kh, kw} : memory::dims{oc, ic, kh, kw}; + } else { + bottom_tz = {n, ic, id, ih, iw}; + bias_tz = {oc}; + top_tz = {n, oc, od, oh, ow}; + weights_tz = ( g!= 1) ? memory::dims{g, oc/g, ic/g, kd, kh, kw} : memory::dims{oc, ic, kd, kh, kw}; + } // ---- Memory descriptors for initializing of convolution primitive descriptor ------------- memory::desc init_bottom_md({bottom_tz}, mpcsn, mfmt_any); @@ -610,7 +667,7 @@ void MKLDNNConvolutionLayer::InitConvolutionBwd(const vector* // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; @@ -684,13 +741,19 @@ void MKLDNNConvolutionLayer::InitConvolutionBwd(const vector* shared_ptr prv_bwdw_weights_diff_memory_pd(new MemPD(convBwdWeights_pd->diff_weights_primitive_desc())); // ---- Create usr memory primitive descriptors ------------- - memory::format mfmt_nchw = memory::format::nchw; - memory::format weights_mfmt = ( g!= 1) ? memory::format::goihw : memory::format::oihw; - + memory::format data_mfmt; + memory::format weights_mfmt; + if (this->num_spatial_axes_ == 2) { + data_mfmt = memory::format::nchw; + weights_mfmt = ( g!= 1) ? memory::format::goihw : memory::format::oihw; + } else { + data_mfmt = memory::format::ncdhw; + weights_mfmt = ( g!= 1) ? memory::format::goidhw : memory::format::oidhw; + } // ???!!! can we use usr memory primitive descrittors for backward?? - shared_ptr usr_bottom_data_memory_pd(new MemPD({{bottom_tz}, mpcsn, mfmt_nchw}, cpu_engine)); + shared_ptr usr_bottom_data_memory_pd(new MemPD({{bottom_tz}, mpcsn, data_mfmt}, cpu_engine)); shared_ptr usr_bias_data_memory_pd(new MemPD({{bias_tz}, mpcsn, memory::format::x}, cpu_engine)); - shared_ptr usr_top_data_memory_pd(new MemPD({{top_tz}, mpcsn, mfmt_nchw}, cpu_engine)); + shared_ptr usr_top_data_memory_pd(new MemPD({{top_tz}, mpcsn, data_mfmt}, cpu_engine)); shared_ptr usr_weights_data_memory_pd(new MemPD({{weights_tz}, mpcsn, weights_mfmt}, cpu_engine)); diff --git a/src/caffe/layers/mkldnn_eltwise_layer.cpp b/src/caffe/layers/mkldnn_eltwise_layer.cpp index beb737e74..aed8db34f 100644 --- a/src/caffe/layers/mkldnn_eltwise_layer.cpp +++ b/src/caffe/layers/mkldnn_eltwise_layer.cpp @@ -147,31 +147,58 @@ void MKLDNNEltwiseLayer::InitEltwiseFwd(const vector*>& botto memory::format mfmt_nchw = memory::format::nchw; // ---- Initialize memory descriptors ------------- + std::vector prv_dt(num_bottoms_, memory::data_type::f32); + for (auto i = 0; i < num_bottoms_; i++) + { + bool bottom_data_is_prv = (const_cast(bottom[i]->prv_data()) != NULL); + if (bottom_data_is_prv) + { + shared_ptr > mem_descr + = get_mkldnn_prv_descriptor(bottom[i]); + prv_dt[i] = static_cast(mem_descr->prv_memory_pd()->desc().data.data_type); + } + } + + memory::data_type first_prv_dt = prv_dt[0]; + bool different_input_dt = false; + for (auto i = 0; i < prv_dt.size(); i++) + { + if (prv_dt[i] != first_prv_dt) + { + different_input_dt = true; + } + } + std::vector bottom_data_mpd; fwd_bottom_data.clear(); fwd_bottom_data_primitives_.clear(); fwd_bottom_data_primitives_at_.clear(); + memory::data_type bottom_data_dt = memory::data_type::f32; for (auto i = 0; i < num_bottoms_; i++) { fwd_bottom_data.push_back(boost::shared_ptr >()); memory::format bottom_data_mfmt = mfmt_nchw; shared_ptr prv_bottom_data_mpd; shared_ptr usr_bottom_data_mpd( - new memory::primitive_desc({{n, ic, ih, iw}, mpcsn, bottom_data_mfmt}, cpu_engine)); + new memory::primitive_desc({{n, ic, ih, iw}, mpcsn, mfmt_nchw}, cpu_engine)); bool bottom_data_is_prv = (const_cast(bottom[i]->prv_data()) != NULL); if (bottom_data_is_prv) { shared_ptr > mem_descr = get_mkldnn_prv_descriptor(bottom[i]); - bottom_data_mfmt = static_cast( - mem_descr->prv_memory_pd()->desc().data.format); + if(!different_input_dt){ + bottom_data_mfmt = static_cast( + mem_descr->prv_memory_pd()->desc().data.format); + bottom_data_dt = static_cast( + mem_descr->prv_memory_pd()->desc().data.data_type); + } prv_bottom_data_mpd.reset(new memory::primitive_desc( - {{n, ic, ih, iw}, mpcsn, bottom_data_mfmt}, cpu_engine)); + {{n, ic, ih, iw}, bottom_data_dt, bottom_data_mfmt}, cpu_engine)); } bottom_data_mpd.push_back(memory::primitive_desc( - {{n, ic, ih, iw}, mpcsn, bottom_data_mfmt}, cpu_engine)); + {{n, ic, ih, iw}, bottom_data_dt, bottom_data_mfmt}, cpu_engine)); fwd_bottom_data[i].reset(new MKLDNNData( usr_bottom_data_mpd, prv_bottom_data_mpd, bottom[i], this)); @@ -185,9 +212,9 @@ void MKLDNNEltwiseLayer::InitEltwiseFwd(const vector*>& botto // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; - eltwiseFwd_pd.reset(new sum::primitive_desc({{n, ic, ih, iw}, mpcsn, memory::format::any}, scale, bottom_data_mpd)); + eltwiseFwd_pd.reset(new sum::primitive_desc({{n, ic, ih, iw}, bottom_data_dt, memory::format::any}, scale, bottom_data_mpd)); CHECK(eltwiseFwd_pd); shared_ptr prv_top_data_mpd(new memory::primitive_desc(eltwiseFwd_pd->dst_primitive_desc())); diff --git a/src/caffe/layers/mkldnn_inner_product_layer.cpp b/src/caffe/layers/mkldnn_inner_product_layer.cpp index b62ec3e4e..8d839ee32 100644 --- a/src/caffe/layers/mkldnn_inner_product_layer.cpp +++ b/src/caffe/layers/mkldnn_inner_product_layer.cpp @@ -195,7 +195,7 @@ void MKLDNNInnerProductLayer::InitInnerProductFwd(const vectorlayer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; @@ -355,18 +355,20 @@ void MKLDNNInnerProductLayer::InitInnerProductBwd(const vector ipBwdData_desc; shared_ptr ipBwdWeights_desc; - if (this->bias_term_) - ipBwdWeights_desc.reset(new inner_product_backward_weights::desc(init_bottom_md, init_weights_md + if (this->bias_term_) { + ipBwdWeights_desc.reset(new inner_product_backward_weights::desc(init_bottom_md, init_weights_md , init_bias_md, init_top_md)); - else - ipBwdWeights_desc.reset(new inner_product_backward_weights::desc(init_bottom_md, init_weights_md + } + else { + ipBwdWeights_desc.reset(new inner_product_backward_weights::desc(init_bottom_md, init_weights_md , init_top_md)); + } ipBwdData_desc.reset(new inner_product_backward_data::desc(init_bottom_md, init_weights_md, init_top_md)); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; diff --git a/src/caffe/layers/mkldnn_lrn_layer.cpp b/src/caffe/layers/mkldnn_lrn_layer.cpp index d3c3147fd..0c834d209 100644 --- a/src/caffe/layers/mkldnn_lrn_layer.cpp +++ b/src/caffe/layers/mkldnn_lrn_layer.cpp @@ -164,7 +164,7 @@ void MKLDNNLRNLayer::InitLRNFwd(const vector*>& bottom, const size_, alpha_, beta_); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; @@ -315,7 +315,7 @@ void MKLDNNLRNLayer::InitLRNBwd(const vector*>& top size_, alpha_, beta_); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; diff --git a/src/caffe/layers/mkldnn_pooling_layer.cpp b/src/caffe/layers/mkldnn_pooling_layer.cpp index c672dd889..17b0ff797 100644 --- a/src/caffe/layers/mkldnn_pooling_layer.cpp +++ b/src/caffe/layers/mkldnn_pooling_layer.cpp @@ -59,18 +59,18 @@ void MKLDNNPoolingLayer::LayerSetUp(const vector*>& bottom PoolingParameter pool_param = this->layer_param_.pooling_param(); if (pool_param.global_pooling()) { - CHECK(!(pool_param.kernel_size_size() || pool_param.has_kernel_h() || pool_param.has_kernel_w())) + CHECK(!(pool_param.has_kernel_size() || pool_param.has_kernel_h() || pool_param.has_kernel_w())) << "With Global_pooling: true Filter size cannot specified"; } else { - CHECK(!pool_param.kernel_size_size() != !(pool_param.has_kernel_h() && pool_param.has_kernel_w())) + CHECK(!pool_param.has_kernel_size() != !(pool_param.has_kernel_h() && pool_param.has_kernel_w())) << "Filter size is kernel_size OR kernel_h and kernel_w; not both"; - CHECK(pool_param.kernel_size_size() ||(pool_param.has_kernel_h() && pool_param.has_kernel_w())) + CHECK(pool_param.has_kernel_size() ||(pool_param.has_kernel_h() && pool_param.has_kernel_w())) << "For non-square filters both kernel_h and kernel_w are required."; } - CHECK((!pool_param.pad_size() && pool_param.has_pad_h() && pool_param.has_pad_w()) + CHECK((!pool_param.has_pad() && pool_param.has_pad_h() && pool_param.has_pad_w()) || (!pool_param.has_pad_h() && !pool_param.has_pad_w())) << "pad is pad OR pad_h and pad_w are required."; - CHECK((!pool_param.stride_size() && pool_param.has_stride_h() && pool_param.has_stride_w()) + CHECK((!pool_param.has_stride() && pool_param.has_stride_h() && pool_param.has_stride_w()) || (!pool_param.has_stride_h() && !pool_param.has_stride_w())) << "Stride is stride OR stride_h and stride_w are required."; @@ -79,15 +79,8 @@ void MKLDNNPoolingLayer::LayerSetUp(const vector*>& bottom kernel_h_ = bottom[0]->height(); kernel_w_ = bottom[0]->width(); } else { - if (pool_param.kernel_size_size()) { - CHECK(pool_param.kernel_size_size() == 1 || pool_param.kernel_size_size() == 2) - << "kernel_size must be specified once, or 2 values for Height and Width"; - if (pool_param.kernel_size_size() == 1) { - kernel_h_ = kernel_w_ = pool_param.kernel_size(0); - } else { - kernel_h_ = pool_param.kernel_size(0); - kernel_w_ = pool_param.kernel_size(1); - } + if (pool_param.has_kernel_size()) { + kernel_h_ = kernel_w_ = pool_param.kernel_size(); } else { kernel_h_ = pool_param.kernel_h(); kernel_w_ = pool_param.kernel_w(); @@ -98,32 +91,14 @@ void MKLDNNPoolingLayer::LayerSetUp(const vector*>& bottom CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero."; if (!pool_param.has_pad_h()) { - CHECK(pool_param.pad_size() < 3) - << "pad must be specified no more than 3 dimensions"; - if (pool_param.pad_size() == 0) { - pad_t_ = pad_b_ = pad_l_ = pad_r_ = 0; - } else if (pool_param.pad_size() == 1) { - pad_t_ = pad_b_ = pad_l_ = pad_r_ = pool_param.pad(0); - } else { - pad_t_ = pad_b_ = pool_param.pad(0); - pad_l_ = pad_r_ = pool_param.pad(1); - } + pad_t_ = pad_b_ = pad_l_ = pad_r_ = pool_param.pad(); } else { pad_t_ = pad_b_ = pool_param.pad_h(); pad_l_ = pad_r_ = pool_param.pad_w(); } if (!pool_param.has_stride_h()) { - CHECK(pool_param.stride_size() < 3) - << "stride must be specified no more than 3 dimensions"; - if (pool_param.stride_size() == 0) { - stride_h_ = stride_w_ = 1; - } else if (pool_param.stride_size() == 1) { - stride_h_ = stride_w_ = pool_param.stride(0); - } else { - stride_h_ = pool_param.stride(0); - stride_w_ = pool_param.stride(1); - } + stride_h_ = stride_w_ = pool_param.stride(); } else { stride_h_ = pool_param.stride_h(); stride_w_ = pool_param.stride_w(); @@ -262,6 +237,7 @@ void MKLDNNPoolingLayer::InitPoolingFwd(const vector*>& botto int32_t pr = this->pad_r_; bool bottom_data_is_prv = (const_cast(bottom[0]->prv_data()) != NULL); + bool top_data_is_prv = (const_cast(top[0]->prv_data()) != NULL); engine cpu_engine = CpuEngine::Instance().get_engine(); memory::data_type mpcsn = memory::data_type::f32; @@ -276,17 +252,15 @@ void MKLDNNPoolingLayer::InitPoolingFwd(const vector*>& botto shared_ptr usr_bottom_data_mpd(new MemPD({{bottom_tz}, mpcsn, mfmt_nchw}, cpu_engine)); shared_ptr usr_top_data_mpd(new MemPD({{top_tz}, mpcsn, mfmt_nchw}, cpu_engine)); - std::vector fl; std::vector scale; - bool bottom_is_float = false; - if (bottom_data_is_prv) { + if (bottom_data_is_prv || top_data_is_prv) { shared_ptr > mem_descr = get_mkldnn_prv_descriptor(bottom[0]); - bottom_is_float = mem_descr->get_float(); cmfmt = static_cast(mem_descr->prv_memory_pd()->desc().data.format); mpcsn = static_cast(mem_descr->prv_memory_pd()->desc().data.data_type); - fl.push_back(mem_descr->get_fl(0)); scale.push_back(mem_descr->get_scale(0)); + } else{ + scale.push_back(1.); } shared_ptr init_fwd_bottom_md(new memory::desc({bottom_tz}, mpcsn, cmfmt)); @@ -297,7 +271,7 @@ void MKLDNNPoolingLayer::InitPoolingFwd(const vector*>& botto , {sh, sw}, {kh, kw}, {pt, pl}, {pb, pr}, padding_kind::zero); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; @@ -319,7 +293,7 @@ void MKLDNNPoolingLayer::InitPoolingFwd(const vector*>& botto // ---- Initialize remaining memory descriptors ------------- shared_ptr prv_fwd_bottom_data_mpd; shared_ptr prv_fwd_top_data_mpd; - if (bottom_data_is_prv) { + if (bottom_data_is_prv || top_data_is_prv) { prv_fwd_bottom_data_mpd.reset(new MemPD(*init_fwd_bottom_md, engine)); prv_fwd_top_data_mpd.reset(new MemPD(*init_fwd_top_md, engine)); // ---- Log prv memory primitive descriptors ------------- @@ -337,18 +311,10 @@ void MKLDNNPoolingLayer::InitPoolingFwd(const vector*>& botto : max_idx_.mutable_cpu_data(); // --- init primitive and prv_memory descriptors ---------------------- - if(bottom_is_float){ - fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_mpd, prv_fwd_bottom_data_mpd, bottom[0], this, true, scale)); - } else { - fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_mpd, prv_fwd_bottom_data_mpd, bottom[0], this, fl)); - } + fwd_bottom_data.reset(new MKLDNNData(usr_bottom_data_mpd, prv_fwd_bottom_data_mpd, bottom[0], this, scale)); fwd_bottom_data_primitive = fwd_bottom_data->create_input(false); - if(bottom_is_float){ - fwd_top_data.reset(new MKLDNNData(usr_top_data_mpd, prv_fwd_top_data_mpd, top[0], this, true, scale)); - } else{ - fwd_top_data.reset(new MKLDNNData(usr_top_data_mpd, prv_fwd_top_data_mpd, top[0], this, fl)); - } + fwd_top_data.reset(new MKLDNNData(usr_top_data_mpd, prv_fwd_top_data_mpd, top[0], this, scale)); fwd_top_data_memory = fwd_top_data->create_output_memory(); if (propagation == prop_kind::forward_training && @@ -479,7 +445,7 @@ void MKLDNNPoolingLayer::InitPoolingBwd(const vector*>& top , {sh, sw}, {kh, kw}, {pt, pl}, {pb, pr}, padding_kind::zero); // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; diff --git a/src/caffe/layers/mkldnn_relu_layer.cpp b/src/caffe/layers/mkldnn_relu_layer.cpp index 364063f12..4c5f83456 100644 --- a/src/caffe/layers/mkldnn_relu_layer.cpp +++ b/src/caffe/layers/mkldnn_relu_layer.cpp @@ -88,19 +88,30 @@ void MKLDNNReLULayer::InitReLUFwd(const vector*>& bottom, con engine cpu_engine = CpuEngine::Instance().get_engine(); memory::data_type mpcsn = memory::data_type::f32; // ---- Initialize memory descriptors ------------- - shared_ptr bottom_data_md, top_data_md; - shared_ptr usr_data_mpd(NULL), prv_data_mpd(NULL); + shared_ptr bottom_data_md; + shared_ptr usr_data_mpd(NULL), prv_data_mpd(NULL), top_data_mpd(NULL); + memory::data_type src_dt = memory::data_type::f32; + memory::data_type top_dt = memory::data_type::f32; + memory::format src_mfmt = memory::format::nchw; + //bottom_data_is_prv = false; + std::vector scale; if (bottom_data_is_prv) { shared_ptr > mem_descr = get_mkldnn_prv_descriptor(bottom[0]); bottom_data_md.reset(new memory::desc(mem_descr->prv_memory_pd()->desc())); usr_data_mpd = mem_descr->usr_memory_pd(); prv_data_mpd = mem_descr->prv_memory_pd(); + scale.push_back(mem_descr->get_scale(0)); + src_dt = static_cast(mem_descr->prv_memory_pd()->desc().data.data_type); + src_mfmt = static_cast(mem_descr->prv_memory_pd()->desc().data.format); } else { bottom_data_md.reset(new memory::desc({{n, ic, ih, iw}}, mpcsn, memory::format::nchw)); usr_data_mpd.reset(new memory::primitive_desc(*bottom_data_md, cpu_engine)); + prv_data_mpd.reset(new memory::primitive_desc(*bottom_data_md, cpu_engine)); + scale.push_back(1.); } - top_data_md = bottom_data_md; + top_dt = src_dt; + top_data_mpd.reset(new memory::primitive_desc({{n,ic,ih,iw}, top_dt, src_mfmt}, cpu_engine)); // ---- Initialize relu primitive descriptor ------------- //relu_forward::desc reluFwd_desc(propagation, *bottom_data_md, negative_slope); @@ -110,7 +121,7 @@ void MKLDNNReLULayer::InitReLUFwd(const vector*>& bottom, con // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; @@ -128,11 +139,11 @@ void MKLDNNReLULayer::InitReLUFwd(const vector*>& bottom, con CHECK(reluFwd_pd); // --- init primitive and prv_memory descriptors ---------------------- - fwd_bottom_data.reset(new MKLDNNData(usr_data_mpd, prv_data_mpd, bottom[0], this)); + fwd_bottom_data.reset(new MKLDNNData(usr_data_mpd, prv_data_mpd, bottom[0], this, scale)); fwd_bottom_data->name = "fwd_bottom_data @ " + this->layer_param_.name(); fwd_bottom_data_primitive = fwd_bottom_data->create_input(false); - fwd_top_data.reset(new MKLDNNData(usr_data_mpd, prv_data_mpd, top[0], this)); + fwd_top_data.reset(new MKLDNNData(usr_data_mpd, top_data_mpd, top[0], this, scale)); fwd_top_data->name = "fwd_top_data @ " + this->layer_param_.name(); fwd_top_data_memory = fwd_top_data->create_output_memory(inplace); @@ -165,6 +176,7 @@ void MKLDNNReLULayer::Forward_cpu(const vector*>& bottom top[0]->ShareData(*bottom[0]); return; } + // making reorders if needed. fwd_bottom_data->sync_before_read(); // update top that head at prv @@ -279,7 +291,7 @@ void MKLDNNReLULayer::InitReLUBwd(const vector*>& top // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; EngineParser ep(subengines); unsigned subEngineIndex = 0; diff --git a/src/caffe/layers/mkldnn_split_layer.cpp b/src/caffe/layers/mkldnn_split_layer.cpp index 359f52462..1abea6169 100644 --- a/src/caffe/layers/mkldnn_split_layer.cpp +++ b/src/caffe/layers/mkldnn_split_layer.cpp @@ -53,7 +53,7 @@ MKLDNNSplitLayer::~MKLDNNSplitLayer() { } template void MKLDNNSplitLayer::Reshape(const vector*>& bottom, const vector*>& top) { - int count = bottom[0]->count(); + size_t count = bottom[0]->count(); for (int i = 0; i < top.size(); ++i) { // Do not allow in-place computation in the SplitLayer. Instead, share data // by reference in the forward pass, and keep separate diff allocations in @@ -99,14 +99,24 @@ void MKLDNNSplitLayer::InitSplitBwd(const vector*>& bottom, memory::data_type data_type = memory::data_type::f32; // TODO: shouldn't we have format here that is well suited for earlier layer. // eg. Netcompiler should some of knowledge provided - memory::format mfmt_nchw = memory::format::nchw; - memory::format diff_dst_mfmt = mfmt_nchw; + memory::format data_mfmt; + memory::format diff_dst_mfmt; + + size_t dim_src = bottom[0]->shape().size(); + if (dim_src == 5) { + data_mfmt = memory::format::ncdhw; + diff_dst_mfmt = data_mfmt; + } else { + data_mfmt = memory::format::nchw; + diff_dst_mfmt = data_mfmt; + } // Dimensions of bottom and top blobs. There is a number of // top blobs each of the same size as the bottom one memory::dims bottom_tz; - bottom_tz.resize(4); - for(int i=0; i<4; i++) { + dim_src = std::max((size_t)4, dim_src); + bottom_tz.resize(dim_src); + for(size_t i=0; isizes_src_.size()) { bottom_tz[i] = static_cast(this->sizes_src_[i]); } else { @@ -116,7 +126,7 @@ void MKLDNNSplitLayer::InitSplitBwd(const vector*>& bottom, shared_ptr prv_diff_dst_mpd; shared_ptr usr_diff_dst_mpd( - new memory::primitive_desc({bottom_tz, data_type, mfmt_nchw}, + new memory::primitive_desc({bottom_tz, data_type, data_mfmt}, cpu_engine)); // We will get final destination layout of bottom diff after first top... @@ -137,7 +147,7 @@ void MKLDNNSplitLayer::InitSplitBwd(const vector*>& bottom, memory::dims top_tz = bottom_tz; shared_ptr usr_diff_src_mpd( - new memory::primitive_desc({top_tz, data_type, mfmt_nchw}, + new memory::primitive_desc({top_tz, data_type, data_mfmt}, cpu_engine)); // Gather diff descriptors of top difs (inputs for BW) @@ -148,7 +158,7 @@ void MKLDNNSplitLayer::InitSplitBwd(const vector*>& bottom, bwd_top_diffs_primitives_at_.clear(); for (int i = 0; i < top.size(); ++i) { // If diff is in private layout then copy descriptor from it - memory::format diff_src_mfmt = mfmt_nchw; + memory::format diff_src_mfmt = data_mfmt; bool top_diff_is_prv = top[i]->prv_diff() != NULL; if (top_diff_is_prv) { shared_ptr > mem_descr @@ -170,7 +180,7 @@ void MKLDNNSplitLayer::InitSplitBwd(const vector*>& bottom, // ---- Determining engine to use ----------------------- std::string subengines = this->layer_param_.engine(); - if (subengines == "" || subengines == "MKLDNN") + if (subengines.find("MKLDNN") == std::string::npos || subengines == "MKLDNN") subengines = "MKLDNN:CPU"; splitBwd_pd_.reset(new sum::primitive_desc({bottom_tz, data_type, diff_dst_mfmt},scale, prv_diff_srcs_mpd)); CHECK(splitBwd_pd_); diff --git a/src/caffe/layers/multibox_loss_layer.cpp b/src/caffe/layers/multibox_loss_layer.cpp index c25109e3f..6f8260294 100644 --- a/src/caffe/layers/multibox_loss_layer.cpp +++ b/src/caffe/layers/multibox_loss_layer.cpp @@ -111,6 +111,8 @@ void MultiBoxLossLayer::LayerSetUp(const vector*>& bottom, layer_param.set_name(this->layer_param_.name() + "_l2_loc"); layer_param.set_type("EuclideanLoss"); layer_param.add_loss_weight(loc_weight_); + layer_param.mutable_loss_param()->set_normalization( + LossParameter_NormalizationMode_NONE); loc_loss_layer_ = LayerRegistry::CreateLayer(layer_param); loc_loss_layer_->SetUp(loc_bottom_vec_, loc_top_vec_); } else if (loc_loss_type_ == MultiBoxLossParameter_LocLossType_SMOOTH_L1) { @@ -118,6 +120,8 @@ void MultiBoxLossLayer::LayerSetUp(const vector*>& bottom, layer_param.set_name(this->layer_param_.name() + "_smooth_L1_loc"); layer_param.set_type("SmoothL1Loss"); layer_param.add_loss_weight(loc_weight_); + layer_param.mutable_loss_param()->set_normalization( + LossParameter_NormalizationMode_NONE); loc_loss_layer_ = LayerRegistry::CreateLayer(layer_param); loc_loss_layer_->SetUp(loc_bottom_vec_, loc_top_vec_); } else { @@ -154,6 +158,8 @@ void MultiBoxLossLayer::LayerSetUp(const vector*>& bottom, layer_param.set_name(this->layer_param_.name() + "_logistic_conf"); layer_param.set_type("SigmoidCrossEntropyLoss"); layer_param.add_loss_weight(Dtype(1.)); + layer_param.mutable_loss_param()->set_normalization( + LossParameter_NormalizationMode_NONE); // Fake reshape. vector conf_shape(1, 1); conf_shape.push_back(num_classes_); @@ -195,8 +201,8 @@ void MultiBoxLossLayer::Forward_cpu(const vector*>& bottom, // Retrieve all prior bboxes. It is same within a batch since we assume all // images in a batch are of same dimension. - vector prior_bboxes; - vector > prior_variances; + vector prior_bboxes(num_priors_); + vector > prior_variances(num_priors_); GetPriorBBoxes(prior_data, num_priors_, &prior_bboxes, &prior_variances); // Retrieve all predictions. @@ -281,15 +287,14 @@ void MultiBoxLossLayer::Forward_cpu(const vector*>& bottom, } top[0]->mutable_cpu_data()[0] = 0; + // To make sure we don't use the cached normalizer value + Dtype normalizer = LossLayer::GetNormalizer( + normalization_, num_, num_priors_, num_matches_); if (this->layer_param_.propagate_down(0)) { - Dtype normalizer = LossLayer::GetNormalizer( - normalization_, num_, num_priors_, num_matches_); top[0]->mutable_cpu_data()[0] += loc_weight_ * loc_loss_.cpu_data()[0] / normalizer; } if (this->layer_param_.propagate_down(1)) { - Dtype normalizer = LossLayer::GetNormalizer( - normalization_, num_, num_priors_, num_matches_); top[0]->mutable_cpu_data()[0] += conf_loss_.cpu_data()[0] / normalizer; } } @@ -322,9 +327,7 @@ void MultiBoxLossLayer::Backward_cpu(const vector*>& top, loc_bottom_vec_); PERFORMANCE_MEASUREMENT_END_STATIC("BW_Smooth_L1");} // Scale gradient. - Dtype normalizer = LossLayer::GetNormalizer( - normalization_, num_, num_priors_, num_matches_); - Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer; + Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_; caffe_scal(loc_pred_.count(), loss_weight, loc_pred_.mutable_cpu_diff()); // Copy gradient back to bottom[0]. const Dtype* loc_pred_diff = loc_pred_.cpu_diff(); @@ -365,9 +368,7 @@ void MultiBoxLossLayer::Backward_cpu(const vector*>& top, conf_bottom_vec_); PERFORMANCE_MEASUREMENT_END_STATIC("BW_Softmax");} // Scale gradient. - Dtype normalizer = LossLayer::GetNormalizer( - normalization_, num_, num_priors_, num_matches_); - Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer; + Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_; caffe_scal(conf_pred_.count(), loss_weight, conf_pred_.mutable_cpu_diff()); // Copy gradient back to bottom[1]. diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp index 87be37910..b40c8ea3c 100644 --- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp +++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp @@ -67,7 +67,16 @@ void MultinomialLogisticLossLayer::Forward_cpu( bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD)); loss -= log(prob); } - top[0]->mutable_cpu_data()[0] = loss / num; + Dtype normalizer = Dtype(bottom[0]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + top[0]->mutable_cpu_data()[0] = loss / normalizer; } template @@ -83,9 +92,18 @@ void MultinomialLogisticLossLayer::Backward_cpu( const Dtype* bottom_label = bottom[1]->cpu_data(); Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); int num = bottom[0]->num(); + Dtype normalizer = Dtype(num); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif int dim = bottom[0]->count() / bottom[0]->num(); caffe_set(bottom[0]->count(), Dtype(0), bottom_diff); - const Dtype scale = - top[0]->cpu_diff()[0] / num; + const Dtype scale = - top[0]->cpu_diff()[0] / normalizer; for (int i = 0; i < num; ++i) { int label = static_cast(bottom_label[i]); Dtype prob = std::max( diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp index f4b0a6294..96613af14 100644 --- a/src/caffe/layers/pooling_layer.cpp +++ b/src/caffe/layers/pooling_layer.cpp @@ -66,22 +66,22 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, if (num_spatial_axes_ == 2) { // Process 2D Pooling if (pool_param.global_pooling()) { - CHECK(!(pool_param.kernel_size_size() || + CHECK(!(pool_param.has_kernel_size() || pool_param.has_kernel_h() || pool_param.has_kernel_w())) << "With Global_pooling: true Filter size cannot specified"; } else { - CHECK(!pool_param.kernel_size_size() != + CHECK(!pool_param.has_kernel_size() != !(pool_param.has_kernel_h() && pool_param.has_kernel_w())) << "Filter size is kernel_size OR kernel_h and kernel_w; not both"; - CHECK(pool_param.kernel_size_size() || + CHECK(pool_param.has_kernel_size() || (pool_param.has_kernel_h() && pool_param.has_kernel_w())) << "For non-square filters both kernel_h and kernel_w are required."; } - CHECK((!pool_param.pad_size() && pool_param.has_pad_h() + CHECK((!pool_param.has_pad() && pool_param.has_pad_h() && pool_param.has_pad_w()) || (!pool_param.has_pad_h() && !pool_param.has_pad_w())) << "pad is pad OR pad_h and pad_w are required."; - CHECK((!pool_param.stride_size() && pool_param.has_stride_h() + CHECK((!pool_param.has_stride() && pool_param.has_stride_h() && pool_param.has_stride_w()) || (!pool_param.has_stride_h() && !pool_param.has_stride_w())) << "Stride is stride OR stride_h and stride_w are required."; @@ -90,15 +90,8 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, kernel_h_ = bottom[0]->height(); kernel_w_ = bottom[0]->width(); } else { - if (pool_param.kernel_size_size()) { - CHECK(pool_param.kernel_size_size() == 1 || pool_param.kernel_size_size() == 2) - << "kernel_size must be specified once, or 2 values for Height and Width"; - if (pool_param.kernel_size_size() == 1) { - kernel_h_ = kernel_w_ = pool_param.kernel_size(0); - } else { - kernel_h_ = pool_param.kernel_size(0); - kernel_w_ = pool_param.kernel_size(1); - } + if (pool_param.has_kernel_size()) { + kernel_h_ = kernel_w_ = pool_param.kernel_size(); } else { kernel_h_ = pool_param.kernel_h(); kernel_w_ = pool_param.kernel_w(); @@ -106,29 +99,15 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, } CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero."; CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero."; - if (pool_param.pad_size() > 0) { - CHECK(pool_param.pad_size() == 1 || pool_param.pad_size() == 2) - << "pad must be specified once, or 2 values for Height and Width"; - if (pool_param.pad_size() == 1) { - pad_h_ = pad_w_ = pool_param.pad(0); - } else { - pad_h_ = pool_param.pad(0); - pad_w_ = pool_param.pad(1); - } - } else { + if (!pool_param.has_pad_h()) { + pad_h_ = pad_w_ = pool_param.pad(); + } else { pad_h_ = pool_param.pad_h(); pad_w_ = pool_param.pad_w(); } - if (pool_param.stride_size() > 0) { - CHECK(pool_param.stride_size() == 1 || pool_param.stride_size() == 2) - << "stride must be specified once, or 2 values for Height and Width"; - if (pool_param.stride_size() == 1) { - stride_h_ = stride_w_ = pool_param.stride(0); - } else { - stride_h_ = pool_param.stride(0); - stride_w_ = pool_param.stride(1); - } - } else { + if (!pool_param.has_stride_h()) { + stride_h_ = stride_w_ = pool_param.stride(); + } else { stride_h_ = pool_param.stride_h(); stride_w_ = pool_param.stride_w(); } @@ -166,7 +145,7 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, if (global_pooling_) { // if global pooling height and width are set the entire blob, // and the layer cannot have a kernel set - CHECK_GE(0, pool_param.kernel_size_size()) + CHECK_GE(0, pool_param.kernel_size_3d_size()) << "With Global_pooling: true Filter size cannot specified."; CHECK(!pool_param.has_kernel_h() || !pool_param.has_kernel_w()) << "With Global_pooling: true Filter size cannot specified."; @@ -179,19 +158,19 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, if (pool_param.has_kernel_h() || pool_param.has_kernel_w()) { CHECK_EQ(num_spatial_axes_, 2) << "kernel_h & kernel_w can only be used for 2D pooling."; - CHECK_EQ(0, pool_param.kernel_size_size()) + CHECK_EQ(0, pool_param.kernel_size_3d_size()) << "Either kernel_size or kernel_h/w should be specified, not both."; kernel_shape_data[0] = pool_param.kernel_h(); kernel_shape_data[1] = pool_param.kernel_w(); } else { // using repeated kernel param - const int num_kernel_dims = pool_param.kernel_size_size(); + const int num_kernel_dims = pool_param.kernel_size_3d_size(); CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_) << "kernel_size must be specified once, or once per spatial dimension" << " (kernel_size specified " << num_kernel_dims << " times " << num_spatial_axes_ << " spatial dims)."; for (int i = 0; i < num_spatial_axes_; ++i) { - kernel_shape_data[i] = pool_param.kernel_size((num_kernel_dims == 1) ? 0 : i); + kernel_shape_data[i] = pool_param.kernel_size_3d((num_kernel_dims == 1) ? 0 : i); } } } @@ -209,13 +188,13 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, // And there must be 2 spatial dims CHECK_EQ(num_spatial_axes_, 2) << "pad_h & pad_w can only be used for 2D convolution."; - CHECK_EQ(0, pool_param.pad_size()) + CHECK_EQ(0, pool_param.pad_3d_size()) << "Either pad or pad_h/w should be specified, not both."; pad_data[0] = pool_param.pad_h(); pad_data[1] = pool_param.pad_w(); } else { // using repeated pad param - const int num_pad_dims = pool_param.pad_size(); + const int num_pad_dims = pool_param.pad_3d_size(); CHECK(num_pad_dims == 0 || num_pad_dims == 1 || num_pad_dims == num_spatial_axes_) << "pad must be specified once, or once per spatial dimension " @@ -224,7 +203,7 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, const int kDefaultPad = 0; for (int i = 0; i < num_spatial_axes_; ++i) { pad_data[i] = (num_pad_dims == 0) ? kDefaultPad : - pool_param.pad((num_pad_dims == 1) ? 0 : i); + pool_param.pad_3d((num_pad_dims == 1) ? 0 : i); if (global_pooling_) { CHECK_EQ(pad_data[i], 0) << "With Global_pooling: true; pool = 0"; @@ -248,13 +227,13 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, if (pool_param.has_stride_h() || pool_param.has_stride_w()) { CHECK_EQ(num_spatial_axes_, 2) << "stride_h & stride_w can only be used for 2D convolution."; - CHECK_EQ(0, pool_param.stride_size()) + CHECK_EQ(0, pool_param.stride_3d_size()) << "Either stride or stride_h/w should be specified, not both."; stride_data[0] = pool_param.stride_h(); stride_data[1] = pool_param.stride_w(); } else { // using repeated stride param - const int num_stride_dims = pool_param.stride_size(); + const int num_stride_dims = pool_param.stride_3d_size(); CHECK(num_stride_dims == 0 || num_stride_dims == 1 || num_stride_dims == num_spatial_axes_) << "stride must be specified once, or once per spatial dimension " @@ -263,7 +242,7 @@ void PoolingLayer::LayerSetUp(const vector*>& bottom, const int kDefaultStride = 1; for (int i = 0; i < num_spatial_axes_; ++i) { stride_data[i] = (num_stride_dims == 0) ? kDefaultStride : - pool_param.stride((num_stride_dims == 1) ? 0 : i); + pool_param.stride_3d((num_stride_dims == 1) ? 0 : i); CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero."; if (global_pooling_) { CHECK_EQ(stride_data[i], 1) diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp index 9e661255e..ddeed2fd7 100644 --- a/src/caffe/layers/prelu_layer.cpp +++ b/src/caffe/layers/prelu_layer.cpp @@ -118,6 +118,7 @@ void PReLULayer::Forward_cpu(const vector*>& bottom, // if channel_shared, channel index in the following computation becomes // always zero. const int div_factor = channel_shared_ ? channels : 1; +#pragma omp parallel for for (long i = 0; i < count; ++i) { long c = (i / dim) % channels / div_factor; top_data[i] = std::max(bottom_data[i], Dtype(0)) @@ -159,6 +160,7 @@ void PReLULayer::Backward_cpu(const vector*>& top, // Propagate to bottom if (propagate_down[0]) { Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); +#pragma omp parallel for for (long i = 0; i < count; ++i) { long c = (i / dim) % channels / div_factor; bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0) diff --git a/src/caffe/layers/region_loss_layer.cpp b/src/caffe/layers/region_loss_layer.cpp new file mode 100644 index 000000000..269507019 --- /dev/null +++ b/src/caffe/layers/region_loss_layer.cpp @@ -0,0 +1,621 @@ +#include +#include +#include +#include +#include +#include +#include + +#include "caffe/layers/region_loss_layer.hpp" +#include "caffe/util/math_functions.hpp" +#include "caffe/layers/sigmoid_layer.hpp" + +int iter = 0; + +namespace caffe { +template +Dtype Overlap(Dtype x1, Dtype w1, Dtype x2, Dtype w2) { + Dtype left = std::max(x1 - w1 / 2, x2 - w2 / 2); + Dtype right = std::min(x1 + w1 / 2, x2 + w2 / 2); + return right - left; +} + +template +Dtype Calc_iou(const vector& box, const vector& truth) { + Dtype w = Overlap(box[0], box[2], truth[0], truth[2]); + Dtype h = Overlap(box[1], box[3], truth[1], truth[3]); + if (w < 0 || h < 0) return 0; + Dtype inter_area = w * h; + Dtype union_area = box[2] * box[3] + truth[2] * truth[3] - inter_area; + return inter_area / union_area; +} + +template +inline Dtype sigmoid(Dtype x) +{ + return 1. / (1. + exp(-x)); +} + +template +void disp(Blob& swap) +{ + std::cout << "#######################################" << std::endl; + for (int b = 0; b < swap.num(); ++b) + for (int c = 0; c < swap.channels(); ++c) + for (int h = 0; h < swap.height(); ++h) + { + std::cout << "["; + for (int w = 0; w < swap.width(); ++w) + { + std::cout << swap.data_at(b, c, h, w) << ","; + } + std::cout << "]" << std::endl; + } + return; +} + +template +Dtype softmax_region(Dtype* input, int classes) +{ + Dtype sum = 0; + Dtype large = input[0]; + for (int i = 0; i < classes; ++i) { + if (input[i] > large) + large = input[i]; + } + for (int i = 0; i < classes; ++i) { + Dtype e = exp(input[i] - large); + sum += e; + input[i] = e; + } + for (int i = 0; i < classes; ++i) { + input[i] = input[i] / sum; + } + return 0; +} + +template +void softmax_tree(Dtype* input, tree *t) +{ + int count = 0; + for (int i = 0; i < t->groups; ++i) { //1 + int group_size = t->group_size[i]; //20 + softmax_region(input + count, group_size); + count += group_size; + } +} + +template +vector get_region_box(Dtype* x, vector biases, int n, int index, int i, int j, int w, int h) { + vector b; + b.clear(); + b.push_back((i + sigmoid(x[index + 0])) / w); + b.push_back((j + sigmoid(x[index + 1])) / h); + b.push_back(exp(x[index + 2]) * biases[2 * n] / w); + b.push_back(exp(x[index + 3]) * biases[2 * n + 1] / h); + return b; +} + +template +Dtype delta_region_box(vector truth, Dtype* x, vector biases, int n, int index, int i, int j, int w, int h, Dtype* delta, float scale) { + vector pred; + pred.clear(); + pred = get_region_box(x, biases, n, index, i, j, w, h); + + float iou = Calc_iou(pred, truth); + //LOG(INFO) << pred[0] << "," << pred[1] << "," << pred[2] << "," << pred[3] << ";"<< truth[0] << "," << truth[1] << "," << truth[2] << "," << truth[3]; + float tx = truth[0] * w - i; //0.5 + float ty = truth[1] * h - j; //0.5 + float tw = log(truth[2] * w / biases[2 * n]); //truth[2]=biases/w tw = 0 + float th = log(truth[3] * h / biases[2 * n + 1]); //th = 0 + + + if (tx - sigmoid(x[index + 0]) > 1) + delta[index + 0] = (-1) * scale * sigmoid(x[index + 0]) * (1 - sigmoid(x[index + 0])); + else if (tx - sigmoid(x[index + 0]) < -1) + delta[index + 0] = scale * sigmoid(x[index + 0]) * (1 - sigmoid(x[index + 0])); + else + delta[index + 0] = (-1) * scale * (tx - sigmoid(x[index + 0])) * sigmoid(x[index + 0]) * (1 - sigmoid(x[index + 0])); + + + if (ty - sigmoid(x[index + 1]) > 1) + delta[index + 1] = (-1) * scale * sigmoid(x[index + 1]) * (1 - sigmoid(x[index + 1])); + else if (ty - sigmoid(x[index + 1]) < -1) + delta[index + 1] = scale * sigmoid(x[index + 1]) * (1 - sigmoid(x[index + 1])); + else + delta[index + 1] = (-1) * scale * (ty - sigmoid(x[index + 1])) * sigmoid(x[index + 1]) * (1 - sigmoid(x[index + 1])); + + + if (tw - x[index + 2] > 1) + delta[index + 2] = -1 * scale; + else if (tw - x[index + 2] < -1) + delta[index + 2] = scale; + else + delta[index + 2] = (-1) * scale * (tw - x[index + 2]); + + if (th - x[index + 3] > 1) + delta[index + 3] = (-1) * scale; + else if (th - x[index + 3] < -1) + delta[index + 3] = scale; + else + delta[index + 3] = (-1) * scale * (th - x[index + 3]); + + /* + delta[index + 0] = (-1) * scale * (tx - sigmoid(x[index + 0])) * (1 - sigmoid(x[index + 0])) * sigmoid(x[index + 0]); + delta[index + 1] = (-1) * scale * (ty - sigmoid(x[index + 1])) * (1 - sigmoid(x[index + 1])) * sigmoid(x[index + 1]); + delta[index + 2] = (-1) * scale * (tw - x[index + 2]); + delta[index + 3] = (-1) * scale * (th - x[index + 3]); + */ + + return iou; +} + +template +void delta_region_class(Dtype* input_data, Dtype* &diff, int index, int class_label, int classes, string softmax_tree, tree *t, float scale, Dtype* avg_cat) +{ + //std::cout << "label: " << class_label << " "; + if (softmax_tree != "") { + float pred = 1; + while (class_label >= 0) + { + pred *= input_data[index + class_label]; + //LOG(INFO) << "class_label: " << class_label << " p: " << pred; + int g = t->group[class_label]; + int offset = t->group_offset[g]; + //LOG(INFO) << "class_label: " << class_label << " p: " << pred << " offset: " << offset; + for (int i = 0; i < t->group_size[g]; ++i) { + diff[index + offset + i] = (-1.0) * scale * (0 - input_data[index + offset + i]); + } + diff[index + class_label] = (-1.0) * scale * (1 - input_data[index + class_label]); + class_label = t->parent[class_label]; + //std::cout << class_label << " "; + } + *avg_cat += pred; + } + else { + for (int n = 0; n < classes; ++n) { + diff[index + n] = (-1.0) * scale * (((n == class_label) ? 1 : 0) - input_data[index + n]); + //std::cout << diff[index + n] << ","; + if (n == class_label) { + *avg_cat += input_data[index + n]; + //std::cout << "avg_cat: " << input_data[index + n] << std::endl; + } + } + } + //std::cout << std::endl; +} + +template +Dtype get_hierarchy_prob(Dtype* input_data, tree *t, int c) +{ + float p = 1; + while (c >= 0) { + p = p * input_data[c]; + c = t->parent[c]; + } + return p; +} + +template +void RegionLossLayer::LayerSetUp( + const vector*>& bottom, const vector*>& top) { + LossLayer::LayerSetUp(bottom, top); + RegionLossParameter param = this->layer_param_.region_loss_param(); + + side_ = param.side(); //13 + bias_match_ = param.bias_match(); // + num_class_ = param.num_class(); //20 + coords_ = param.coords(); //4 + num_ = param.num(); //5 + softmax_ = param.softmax(); // + softmax_tree_ = param.softmax_tree(); //string + if (softmax_tree_ != "") + t_ = tree(softmax_tree_); + + class_map_ = param.class_map(); + if (class_map_ != "") { + string line; + std::fstream fin(class_map_.c_str()); + if (!fin) { + LOG(INFO) << "no map file"; + } + + int index = 0; + int id = 0; + while (getline(fin, line)) { + stringstream ss; + ss << line; + ss >> id; + + cls_map_[index] = id; //class map + index++; + } + fin.close(); + } + + //LOG(INFO) << "t_.groups: " << t_.groups; + //jitter_ = param.jitter(); + //rescore_ = param.rescore(); + + object_scale_ = param.object_scale(); //5.0 + noobject_scale_ = param.noobject_scale(); //1.0 + class_scale_ = param.class_scale(); //1.0 + coord_scale_ = param.coord_scale(); //1.0 + + //absolute_ = param.absolute(); + thresh_ = param.thresh(); //0.6 + //random_ = param.random(); + + for (int c = 0; c < param.biases_size(); ++c) { + biases_.push_back(param.biases(c)); + } //0.73 0.87;2.42 2.65;4.30 7.04;10.24 4.59;12.68 11.87; + + //int input_count = bottom[0]->count(1); //h*w*n*(classes+coords+1) = 13*13*5*(20+4+1) + //int label_count = bottom[1]->count(1); //30*5 + + // outputs: classes, iou, coordinates + //int tmp_input_count = side_ * side_ * num_ * (coords_ + num_class_ + 1); //13*13*5*(20+4+1) label: isobj, class_label, coordinates + //int tmp_label_count = 30 * (1 + coords_); + //CHECK_EQ(input_count, tmp_input_count); + //CHECK_EQ(label_count, tmp_label_count); +} + + +template +void RegionLossLayer::Reshape( + const vector*>& bottom, const vector*>& top) { + //LossLayer::Reshape(bottom, top); + vector loss_shape(0); // Loss layers output a scalar; 0 axes. + top[0]->Reshape(loss_shape); + + diff_.ReshapeLike(*bottom[0]); + real_diff_.ReshapeLike(*bottom[0]); +} + +template +void RegionLossLayer::Forward_cpu( + const vector*>& bottom, const vector*>& top) { + //LOG(INFO) << "forward cpu"; + + const Dtype* label_data = bottom[1]->cpu_data(); //[label,x,y,w,h] + + Dtype* diff = diff_.mutable_cpu_data(); + caffe_set(diff_.count(), Dtype(0.0), diff); + + Dtype avg_anyobj(0.0), avg_obj(0.0), avg_iou(0.0), avg_cat(0.0), recall(0.0), loss(0.0); + int count = 0; + int class_count = 0; + //LOG(INFO) << "data reshape"; + //*********************************************************Reshape********************************************************// + Blob swap; + swap.Reshape(bottom[0]->num(), bottom[0]->height()*bottom[0]->width(), num_, bottom[0]->channels() / num_); + + Dtype* swap_data = swap.mutable_cpu_data(); + int index = 0; + for (int b = 0; b < bottom[0]->num(); ++b) + for (int h = 0; h < bottom[0]->height(); ++h) + for (int w = 0; w < bottom[0]->width(); ++w) + for (int c = 0; c < bottom[0]->channels(); ++c) + { + swap_data[index++] = bottom[0]->data_at(b, c, h, w); +#ifdef DEBUG + std::cout << "Debug: swap_data[" << index-1 << "]: " << swap_data[index] << std::endl; +#endif + } + + //CHECK_EQ(bottom[0]->data_at(0,4,1,2),swap.data_at(0,15,0,4)); + for (int b = 0; b < swap.num(); ++b) + for (int c = 0; c < swap.channels(); ++c) + for (int h = 0; h < swap.height(); ++h) + { + int index = b * swap.channels() * swap.height() * swap.width() + c * swap.height() * swap.width() + h * swap.width() + 4; +#ifdef DEBUG + std::cout << "Debug: Before sigmoid, swap_data[" << index << "]: " << swap_data[index] << std::endl; +#endif + swap_data[index] = sigmoid(swap_data[index]); +#ifdef DEBUG + std::cout << "Debug: After sigmoid, swap_data[" << index << "]: " << swap_data[index] << std::endl; +#endif + CHECK_GE(swap_data[index], 0); + //CHECK_GE(swap_data[index], 0) << "swap_data: " << swap_data[index]; + } + + if (softmax_tree_ != "") { + for (int b = 0; b < swap.num(); ++b) + for (int c = 0; c < swap.channels(); ++c) + for (int h = 0; h < swap.height(); ++h) + { + int index = b * swap.channels() * swap.height() * swap.width() + c * swap.height() * swap.width() + h * swap.width() + 5; + softmax_tree(swap_data + index, &t_); //softmax tree activation. + } + } + else { + for (int b = 0; b < swap.num(); ++b) + for (int c = 0; c < swap.channels(); ++c) + for (int h = 0; h < swap.height(); ++h) + { + int index = b * swap.channels() * swap.height() * swap.width() + c * swap.height() * swap.width() + h * swap.width() + 5; + + softmax_region(swap_data + index, num_class_); + for (int i = 0; i < num_class_; ++i) + CHECK_GE(swap_data[index + i], 0); + } + } + + //LOG(INFO) << "label transform"; + //******************************************************** Label ********************************************************// + vector > > labels; + labels.resize(bottom[0]->num()); //batch_size + int num_boxes = bottom[1]->height(); + //int start = -1; + + for (int i = 0; i < num_boxes; ++i) { + vector box; + int item_id = label_data[i * 8 + 0]; //item_id + + if (item_id == -1) + continue; + + Dtype xmin = label_data[i * 8 + 3]; + Dtype ymin = label_data[i * 8 + 4]; + Dtype xmax = label_data[i * 8 + 5]; + Dtype ymax = label_data[i * 8 + 6]; + + Dtype cx = (xmin + xmax) / 2.0; + Dtype cy = (ymin + ymax) / 2.0; + Dtype w = xmax - xmin; + Dtype h = ymax - ymin; + + if (softmax_tree_ != "") + box.push_back(label_data[i * 8 + 1]); //0-n:classes + else + box.push_back(label_data[i * 8 + 1] - 1); //0: bg 1-n: classes + + box.push_back(cx); //1 xmin + box.push_back(cy); //2 ymin + box.push_back(w); //3 xmax + box.push_back(h); //4 ymax + +#ifdef DEBUG + //LOG(INFO) << "item_id: "<< item_id << " label: " << box[0] << " box: [" << box[1] << " "<< box[2] << " " < truth; + //Dtype x = label_data[b * 30 * 5 + t * 5 + 1]; + //Dtype y = label_data[b * 30 * 5 + t * 5 + 2]; + vector box = labels[b][t]; + Dtype x = box[1]; + Dtype y = box[2]; + + //if (!x) break; + + //int class_label = label_data[b * 30 * 5 + t * 5 + 0]; + int class_label = box[0]; + + float maxp = 0; + int maxi = 0; + if (x > 100000 && y > 100000) { + for (int j = 0; j < side_; ++j) + for (int i = 0; i < side_; ++i) + for (int n = 0; n < num_; ++n) { + int index = b * swap.channels() * swap.height() * swap.width() + (j * side_ + i) * swap.height() * swap.width() + n * swap.width(); + float scale = swap_data[index + 4]; + diff[index + 4] = (-1.0) * noobject_scale_ * (0 - swap_data[index + 4]) * (swap_data[index + 4]) * (1 - swap_data[index + 4]); + float p = scale * get_hierarchy_prob(swap_data + index + 5, &t_, class_label); //p(c, pc, ppc .. ) + if (p > maxp) { + maxp = p; + maxi = index; //find maximum pos. + } + } + //LOG(INFO) << "delta hierarchy prob"; + delta_region_class(swap_data, diff, maxi + 5, class_label, num_class_, softmax_tree_, &t_, class_scale_, &avg_cat); + if (swap_data[maxi + 4] < 0.3) + diff[maxi + 4] = -1 * object_scale_ * (0.3 - swap_data[maxi + 4]) * (swap_data[maxi + 4]) * (1 - swap_data[maxi + 4]); + else + diff[maxi + 4] = 0; + ++class_count; + onlyclass = 1; + break; + } + } + if (onlyclass) + continue; + } + + for (int j = 0; j < side_; ++j) + for (int i = 0; i < side_; ++i) + for (int n = 0; n < num_; ++n) { + int index = b * swap.channels() * swap.height() * swap.width() + (j * side_ + i) * swap.height() * swap.width() + n * swap.width(); + CHECK_EQ(swap_data[index], swap.data_at(b, j * side_ + i, n, 0)); + + vector pred = get_region_box(swap_data, biases_, n, index, i, j, side_, side_); + float best_iou = 0; + //for (int t = 0; t < 30; ++t){ + //****************************************************************// + for (int t = 0; t < labels[b].size(); t++) { + vector box = labels[b][t]; + vector truth; + Dtype x = box[1]; + Dtype y = box[2]; + Dtype w = box[3]; + Dtype h = box[4]; + + //if (!w || !h) break; + truth.push_back(x); + truth.push_back(y); + truth.push_back(w); + truth.push_back(h); + Dtype iou = Calc_iou(pred, truth); + if (iou > best_iou) best_iou = iou; + } + + avg_anyobj += swap_data[index + 4]; + + //==================== focal negative ===============// + //noobject_scale_ = 1 + best_iou * best_iou; + + diff[index + 4] = -1 * noobject_scale_ * (0 - swap_data[index + 4]) * (swap_data[index + 4]) * (1 - swap_data[index + 4]); + + if (best_iou > thresh_) { + diff[index + 4] = 0; + } + + //if (iter < 12800 / bottom[0]->num()){ + // vector truth; + // truth.clear(); + // truth.push_back((i + .5) / side_); //center of i,j + // truth.push_back((j + .5) / side_); + // truth.push_back((biases_[2 * n]) / side_); //anchor boxes + // truth.push_back((biases_[2 * n + 1]) / side_); + // delta_region_box(truth, swap_data, biases_, n, index, i, j, side_, side_, diff, .01); + //} + } + + //for (int t = 0; t < 30; ++t) { + for (int t = 0; t < labels[b].size(); t++) { + vector truth; + vector box = labels[b][t]; + truth.clear(); + int class_label = box[0]; + Dtype x = box[1]; + Dtype y = box[2]; + Dtype w = box[3]; + Dtype h = box[4]; + + //if (!w || !h) break; + + truth.push_back(x); + truth.push_back(y); + truth.push_back(w); + truth.push_back(h); + float best_iou = 0; + int best_index = 0; + int best_n = 0; + int i = truth[0] * side_; //match which i,j + int j = truth[1] * side_; + int pos = j * side_ + i; + + vector truth_shift; + truth_shift.clear(); + truth_shift.push_back(0); + truth_shift.push_back(0); + truth_shift.push_back(w); + truth_shift.push_back(h); + + int size = coords_ + num_class_ + 1; //4 + 20 + 1 + + for (int n = 0; n < num_; ++n) { //search 5 anchor in i,j + int index = b * bottom[0]->count(1) + pos * size * num_ + n * size; + + vector pred = get_region_box(swap_data, biases_, n, index, i, j, side_, side_); // + if (bias_match_) { + pred[2] = biases_[2 * n] / side_; + pred[3] = biases_[2 * n + 1] / side_; + } + + pred[0] = 0; + pred[1] = 0; + float iou = Calc_iou(pred, truth_shift); + if (iou > best_iou) { + best_index = index; + best_iou = iou; + best_n = n; + } + } + + //=================== focal postive =========================// + //float iou_scale = 1 + pow((1 - best_iou), 2); + //coord_scale_ = iou_scale; + + float iou = delta_region_box(truth, swap_data, biases_, best_n, best_index, i, j, side_, side_, diff, coord_scale_); + + if (iou > 0.5) + recall += 1; + avg_iou += iou; + avg_obj += swap_data[best_index + 4]; + + //diff[best_index + 4] = (-1.0) * object_scale_ * (1 - swap_data[best_index + 4]); + diff[best_index + 4] = (-1.0) * object_scale_ * 2 * pow((1 - swap_data[best_index + 4]), 3) * (swap_data[best_index + 4] * (1 - swap_data[best_index + 4])); + + //std::cout << "select label: " << class_label << std::endl; + + if (class_map_ != "") + class_label = cls_map_[class_label]; //class_label --> which position of blob. + //std::cout << "select label: " << class_label << std::endl; + + delta_region_class(swap_data, diff, best_index + 5, class_label, num_class_, softmax_tree_, &t_, class_scale_, &avg_cat); //softmax_tree_ + + ++count; + ++class_count; + } + } + + //LOG(INFO) << "show loss"; + //diff_.Reshape(bottom[0]->num(), bottom[0]->height()*bottom[0]->width(), num_, bottom[0]->channels() / num_); + //FIXME: Fix the caffe time crash with BS > 1 (Chong), because this reshape is unnecessary and will cause diff to be cannot acess memory + Dtype* real_diff = real_diff_.mutable_cpu_data(); + int sindex = 0; + + for (int b = 0; b < real_diff_.num(); ++b) + for (int h = 0; h < real_diff_.height(); ++h) + for (int w = 0; w < real_diff_.width(); ++w) + for (int c = 0; c < real_diff_.channels(); ++c) + { + int rindex = b * real_diff_.height() * real_diff_.width() * real_diff_.channels() + c * real_diff_.height() * real_diff_.width() + h * real_diff_.width() + w; + Dtype e = diff[sindex]; + real_diff[rindex] = e; + sindex++; + } + + for (int i = 0; i < real_diff_.count(); ++i) + { + loss += real_diff[i] * real_diff[i]; + } + top[0]->mutable_cpu_data()[0] = loss; + + iter++; + if (count == 0) count++; // 0 can be divided +} + +template +void RegionLossLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + //LOG(INFO) <<" propagate_down: "<< propagate_down[1] << " " << propagate_down[0]; + //LOG(INFO) << "backward cpu"; + + if (propagate_down[1]) { + LOG(FATAL) << this->type() + << " Layer cannot backpropagate to label inputs."; + } + if (propagate_down[0]) { + const Dtype sign(1.); + const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[0]->num(); + + caffe_cpu_axpby( + bottom[0]->count(), + alpha, + real_diff_.cpu_data(), + Dtype(0), + bottom[0]->mutable_cpu_diff()); + } +} + +#ifdef CPU_ONLY + //STUB_GPU(DetectionLossLayer); +#endif + + INSTANTIATE_CLASS(RegionLossLayer); + REGISTER_LAYER_CLASS(RegionLoss); + +} // namespace caffe diff --git a/src/caffe/layers/reorg_layer.cpp b/src/caffe/layers/reorg_layer.cpp new file mode 100644 index 000000000..0504cf36a --- /dev/null +++ b/src/caffe/layers/reorg_layer.cpp @@ -0,0 +1,68 @@ +#include "caffe/layers/reorg_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + + template + void ReorgLayer::LayerSetUp(const vector *> &bottom, + const vector *> &top) { + CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " + "allow in-place computation."; + ReorgParameter reorg_param = this->layer_param_.reorg_param(); + CHECK_EQ(reorg_param.has_stride(), true) << this->type() << " Layer needs stride param."; + reverse_ = reorg_param.reverse(); + stride_ = reorg_param.stride(); + channels_ = bottom[0]->channels(); + height_ = bottom[0]->height(); + width_ = bottom[0]->width(); + batch_num_ = bottom[0]->num(); + + diff_.Reshape(batch_num_, channels_, height_, width_); + + if (reverse_) { + reorged_channels_ = channels_ / (stride_ * stride_); + reorged_width_ = width_ * stride_; + reorged_height_ = height_ * stride_; + } else { + reorged_channels_ = channels_ * stride_ * stride_; + reorged_height_ = height_ / stride_; + reorged_width_ = width_ / stride_; + } + } + + template + void ReorgLayer::Reshape(const vector *> &bottom, + const vector *> &top) { + top[0]->Reshape(batch_num_, reorged_channels_, + reorged_height_, reorged_width_); + } + + template + void ReorgLayer::Forward_cpu(const vector *> &bottom, + const vector *> &top) { + const Dtype *bottom_data = bottom[0]->cpu_data(); + Dtype *top_data = top[0]->mutable_cpu_data(); + reorg_cpu(bottom_data, width_, height_, + channels_, batch_num_, stride_, reverse_, top_data); + } + + template + void ReorgLayer::Backward_cpu(const vector *> &top, const vector &propagate_down, + const vector *> &bottom) { + if(!propagate_down[0]){ + return; + } + //const Dtype *top_diff = top[0]->cpu_diff(); + const Dtype *top_diff = diff_.mutable_cpu_diff(); + Dtype *bottom_diff = bottom[0]->mutable_cpu_diff(); + reorg_cpu(top_diff, width_, height_, + channels_, batch_num_, stride_, !reverse_, bottom_diff); + } +#ifdef CPU_ONLY +STUB_GPU(ReorgLayer); +#endif + INSTANTIATE_CLASS(ReorgLayer); + + REGISTER_LAYER_CLASS(Reorg); + +} // namespace caffe diff --git a/src/caffe/layers/reorg_layer.cu b/src/caffe/layers/reorg_layer.cu new file mode 100644 index 000000000..3b7e35feb --- /dev/null +++ b/src/caffe/layers/reorg_layer.cu @@ -0,0 +1,65 @@ +#include "caffe/layers/reorg_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + template + __global__ void reorg_kernel(const Dtype *x, int w, int h, int c, int batch, int stride, int forward, Dtype *out) + { + int size = batch*c*h*w; + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i >= size) return; + int in_index = i; + int in_w = i%w; + i = i/w; + int in_h = i%h; + i = i/h; + int in_c = i%c; + i = i/c; + int b = i%batch; + + int out_c = c/(stride*stride); + + int c2 = in_c % out_c; + int offset = in_c / out_c; + int w2 = in_w*stride + offset % stride; + int h2 = in_h*stride + offset / stride; + int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); + + if(forward) + { + out[out_index] = x[in_index]; + } + else + { + out[in_index] = x[out_index]; + } + } + + template + void ReorgLayer::Forward_gpu(const vector *> &bottom, + const vector *> &top) { + const Dtype *bottom_data = bottom[0]->gpu_data(); + int count = bottom[0]->count(); + Dtype *top_data = top[0]->mutable_gpu_data(); + reorg_kernel + <<>>(bottom_data, width_, height_, + channels_, batch_num_, stride_, reverse_, top_data); + } + + template + void ReorgLayer::Backward_gpu(const vector *> &top, const vector &propagate_down, + const vector *> &bottom) { + if(!propagate_down[0]){ + return; + } + int count = diff_.count(); + const Dtype *top_diff = diff_.mutable_gpu_diff(); + Dtype *bottom_diff = bottom[0]->mutable_gpu_diff(); + reorg_kernel + <<>>(top_diff, width_, height_, + channels_, batch_num_, stride_, !reverse_, bottom_diff); + } + +INSTANTIATE_LAYER_GPU_FUNCS(ReorgLayer); + +} // namespace caffe diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp index 20854ee24..0a2e3329e 100644 --- a/src/caffe/layers/reshape_layer.cpp +++ b/src/caffe/layers/reshape_layer.cpp @@ -46,85 +46,201 @@ void ReshapeLayer::LayerSetUp(const vector*>& bottom, const vector*>& top) { CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not " "allow in-place computation."; - inferred_axis_ = -1; - copy_axes_.clear(); - const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); - const int top_num_axes = top_blob_shape.dim_size(); - constant_count_ = 1; - for (int i = 0; i < top_num_axes; ++i) { - const int top_dim = top_blob_shape.dim(i); - if (top_dim == 0) { - copy_axes_.push_back(i); - } else if (top_dim == -1) { - CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple " - << "-1 dims; at most a single (1) value of -1 may be specified"; - inferred_axis_ = i; - } else { - constant_count_ *= top_dim; - } + const int ps = this->layer_param_.reshape_param().pixelshuffler(); + if(ps == 1){ + inferred_axis_ = -1; + copy_axes_.clear(); + const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); + const int top_num_axes = top_blob_shape.dim_size(); + constant_count_ = 1; + for (int i = 0; i < top_num_axes; ++i) { + const int top_dim = top_blob_shape.dim(i); + if (top_dim == 0) { + copy_axes_.push_back(i); + } else if (top_dim == -1) { + CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple " + << "-1 dims; at most a single (1) value of -1 may be specified"; + inferred_axis_ = i; + } else { + constant_count_ *= top_dim; + } + } } + } template void ReshapeLayer::Reshape(const vector*>& bottom, const vector*>& top) { - const int input_start_axis = this->layer_param_.reshape_param().axis(); - const int start_axis = (input_start_axis >= 0) ? input_start_axis : - bottom[0]->num_axes() + input_start_axis + 1; - CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range"; - CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis - << " out of range for " << bottom[0]->num_axes() << "-D input blob"; - const int num_axes = this->layer_param_.reshape_param().num_axes(); - CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all"; - const int end_axis = - (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes); - CHECK_LE(end_axis, bottom[0]->num_axes()) - << "end_axis = axis + num_axes is out of range"; - const int num_axes_replaced = end_axis - start_axis; - const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced; - const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); - const int num_new_axes = top_blob_shape.dim_size(); - vector top_shape(num_axes_retained + num_new_axes); - int top_shape_index = 0; - for (int i = 0; i < start_axis; ++i) { - top_shape[top_shape_index++] = bottom[0]->shape(i); - } - for (int i = 0; i < num_new_axes; ++i) { - top_shape[top_shape_index++] = top_blob_shape.dim(i); + const int ps = this->layer_param_.reshape_param().pixelshuffler(); + if (ps ==1 ){ + const int input_start_axis = this->layer_param_.reshape_param().axis(); + const int start_axis = (input_start_axis >= 0) ? input_start_axis : + bottom[0]->num_axes() + input_start_axis + 1; + CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range"; + CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis + << " out of range for " << bottom[0]->num_axes() << "-D input blob"; + const int num_axes = this->layer_param_.reshape_param().num_axes(); + CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all"; + const int end_axis = + (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes); + CHECK_LE(end_axis, bottom[0]->num_axes()) + << "end_axis = axis + num_axes is out of range"; + const int num_axes_replaced = end_axis - start_axis; + const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced; + const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape(); + const int num_new_axes = top_blob_shape.dim_size(); + vector top_shape(num_axes_retained + num_new_axes); + int top_shape_index = 0; + for (int i = 0; i < start_axis; ++i) { + top_shape[top_shape_index++] = bottom[0]->shape(i); + } + for (int i = 0; i < num_new_axes; ++i) { + top_shape[top_shape_index++] = top_blob_shape.dim(i); + } + for (int i = end_axis; i < bottom[0]->num_axes(); ++i) { + top_shape[top_shape_index++] = bottom[0]->shape(i); + } + CHECK_EQ(top_shape_index, top_shape.size()); + for (int i = 0; i < copy_axes_.size(); ++i) { + const int copy_axis_index = copy_axes_[i]; + CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index) + << "new shape contains a 0, but there was no corresponding bottom axis " + << "to copy"; + top_shape[start_axis + copy_axis_index] = + bottom[0]->shape(start_axis + copy_axis_index); + } + if (inferred_axis_ >= 0) { + // A -1 dim was specified; infer the correct dimension by computing the + // product of the other dimensions. + int explicit_count = constant_count_; + explicit_count *= bottom[0]->count(0, start_axis); + explicit_count *= bottom[0]->count(end_axis); + for (int i = 0; i < copy_axes_.size(); ++i) { + const int copy_axis_index = copy_axes_[i]; + explicit_count *= top_shape[start_axis + copy_axis_index]; + } + CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count (" + << bottom[0]->count() << ") must be divisible by the product of " + << "the specified dimensions (" << explicit_count << ")"; + const int inferred_dim = bottom[0]->count() / explicit_count; + top_shape[start_axis + inferred_axis_] = inferred_dim; + } + top[0]->Reshape(top_shape); + CHECK_EQ(top[0]->count(), bottom[0]->count()) + << "output count must match input count"; + top[0]->ShareData(*bottom[0]); + top[0]->ShareDiff(*bottom[0]); } - for (int i = end_axis; i < bottom[0]->num_axes(); ++i) { - top_shape[top_shape_index++] = bottom[0]->shape(i); + else { + + // Input: (N, C * r^2, H, W) + // Output: (N, C, H * r, W * r) + // Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network + // https://arxiv.org/abs/1609.05158 + vector bottom_shape = bottom[0]->shape(); + const int bn = bottom_shape[0]; + const int bc = bottom_shape[1]; + const int bh = bottom_shape[2]; + const int bw = bottom_shape[3]; + vector top_shape(4); + top_shape[0] = bn; + top_shape[1] = bc/(ps * ps); + top_shape[2] = bh * ps; + top_shape[3] = bw * ps; + top[0]->Reshape(top_shape); + CHECK_EQ(top[0]->count(), bottom[0]->count()) + << "output count must match input count"; } - CHECK_EQ(top_shape_index, top_shape.size()); - for (int i = 0; i < copy_axes_.size(); ++i) { - const int copy_axis_index = copy_axes_[i]; - CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index) - << "new shape contains a 0, but there was no corresponding bottom axis " - << "to copy"; - top_shape[start_axis + copy_axis_index] = - bottom[0]->shape(start_axis + copy_axis_index); + +} + +template +void ReshapeLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top){ + const int ps = this->layer_param_.reshape_param().pixelshuffler(); + if( ps == 1){} + else{ + vector bottom_shape = bottom[0]->shape(); + const int bn = bottom_shape[0]; + const int bc = bottom_shape[1]; + const int bh = bottom_shape[2]; + const int bw = bottom_shape[3]; + //const int tn = top_shape[0]; + vector top_shape = top[0]->shape(); + const int tc = top_shape[1]; + const int th = top_shape[2]; + const int tw = top_shape[3]; + + int test_r1 = bc/tc; + const int r = th / bh; + int test_r2 = r * r; + CHECK_EQ( test_r1, test_r2) << "Pixelshuffler output is illegal"; + + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + + #ifdef _OPENMP + #pragma omp parallel for collapse(2) + #endif + for(int n = 0; n < bn; n++) { + for(int c = 0; c < bc; c++) { + for(int h = 0; h < bh; h++) { + for(int w = 0; w < bw; w++) { + int new_c = static_cast(floor(c/(r*r))); + int new_h = h*r + (static_cast(floor(c/r)))%r; + int new_w = w*r+ (c%(r*r))%r; + int top_index = n*(tc*th*tw)+ new_c*(th*tw)+ new_h*tw+ new_w; + int bottom_index = n*bc*bh*bw + c*(bh*bw) + h*bw + w; + top_data[top_index] = bottom_data[bottom_index]; + } + } + } + } } - if (inferred_axis_ >= 0) { - // A -1 dim was specified; infer the correct dimension by computing the - // product of the other dimensions. - int explicit_count = constant_count_; - explicit_count *= bottom[0]->count(0, start_axis); - explicit_count *= bottom[0]->count(end_axis); - for (int i = 0; i < copy_axes_.size(); ++i) { - const int copy_axis_index = copy_axes_[i]; - explicit_count *= top_shape[start_axis + copy_axis_index]; +} + +template +void ReshapeLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom){ + const int ps = this->layer_param_.reshape_param().pixelshuffler(); + if( ps == 1){} + else{ + vector top_shape = top[0]->shape(); + const int tn = top_shape[0]; + const int tc = top_shape[1]; + const int th = top_shape[2]; + const int tw = top_shape[3]; + vector bottom_shape = bottom[0]->shape(); + const int bc = bottom_shape[1]; + const int bh = bottom_shape[2]; + const int bw = bottom_shape[3]; + + //int test_r1 = bc/tc; + const int r = th / bh; + //int test_r2 = r * r; + //CHECK_EQ( test_r1, test_r2) << "Pixelshuffler output is illegal"; + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + const Dtype* top_diff = top[0]->cpu_diff(); + + #ifdef _OPENMP + #pragma omp parallel for collapse(2) + #endif + for(int n = 0; n < tn; n++) { + for(int c = 0; c < tc; c++) { + for(int h = 0; h < th; h++) { + for(int w = 0; w < tw; w++) { + int old_c = c*r*r + (h%r)*r + w%r; + int old_h = static_cast(floor(h/r)); + int old_w = static_cast(floor(w/r)); + int bottom_index = n*(bc*bh*bw)+ old_c*(bh*bw)+ old_h*bw+ old_w; + int top_index = n*(tc*th*tw)+ c*(th*tw)+ h*tw+ w; + bottom_diff[bottom_index] = top_diff[top_index]; + } + } + } } - CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count (" - << bottom[0]->count() << ") must be divisible by the product of " - << "the specified dimensions (" << explicit_count << ")"; - const int inferred_dim = bottom[0]->count() / explicit_count; - top_shape[start_axis + inferred_axis_] = inferred_dim; } - top[0]->Reshape(top_shape); - CHECK_EQ(top[0]->count(), bottom[0]->count()) - << "output count must match input count"; - top[0]->ShareData(*bottom[0]); - top[0]->ShareDiff(*bottom[0]); } INSTANTIATE_CLASS(ReshapeLayer); diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp index c5695d81f..7b75b4bd3 100644 --- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp +++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp @@ -79,7 +79,16 @@ void SigmoidCrossEntropyLossLayer::Forward_cpu( loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) - log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0))); } - top[0]->mutable_cpu_data()[0] = loss / num; + Dtype normalizer = Dtype(num); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + top[0]->mutable_cpu_data()[0] = loss / normalizer; } template @@ -100,7 +109,16 @@ void SigmoidCrossEntropyLossLayer::Backward_cpu( caffe_sub(count, sigmoid_output_data, target, bottom_diff); // Scale down gradient const Dtype loss_weight = top[0]->cpu_diff()[0]; - caffe_scal(count, loss_weight / num, bottom_diff); + Dtype normalizer = Dtype(num); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + caffe_scal(count, loss_weight / normalizer, bottom_diff); } } diff --git a/src/caffe/layers/smooth_L1_loss_layer.cpp b/src/caffe/layers/smooth_L1_loss_layer.cpp index a660a1fa8..c8ee3ff68 100644 --- a/src/caffe/layers/smooth_L1_loss_layer.cpp +++ b/src/caffe/layers/smooth_L1_loss_layer.cpp @@ -115,7 +115,16 @@ void SmoothL1LossLayer::Forward_cpu(const vector*>& bottom, c } Dtype loss = caffe_cpu_dot(count, ones_.cpu_data(), errors_.cpu_data()); - top[0]->mutable_cpu_data()[0] = loss / bottom[0]->num(); + Dtype normalizer = Dtype(bottom[0]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif + top[0]->mutable_cpu_data()[0] = loss / normalizer; } @@ -125,6 +134,15 @@ void SmoothL1LossLayer::Backward_cpu(const vector*>& top, con // after forwards, diff_ holds w_in * (b0 - b1) int count = diff_.count(); + Dtype normalizer = Dtype(bottom[0]->num()); + if (this->layer_param_.loss_param().normalization() == LossParameter_NormalizationMode_NONE) + normalizer = 1.f; +#ifdef USE_MLSL + else { + // We assume local bs is same across all nodes + normalizer *= mn::get_group_size(); + } +#endif for (int i = 0; i < count; i++) { // f'(x) = sigma * sigma * x if |x| < 1 / sigma / sigma // = sign(x) otherwise @@ -141,7 +159,7 @@ void SmoothL1LossLayer::Backward_cpu(const vector*>& top, con for (int i = 0; i < 2; ++i) { if (propagate_down[i]) { const Dtype sign = (i == 0) ? 1 : -1; - const Dtype alpha = sign * top[0]->cpu_diff()[0] / bottom[i]->num(); + const Dtype alpha = sign * top[0]->cpu_diff()[0] / normalizer; caffe_cpu_axpby( count, // count alpha, // alpha diff --git a/src/caffe/layers/smooth_L1_loss_ohem_layer.cpp b/src/caffe/layers/smooth_L1_loss_ohem_layer.cpp index e0ec66f4b..383f4ed6d 100644 --- a/src/caffe/layers/smooth_L1_loss_ohem_layer.cpp +++ b/src/caffe/layers/smooth_L1_loss_ohem_layer.cpp @@ -116,9 +116,24 @@ Dtype SmoothL1LossOHEMLayer::get_normalizer( LOG(FATAL) << "Unknown normalization mode: " << LossParameter_NormalizationMode_Name(normalization_mode); } +#ifdef USE_MLSL + if (normalization_mode != LossParameter_NormalizationMode_NONE) { + Dtype allnodes_normalizer(normalizer); + DLOG(INFO) << "node id: " << mn::get_node_id() << ", normalizer: " << normalizer; + if (normalization_mode == LossParameter_NormalizationMode_VALID) { + mn::allreduce(&allnodes_normalizer, 1); + } else { + // We assume local bs or pre_fixed_normalizer is same across all nodes + allnodes_normalizer *= mn::get_group_size(); + } + normalizer = allnodes_normalizer; + } +#endif + DLOG(INFO) << "Final normalizer: " << normalizer; // Some users will have no labels for some examples in order to 'turn off' a // particular loss in a multi-task setup. The max prevents NaNs in that case. - return std::max(Dtype(1.0), normalizer); + this->cached_normalizer_ = std::max(Dtype(1.0), normalizer); + return this->cached_normalizer_; } template @@ -216,11 +231,7 @@ void SmoothL1LossOHEMLayer::Backward_cpu( for (int i = 0; i < 2; ++i) { if (propagate_down[i]) { const Dtype sign = (i == 0) ? 1 : -1; - - Dtype pre_fixed_normalizer = - this->layer_param_.loss_param().pre_fixed_normalizer(); - Dtype normalizer = get_normalizer(normalization_, pre_fixed_normalizer); - Dtype alpha = sign * top[0]->cpu_diff()[0] / normalizer; + Dtype alpha = sign * top[0]->cpu_diff()[0] / this->cached_normalizer_; caffe_cpu_axpby( bottom[i]->count(), // count diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp index b9e3fc3b8..a758bfcc7 100644 --- a/src/caffe/layers/softmax_loss_layer.cpp +++ b/src/caffe/layers/softmax_loss_layer.cpp @@ -112,6 +112,9 @@ Dtype SoftmaxWithLossLayer::get_normalizer( case LossParameter_NormalizationMode_BATCH_SIZE: normalizer = Dtype(outer_num_); break; + case LossParameter_NormalizationMode_PRE_FIXED: + normalizer = Dtype(this->layer_param_.loss_param().pre_fixed_normalizer()); + break; case LossParameter_NormalizationMode_NONE: normalizer = Dtype(1); break; @@ -119,9 +122,24 @@ Dtype SoftmaxWithLossLayer::get_normalizer( LOG(FATAL) << "Unknown normalization mode: " << LossParameter_NormalizationMode_Name(normalization_mode); } +#ifdef USE_MLSL + if (normalization_mode != LossParameter_NormalizationMode_NONE) { + Dtype allnodes_normalizer(normalizer); + DLOG(INFO) << "node id: " << mn::get_node_id() << ", normalizer: " << normalizer; + if (has_ignore_label_) { + mn::allreduce(&allnodes_normalizer, 1); + } else { + // We assume local bs is same across all nodes + allnodes_normalizer *= mn::get_group_size(); + } + normalizer = allnodes_normalizer; + } +#endif + DLOG(INFO) << "Final normalizer: " << normalizer; // Some users will have no labels for some examples in order to 'turn off' a // particular loss in a multi-task setup. The max prevents NaNs in that case. - return std::max(Dtype(1.0), normalizer); + this->cached_normalizer_ = std::max(Dtype(1.0), normalizer); + return this->cached_normalizer_; } template @@ -196,7 +214,7 @@ void SoftmaxWithLossLayer::Forward_cpu( loss -= loss_local; } - Dtype normalizer = LossLayer::GetNormalizer(normalization_, outer_num_, inner_num_, count); + Dtype normalizer = this->get_normalizer(normalization_, count); top[0]->mutable_cpu_data()[0] = loss / normalizer; if (top.size() == 2) { top[1]->ShareData(prob_); @@ -282,8 +300,7 @@ void SoftmaxWithLossLayer::Backward_cpu(const vector*>& top, } } // Scale gradient - Dtype normalizer = LossLayer::GetNormalizer(normalization_, outer_num_, inner_num_, count); - Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer; + Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_; caffe_scal(prob_.count(), loss_weight, bottom_diff); } } diff --git a/src/caffe/layers/softmax_loss_ohem_layer.cpp b/src/caffe/layers/softmax_loss_ohem_layer.cpp index a57545274..9b496fa46 100644 --- a/src/caffe/layers/softmax_loss_ohem_layer.cpp +++ b/src/caffe/layers/softmax_loss_ohem_layer.cpp @@ -118,6 +118,9 @@ Dtype SoftmaxWithLossOHEMLayer::get_normalizer( case LossParameter_NormalizationMode_BATCH_SIZE: normalizer = Dtype(outer_num_); break; + case LossParameter_NormalizationMode_PRE_FIXED: + normalizer = Dtype(this->layer_param_.loss_param().pre_fixed_normalizer()); + break; case LossParameter_NormalizationMode_NONE: normalizer = Dtype(1); break; @@ -125,9 +128,24 @@ Dtype SoftmaxWithLossOHEMLayer::get_normalizer( LOG(FATAL) << "Unknown normalization mode: " << LossParameter_NormalizationMode_Name(normalization_mode); } +#ifdef USE_MLSL + if (normalization_mode != LossParameter_NormalizationMode_NONE) { + Dtype allnodes_normalizer(normalizer); + DLOG(INFO) << "node id: " << mn::get_node_id() << ", normalizer: " << normalizer; + if (has_ignore_label_) { + mn::allreduce(&allnodes_normalizer, 1); + } else { + // We assume local bs is same across all nodes + allnodes_normalizer *= mn::get_group_size(); + } + normalizer = allnodes_normalizer; + } +#endif + DLOG(INFO) << "Final normalizer: " << normalizer; // Some users will have no labels for some examples in order to 'turn off' a // particular loss in a multi-task setup. The max prevents NaNs in that case. - return std::max(Dtype(1.0), normalizer); + this->cached_normalizer_ = std::max(Dtype(1.0), normalizer); + return this->cached_normalizer_; } template @@ -160,7 +178,7 @@ void SoftmaxWithLossOHEMLayer::Forward_cpu( } } loss = caffe_cpu_asum(count, loss_data); - top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count); + top[0]->mutable_cpu_data()[0] = loss / this->get_normalizer(normalization_, count); if (top.size() == 2) { top[1]->ShareData(prob_); } @@ -204,8 +222,7 @@ void SoftmaxWithLossOHEMLayer::Backward_cpu( } } // Scale gradient - Dtype loss_weight = top[0]->cpu_diff()[0] / - get_normalizer(normalization_, count); + Dtype loss_weight = top[0]->cpu_diff()[0] / this->cached_normalizer_; caffe_scal(prob_.count(), loss_weight, bottom_diff); } } diff --git a/src/caffe/layers/swish_layer.cpp b/src/caffe/layers/swish_layer.cpp new file mode 100644 index 000000000..e94e9cac9 --- /dev/null +++ b/src/caffe/layers/swish_layer.cpp @@ -0,0 +1,105 @@ +/* +All modification made by Intel Corporation: © 2018 Intel Corporation + +All contributions by the University of California: +Copyright (c) 2014, 2015, The Regents of the University of California (Regents) +All rights reserved. + +All other contributions: +Copyright (c) 2014, 2015, the respective contributors +All rights reserved. +For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md + + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of Intel Corporation nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#include +#include + +#include "caffe/layers/swish_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void SwishLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + NeuronLayer::LayerSetUp(bottom, top); + sigmoid_bottom_vec_.clear(); + sigmoid_bottom_vec_.push_back(sigmoid_input_.get()); + sigmoid_top_vec_.clear(); + sigmoid_top_vec_.push_back(sigmoid_output_.get()); + sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_); +} + +template +void SwishLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + NeuronLayer::Reshape(bottom, top); + sigmoid_input_->ReshapeLike(*bottom[0]); + sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_); +} + +template +void SwishLayer::Forward_cpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* sigmoid_input_data = sigmoid_input_->mutable_cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + const int count = bottom[0]->count(); + Dtype beta = this->layer_param_.swish_param().beta(); + caffe_copy(count, bottom_data, sigmoid_input_data); + caffe_scal(count, beta, sigmoid_input_data); + sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_); + caffe_mul(count, bottom_data, sigmoid_output_->cpu_data(), top_data); +} + +template +void SwishLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + if (propagate_down[0]) { + const Dtype* top_data = top[0]->cpu_data(); + const Dtype* top_diff = top[0]->cpu_diff(); + const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + const int count = bottom[0]->count(); + Dtype beta = this->layer_param_.swish_param().beta(); + for (int i = 0; i < count; ++i) { + const Dtype swish_x = top_data[i]; + bottom_diff[i] = top_diff[i] * (beta * swish_x + sigmoid_output_data[i] + * (1. - beta * swish_x)); + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(SwishLayer); +#endif + +INSTANTIATE_CLASS(SwishLayer); +REGISTER_LAYER_CLASS(Swish); + +} // namespace caffe diff --git a/src/caffe/layers/swish_layer.cu b/src/caffe/layers/swish_layer.cu new file mode 100644 index 000000000..c4fef53bf --- /dev/null +++ b/src/caffe/layers/swish_layer.cu @@ -0,0 +1,54 @@ +#include +#include + +#include "caffe/layers/swish_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void SwishLayer::Forward_gpu(const vector*>& bottom, + const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* sigmoid_input_data = sigmoid_input_->mutable_gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const int count = bottom[0]->count(); + Dtype beta = this->layer_param_.swish_param().beta(); + caffe_copy(count, bottom_data, sigmoid_input_data); + caffe_gpu_scal(count, beta, sigmoid_input_data); + sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_); + caffe_gpu_mul(count, bottom_data, sigmoid_output_->gpu_data(), top_data); +} + +template +__global__ void SwishBackward(const int n, const Dtype* in_diff, + const Dtype* out_data, const Dtype* sigmoid_output_data, Dtype* out_diff, + const Dtype beta) { + CUDA_KERNEL_LOOP(index, n) { + const Dtype swish_x = out_data[index]; + out_diff[index] = in_diff[index] * (beta * swish_x + + sigmoid_output_data[index] * (1 - beta * swish_x)); + } +} + +template +void SwishLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, + const vector*>& bottom) { + if (propagate_down[0]) { + const Dtype* top_data = top[0]->gpu_data(); + const Dtype* top_diff = top[0]->gpu_diff(); + const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const int count = bottom[0]->count(); + Dtype beta = this->layer_param_.swish_param().beta(); + // NOLINT_NEXT_LINE(whitespace/operators) + SwishBackward<<>>( + count, top_diff, top_data, sigmoid_output_data, bottom_diff, beta); + CUDA_POST_KERNEL_CHECK; + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(SwishLayer); + +} // namespace caffe diff --git a/src/caffe/mkldnn_memory.cpp b/src/caffe/mkldnn_memory.cpp index 6ba28da3e..eb522b2b0 100644 --- a/src/caffe/mkldnn_memory.cpp +++ b/src/caffe/mkldnn_memory.cpp @@ -47,41 +47,20 @@ MKLDNNMemoryDescriptorBase::MKLDNNMemoryDescriptorBase(shared_ptr prv_memory_pd , Blob* blob , MKLDNNLayer* mkldnn_layer - , bool is_float , std::vector scale - , bool is_sum) + , int mask + , bool is_sum + , bool is_wino) : name("MKLDNNMemoryDescriptorBase") , _reorder_usr2prv_pd(), _reorder_prv2usr_pd(), _reorder_extprv2prv_pd() ,_prv_memory(), _internal_ptr(NULL), _usr_memory(), _cpu_ptr(NULL) , _mkldnn_layer(NULL) { set_usr_memory_pd(usr_memory_pd, scale); - set_prv_memory_pd(prv_memory_pd, scale); + set_prv_memory_pd(prv_memory_pd, scale, mask, is_wino); set_mkldnn_layer(mkldnn_layer); this->set_scale(scale); this->set_sum(is_sum); - this->set_float(is_float); - this->_blob = blob; -} - -template -MKLDNNMemoryDescriptorBase::MKLDNNMemoryDescriptorBase(shared_ptr usr_memory_pd - , shared_ptr prv_memory_pd - , Blob* blob - , MKLDNNLayer* mkldnn_layer - , std::vector fl - , bool is_sum) - : name("MKLDNNMemoryDescriptorBase") - , _reorder_usr2prv_pd(), _reorder_prv2usr_pd(), _reorder_extprv2prv_pd() - ,_prv_memory(), _internal_ptr(NULL), _usr_memory(), _cpu_ptr(NULL) - , _mkldnn_layer(NULL) -{ - set_usr_memory_pd(usr_memory_pd, fl); - set_prv_memory_pd(prv_memory_pd, fl); - set_mkldnn_layer(mkldnn_layer); - this->set_fl(fl); - this->set_sum(is_sum); - this->set_float(false); this->_blob = blob; } @@ -101,21 +80,12 @@ void MKLDNNMemoryDescriptorBase::check_usr_with_prv_descriptors() } template -void MKLDNNMemoryDescriptorBase::create_reorder_descriptors(std::vector scale, std::vector scale_ext, bool is_sum) +void MKLDNNMemoryDescriptorBase::create_reorder_descriptors(std::vector scale, int mask, std::vector scale_ext, bool is_sum, bool is_wino) { CHECK(_usr_memory_pd); CHECK(_prv_memory_pd); primitive_attr attri; - int mask = 0; - if(scale.size() > 1 || scale_ext.size() > 1){ -#ifdef DEBUG - LOG(INFO)<<"multi-channel"; -#endif - int oc_dim_id = 0; - mask = 1 << oc_dim_id; - } - int count = scale.size(); if ( *_usr_memory_pd != *_prv_memory_pd) { std::vector scales_u2p(count); @@ -135,8 +105,9 @@ void MKLDNNMemoryDescriptorBase::create_reorder_descriptors(std::vector( - new reorder::primitive_desc(*_prv_memory_pd, *_usr_memory_pd, attri)); + if(!is_wino) + _reorder_prv2usr_pd = shared_ptr( + new reorder::primitive_desc(*_prv_memory_pd, *_usr_memory_pd, attri)); } if ( _extprv_memory_pd && (*_prv_memory_pd != *_extprv_memory_pd || scale != scale_ext)) { if(is_sum == true && scale == scale_ext && _extprv_memory_pd->desc().data.data_type == memory::data_type::s8 && _prv_memory_pd->desc().data.data_type == memory::data_type::u8){ @@ -160,78 +131,15 @@ void MKLDNNMemoryDescriptorBase::create_reorder_descriptors(std::vector -void MKLDNNMemoryDescriptorBase::create_reorder_descriptors(std::vector fl, std::vector fl_ext, bool is_sum) -{ - CHECK(_usr_memory_pd); - CHECK(_prv_memory_pd); - - primitive_attr attri; - int mask = 0; - if(fl.size() > 1 || fl_ext.size() > 1){ -#ifdef DEBUG - LOG(INFO)<<"multi-channel"; -#endif - int oc_dim_id = 0; - mask = 1 << oc_dim_id; - } - - int count = fl.size(); - float scale; - if ( *_usr_memory_pd != *_prv_memory_pd) { - std::vector scales_u2p(count); - #pragma omp parallel for if (count > 1) - for(int i = 0; i < count; i++){ - scale = pow(2, fl[i]); - scales_u2p[i] = scale; - } - attri.set_output_scales(mask, scales_u2p); - attri.set_int_output_round_mode(round_nearest); - _reorder_usr2prv_pd = shared_ptr( - new reorder::primitive_desc(*_usr_memory_pd, *_prv_memory_pd, attri)); - - std::vector scales_p2u(count); - #pragma omp parallel for if (count > 1) - for(int i = 0; i < count; i++){ - scale = pow(2, -fl[i]); - scales_p2u[i] = scale; - } - attri.set_output_scales(mask, scales_p2u); - attri.set_int_output_round_mode(round_nearest); - _reorder_prv2usr_pd = shared_ptr( - new reorder::primitive_desc(*_prv_memory_pd, *_usr_memory_pd, attri)); - } - if ( _extprv_memory_pd && (*_prv_memory_pd != *_extprv_memory_pd || fl != fl_ext)) { - if(is_sum == true && fl == fl_ext && _extprv_memory_pd->desc().data.data_type == memory::data_type::s8 && _prv_memory_pd->desc().data.data_type == memory::data_type::u8){ -#ifdef DEBUG - LOG(INFO) << "skip s8 to u8 reorder...."; -#endif - _reorder_extprv2prv_pd = NULL; - }else{ - std::vector scales_e2p(count); - int shift_fl; - #pragma omp parallel for if (count > 1) - for(int i = 0; i < count; i++){ - shift_fl = fl[i] - fl_ext[i]; //fp32->int8 blob_prv_mkldnn_mem_descr->get_fl() will always be 0 ? - scale = pow(2, shift_fl); - scales_e2p[i] = scale; - } - attri.set_output_scales(mask, scales_e2p); - attri.set_int_output_round_mode(round_nearest); - _reorder_extprv2prv_pd = shared_ptr(new reorder::primitive_desc(*_extprv_memory_pd, *_prv_memory_pd, attri)); - - } - } -} - template MKLDNNMemoryDescriptor::MKLDNNMemoryDescriptor(shared_ptr usr_memory_pd , shared_ptr prv_memory_pd , Blob* blob, MKLDNNLayer* mkldnn_layer - , bool is_float , std::vector scale - , bool is_sum) - : MKLDNNMemoryDescriptorBase(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, is_float, scale, is_sum) + , int mask + , bool is_sum + , bool is_wino) + : MKLDNNMemoryDescriptorBase(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, scale, mask, is_sum, is_wino) { const Dtype* prv_ptr = is_diff ? blob->prv_diff() : blob->prv_data(); @@ -241,7 +149,7 @@ template LOG(INFO) << "Format of blob-prv-memory-pd: " << blob_prv_mkldnn_mem_descr->prv_memory_pd()->desc().data.format; LOG(INFO) << "Format of this-prv-memory-pd: " << this->prv_memory_pd()->desc().data.format; #endif - if (*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_scale() != this->get_scale()) { + if (blob_prv_mkldnn_mem_descr->prv_memory_pd()->desc().data.format != this->prv_memory_pd()->desc().data.format || blob_prv_mkldnn_mem_descr->prv_memory_pd()->desc().data.data_type != this->prv_memory_pd()->desc().data.data_type || blob_prv_mkldnn_mem_descr->get_scale() != this->get_scale()) { #ifdef DEBUG LOG(INFO) << "Formats of blob-prv-memory-pd and this-prv-memory-pd are not equal !"; #endif @@ -250,31 +158,6 @@ template } } -template - MKLDNNMemoryDescriptor::MKLDNNMemoryDescriptor(shared_ptr usr_memory_pd - , shared_ptr prv_memory_pd - , Blob* blob, MKLDNNLayer* mkldnn_layer - , std::vector fl - , bool is_sum) - : MKLDNNMemoryDescriptorBase(usr_memory_pd, prv_memory_pd, blob, mkldnn_layer, fl, is_sum) -{ - const Dtype* prv_ptr = is_diff ? blob->prv_diff() : blob->prv_data(); - - if (prv_ptr != NULL) { - shared_ptr > blob_prv_mkldnn_mem_descr = get_mkldnn_prv_descriptor(blob); -#ifdef DEBUG - LOG(INFO) << "Format of blob-prv-memory-pd: " << blob_prv_mkldnn_mem_descr->prv_memory_pd()->desc().data.format; - LOG(INFO) << "Format of this-prv-memory-pd: " << this->prv_memory_pd()->desc().data.format; -#endif - if (*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_fl() != this->get_fl()) { -#ifdef DEBUG - LOG(INFO) << "Formats of blob-prv-memory-pd and this-prv-memory-pd are not equal !"; -#endif - this->set_extprv_memory_pd(blob_prv_mkldnn_mem_descr->prv_memory_pd(), fl, blob_prv_mkldnn_mem_descr->get_fl(), blob_prv_mkldnn_mem_descr->get_sum()); - } - } -} - template void MKLDNNMemoryDescriptor::create_reorder_to_prv(void* cpu_ptr) { @@ -498,35 +381,19 @@ shared_ptr MKLDNNMemoryDescriptor::get_blob_prv_primi return this->reorder_usr2prv(); } else { shared_ptr > blob_prv_mkldnn_mem_descr = get_mkldnn_prv_descriptor(blob); - if(blob_prv_mkldnn_mem_descr->get_float() || this->get_float()){ - if ((*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_scale() != this->get_scale()) && this->_reorder_extprv2prv_pd != NULL) { - // prv in blob and in this descrptor may have different layouts - if(convert) { - LOG(INFO) << "BAD CONVERT"; - this->convert_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); - } - else { - this->create_reorder_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); - } - return this->reorder_extprv2prv(); - } else if (blob_prv_mkldnn_mem_descr.get() != this) { - VLOG(1) << "layout OK " << blob_prv_mkldnn_mem_descr->name << " == " << this->name; + if ((*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_scale() != this->get_scale()) && this->_reorder_extprv2prv_pd != NULL) { + // prv in blob and in this descrptor may have different layouts + if(convert) { + LOG(INFO) << "BAD CONVERT"; + this->convert_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); } - } else{ - if ((*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_fl() != this->get_fl()) && this->_reorder_extprv2prv_pd != NULL) { - // prv in blob and in this descrptor may have different layouts - if(convert) { - LOG(INFO) << "BAD CONVERT"; - this->convert_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); - } - else { - this->create_reorder_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); - } - return this->reorder_extprv2prv(); - } else if (blob_prv_mkldnn_mem_descr.get() != this) { - VLOG(1) << "layout OK " << blob_prv_mkldnn_mem_descr->name << " == " << this->name; + else { + this->create_reorder_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); } - } + return this->reorder_extprv2prv(); + } else if (blob_prv_mkldnn_mem_descr.get() != this) { + VLOG(1) << "layout OK " << blob_prv_mkldnn_mem_descr->name << " == " << this->name; + } return blob_prv_mkldnn_mem_descr->aprimitive(); } NOT_IMPLEMENTED; @@ -573,33 +440,17 @@ void MKLDNNMemoryDescriptor::sync_before_read() } else { shared_ptr > blob_prv_mkldnn_mem_descr = get_mkldnn_prv_descriptor(this->_blob); - if(blob_prv_mkldnn_mem_descr->get_float() || this->get_float()){ - if (*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_scale() != this->get_scale()) { - // prv in blob and in this descrptor may have different layouts -#ifdef DEBUG - LOG(INFO) << "Convert from extprv"; -#endif - this->convert_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); - } else { - if (is_diff) { - this->_blob->mutable_prv_diff(); - } else { - this->_blob->mutable_prv_data(); - } - } - } else{ - if (*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_fl() != this->get_fl()) { - // prv in blob and in this descrptor may have different layouts -#ifdef DEBUG - LOG(INFO) << "Convert from extprv"; + if (*blob_prv_mkldnn_mem_descr->prv_memory_pd() != *this->prv_memory_pd() || blob_prv_mkldnn_mem_descr->get_scale() != this->get_scale()) { + // prv in blob and in this descrptor may have different layouts +#ifdef D + LOG(INFO) << "Convert from extprv"; #endif - this->convert_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); + this->convert_from_extprv(blob_prv_mkldnn_mem_descr->aprimitive()); + } else { + if (is_diff) { + this->_blob->mutable_prv_diff(); } else { - if (is_diff) { - this->_blob->mutable_prv_diff(); - } else { - this->_blob->mutable_prv_data(); - } + this->_blob->mutable_prv_data(); } } } diff --git a/src/caffe/multinode/mlsl.cpp b/src/caffe/multinode/mlsl.cpp index 69b3f0a66..87027c9c6 100644 --- a/src/caffe/multinode/mlsl.cpp +++ b/src/caffe/multinode/mlsl.cpp @@ -50,12 +50,15 @@ namespace caffe { int nServer = 0; boost::mutex distrib_lock; std::map, boost::shared_ptr> *distrib_map; + MLSL::Distribution *global_distrib = nullptr; void init(int* argc, char **argv[]) { static class initialize { public: initialize(int* argc, char** argv[]) { MLSL::Environment::GetEnv().Init(argc, argv); + global_distrib = + MLSL::Environment::GetEnv().CreateDistribution(get_nodes_count(), 1); distrib_map = new std::map, boost::shared_ptr>(); if (use_param_server()) { @@ -77,6 +80,7 @@ namespace caffe { } ~initialize() { delete distrib_map; + MLSL::Environment::GetEnv().DeleteDistribution(global_distrib); MLSL::Environment::GetEnv().Finalize(); } } __init{ argc, argv }; diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp index 89a741bd2..7019a1453 100644 --- a/src/caffe/net.cpp +++ b/src/caffe/net.cpp @@ -535,7 +535,8 @@ void Net::CompileNet(const NetParameter& param, NetParameter param_temp[2]; void (*CompileRules[]) (const NetParameter& param, NetParameter* param_compiled) = {RemoveBNScale, CompilationRuleRemoveScale, CompilationRuleConvReluFusion, - CompilationRuleFuseBnRelu, CompilationRuleBNInplace, CompilationRuleSparse, CompilationRuleConvSumFusion}; + CompilationRuleFuseBnRelu, CompilationRuleBNInplace, CompilationRuleSparse, + CompilationRuleConvSumFusion}; bool disabled[NUM_OF_RULES] = {false}; @@ -568,7 +569,7 @@ void Net::CompileNet(const NetParameter& param, #undef COMPILE_BN_FOLDING_INDEX #undef COMPILE_CONV_RELU_FUSION_INDEX #undef COMPILE_BN_RELU_FUSION_INDEX - #undef DISABLE_SPARSE_INDEX + #undef COMPILE_SPARSE_INDEX #undef COMPILE_CONV_SUM_FUSION_INDEX } @@ -601,14 +602,16 @@ void Net::CompilationRuleRemoveScale(const NetParameter& param, ((layer_param->batch_norm_param().engine() == BatchNormParameter_Engine_DEFAULT) && (layer_param->has_engine() == false) && (param.engine().compare("MKL2017") == 0)) || - (param.engine() == "" && layer_param->engine().compare("MKL2017") == 0))) || + ((layer_param->batch_norm_param().has_engine() == false) && + (layer_param->engine().compare("MKL2017") == 0)))) || // If current layer is BatchNorm of MKLDNN engine.. ((layer_param->type().compare("BatchNorm") == 0) && ((layer_param->batch_norm_param().engine() == BatchNormParameter_Engine_MKLDNN) || ((layer_param->batch_norm_param().engine() == BatchNormParameter_Engine_DEFAULT) && (layer_param->has_engine() == false) && (param.engine().compare("MKLDNN") == 0)) || - (param.engine() == "" && layer_param->engine().compare("MKLDNN") == 0)))) { + ((layer_param->batch_norm_param().has_engine() == false) && + (layer_param->engine().compare("MKLDNN") == 0))))) { std::vector consumer_layer_params; GetBlobConsumers(consumer_layer_params, layer_param->top(0), @@ -643,6 +646,10 @@ void Net::CompilationRuleRemoveScale(const NetParameter& param, layer_param->add_blobs()->CopyFrom(consumer_layer_param.blobs(0)); layer_param->add_blobs()->CopyFrom(consumer_layer_param.blobs(1)); } + if (consumer_layer_param.param_size() == 2) { + layer_param->add_param()->CopyFrom(consumer_layer_param.param(0)); + layer_param->add_param()->CopyFrom(consumer_layer_param.param(1)); + } } } @@ -709,7 +716,12 @@ void Net::CompilationRuleConvReluFusion(const NetParameter& param, string& convolution_top_blob_name = const_cast(layer_param->top(0)); - if(param.state().phase() == TEST) { + float negative_slope1 = + consumer_layer_param.relu_param().negative_slope(); + if (negative_slope1 != 0) { + use_negative_slope = true; + } + if(param.state().phase() == TEST && !negative_slope1) { const string& scale_top_blob_name = consumer_layer_param.top(0); // Mark Consumer layer (its name) as the one marked for dropping layers_to_drop.insert(consumer_layer_param.name()); @@ -720,13 +732,9 @@ void Net::CompilationRuleConvReluFusion(const NetParameter& param, scale_top_blob_name.size(), scale_top_blob_name); } - float negative_slope1 = - consumer_layer_param.relu_param().negative_slope(); - if (negative_slope1 != 0) { - use_negative_slope = true; - } else { - layer_param->mutable_convolution_param()->set_relu(true); - layer_param->mutable_convolution_param()->set_negative_slope(0); + if(!use_negative_slope) { + layer_param->mutable_convolution_param()->set_relu(true); + layer_param->mutable_convolution_param()->set_negative_slope(0); } if(param.state().phase() == TRAIN && !use_negative_slope) { if(i+1 < param.layer_size()) { @@ -862,7 +870,7 @@ void Net::CompilationRuleBNInplace(const NetParameter& param, template void Net::CompilationRuleConvSumFusion(const NetParameter& param, - NetParameter* param_compiled) { + NetParameter* param_compiled) { // only apply this rule for inference(TEST) phase if (param.state().phase() != TEST || param.engine().compare("MKLDNN") != 0) { param_compiled->CopyFrom(param); @@ -870,19 +878,44 @@ void Net::CompilationRuleConvSumFusion(const NetParameter& param, } string blob_need_to_insert; LayerParameter* need_to_convert_layer = NULL; + bool has_relu_flag = true; + bool need_fusion_flag; + std::set invalid_fusion_blob_names; + + for (int i = 0; i < param.layer_size(); i++) { + need_fusion_flag = true; LayerParameter* layer_param = (const_cast(param)).mutable_layer(i); - if (layer_param->type().compare("Convolution") == 0 - && (layer_param->has_engine() == false - || (layer_param->has_engine() == true - && layer_param->engine().compare("MKLDNN") ==0))) { + if (layer_param->type().compare("Split") == 0 && + layer_param->top_size() > 2) { + for (int j = 0; j < layer_param->top_size() - 1; j++) { + invalid_fusion_blob_names.insert(layer_param->top(j)); + } + } + if (layer_param->type().compare("Convolution") == 0 && + (layer_param->has_engine() == false || + (layer_param->has_engine() == true && + layer_param->engine().compare("MKLDNN") == 0))) { std::vector child_layers_params; Net::GetBlobConsumers(child_layers_params, layer_param->top(0), param, i + 1 < param.layer_size() ? i + 1 : i); + if (child_layers_params.size() > 0 && + child_layers_params[0]->type().compare("Eltwise") == 0) { + + for (int k = 0; k < child_layers_params[0]->bottom_size(); k++) { + if (invalid_fusion_blob_names.count( + child_layers_params[0]->bottom(k)) > 0) { + need_fusion_flag = false; + break; + } + } + if (!need_fusion_flag) { + param_compiled->add_layer()->CopyFrom(*layer_param); + continue; + } - if (child_layers_params.size() > 0 && child_layers_params[0]->type().compare("Eltwise") == 0) { std::vector grand_child_layers_params; Net::GetBlobConsumers(grand_child_layers_params, @@ -894,18 +927,23 @@ void Net::CompilationRuleConvSumFusion(const NetParameter& param, : *layer_param; if (grand_child_layer_param.type().compare("ReLU") != 0) { - param_compiled->add_layer()->CopyFrom(*layer_param); - continue; + has_relu_flag = false; } - if (child_layers_params[0]->bottom(0) == layer_param->top(0) ) { + if (child_layers_params[0]->bottom(0) == layer_param->top(0)) { param_compiled->add_layer()->CopyFrom(*layer_param); need_to_convert_layer = layer_param; continue; } - const_cast(layer_param->top(0)) = - grand_child_layer_param.top(0); + if (has_relu_flag) { + const_cast(layer_param->top(0)) = + grand_child_layer_param.top(0); + } else { + const_cast(layer_param->top(0)) = + child_layers_params[0]->top(0); + } + if (need_to_convert_layer != NULL) { layer_param->add_bottom( const_cast(need_to_convert_layer->top(0))); @@ -915,7 +953,22 @@ void Net::CompilationRuleConvSumFusion(const NetParameter& param, const_cast(child_layers_params[0]->bottom(0))); } - i += 2; // skip next eltwise and relu + if (has_relu_flag) { + i += 2; // skip next eltwise and relu + layer_param->mutable_convolution_param()->set_relu(true); + } else { + i += 1; + } + + layer_param->mutable_convolution_param()->set_fusion_type(ConvolutionParameter::SUM_FUSION); + size_t coeff_size = child_layers_params[0]->eltwise_param().coeff_size(); + if (coeff_size > 0) + { + for (int i = 0; i < coeff_size; ++i) + { + layer_param->mutable_convolution_param()->add_coeff(child_layers_params[0]->eltwise_param().coeff(i)); + } + } } } @@ -983,6 +1036,8 @@ void Net::CompilationRuleSparse(const NetParameter& param, (layer_param->convolution_param().pad_size() == 0 || (layer_param->convolution_param().pad_size() > 0 && layer_param->convolution_param().pad(0) == 0))) { + if (potential_sparse_layer == NULL) + continue; confirmed_sparse_layer = potential_sparse_layer; if (trigger_sparse_layers.size() > 0) { @@ -1070,9 +1125,9 @@ void Net::CompilationRuleSparse(const NetParameter& param, each_layer_param->add_bottom(pooling_layer_id_top_blob[i]); each_layer_param->add_top(pooling_layer_id_top_blob[i] + "_p"); - each_layer_param->mutable_pooling_param()->add_stride( + each_layer_param->mutable_pooling_param()->set_stride( pooling_layer_id_stride[i]); - each_layer_param->mutable_pooling_param()->add_kernel_size(1); + each_layer_param->mutable_pooling_param()->set_kernel_size(1); each_layer_param->mutable_pooling_param()->set_pool( PoolingParameter_PoolMethod_MAX); @@ -1368,6 +1423,7 @@ vector Net::FindMax(Blob* blob, bool is_single) { if((i + 1) % step == 0) { max_vals.at(index) = std::max(max_val, (Dtype)fabs(data[i])); ++index; + max_val = (Dtype)(-10); } else { max_val = std::max(max_val, (Dtype)fabs(data[i])); } @@ -1388,6 +1444,7 @@ vector Net::FindMax(Blob* blob, bool is_single) { if((i + 1) % step == 0) { max_vals.at(index) = std::max(max_val, (Dtype)fabs(data[i])); ++index; + max_val = (Dtype)(-10); } else { max_val = std::max(max_val, (Dtype)fabs(data[i])); } @@ -1857,6 +1914,26 @@ void Net::CopyTrainedLayersFrom(const NetParameter& param_inp) { for (vector::iterator it = this->kept_bn_layers_.begin(); it != this->kept_bn_layers_.end(); it++) { param_tmp.mutable_compile_net_state()->add_kept_bn_layers(*it); } + int num_source_layers = param.layer_size(); + for (int i = 0; i < num_source_layers; ++i) { + LayerParameter* source_layer = param.mutable_layer(i); + const string& source_layer_name = source_layer->name(); + int target_layer_id = 0; + while (target_layer_id != layer_names_.size() && + layer_names_[target_layer_id] != source_layer_name) { + ++target_layer_id; + } + if (target_layer_id == layer_names_.size()) { + continue; + } + const LayerParameter& layer_param = layers_[target_layer_id]->layer_param(); + const string& engine_name = layer_param.engine(); + source_layer->set_engine(engine_name); + if ((layer_param.type().compare("BatchNorm") == 0) && + (layer_param.batch_norm_param().has_engine())) { + source_layer->mutable_batch_norm_param()->set_engine(layer_param.batch_norm_param().engine()); + } + } NetParameter param_compiled; CompileNet(param, ¶m_compiled); param = param_compiled; @@ -1869,7 +1946,7 @@ void Net::CopyTrainedLayersFrom(const NetParameter& param_inp) { } #endif - int num_source_layers = param.layer_size(); + num_source_layers = param.layer_size(); for (int i = 0; i < num_source_layers; ++i) { const LayerParameter& source_layer = param.layer(i); const string& source_layer_name = source_layer.name(); diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto index f850864ce..09ebd330f 100755 --- a/src/caffe/proto/caffe.proto +++ b/src/caffe/proto/caffe.proto @@ -230,6 +230,8 @@ message CompileNetState { optional bool bn_scale_remove = 2 [default = false]; optional bool bn_scale_merge = 3 [default = false]; repeated string kept_bn_layers = 4; + repeated string negative_conv_names = 5; + repeated uint32 negative_conv_indexes = 6; } message MultinodeParameter { @@ -358,6 +360,10 @@ message SolverParameter { repeated int32 stepvalue = 34; // the stepsize for learning rate policy "plateau" repeated int32 plateau_winsize = 44; + + // for rate policy "multifixed" + repeated float stagelr = 150; + repeated int32 stageiter = 151; // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm, // whenever their actual L2 norm is larger. @@ -596,8 +602,9 @@ message LayerParameter { optional SmoothL1LossParameter smooth_l1_loss_param = 148; optional SoftmaxParameter softmax_param = 125; optional SPPParameter spp_param = 132; - optional SplitParameter split_param = 147; + optional SplitParameter split_param = 208; optional SliceParameter slice_param = 126; + optional SwishParameter swish_param = 147; optional TanHParameter tanh_param = 127; optional ThresholdParameter threshold_param = 128; optional TileParameter tile_param = 138; @@ -610,6 +617,52 @@ message LayerParameter { optional MnActivationParameter mn_activation_param = 151; optional MnParamGradCompressParameter mn_grad_compress_param = 156; optional QuantizationParameter quantization_param = 158; + + optional ReorgParameter reorg_param = 159; + optional RegionLossParameter region_loss_param = 209; + // Yolo detection evaluation layer + optional EvalDetectionParameter eval_detection_param = 301; +} + +message RegionLossParameter{ + //Yolo 9000 + optional uint32 side = 1 [default = 13]; + optional uint32 num_class = 2 [default = 20]; + optional uint32 bias_match = 3 [default = 1]; + optional uint32 coords = 4 [default = 4]; + optional uint32 num = 5 [default = 5]; + optional uint32 softmax = 6 [default = 1]; + optional float jitter = 7 [default = 0.2]; + optional uint32 rescore = 8 [default = 1]; + + optional float object_scale = 9 [default = 1.0]; + optional float class_scale = 10 [default = 1.0]; + optional float noobject_scale = 11 [default = 0.5]; + optional float coord_scale = 12 [default = 5.0]; + optional uint32 absolute = 13 [default = 1]; + optional float thresh = 14 [default = 0.2]; + optional uint32 random = 15 [default = 1]; + repeated float biases = 16; + optional string softmax_tree = 17; + optional string class_map = 18; +} + +message EvalDetectionParameter { + enum ScoreType { + OBJ = 0; + PROB = 1; + MULTIPLY = 2; + } + // Yolo detection evaluation layer + optional uint32 side = 1 [default = 7]; + optional uint32 num_class = 2 [default = 20]; + optional uint32 num_object = 3 [default = 2]; + optional float threshold = 4 [default = 0.5]; + optional bool sqrt = 5 [default = true]; + optional bool constriant = 6 [default = true]; + optional ScoreType score_type = 7 [default = MULTIPLY]; + optional float nms = 8 [default = -1]; + repeated float biases = 9; } message MultinodeLayerParameter { @@ -697,6 +750,8 @@ message TransformationParameter { //will flip x flow if flow image input optional bool flow = 17 [default = false]; + + optional bool bgr2rgb = 18 [ default = false ]; } message RandomResizeParameter { @@ -923,6 +978,9 @@ message ConcatParameter { // DEPRECATED: alias for "axis" -- does not support negative indexing. optional uint32 concat_dim = 1 [default = 1]; + // For permute and flatten fuse in SSD-VGG16 + optional bool per_fla_fuse = 4 [default = false]; + enum Engine { DEFAULT = 0; CAFFE = 1; @@ -1017,6 +1075,11 @@ message ContrastiveLossParameter { optional bool legacy_version = 2 [default = false]; } +message ReorgParameter { + optional uint32 stride = 1; + optional bool reverse = 2 [default = false]; +} + message ConvolutionParameter { optional uint32 num_output = 1; // The number of outputs for the layer optional bool bias_term = 2 [default = true]; // whether to have bias terms @@ -1074,6 +1137,13 @@ message ConvolutionParameter { optional bool relu = 19 [default = false]; optional float negative_slope = 20 [default = 0]; optional string conv_algorithm = 21 [default = "direct"]; + repeated float coeff = 23; // bypass R-FCN test issue + enum FusionType { + NONE_FUSION = 0; + SUM_FUSION = 1; + } + optional FusionType fusion_type = 24 [default = NONE_FUSION]; + } message CropParameter { @@ -1128,6 +1198,8 @@ message DataParameter { optional uint32 prefetch = 10 [default = 4]; // Whether or not DataLayer should shuffle the images at every epoch. optional bool shuffle = 11 [default = false]; + + repeated uint32 side = 12; } // Message that store parameters used by DetectionEvaluateLayer @@ -1377,6 +1449,7 @@ message ImageDataParameter { message InfogainLossParameter { // Specify the infogain matrix source. optional string source = 1; + optional int32 axis = 2 [default = 1]; // axis of prob } message InnerProductParameter { @@ -1560,22 +1633,24 @@ message PoolingParameter { AVE = 1; STOCHASTIC = 2; } - optional bool avg_include_pad = 13 [default = true]; optional PoolMethod pool = 1 [default = MAX]; // The pooling method - // Pad, kernel size, and stride are all given as a single value for equal - // dimensions in all spatial dimensions, or once per spatial dimension. - repeated uint32 pad = 2; // The padding size defaults to 0 - repeated uint32 kernel_size = 3; // The kernel size (square) - repeated uint32 stride = 4; // defaults to 1 + // For 2D pooling only, Pad, kernel size, and stride are all given as a + // single value for equal dimensions in height and width or as Y, X pairs. + optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X) + optional uint32 pad_h = 9 [default = 0]; // The padding height + optional uint32 pad_w = 10 [default = 0]; // The padding width + optional uint32 kernel_size = 2; // The kernel size (square) + optional uint32 kernel_h = 5; // The kernel height + optional uint32 kernel_w = 6; // The kernel width + optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X) + optional uint32 stride_h = 7; // The stride height + optional uint32 stride_w = 8; // The stride width + // For 3D pooling only, dimensions in all spatial dimensions, or once + // per spatial dimension. + repeated uint32 pad_3d = 15; // The padding size defaults to 0 + repeated uint32 kernel_size_3d = 16; // The kernel size (square) + repeated uint32 stride_3d = 17; // defaults to 1 - // For 2D pooling only, the *_h and *_w versions may also be used to - // specify both spatial dimensions. - optional uint32 pad_h = 5 [default = 0]; // The padding height (2D only) - optional uint32 pad_w = 6 [default = 0]; // The padding width (2D only) - optional uint32 kernel_h = 7; // The kernel height (2D only) - optional uint32 kernel_w = 8; // The kernel width (2D only) - optional uint32 stride_h = 9 [default = 1]; // The stride height (2D only) - optional uint32 stride_w = 10 [default = 1]; // The stride width (2D only) enum Engine { DEFAULT = 0; CAFFE = 1; @@ -1587,6 +1662,7 @@ message PoolingParameter { // If global_pooling then it will pool over the size of the bottom by doing // kernel_h = bottom->height and kernel_w = bottom->width optional bool global_pooling = 12 [default = false]; + optional bool avg_include_pad = 13 [default = true]; // The axis to interpret as "channels" when performing pooling. // Preceding dimensions are treated as independent inputs; // succeeding dimensions are treated as "spatial". @@ -1791,6 +1867,7 @@ message ReshapeParameter { // optional int32 axis = 2 [default = 0]; optional int32 num_axes = 3 [default = -1]; + optional int32 pixelshuffler = 4 [default = 1]; } // Message that stores parameters used by ROIPoolingLayer @@ -1884,6 +1961,15 @@ message SoftmaxParameter { optional int32 axis = 2 [default = 1]; } +// Message that stores parameters used by SwishLayer +message SwishParameter { + // Beta parameter for the Swish activation function + // Described in: + // Prajit Ramachandran, Barret Zoph, Quoc V. Le. (2017). Searching for + // Activation Functions. https://arxiv.org/abs/1710.05941v2 + optional float beta = 1 [default = 1]; +} + message TanHParameter { enum Engine { DEFAULT = 0; @@ -2161,6 +2247,6 @@ message PReLUParameter { // Initial value of a_i. Default is a_i=0.25 for all i. optional FillerParameter filler = 1; - // Whether or not slope paramters are shared across channels. + // Whether or not slope parameters are shared across channels. optional bool channel_shared = 2 [default = false]; } diff --git a/src/caffe/quant/layers/base_quant_layer.cpp b/src/caffe/quant/layers/base_quant_layer.cpp index ea48d2296..bcfb09378 100644 --- a/src/caffe/quant/layers/base_quant_layer.cpp +++ b/src/caffe/quant/layers/base_quant_layer.cpp @@ -48,7 +48,7 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. namespace caffe { template -BaseQuantLayer::BaseQuantLayer() : need_quantize_(false), is_float_(false) { +BaseQuantLayer::BaseQuantLayer() : need_quantize_(false) { } INSTANTIATE_CLASS(BaseQuantLayer); diff --git a/src/caffe/quant/quantization.cpp b/src/caffe/quant/quantization.cpp index 4429f3621..e941c1d11 100644 --- a/src/caffe/quant/quantization.cpp +++ b/src/caffe/quant/quantization.cpp @@ -52,7 +52,7 @@ using caffe::LayerParameter; using caffe::NetParameter; Quantization::Quantization(string model, string weights, string model_quantized, - int iterations, string trimming_mode, double error_margin, int score_number, string scaling, int detection, int power) { + int iterations, string trimming_mode, double error_margin, int score_number, string scaling, int detection) { this->model_ = model; this->weights_ = weights; this->model_quantized_ = model_quantized; @@ -62,7 +62,6 @@ Quantization::Quantization(string model, string weights, string model_quantized, this->score_number = score_number; this->scaling = scaling; this->detection = detection; - this->power = power; } void Quantization::QuantizeNet() { @@ -153,65 +152,32 @@ void Quantization::Quantize2DynamicFixedPoint() { vector lens; vector scales; for (int i = 0; i < layer_names_.size(); ++i) { - if (this->power) { - il_in_.push_back((int)ceil(log2(max_in_[i]))); - il_out_.push_back((int)ceil(log2(max_out_[i]))); - } else { - scale_in_.push_back(max_in_[i]); - scale_out_.push_back(max_out_[i]); - } + scale_in_.push_back(max_in_[i]); + scale_out_.push_back(max_out_[i]); if (this->scaling == "single") { - if (this->power) - lens.push_back((int)ceil(log2(max_params_[i][0])+1)); - else - scales.push_back(max_params_[i][0]); + scales.push_back(max_params_[i][0]); } else { for (int j = 0; j < max_params_[i].size(); j++) { - if (this->power) - lens.push_back((int)ceil(log2(max_params_[i][j])+1)); - else - scales.push_back(max_params_[i][j]+0.0); + scales.push_back(max_params_[i][j]+0.0); } } - if (this->power) { - il_params_.push_back(lens); - lens.clear(); - } else { - scale_params_.push_back(scales); - scales.clear(); - } + scale_params_.push_back(scales); + scales.clear(); } // Debug for (int k = 0; k < layer_names_.size(); ++k) { if (this->scaling != "single") { - if (this->power) - LOG(INFO) << "Layer " << layer_names_[k] << ", parameters channel=" << il_params_[k].size(); - else - LOG(INFO) << "Layer " << layer_names_[k] << ", parameters channel=" << scale_params_[k].size(); + LOG(INFO) << "Layer " << layer_names_[k] << ", parameters channel=" << scale_params_[k].size(); } - if (this->power) { - LOG(INFO) << "Integer length input=" << il_in_[k]; - LOG(INFO) << "Integer length output=" << il_out_[k]; - } else { - LOG(INFO) << "Scale input=" << scale_in_[k]; - LOG(INFO) << "Scale output=" << scale_out_[k]; - } + LOG(INFO) << "Scale input=" << scale_in_[k]; + LOG(INFO) << "Scale output=" << scale_out_[k]; if (this->scaling == "single") { - if (this->power) - LOG(INFO) << "Integer length param=" << il_params_[k][0]; - else - LOG(INFO) << "Scale param=" << scale_params_[k][0]; + LOG(INFO) << "Scale param=" << scale_params_[k][0]; } else { - if (this->power){ - for (int j = 0; j < il_params_[k].size(); j++) { - LOG(INFO) << "Integer length params[" << j << "]=" << il_params_[k][j]; - } - } else{ - for (int j = 0; j < scale_params_[k].size(); j++) { - LOG(INFO) << "Scale params[" << j << "]=" << scale_params_[k][j]; - } + for (int j = 0; j < scale_params_[k].size(); j++) { + LOG(INFO) << "Scale params[" << j << "]=" << scale_params_[k][j]; } } } @@ -252,17 +218,9 @@ void Quantization::EditNetDescriptionDynamicFixedPoint(NetParameter* param, param_layer->set_type("Convolution"); if (trimming_mode_ == "dynamic_fixed_point") { param_layer->mutable_quantization_param()->set_bw_params(bw_conv); - if (this->power) { - vector vals = GetIntegerLengthParams(param->layer(i).name()); - for (int j = 0; j < vals.size(); j++) { - vals[j] = bw_conv - vals[j]; - param_layer->mutable_quantization_param()->add_fl_params(vals[j]); - } - } else { - vector vals = GetScaleParams(param->layer(i).name()); - for (int j = 0; j < vals.size(); j++) { - param_layer->mutable_quantization_param()->add_scale_params(vals[j]); - } + vector vals = GetScaleParams(param->layer(i).name()); + for (int j = 0; j < vals.size(); j++) { + param_layer->mutable_quantization_param()->add_scale_params(vals[j]); } } } @@ -273,17 +231,10 @@ void Quantization::EditNetDescriptionDynamicFixedPoint(NetParameter* param, if (trimming_mode_ == "dynamic_fixed_point") { param_layer->mutable_quantization_param()->set_bw_layer_in(bw_in); param_layer->mutable_quantization_param()->set_bw_layer_out(bw_out); - if (this->power) { - int val = GetIntegerLengthIn(param->layer(i).name()); - param_layer->mutable_quantization_param()->add_fl_layer_in(bw_in - val); - val = GetIntegerLengthOut(param->layer(i).name()); - param_layer->mutable_quantization_param()->add_fl_layer_out(bw_out - val); - } else { - float val = GetScaleIn(param->layer(i).name()); - param_layer->mutable_quantization_param()->add_scale_in(val); - val = GetScaleOut(param->layer(i).name()); - param_layer->mutable_quantization_param()->add_scale_out(val); - } + float val = GetScaleIn(param->layer(i).name()); + param_layer->mutable_quantization_param()->add_scale_in(val); + val = GetScaleOut(param->layer(i).name()); + param_layer->mutable_quantization_param()->add_scale_out(val); } } LayerParameter* param_layer = param->mutable_layer(i); @@ -297,24 +248,6 @@ void Quantization::EditNetDescriptionDynamicFixedPoint(NetParameter* param, } } -vector Quantization::GetIntegerLengthParams(const string layer_name) { - int pos = find(layer_names_.begin(), layer_names_.end(), layer_name) - - layer_names_.begin(); - return il_params_[pos]; -} - -int Quantization::GetIntegerLengthIn(const string layer_name) { - int pos = find(layer_names_.begin(), layer_names_.end(), layer_name) - - layer_names_.begin(); - return il_in_[pos]; -} - -int Quantization::GetIntegerLengthOut(const string layer_name) { - int pos = find(layer_names_.begin(), layer_names_.end(), layer_name) - - layer_names_.begin(); - return il_out_[pos]; -} - vector Quantization::GetScaleParams(const string layer_name) { int pos = find(layer_names_.begin(), layer_names_.end(), layer_name) - layer_names_.begin(); diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp index 304adf43c..dcc0103cc 100644 --- a/src/caffe/solvers/sgd_solver.cpp +++ b/src/caffe/solvers/sgd_solver.cpp @@ -161,6 +161,16 @@ Dtype SGDSolver::GetLearningRate() { rate = this->param_.base_lr() * pow(this->param_.gamma(), this->current_step_); + } else if (lr_policy == "multifixed") { + CHECK_EQ(this->param_.stageiter_size(), this->param_.stagelr_size()); + int num_stages = this->param_.stagelr_size(); + int stage = 0; + for (; stage < num_stages; ++stage) { + if (this->iter_ <= this->param_.stageiter(stage)) + break; + } + stage = (stage == num_stages) ? stage - 1 : stage; + rate = this->param_.stagelr(stage); } else { LOG(FATAL) << "Unknown learning rate policy: " << lr_policy; } @@ -263,6 +273,28 @@ void SGDSolver::ApplyUpdate(int param_id) { } #endif /* ENABLE_SGD_FUSION */ + const vector*>& net_params = this->net_->learnable_params(); + bool need_sync_data_back_to_prv = false; + bool need_sync_diff_back_to_prv = false; + // This sync logic is specific for the case of oc not exactly being divided by 16. + // mkldnn primitive have padding dimensions for perf boosting on such case. + // This causes prv_diff_count() is not equal to count() and makes weight diff point + // to cpu buffer rather than mkldnn prv buffer. but if iter_size is larger than 1 at + // this time, mkldnn conv layer would directly use the prv buffer of weight diff as + // input&output for weight diff accumulation. This makes weight diff calculation wrong. + // Syncing weight diff to be in mkldnn prv buffer would work around this issue in mkldnn + // conv layer. + if (net_params[param_id]->prv_data() + && (net_params[param_id]->prv_data_count() + != net_params[param_id]->count())) { + need_sync_data_back_to_prv = true; + } + if (net_params[param_id]->prv_diff() + && (net_params[param_id]->prv_diff_count() + != net_params[param_id]->count())) { + need_sync_diff_back_to_prv = true; + } + //LOG(INFO) << "No Fusion: Param_id: " << param_id; Normalize(param_id); @@ -274,12 +306,20 @@ void SGDSolver::ApplyUpdate(int param_id) { ComputeUpdateValue(param_id, rate); + LOG_PARAM_BLOB(this->net_->learnable_params()[param_id], diff, param_id, "ApplyUpdate: wtinc:"); LOG_PARAM_BLOB(this->net_->learnable_params()[param_id], data, param_id, "ApplyUpdate: weight before update:"); this->net_->learnable_params()[param_id]->Update(); + if (need_sync_diff_back_to_prv) { + net_params[param_id]->mutable_prv_diff(); + } + if (need_sync_data_back_to_prv) { + net_params[param_id]->mutable_prv_data(); + } + LOG_PARAM_BLOB(this->net_->learnable_params()[param_id], data, param_id, "ApplyUpdate: weight after update:"); } @@ -378,11 +418,23 @@ void SGDSolver::SGDFusion(int param_id, Dtype rate) { //#pragma region 2. Common condition judgement bool prv_diff_condition_flag = false; + bool need_sync_data_back_to_prv = false; + bool need_sync_diff_back_to_prv = false; if (net_params[param_id]->prv_diff() && (net_params[param_id]->prv_diff_count() == net_params[param_id]->count())) { prv_diff_condition_flag = true; } + if (net_params[param_id]->prv_diff() + && (net_params[param_id]->prv_diff_count() + != net_params[param_id]->count())) { + need_sync_diff_back_to_prv = true; + } + if (net_params[param_id]->prv_data() + && (net_params[param_id]->prv_data_count() + != net_params[param_id]->count())) { + need_sync_data_back_to_prv = true; + } //#pragma endregion //#pragma region 3. Normalize stage @@ -484,6 +536,12 @@ void SGDSolver::SGDFusion(int param_id, Dtype rate) { //Update stage (separate) net_params[param_id]->Update(); } + if (need_sync_data_back_to_prv) { + net_params[param_id]->mutable_prv_data(); + } + if (need_sync_diff_back_to_prv) { + net_params[param_id]->mutable_prv_diff(); + } } #endif /* ENABLE_SGD_FUSION */ @@ -645,12 +703,6 @@ void SGDSolver::ComputeUpdateValue(int param_id, Dtype rate) { caffe_copy(net_params[param_id]->count(), history_[param_id]->cpu_data(), net_params[param_id]->mutable_cpu_diff()); - - if (net_params[param_id]->prv_diff() - && (net_params[param_id]->prv_diff_count() - != net_params[param_id]->count())) { - net_params[param_id]->mutable_prv_diff(); - } } break; } diff --git a/src/caffe/test/test_bbox_util.cpp b/src/caffe/test/test_bbox_util.cpp index 02782f128..91b624f1a 100644 --- a/src/caffe/test/test_bbox_util.cpp +++ b/src/caffe/test/test_bbox_util.cpp @@ -1232,8 +1232,8 @@ TEST_F(CPUBBoxUtilTest, TestGetPriorBBoxes) { } } - vector prior_bboxes; - vector > prior_variances; + vector prior_bboxes(num_priors); + vector > prior_variances(num_priors); GetPriorBBoxes(prior_data, num_priors, &prior_bboxes, &prior_variances); EXPECT_EQ(prior_bboxes.size(), num_priors); diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp index edd22549f..bec5a24a5 100644 --- a/src/caffe/test/test_layer_factory.cpp +++ b/src/caffe/test/test_layer_factory.cpp @@ -67,7 +67,7 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) { if (iter->first == "Python") { continue; } LayerParameter layer_param; // Data layers expect a DB - if (iter->first == "Data" || iter->first == "AnnotatedData") { + if (iter->first == "Data" || iter->first == "AnnotatedData" || iter->first == "BoxData") { #ifdef USE_LEVELDB string tmp; MakeTempDir(&tmp); diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp index ad9d3321d..3d9b07a80 100644 --- a/src/caffe/test/test_maxpool_dropout_layers.cpp +++ b/src/caffe/test/test_maxpool_dropout_layers.cpp @@ -81,8 +81,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestSetup) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); PoolingLayer max_layer(layer_param); max_layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); DropoutLayer dropout_layer(layer_param); @@ -98,8 +98,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestForward) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); PoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); @@ -128,8 +128,8 @@ TYPED_TEST(MaxPoolingDropoutTest, TestBackward) { LayerParameter layer_param; layer_param.set_phase(TRAIN); PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); PoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); diff --git a/src/caffe/test/test_mkl_pooling_layer.cpp b/src/caffe/test/test_mkl_pooling_layer.cpp index 19347d185..3df1590f1 100644 --- a/src/caffe/test/test_mkl_pooling_layer.cpp +++ b/src/caffe/test/test_mkl_pooling_layer.cpp @@ -83,7 +83,7 @@ class MKLPoolingLayerTest : public MultiDeviceTest { void TestForwardSquare() { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(2); + pooling_param->set_kernel_size(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); const int num = 2; const int channels = 2; @@ -413,8 +413,8 @@ TYPED_TEST(MKLPoolingLayerTest, TestSetup) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); MKLPoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num()); @@ -427,9 +427,9 @@ TYPED_TEST(MKLPoolingLayerTest, TestSetupPadded) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); MKLPoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -502,8 +502,8 @@ TYPED_TEST(MKLPoolingLayerTest, TestGradientMax) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); MKLPoolingLayer layer(layer_param); GradientChecker checker(1e-4, 1e-2); @@ -517,9 +517,9 @@ TYPED_TEST(MKLPoolingLayerTest, TestForwardMaxPadded) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); this->blob_bottom_->Reshape(1, 1, 3, 3); // Input: diff --git a/src/caffe/test/test_mkldnn_pooling_layer.cpp b/src/caffe/test/test_mkldnn_pooling_layer.cpp index 04cf68bed..a9d37010a 100644 --- a/src/caffe/test/test_mkldnn_pooling_layer.cpp +++ b/src/caffe/test/test_mkldnn_pooling_layer.cpp @@ -83,7 +83,7 @@ class MKLDNNPoolingLayerTest : public MultiDeviceTest { void TestForwardSquare() { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(2); + pooling_param->set_kernel_size(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); const int num = 2; const int channels = 2; @@ -415,8 +415,8 @@ TYPED_TEST(MKLDNNPoolingLayerTest, TestSetup) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); MKLDNNPoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num()); @@ -429,9 +429,9 @@ TYPED_TEST(MKLDNNPoolingLayerTest, TestSetupPadded) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); MKLDNNPoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -506,8 +506,8 @@ TYPED_TEST(MKLDNNPoolingLayerTest, TestGradientMax) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); MKLDNNPoolingLayer layer(layer_param); GradientChecker checker(1e-4, 1e-2); @@ -521,9 +521,9 @@ TYPED_TEST(MKLDNNPoolingLayerTest, TestForwardMaxPadded) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); this->blob_bottom_->Reshape(1, 1, 3, 3); // Input: @@ -590,9 +590,9 @@ TYPED_TEST(MKLDNNPoolingLayerTest, TestForwardAve) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(1); - pooling_param->add_pad(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(1); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); this->blob_bottom_->Reshape(1, 1, 3, 3); FillerParameter filler_param; @@ -653,7 +653,7 @@ TYPED_TEST(MKLDNNPoolingLayerTest, DISABLED_TestGradientAve) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); + pooling_param->set_stride(2); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); MKLDNNPoolingLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); @@ -672,8 +672,8 @@ TYPED_TEST(MKLDNNPoolingLayerTest, TestGradientAvePadded) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); MKLDNNPoolingLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); diff --git a/src/caffe/test/test_multibox_loss_layer.cpp b/src/caffe/test/test_multibox_loss_layer.cpp index 1b2cb4be6..96dd65f25 100644 --- a/src/caffe/test/test_multibox_loss_layer.cpp +++ b/src/caffe/test/test_multibox_loss_layer.cpp @@ -235,8 +235,8 @@ class MultiBoxLossLayerTest : public MultiDeviceTest { // Fake layer PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); - pooling_param->add_kernel_size(10); - pooling_param->add_stride(10); + pooling_param->set_kernel_size(10); + pooling_param->set_stride(10); PoolingLayer pooling_layer(layer_param); Blob* fake_blob = new Blob(num_, 5, height_, width_); diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp index 53c4e2e1a..c4c4ceac5 100644 --- a/src/caffe/test/test_net.cpp +++ b/src/caffe/test/test_net.cpp @@ -3558,6 +3558,8 @@ TEST_F(CompileNetTest, TestCompileNetConvEltReluFusionMKLDNN) { " type: 'Convolution' " " convolution_param { " " engine: MKLDNN " + " relu: true " + " fusion_type: SUM_FUSION " " } " "} " "layer { " diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp index ab4cc5496..7f76b394b 100644 --- a/src/caffe/test/test_pooling_layer.cpp +++ b/src/caffe/test/test_pooling_layer.cpp @@ -86,7 +86,7 @@ class PoolingLayerTest : public MultiDeviceTest { void TestForwardSquare() { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(2); + pooling_param->set_kernel_size(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); const int num = 2; const int channels = 2; @@ -414,8 +414,8 @@ TYPED_TEST(PoolingLayerTest, TestSetup) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); PoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num()); @@ -428,9 +428,9 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); PoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -501,8 +501,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientMax) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); PoolingLayer layer(layer_param); GradientChecker checker(1e-4, 1e-2); @@ -516,9 +516,9 @@ TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); this->blob_bottom_->Reshape(1, 1, 3, 3); // Input: @@ -565,7 +565,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); + pooling_param->set_stride(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); this->blob_top_vec_.push_back(this->blob_top_mask_); PoolingLayer layer(layer_param); @@ -581,9 +581,9 @@ TYPED_TEST(PoolingLayerTest, TestForwardAve) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(1); - pooling_param->add_pad(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(1); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); this->blob_bottom_->Reshape(1, 1, 3, 3); FillerParameter filler_param; @@ -617,7 +617,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAve) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); + pooling_param->set_stride(2); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); PoolingLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); @@ -635,8 +635,8 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); PoolingLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); @@ -678,7 +678,7 @@ class CuDNNPoolingLayerTest : public GPUDeviceTest { void TestForwardSquare() { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(2); + pooling_param->set_kernel_size(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); const int num = 2; const int channels = 2; @@ -1005,8 +1005,8 @@ TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes); TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); CuDNNPoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num()); @@ -1018,9 +1018,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) { TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(1); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); CuDNNPoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); @@ -1080,9 +1080,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); + pooling_param->set_stride(2); // currenty, cuDNN pooling does not support padding - pooling_param->add_pad(0); + pooling_param->set_pad(0); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); CuDNNPoolingLayer layer(layer_param); GradientChecker checker(1e-4, 1e-2); @@ -1095,9 +1095,9 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) { TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_MAX); this->blob_bottom_->Reshape(1, 1, 3, 3); // Input: @@ -1160,11 +1160,11 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) { TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(1); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(1); // Currently, cuDNN pooling does not support padding, so we use // a simplified version of this test. - pooling_param->add_pad(0); + pooling_param->set_pad(0); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); this->blob_bottom_->Reshape(1, 1, 3, 3); FillerParameter filler_param; @@ -1189,7 +1189,7 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); + pooling_param->set_stride(2); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); CuDNNPoolingLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); @@ -1206,8 +1206,8 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) { PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); pooling_param->set_kernel_h(kernel_h); pooling_param->set_kernel_w(kernel_w); - pooling_param->add_stride(2); - pooling_param->add_pad(2); + pooling_param->set_stride(2); + pooling_param->set_pad(2); pooling_param->set_pool(PoolingParameter_PoolMethod_AVE); CuDNNPoolingLayer layer(layer_param); GradientChecker checker(1e-2, 1e-2); diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp index 59ea570a9..e6eb4b2aa 100644 --- a/src/caffe/test/test_stochastic_pooling.cpp +++ b/src/caffe/test/test_stochastic_pooling.cpp @@ -93,8 +93,8 @@ TYPED_TEST_CASE(CPUStochasticPoolingLayerTest, TestDtypes); TYPED_TEST(CPUStochasticPoolingLayerTest, TestSetup) { LayerParameter layer_param; PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); - pooling_param->add_kernel_size(3); - pooling_param->add_stride(2); + pooling_param->set_kernel_size(3); + pooling_param->set_stride(2); PoolingLayer layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num()); diff --git a/src/caffe/util/bbox_util.cpp b/src/caffe/util/bbox_util.cpp index 715f84685..27f62bb42 100644 --- a/src/caffe/util/bbox_util.cpp +++ b/src/caffe/util/bbox_util.cpp @@ -48,7 +48,10 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #include "boost/iterator/counting_iterator.hpp" #include "caffe/util/bbox_util.hpp" - +#ifdef ENABLE_NMS_OPTIMIZATION +#include +#include "omp.h" +#endif namespace caffe { bool SortBBoxAscend(const NormalizedBBox& bbox1, const NormalizedBBox& bbox2) { @@ -82,6 +85,437 @@ template bool SortScorePairDescend(const pair& pair1, const pair& pair2); template bool SortScorePairDescend(const pair >& pair1, const pair >& pair2); +#ifdef ENABLE_NMS_OPTIMIZATION +#define DIVUP(m, n)((m) / (n) + ((m) % (n) > 0)) + +typedef unsigned long long int uint64_t; + +static inline float max(float a, float b) { return (a) > (b) ? (a) : (b); } +static inline float min(float a, float b) { return (a) < (b) ? (a) : (b); } + +#define MASK_ELEM(x, y) \ + ((uint64_t*)((char*)mask_tbl + x * blocks * sizeof(uint64_t)) + y) + +void cpu_nms_avx512_parallize_inner(int* __restrict keep_out, + int* __restrict num_out, + const float* __restrict x1, + const float* __restrict y1, + const float* __restrict x2, + const float* __restrict y2, + int64_t boxes_num, + float thresh_f) +{ + const int blocks = DIVUP(boxes_num, 64); + uint64_t *mask_tbl = (uint64_t *)aligned_alloc(64, blocks * sizeof(uint64_t)); + memset(mask_tbl, 0, sizeof(uint64_t) * blocks); + + uint64_t i = 0, j = 0; + uint64_t num_to_keep = 0; + + for (i = 0; i < boxes_num; i++) { + + if (mask_tbl[i/64] & (1ull << (i % 64))) { + continue; + } + + keep_out[num_to_keep++] = i; + + __m512 x1_a = _mm512_set1_ps(x1[i]); + __m512 x2_a = _mm512_set1_ps(x2[i]); + __m512 y1_a = _mm512_set1_ps(y1[i]); + __m512 y2_a = _mm512_set1_ps(y2[i]); + + __m512 zero = _mm512_setzero_ps(); + __m512 Sa_w = _mm512_sub_ps(x2_a, x1_a); + __m512 Sa_h = _mm512_sub_ps(y2_a, y1_a); + + __m512 Sa = _mm512_mul_ps(Sa_w, Sa_h); + + __m512 thresh = _mm512_set1_ps(thresh_f); + + + for (j = i + 1; (j & 0xf) && (j < boxes_num); j ++) { + + if (mask_tbl[j/64] & (1ull << (j % 64))) { + continue; + } + + float xx1 = max(x1[i], x1[j]); + float yy1 = max(y1[i], y1[j]); + float xx2 = min(x2[i], x2[j]); + float yy2 = min(y2[i], y2[j]); + + float w = max(0.0f, xx2 - xx1 ); + float h = max(0.0f, yy2 - yy1 ); + + float interS = w * h; + float Sa = (x2[i] - x1[i] ) * (y2[i] - y1[i] ); + float Sb = (x2[j] - x1[j] ) * (y2[j] - y1[j] ); + + if (interS > thresh_f * (Sa + Sb - interS)) { + mask_tbl[j/64] |= 1ull << (j % 64); + } + } + + if (!(j & 0xf) && (j & 0x1f) && (j + 16 <= boxes_num)) { + + __m512 x1_b = _mm512_load_ps(x1 + j); + __m512 y1_b = _mm512_load_ps(y1 + j); + __m512 x2_b = _mm512_load_ps(x2 + j); + __m512 y2_b = _mm512_load_ps(y2 + j); + + __m512 xx1 = _mm512_max_ps(x1_a, x1_b); + __m512 yy1 = _mm512_max_ps(y1_a, y1_b); + __m512 xx2 = _mm512_min_ps(x2_a, x2_b); + __m512 yy2 = _mm512_min_ps(y2_a, y2_b); + + __m512 w = _mm512_sub_ps(xx2, xx1); + __m512 h = _mm512_sub_ps(yy2, yy1); + + w = _mm512_max_ps(w, zero); + h = _mm512_max_ps(h, zero); + + __m512 interS = _mm512_mul_ps(w, h); + + __m512 Sb_w = _mm512_sub_ps(x2_b, x1_b); + __m512 Sb_h = _mm512_sub_ps(y2_b, y1_b); + + __m512 Sb = _mm512_mul_ps(Sb_w, Sb_h); + + __m512 ovr = _mm512_add_ps(Sa, Sb); + ovr = _mm512_sub_ps(ovr, interS); + ovr = _mm512_mul_ps(ovr, thresh); + + __mmask16 gt = _mm512_cmp_ps_mask(interS, ovr, _CMP_GT_OS); + + uint64_t mask = gt; + + mask_tbl[j/64] |= (mask) << (j % 64); + + j += 16; + } + + if (!(j & 0x1f) && (j & 0x3f) && (j + 32 <= boxes_num)) { + + __m512 x1_b0 = _mm512_load_ps(x1 + j); + __m512 y1_b0 = _mm512_load_ps(y1 + j); + __m512 x2_b0 = _mm512_load_ps(x2 + j); + __m512 y2_b0 = _mm512_load_ps(y2 + j); + + __m512 x1_b1 = _mm512_load_ps(x1 + j + 16); + __m512 y1_b1 = _mm512_load_ps(y1 + j + 16); + __m512 x2_b1 = _mm512_load_ps(x2 + j + 16); + __m512 y2_b1 = _mm512_load_ps(y2 + j + 16); + + __m512 xx1_0 = _mm512_max_ps(x1_a, x1_b0); + __m512 yy1_0 = _mm512_max_ps(y1_a, y1_b0); + __m512 xx2_0 = _mm512_min_ps(x2_a, x2_b0); + __m512 yy2_0 = _mm512_min_ps(y2_a, y2_b0); + + __m512 xx1_1 = _mm512_max_ps(x1_a, x1_b1); + __m512 yy1_1 = _mm512_max_ps(y1_a, y1_b1); + __m512 xx2_1 = _mm512_min_ps(x2_a, x2_b1); + __m512 yy2_1 = _mm512_min_ps(y2_a, y2_b1); + + __m512 w0 = _mm512_sub_ps(xx2_0, xx1_0); + __m512 h0 = _mm512_sub_ps(yy2_0, yy1_0); + __m512 w1 = _mm512_sub_ps(xx2_1, xx1_1); + __m512 h1 = _mm512_sub_ps(yy2_1, yy1_1); + + w0 = _mm512_max_ps(w0, zero); + h0 = _mm512_max_ps(h0, zero); + w1 = _mm512_max_ps(w1, zero); + h1 = _mm512_max_ps(h1, zero); + + __m512 interS_0 = _mm512_mul_ps(w0, h0); + __m512 interS_1 = _mm512_mul_ps(w1, h1); + + __m512 Sb_w0 = _mm512_sub_ps(x2_b0, x1_b0); + __m512 Sb_h0 = _mm512_sub_ps(y2_b0, y1_b0); + __m512 Sb_w1 = _mm512_sub_ps(x2_b1, x1_b1); + __m512 Sb_h1 = _mm512_sub_ps(y2_b1, y1_b1); + + __m512 ovr0 = _mm512_fmadd_ps(Sb_w0, Sb_h0, Sa); + __m512 ovr1 = _mm512_fmadd_ps(Sb_w1, Sb_h1, Sa); + + ovr0 = _mm512_sub_ps(ovr0, interS_0); + ovr1 = _mm512_sub_ps(ovr1, interS_1); + + ovr0 = _mm512_mul_ps(ovr0, thresh); + ovr1 = _mm512_mul_ps(ovr1, thresh); + + __mmask16 gt0 = _mm512_cmp_ps_mask(interS_0, ovr0, _CMP_GT_OS); + __mmask16 gt1 = _mm512_cmp_ps_mask(interS_1, ovr1, _CMP_GT_OS); + + uint64_t mask = (gt0) | ((uint64_t)gt1 << 16); + + mask_tbl[j/64] |= (mask) << (j % 64); + + j += 32; + } + //#pragma omp parallel for + for (; (j + 64) <= boxes_num; j += 64) { + + __m512 x1_b0 = _mm512_load_ps(x1 + j); + __m512 y1_b0 = _mm512_load_ps(y1 + j); + __m512 x2_b0 = _mm512_load_ps(x2 + j); + __m512 y2_b0 = _mm512_load_ps(y2 + j); + + __m512 x1_b1 = _mm512_load_ps(x1 + j + 16); + __m512 y1_b1 = _mm512_load_ps(y1 + j + 16); + __m512 x2_b1 = _mm512_load_ps(x2 + j + 16); + __m512 y2_b1 = _mm512_load_ps(y2 + j + 16); + + __m512 x1_b2 = _mm512_load_ps(x1 + j + 32); + __m512 y1_b2 = _mm512_load_ps(y1 + j + 32); + __m512 x2_b2 = _mm512_load_ps(x2 + j + 32); + __m512 y2_b2 = _mm512_load_ps(y2 + j + 32); + + __m512 x1_b3 = _mm512_load_ps(x1 + j + 48); + __m512 y1_b3 = _mm512_load_ps(y1 + j + 48); + __m512 x2_b3 = _mm512_load_ps(x2 + j + 48); + __m512 y2_b3 = _mm512_load_ps(y2 + j + 48); + + __m512 xx1_0 = _mm512_max_ps(x1_a, x1_b0); + __m512 yy1_0 = _mm512_max_ps(y1_a, y1_b0); + __m512 xx2_0 = _mm512_min_ps(x2_a, x2_b0); + __m512 yy2_0 = _mm512_min_ps(y2_a, y2_b0); + + __m512 xx1_1 = _mm512_max_ps(x1_a, x1_b1); + __m512 yy1_1 = _mm512_max_ps(y1_a, y1_b1); + __m512 xx2_1 = _mm512_min_ps(x2_a, x2_b1); + __m512 yy2_1 = _mm512_min_ps(y2_a, y2_b1); + + __m512 Sb_w0 = _mm512_sub_ps(x2_b0, x1_b0); + __m512 Sb_h0 = _mm512_sub_ps(y2_b0, y1_b0); + __m512 Sb_w1 = _mm512_sub_ps(x2_b1, x1_b1); + __m512 Sb_h1 = _mm512_sub_ps(y2_b1, y1_b1); + __m512 Sb_w2 = _mm512_sub_ps(x2_b2, x1_b2); + __m512 Sb_h2 = _mm512_sub_ps(y2_b2, y1_b2); + __m512 Sb_w3 = _mm512_sub_ps(x2_b3, x1_b3); + __m512 Sb_h3 = _mm512_sub_ps(y2_b3, y1_b3); + + __m512 xx1_2 = _mm512_max_ps(x1_a, x1_b2); + __m512 yy1_2 = _mm512_max_ps(y1_a, y1_b2); + __m512 xx2_2 = _mm512_min_ps(x2_a, x2_b2); + __m512 yy2_2 = _mm512_min_ps(y2_a, y2_b2); + + __m512 xx1_3 = _mm512_max_ps(x1_a, x1_b3); + __m512 yy1_3 = _mm512_max_ps(y1_a, y1_b3); + __m512 xx2_3 = _mm512_min_ps(x2_a, x2_b3); + __m512 yy2_3 = _mm512_min_ps(y2_a, y2_b3); + + __m512 w0 = _mm512_sub_ps(xx2_0, xx1_0); + __m512 h0 = _mm512_sub_ps(yy2_0, yy1_0); + __m512 w1 = _mm512_sub_ps(xx2_1, xx1_1); + __m512 h1 = _mm512_sub_ps(yy2_1, yy1_1); + __m512 w2 = _mm512_sub_ps(xx2_2, xx1_2); + __m512 h2 = _mm512_sub_ps(yy2_2, yy1_2); + __m512 w3 = _mm512_sub_ps(xx2_3, xx1_3); + __m512 h3 = _mm512_sub_ps(yy2_3, yy1_3); + + w0 = _mm512_max_ps(w0, zero); + h0 = _mm512_max_ps(h0, zero); + w1 = _mm512_max_ps(w1, zero); + h1 = _mm512_max_ps(h1, zero); + w2 = _mm512_max_ps(w2, zero); + h2 = _mm512_max_ps(h2, zero); + w3 = _mm512_max_ps(w3, zero); + h3 = _mm512_max_ps(h3, zero); + + __m512 interS_0 = _mm512_mul_ps(w0, h0); + __m512 interS_1 = _mm512_mul_ps(w1, h1); + __m512 interS_2 = _mm512_mul_ps(w2, h2); + __m512 interS_3 = _mm512_mul_ps(w3, h3); + + __m512 ovr0 = _mm512_fmadd_ps(Sb_w0, Sb_h0, Sa); + __m512 ovr1 = _mm512_fmadd_ps(Sb_w1, Sb_h1, Sa); + __m512 ovr2 = _mm512_fmadd_ps(Sb_w2, Sb_h2, Sa); + __m512 ovr3 = _mm512_fmadd_ps(Sb_w3, Sb_h3, Sa); + + ovr0 = _mm512_sub_ps(ovr0, interS_0); + ovr1 = _mm512_sub_ps(ovr1, interS_1); + ovr2 = _mm512_sub_ps(ovr2, interS_2); + ovr3 = _mm512_sub_ps(ovr3, interS_3); + + ovr0 = _mm512_mul_ps(ovr0, thresh); + ovr1 = _mm512_mul_ps(ovr1, thresh); + ovr2 = _mm512_mul_ps(ovr2, thresh); + ovr3 = _mm512_mul_ps(ovr3, thresh); + + __mmask16 gt0 = _mm512_cmp_ps_mask(interS_0, ovr0, _CMP_GT_OS); + __mmask16 gt1 = _mm512_cmp_ps_mask(interS_1, ovr1, _CMP_GT_OS); + __mmask16 gt2 = _mm512_cmp_ps_mask(interS_2, ovr2, _CMP_GT_OS); + __mmask16 gt3 = _mm512_cmp_ps_mask(interS_3, ovr3, _CMP_GT_OS); + + uint64_t mask = ((uint64_t)(gt0)) | (((uint64_t)gt1) << 16) | (((uint64_t)gt2) << 32) | (((uint64_t)gt3) << 48); + + mask_tbl[j / 64] |= mask; + } + + if ((j + 32) <= boxes_num) { + + __m512 x1_b0 = _mm512_load_ps(x1 + j); + __m512 y1_b0 = _mm512_load_ps(y1 + j); + __m512 x2_b0 = _mm512_load_ps(x2 + j); + __m512 y2_b0 = _mm512_load_ps(y2 + j); + + __m512 x1_b1 = _mm512_load_ps(x1 + j + 16); + __m512 y1_b1 = _mm512_load_ps(y1 + j + 16); + __m512 x2_b1 = _mm512_load_ps(x2 + j + 16); + __m512 y2_b1 = _mm512_load_ps(y2 + j + 16); + + __m512 xx1_0 = _mm512_max_ps(x1_a, x1_b0); + __m512 yy1_0 = _mm512_max_ps(y1_a, y1_b0); + __m512 xx2_0 = _mm512_min_ps(x2_a, x2_b0); + __m512 yy2_0 = _mm512_min_ps(y2_a, y2_b0); + + __m512 xx1_1 = _mm512_max_ps(x1_a, x1_b1); + __m512 yy1_1 = _mm512_max_ps(y1_a, y1_b1); + __m512 xx2_1 = _mm512_min_ps(x2_a, x2_b1); + __m512 yy2_1 = _mm512_min_ps(y2_a, y2_b1); + + __m512 w0 = _mm512_sub_ps(xx2_0, xx1_0); + __m512 h0 = _mm512_sub_ps(yy2_0, yy1_0); + __m512 w1 = _mm512_sub_ps(xx2_1, xx1_1); + __m512 h1 = _mm512_sub_ps(yy2_1, yy1_1); + + w0 = _mm512_max_ps(w0, zero); + h0 = _mm512_max_ps(h0, zero); + w1 = _mm512_max_ps(w1, zero); + h1 = _mm512_max_ps(h1, zero); + + __m512 interS_0 = _mm512_mul_ps(w0, h0); + __m512 interS_1 = _mm512_mul_ps(w1, h1); + + __m512 Sb_w0 = _mm512_sub_ps(x2_b0, x1_b0); + __m512 Sb_h0 = _mm512_sub_ps(y2_b0, y1_b0); + __m512 Sb_w1 = _mm512_sub_ps(x2_b1, x1_b1); + __m512 Sb_h1 = _mm512_sub_ps(y2_b1, y1_b1); + + __m512 Sb0 = _mm512_mul_ps(Sb_w0, Sb_h0); + __m512 Sb1 = _mm512_mul_ps(Sb_w1, Sb_h1); + + __m512 ovr0 = _mm512_add_ps(Sa, Sb0); + __m512 ovr1 = _mm512_add_ps(Sa, Sb1); + + ovr0 = _mm512_sub_ps(ovr0, interS_0); + ovr1 = _mm512_sub_ps(ovr1, interS_1); + + ovr0 = _mm512_mul_ps(ovr0, thresh); + ovr1 = _mm512_mul_ps(ovr1, thresh); + + __mmask16 gt0 = _mm512_cmp_ps_mask(interS_0, ovr0, _CMP_GT_OS); + __mmask16 gt1 = _mm512_cmp_ps_mask(interS_1, ovr1, _CMP_GT_OS); + + uint64_t mask = (gt0) | ((uint64_t)gt1 << 16); + + mask_tbl[(j + 0) / 64] |= (mask) << (j % 64); + mask_tbl[(j + 32) / 64] |= (mask) >> (64-(j % 64)); + + j += 32; + } + + if ((j + 16) <= boxes_num) { + + __m512 x1_b = _mm512_load_ps(x1 + j); + __m512 y1_b = _mm512_load_ps(y1 + j); + __m512 x2_b = _mm512_load_ps(x2 + j); + __m512 y2_b = _mm512_load_ps(y2 + j); + + __m512 xx1 = _mm512_max_ps(x1_a, x1_b); + __m512 yy1 = _mm512_max_ps(y1_a, y1_b); + __m512 xx2 = _mm512_min_ps(x2_a, x2_b); + __m512 yy2 = _mm512_min_ps(y2_a, y2_b); + + __m512 w = _mm512_sub_ps(xx2, xx1); + __m512 h = _mm512_sub_ps(yy2, yy1); + + w = _mm512_max_ps(w, zero); + h = _mm512_max_ps(h, zero); + + __m512 interS = _mm512_mul_ps(w, h); + + __m512 Sb_w = _mm512_sub_ps(x2_b, x1_b); + __m512 Sb_h = _mm512_sub_ps(y2_b, y1_b); + + __m512 Sb = _mm512_mul_ps(Sb_w, Sb_h); + + __m512 ovr = _mm512_add_ps(Sa, Sb); + ovr = _mm512_sub_ps(ovr, interS); + ovr = _mm512_mul_ps(ovr, thresh); + + __mmask16 gt = _mm512_cmp_ps_mask(interS, ovr, _CMP_GT_OS); + + uint64_t mask = gt; + + mask_tbl[(j + 0) / 64] |= (mask) << (j % 64); + mask_tbl[(j + 16) / 64] |= (mask) >> (64-(j % 64)); + + j += 16; + } + + for (; j < boxes_num; j ++) { + + if (mask_tbl[j/64] & (1ull << (j % 64))) { + continue; + } + + float xx1 = max(x1[i], x1[j]); + float yy1 = max(y1[i], y1[j]); + float xx2 = min(x2[i], x2[j]); + float yy2 = min(y2[i], y2[j]); + + float w = max(0.0f, xx2 - xx1 ); + float h = max(0.0f, yy2 - yy1 ); + + float interS = w * h; + float Sa = (x2[i] - x1[i] ) * (y2[i] - y1[i] ); + float Sb = (x2[j] - x1[j] ) * (y2[j] - y1[j] ); + + if (interS > thresh_f * (Sa + Sb - interS)) { + mask_tbl[j/64] |= 1ull << (j % 64); + } + } + } + + free(mask_tbl); + + *num_out = num_to_keep; +} + +void cpu_nms_avx512_caffe(int* __restrict keep_out, int* __restrict num_out, + const vector& bboxes, + vector >& score_index_vec, + float thresh) { + uint64_t boxes_num = score_index_vec.size(); + float* x1 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + float* y1 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + float* x2 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + float* y2 = (float*)aligned_alloc(64, boxes_num * sizeof(float)); + + uint64_t i = 0; + +#pragma omp parallel for + for (i = 0; i < boxes_num; ++i) { + x1[i] = bboxes[score_index_vec[i].second].xmin(); + y1[i] = bboxes[score_index_vec[i].second].ymin(); + x2[i] = bboxes[score_index_vec[i].second].xmax(); + y2[i] = bboxes[score_index_vec[i].second].ymax(); + } + + cpu_nms_avx512_parallize_inner(keep_out, num_out, x1, y1, x2, y2, boxes_num, + thresh); +#pragma omp parallel for + for (int i = 0; i < *num_out; i++) { + keep_out[i] = score_index_vec[keep_out[i]].second; + } + free(x1); + free(x2); + free(y1); + free(y2); +} +#endif NormalizedBBox UnitBBox() { NormalizedBBox unit_bbox; @@ -566,25 +1000,33 @@ void DecodeBBox( } } -void DecodeBBoxes( - const vector& prior_bboxes, - const vector >& prior_variances, - const CodeType code_type, const bool variance_encoded_in_target, - const bool clip_bbox, const vector& bboxes, - vector* decode_bboxes) { +void DecodeBBoxes(const vector& prior_bboxes, + const vector >& prior_variances, + const CodeType code_type, + const bool variance_encoded_in_target, const bool clip_bbox, + const vector& bboxes, + vector* decode_bboxes) { CHECK_EQ(prior_bboxes.size(), prior_variances.size()); CHECK_EQ(prior_bboxes.size(), bboxes.size()); int num_bboxes = prior_bboxes.size(); if (num_bboxes >= 1) { CHECK_EQ(prior_variances[0].size(), 4); } - decode_bboxes->clear(); + // decode_bboxes->clear(); + decode_bboxes->reserve(num_bboxes); + + NormalizedBBox* result = new NormalizedBBox[num_bboxes]; +#ifdef _OPENMP + #pragma omp parallel for +#endif for (int i = 0; i < num_bboxes; ++i) { - NormalizedBBox decode_bbox; + DecodeBBox(prior_bboxes[i], prior_variances[i], code_type, - variance_encoded_in_target, clip_bbox, bboxes[i], &decode_bbox); - decode_bboxes->push_back(decode_bbox); + variance_encoded_in_target, clip_bbox, bboxes[i], &result[i]); } + + decode_bboxes->assign(&result[0], &result[num_bboxes]); + delete[] result; } void DecodeBBoxesAll(const vector& all_loc_preds, @@ -597,9 +1039,10 @@ void DecodeBBoxesAll(const vector& all_loc_preds, CHECK_EQ(all_loc_preds.size(), num); all_decode_bboxes->clear(); all_decode_bboxes->resize(num); +#ifdef _OPENMP + #pragma omp parallel for +#endif for (int i = 0; i < num; ++i) { - // Decode predictions into bboxes. - LabelBBox& decode_bboxes = (*all_decode_bboxes)[i]; for (int c = 0; c < num_loc_classes; ++c) { int label = share_location ? -1 : c; if (label == background_label_id) { @@ -614,7 +1057,7 @@ void DecodeBBoxesAll(const vector& all_loc_preds, all_loc_preds[i].find(label)->second; DecodeBBoxes(prior_bboxes, prior_variances, code_type, variance_encoded_in_target, clip, - label_loc_preds, &(decode_bboxes[label])); + label_loc_preds, &((*all_decode_bboxes)[i][label])); } } } @@ -1177,22 +1620,32 @@ void GetLocPredictions(const Dtype* loc_data, const int num, CHECK_EQ(num_loc_classes, 1); } loc_preds->resize(num); +#ifdef _OPENMP + #pragma omp parallel for +#endif for (int i = 0; i < num; ++i) { - LabelBBox& label_bbox = (*loc_preds)[i]; - for (int p = 0; p < num_preds_per_class; ++p) { - int start_idx = p * num_loc_classes * 4; - for (int c = 0; c < num_loc_classes; ++c) { - int label = share_location ? -1 : c; - if (label_bbox.find(label) == label_bbox.end()) { - label_bbox[label].resize(num_preds_per_class); - } - label_bbox[label][p].set_xmin(loc_data[start_idx + c * 4]); - label_bbox[label][p].set_ymin(loc_data[start_idx + c * 4 + 1]); - label_bbox[label][p].set_xmax(loc_data[start_idx + c * 4 + 2]); - label_bbox[label][p].set_ymax(loc_data[start_idx + c * 4 + 3]); + for (int c = 0; c < num_loc_classes; ++c) { + int label = share_location ? -1 : c; + if ((*loc_preds)[i].find(label) == (*loc_preds)[i].end()) { + (*loc_preds)[i][label].resize(num_preds_per_class); + } else { + break; + } + } + } +#ifdef _OPENMP + #pragma omp parallel for collapse(3) +#endif + for (int i = 0; i < num; ++i) { + for (int p = 0; p < num_preds_per_class; ++p) { + for (int c = 0; c < num_loc_classes; ++c) { + int label = share_location ? -1 : c; + (*loc_preds)[i][label][p].set_xmin(loc_data[ i* num_preds_per_class * num_loc_classes * 4 + p * num_loc_classes * 4 + c * 4]); + (*loc_preds)[i][label][p].set_ymin(loc_data[ i* num_preds_per_class * num_loc_classes * 4 + p * num_loc_classes * 4 + c * 4 + 1]); + (*loc_preds)[i][label][p].set_xmax(loc_data[ i* num_preds_per_class * num_loc_classes * 4 + p * num_loc_classes * 4 + c * 4 + 2]); + (*loc_preds)[i][label][p].set_ymax(loc_data[ i* num_preds_per_class * num_loc_classes * 4 + p * num_loc_classes * 4 + c * 4 + 3]); } } - loc_data += num_preds_per_class * num_loc_classes * 4; } } @@ -1373,20 +1826,42 @@ template void ComputeLocLoss(const Blob& loc_pred, template void GetConfidenceScores(const Dtype* conf_data, const int num, - const int num_preds_per_class, const int num_classes, - vector > >* conf_preds) { + const int num_preds_per_class, const int num_classes, + vector > >* conf_preds) { conf_preds->clear(); conf_preds->resize(num); + Dtype* buffer = new Dtype[num * num_preds_per_class * num_classes]; +#ifdef _OPENMP + #pragma omp parallel for collapse(3) +#endif for (int i = 0; i < num; ++i) { - map >& label_scores = (*conf_preds)[i]; - for (int p = 0; p < num_preds_per_class; ++p) { - int start_idx = p * num_classes; - for (int c = 0; c < num_classes; ++c) { - label_scores[c].push_back(conf_data[start_idx + c]); + for (int c = 0; c < num_classes; ++c) { + for (int p = 0; p < num_preds_per_class; ++p) { + buffer[i * num_classes * num_preds_per_class + c * num_preds_per_class + + p] = conf_data[i * num_classes * num_preds_per_class + + p * num_classes + c]; } } - conf_data += num_preds_per_class * num_classes; } +#ifdef _OPENMP + #pragma omp parallel for +#endif + for (int i = 0; i < num; i++) { + // #pragma omp parallel + // map >& label_scores = (*conf_preds)[i]; + for (int c = 0; c < num_classes; ++c) { + // label_scores[c].reserve(num_preds_per_class); + (*conf_preds)[i][c].reserve(num_preds_per_class); + + (*conf_preds)[i][c].assign( + &buffer[i * num_classes * num_preds_per_class + + c * num_preds_per_class], + &buffer[i * num_classes * num_preds_per_class + + c * num_preds_per_class + num_preds_per_class]); + } + } + + delete[] buffer; } // Explicit initialization. @@ -1694,27 +2169,28 @@ template void GetPriorBBoxes(const Dtype* prior_data, const int num_priors, vector* prior_bboxes, vector >* prior_variances) { - prior_bboxes->clear(); - prior_variances->clear(); +#ifdef _OPENMP + #pragma omp parallel for +#endif for (int i = 0; i < num_priors; ++i) { - int start_idx = i * 4; NormalizedBBox bbox; - bbox.set_xmin(prior_data[start_idx]); - bbox.set_ymin(prior_data[start_idx + 1]); - bbox.set_xmax(prior_data[start_idx + 2]); - bbox.set_ymax(prior_data[start_idx + 3]); - float bbox_size = BBoxSize(bbox); - bbox.set_size(bbox_size); - prior_bboxes->push_back(bbox); + bbox.set_xmin(prior_data[i*4]); + bbox.set_ymin(prior_data[i*4 + 1]); + bbox.set_xmax(prior_data[i*4 + 2]); + bbox.set_ymax(prior_data[i*4 + 3]); + + bbox.set_size(BBoxSize(bbox)); + + prior_bboxes->at(i) = bbox; } + vector var(4, 0); + for (int i = 0; i < num_priors; ++i) { - int start_idx = (num_priors + i) * 4; - vector var; for (int j = 0; j < 4; ++j) { - var.push_back(prior_data[start_idx + j]); + var.at(j) = (prior_data[(num_priors + i) * 4 + j]); } - prior_variances->push_back(var); + prior_variances->at(i) = var; } } @@ -1782,9 +2258,12 @@ void GetTopKScoreIndex(const vector& scores, const vector& indices, void GetMaxScoreIndex(const vector& scores, const float threshold, const int top_k, vector >* score_index_vec) { // Generate index score pairs. +#ifdef _OPENMP + #pragma omp parallel for +#endif for (int i = 0; i < scores.size(); ++i) { if (scores[i] > threshold) { - score_index_vec->push_back(std::make_pair(scores[i], i)); + score_index_vec->at(i) = std::make_pair(scores[i], i); } } @@ -1938,40 +2417,52 @@ inline int clamp(const int v, const int a, const int b) { } void ApplyNMSFast(const vector& bboxes, - const vector& scores, const float score_threshold, - const float nms_threshold, const float eta, const int top_k, - vector* indices) { + const vector& scores, const float score_threshold, + const float nms_threshold, const float eta, const int top_k, + vector* indices) { // Sanity check. CHECK_EQ(bboxes.size(), scores.size()) << "bboxes and scores have different size."; - // Get top_k scores (with corresponding indices). - vector > score_index_vec; + vector > score_index_vec(scores.size()); GetMaxScoreIndex(scores, score_threshold, top_k, &score_index_vec); - // Do nms. float adaptive_threshold = nms_threshold; indices->clear(); - while (score_index_vec.size() != 0) { - const int idx = score_index_vec.front().second; - bool keep = true; - for (int k = 0; k < indices->size(); ++k) { +#ifdef ENABLE_NMS_OPTIMIZATION + if (_may_i_use_cpu_feature(_FEATURE_AVX512CD | _FEATURE_AVX512F)) { + if (score_index_vec.size() == 0) return; + indices->resize(score_index_vec.size()); + int* p = (int*)malloc(sizeof(int) * score_index_vec.size()); + int num_out = 0; + cpu_nms_avx512_caffe(p, &num_out, bboxes, score_index_vec, nms_threshold); + indices->assign(p, p + num_out); + free(p); + } else { +#endif + while (score_index_vec.size() != 0) { + const int idx = score_index_vec.front().second; + bool keep = true; + for (int k = 0; k < indices->size(); ++k) { + if (keep) { + const int kept_idx = (*indices)[k]; + float overlap = JaccardOverlap(bboxes[idx], bboxes[kept_idx]); + keep = overlap <= adaptive_threshold; + } else { + break; + } + } if (keep) { - const int kept_idx = (*indices)[k]; - float overlap = JaccardOverlap(bboxes[idx], bboxes[kept_idx]); - keep = overlap <= adaptive_threshold; - } else { - break; + indices->push_back(idx); + } + score_index_vec.erase(score_index_vec.begin()); + if (keep && eta < 1 && adaptive_threshold > 0.5) { + adaptive_threshold *= eta; } } - if (keep) { - indices->push_back(idx); - } - score_index_vec.erase(score_index_vec.begin()); - if (keep && eta < 1 && adaptive_threshold > 0.5) { - adaptive_threshold *= eta; - } +#ifdef ENABLE_NMS_OPTIMIZATION } +#endif } template diff --git a/src/caffe/util/remove_batch_norm.cpp b/src/caffe/util/remove_batch_norm.cpp index 8c56639fc..bf42d7ad0 100644 --- a/src/caffe/util/remove_batch_norm.cpp +++ b/src/caffe/util/remove_batch_norm.cpp @@ -209,7 +209,7 @@ void RemoveBNScale(const NetParameter& param, NetParameter* param_compiled) { bool bn_use_global_stats_set = true; if (layer_param->type().compare("Convolution") == 0) { std::vector child_layers_params; - Net::GetBlobConsumers(child_layers_params, layer_param->top(0), param, i + 1 < param.layer_size() ? i + 1 : i); + Net::GetBlobConsumers(child_layers_params, layer_param->top(0), param, i + 1 < param.layer_size() ? i + 1 : i); const LayerParameter &child_layer_param = child_layers_params.size() > 0 ? *(child_layers_params[0]) : *layer_param; // check whether child layer is BatchNorm if (child_layer_param.type().compare("BatchNorm") == 0) { @@ -243,11 +243,11 @@ void RemoveBNScale(const NetParameter& param, NetParameter* param_compiled) { if (!bn_use_global_stats_set) { //Even in caffe TEST phase, current batch norm layer has set use_global_stats = false in protxt file, so we won't //merge this layer into convolution layer. - param_compiled->add_layer()->CopyFrom(*layer_param); + param_compiled->add_layer()->CopyFrom(*layer_param); continue; } std::vector grandchild_layers_params; - Net::GetBlobConsumers(grandchild_layers_params, child_layer_param.top(0), param, i + 2 < param.layer_size() ? i + 2 : i); + Net::GetBlobConsumers(grandchild_layers_params, child_layer_param.top(0), param, i + 2 < param.layer_size() ? i + 2 : i); const LayerParameter &grandchild_layer_param = (grandchild_layers_params.size() > 0) ? *(grandchild_layers_params[0]) : child_layer_param; if (grandchild_layer_param.type().compare("Scale") == 0) { MergeLayer(*layer_param, grandchild_layer_param); @@ -279,10 +279,10 @@ void RemoveBNScale(const NetParameter& param, NetParameter* param_compiled) { layers_to_drop.erase(layers_to_drop.find(layer_param->name())); } if (layer_included) { - if (layer_param->type().compare("BatchNorm") == 0) { - param_compiled->mutable_compile_net_state()->add_kept_bn_layers(layer_param->name()); - } - param_compiled->add_layer()->CopyFrom(*layer_param); + if (layer_param->type().compare("BatchNorm") == 0) { + param_compiled->mutable_compile_net_state()->add_kept_bn_layers(layer_param->name()); + } + param_compiled->add_layer()->CopyFrom(*layer_param); } } diff --git a/src/caffe/util/tree.cpp b/src/caffe/util/tree.cpp new file mode 100755 index 000000000..bb39ce776 --- /dev/null +++ b/src/caffe/util/tree.cpp @@ -0,0 +1,73 @@ +#include "caffe/util/tree.hpp" +#include +#include +#include + +using namespace std; + +tree::tree() +{ + +} + +tree::tree(string filename) +{ + //char file[20] = filename.c_str(); + //std::cout<< "filename: " << filename <> parent; + //parent = std::atoi(line.substr(10).c_str()); + //std::cout<< line << " p:" << parent <parent.push_back(parent); + this->child.push_back(-1); + this->name.push_back(id); + + if (parent != last_parent) + { + ++groups; + this->group_offset.push_back(n - group_size); + this->group_size.push_back(group_size); + group_size = 0; + last_parent = parent; + } + this->group.push_back(groups); + if (parent >= 0) + { + this->child[parent] = groups; + } + ++n; + ++group_size; + } + ++groups; + this->group_offset.push_back(n-group_size); + this->group_size.push_back(group_size); + this->n = n; + this->groups = groups; + + for (int i = 0; i < n; ++ i) + this->leaf.push_back(1); + for (int i = 0; i < n; ++ i) + if (this->parent[i] >= 0) + this->leaf[this->parent[i]] = 0; + + fin.close(); + //std::cout<< "groups: "<< groups << "\t" << this->groups << std::endl; +} diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp index b29720276..05a743bc5 100644 --- a/src/caffe/util/upgrade_proto.cpp +++ b/src/caffe/util/upgrade_proto.cpp @@ -313,7 +313,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection, if (type == "conv") { layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad()); } else if (type == "pool") { - layer_param->mutable_pooling_param()->add_pad(v0_layer_param.pad()); + layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad()); } else { LOG(ERROR) << "Unknown parameter pad for layer type " << type; is_fully_compatible = false; @@ -324,7 +324,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection, layer_param->mutable_convolution_param()->add_kernel_size( v0_layer_param.kernelsize()); } else if (type == "pool") { - layer_param->mutable_pooling_param()->add_kernel_size( + layer_param->mutable_pooling_param()->set_kernel_size( v0_layer_param.kernelsize()); } else { LOG(ERROR) << "Unknown parameter kernelsize for layer type " << type; @@ -345,7 +345,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection, layer_param->mutable_convolution_param()->add_stride( v0_layer_param.stride()); } else if (type == "pool") { - layer_param->mutable_pooling_param()->add_stride( + layer_param->mutable_pooling_param()->set_stride( v0_layer_param.stride()); } else { LOG(ERROR) << "Unknown parameter stride for layer type " << type; diff --git a/tools/caffe.cpp b/tools/caffe.cpp index f22af5e11..9ec4285c0 100644 --- a/tools/caffe.cpp +++ b/tools/caffe.cpp @@ -117,6 +117,8 @@ DEFINE_bool(detection, false, "By default it is false and classification is on."); DEFINE_bool(fast_compare, false, "Optional; Break layer comparison after fast_compare_max errors found"); +DEFINE_bool(sampling, false, + "Optional; Caffe test with sampling mode"); DEFINE_int32(fast_compare_max, 50, "Optional; Max errors for fast_compare"); DEFINE_double(buffer_filler, std::nanf(""), "Buffer filler for compare tool"); @@ -321,7 +323,7 @@ int train() { #ifdef USE_MLSL if (caffe::mn::is_multinode()) { - MPI_Barrier(MPI_COMM_WORLD); + caffe::mn::barrier(); LOG(INFO) << "Configuring multinode setup"; if (!caffe::mn::is_param_server()) { caffe::MultiSync sync(solver); @@ -469,6 +471,10 @@ int test() { Net caffe_net(FLAGS_model, caffe::TEST, FLAGS_level, &stages, NULL, FLAGS_engine); caffe_net.CopyTrainedLayersFrom(FLAGS_weights); + + if (FLAGS_sampling) + return 0; + LOG(INFO) << "Running for " << FLAGS_iterations << " iterations."; if (FLAGS_detection) { @@ -495,7 +501,7 @@ int test() { } else { test_score[idx] += score; } - const std::string& output_name = caffe_net.blob_names()[ + const std::string& output_name = caffe_net.blob_names()[ caffe_net.output_blob_indices()[j]]; LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score; } diff --git a/tools/extra/plot_loss_trends.py b/tools/extra/plot_loss_trends.py index 80fc611ef..f11215654 100644 --- a/tools/extra/plot_loss_trends.py +++ b/tools/extra/plot_loss_trends.py @@ -3,11 +3,13 @@ import argparse from collections import defaultdict import random -import matplotlib.colors as colors +# In case you can't install Tkinter on your system, please uncomment below two lines to have another try. +#import matplotlib +#matplotlib.use('agg') import matplotlib.pyplot as plt +import matplotlib.colors as colors import matplotlib.legend as lgd import matplotlib.markers as mks -import pdb class TrainLog(object): '''train log file analyze and data collection''' @@ -20,6 +22,8 @@ def __init__(self, log_files): self._multi_mbox_losses = defaultdict(list) self._multi_test_iterations = defaultdict(list) self._multi_detection_accuracies = defaultdict(list) + self._multi_top1_accuracies = defaultdict(list) + self._multi_top5_accuracies = defaultdict(list) self._parse_log() self._is_detection = True if len(self._multi_mbox_losses) > 0 else False @@ -42,6 +46,8 @@ def obtain_process_id(line): lr_pattern = re.compile('.* Iteration \d+, lr = .*') mbox_loss_pattern = re.compile('.* Train net output #\d+: mbox_loss = .*') detection_accuracy_pattern = re.compile('.* Test net output #\d+: detection_eval = .*') + top1_accuracy_pattern = re.compile('.* Test net output #\d+: .*top-1 = .*') + top5_accuracy_pattern = re.compile('.* Test net output #\d+: .*top-5 = .*') test_iteration_pattern = re.compile('.* Iteration \d+, Testing net \(#\d+\).*') for log_file in self._log_files: log_name = obtain_log_name(log_file) @@ -68,6 +74,14 @@ def obtain_process_id(line): if '0' != obtain_process_id(line): continue self._multi_detection_accuracies[log_name].append(float(line.split()[-1])) + elif re.match(top1_accuracy_pattern, line): + if '0' != obtain_process_id(line): + continue + self._multi_top1_accuracies[log_name].append(float(line.split()[-1])) + elif re.match(top5_accuracy_pattern, line): + if '0' != obtain_process_id(line): + continue + self._multi_top5_accuracies[log_name].append(float(line.split()[-1])) @property def is_detection(self): @@ -104,6 +118,16 @@ def detection_accuracies(self): '''get detection accuracies data showed within log file''' return self._multi_detection_accuracies + @property + def top1_accuracies(self): + '''get top-1 accuracies data showed within log file''' + return self._multi_top1_accuracies + + @property + def top5_accuracies(self): + '''get top-5 accuracies data showed within log file''' + return self._multi_top5_accuracies + class PlotTrend(object): '''plot single or multi trends within a fig''' def __init__(self, y_items, x_items, out_dir): @@ -176,12 +200,12 @@ def plot_multiple_trends(self, y_dict, x_dict): png_file = self.result_png_file(self.y_axis_name) plt.savefig(png_file) plt.show() + plt.close() def plot_loss_trends(train_log, out_dir): '''plot loss trends of train logs''' y_items = {'loss' : train_log.losses} x_items = {'Iterations' : train_log.iterations} - #pdb.set_trace() plot_trend = PlotTrend(y_items, x_items, out_dir) plot_trend.plot() @@ -208,6 +232,22 @@ def plot_detection_accuracy_trends(train_log, out_dir): plot_trend = PlotTrend(y_items, x_items, out_dir) plot_trend.plot() +def plot_top1_accuracy_trends(train_log, out_dir): + '''plot top-1 accuracy trend of train logs''' + if train_log.is_detection: return + y_items = {'top-1 accuracy' : train_log.top1_accuracies} + x_items = {'Test iterations' : train_log.test_iterations} + plot_trend = PlotTrend(y_items, x_items, out_dir) + plot_trend.plot() + +def plot_top5_accuracy_trends(train_log, out_dir): + '''plot top-5 accuracy trend of train logs''' + if train_log.is_detection: return + y_items = {'top-5 accuracy' : train_log.top5_accuracies} + x_items = {'Test iterations' : train_log.test_iterations} + plot_trend = PlotTrend(y_items, x_items, out_dir) + plot_trend.plot() + def parse_args(): '''parse program arguments''' description = ('Parse Caffe training log and plot loss trends,\ @@ -233,6 +273,8 @@ def main(): plot_mbox_loss_trends(train_log, args.output_dir) plot_lr_trends(train_log, args.output_dir) plot_detection_accuracy_trends(train_log, args.output_dir) + plot_top1_accuracy_trends(train_log, args.output_dir) + plot_top5_accuracy_trends(train_log, args.output_dir) if __name__ == '__main__': main() diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp index 56ee2f555..76a8943da 100644 --- a/tools/extract_features.cpp +++ b/tools/extract_features.cpp @@ -60,6 +60,9 @@ template int feature_extraction_pipeline(int argc, char** argv); int main(int argc, char** argv) { +#ifdef USE_MLSL + caffe::mn::init(&argc, &argv); +#endif return feature_extraction_pipeline(argc, argv); // return feature_extraction_pipeline(argc, argv); } diff --git a/tools/sample.cpp b/tools/sample.cpp index 3212cbe9b..4903824ba 100644 --- a/tools/sample.cpp +++ b/tools/sample.cpp @@ -127,7 +127,7 @@ int quantize(){ CHECK_GT(FLAGS_trimming_mode.size(), 0) << "Need trimming mode."; Quantization* q = new Quantization(FLAGS_model, FLAGS_weights, FLAGS_model_quantized, FLAGS_iterations, FLAGS_trimming_mode, - FLAGS_error_margin, FLAGS_score_number, FLAGS_scaling, FLAGS_detection, FLAGS_power); + FLAGS_error_margin, FLAGS_score_number, FLAGS_scaling, FLAGS_detection); q->QuantizeNet(); delete q; return 0; @@ -146,6 +146,9 @@ int main(int argc, char** argv) { " quantize Trim 32bit floating point net\n"); // Run tool or show usage. caffe::GlobalInit(&argc, &argv); +#ifdef USE_MLSL + caffe::mn::init(&argc, &argv); +#endif if (argc == 2) { return GetBrewFunction(caffe::string(argv[1]))(); } else { diff --git a/tools/test_detection.cpp b/tools/test_detection.cpp new file mode 100644 index 000000000..7c2226938 --- /dev/null +++ b/tools/test_detection.cpp @@ -0,0 +1,319 @@ +#include +#include + +#include +#include +#include +#include + +#include "boost/algorithm/string.hpp" +#include "caffe/caffe.hpp" +#include "caffe/util/signal_handler.h" +#include "caffe/util/benchmark.hpp" + +using caffe::Blob; +using caffe::Caffe; +using caffe::Net; +using caffe::Layer; +using caffe::string; +using caffe::vector; +using std::map; +using std::pair; + +DEFINE_string(gpu, "", + "Optional; run in GPU mode on given device IDs separated by ','." + "Use '-gpu all' to run on all available GPUs. The effective training " + "batch size is multiplied by the number of devices."); +DEFINE_string(model, "", + "The model definition protocol buffer text file."); +DEFINE_string(weights, "", + "Optional; the pretrained weights to initialize finetuning, " + "separated by ','. Cannot be set simultaneously with snapshot."); +DEFINE_int32(iterations, 50, + "The number of iterations to run."); + +DEFINE_int32(side, 17, "side of image"); +DEFINE_int32(objects, 5, "num of box for every point"); +DEFINE_int32(classes, 20, "num of classes"); + +// Parse GPU ids or use all available devices +static void get_gpus(vector* gpus) { + if (FLAGS_gpu == "all") { + int count = 0; +#ifndef CPU_ONLY + CUDA_CHECK(cudaGetDeviceCount(&count)); +#else + NO_GPU; +#endif + for (int i = 0; i < count; ++i) { + gpus->push_back(i); + } + } else if (FLAGS_gpu.size()) { + vector strings; + boost::split(strings, FLAGS_gpu, boost::is_any_of(",")); + for (int i = 0; i < strings.size(); ++i) { + gpus->push_back(boost::lexical_cast(strings[i])); + } + } else { + CHECK_EQ(gpus->size(), 0); + } +} + +template +bool SortScorePairDescend(const pair& pair1, + const pair& pair2) { + return pair1.first > pair2.first; +} + +// Explicit initialization. +template bool SortScorePairDescend(const pair& pair1, + const pair& pair2); +template bool SortScorePairDescend(const pair >& pair1, + const pair >& pair2); + +void CumSum(const vector >& pairs, vector* cumsum) { + // Sort the pairs based on first item of the pair. + vector > sort_pairs = pairs; + std::stable_sort(sort_pairs.begin(), sort_pairs.end(), + SortScorePairDescend); + + cumsum->clear(); + for (int i = 0; i < sort_pairs.size(); ++i) { + if (i == 0) { + cumsum->push_back(sort_pairs[i].second); + } else { + cumsum->push_back(cumsum->back() + sort_pairs[i].second); + } + } +} + +void ComputeAP(const vector >& tp, const int num_pos, + const vector >& fp, const string ap_version, + vector* prec, vector* rec, float* ap) { + const float eps = 1e-6; + CHECK_EQ(tp.size(), fp.size()) << "tp must have same size as fp."; + const int num = tp.size(); + // Make sure that tp and fp have complement value. + for (int i = 0; i < num; ++i) { + CHECK_LE(fabs(tp[i].first - fp[i].first), eps); + CHECK_GE(tp[i].second, 0); + CHECK_GE(fp[i].second, 0); + } + prec->clear(); + rec->clear(); + *ap = 0; + if (tp.size() == 0 || num_pos == 0) { + return; + } + + // Compute cumsum of tp. + vector tp_cumsum; + CumSum(tp, &tp_cumsum); + CHECK_EQ(tp_cumsum.size(), num); + + // Compute cumsum of fp. + vector fp_cumsum; + CumSum(fp, &fp_cumsum); + CHECK_EQ(fp_cumsum.size(), num); + + // Compute precision. + for (int i = 0; i < num; ++i) { + prec->push_back(static_cast(tp_cumsum[i]) / + (tp_cumsum[i] + fp_cumsum[i])); + } + + // Compute recall. + for (int i = 0; i < num; ++i) { + CHECK_LE(tp_cumsum[i], num_pos); + rec->push_back(static_cast(tp_cumsum[i]) / num_pos); + } + + // for (int i = 0; i < num; ++i) { + // std::cout << (*prec)[i] << std::endl; + // std::cout << (*rec)[i] << std::endl; + // } + + if (ap_version == "11point") { + // VOC2007 style for computing AP. + vector max_precs(11, 0.); + int start_idx = num - 1; + for (int j = 10; j >= 0; --j) { + for (int i = start_idx; i >= 0 ; --i) { + if ((*rec)[i] < j / 10.) { + start_idx = i; + if (j > 0) { + max_precs[j-1] = max_precs[j]; + } + break; + } else { + if (max_precs[j] < (*prec)[i]) { + max_precs[j] = (*prec)[i]; + } + } + } + } + for (int j = 10; j >= 0; --j) { + *ap += max_precs[j] / 11; + } + } else if (ap_version == "MaxIntegral") { + // VOC2012 or ILSVRC style for computing AP. + float cur_rec = rec->back(); + float cur_prec = prec->back(); + for (int i = num - 2; i >= 0; --i) { + cur_prec = std::max((*prec)[i], cur_prec); + if (fabs(cur_rec - (*rec)[i]) > eps) { + *ap += cur_prec * fabs(cur_rec - (*rec)[i]); + } + cur_rec = (*rec)[i]; + } + *ap += cur_rec * cur_prec; + } else if (ap_version == "Integral") { + // Natural integral. + float prev_rec = 0.; + for (int i = 0; i < num; ++i) { + if (fabs((*rec)[i] - prev_rec) > eps) { + *ap += (*prec)[i] * fabs((*rec)[i] - prev_rec); + } + prev_rec = (*rec)[i]; + } + } else { + LOG(FATAL) << "Unknown ap_version: " << ap_version; + } +} + +// Test: score a model. +int test_detection() { + CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score."; + CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score."; + + // Set device id and mode + vector gpus; + get_gpus(&gpus); + if (gpus.size() != 0) { + LOG(INFO) << "Use GPU with device ID " << gpus[0]; +#ifndef CPU_ONLY + cudaDeviceProp device_prop; + cudaGetDeviceProperties(&device_prop, gpus[0]); + LOG(INFO) << "GPU device name: " << device_prop.name; +#endif + Caffe::SetDevice(gpus[0]); + Caffe::set_mode(Caffe::GPU); + } else { + LOG(INFO) << "Use CPU."; + Caffe::set_mode(Caffe::CPU); + } + //***************************************************// + int side = FLAGS_side; //17 + int num_object = FLAGS_objects; //5 + int num_class = FLAGS_classes; //20 + + caffe::CPUTimer timer; + // Instantiate the caffe net. + Net caffe_net(FLAGS_model, caffe::TEST); + caffe_net.CopyTrainedLayersFrom(FLAGS_weights); + LOG(INFO) << "Running for " << FLAGS_iterations << " iterations."; + + map > > > true_poss, false_poss; + map > num_gts; + vector test_score_output_id; + float loss = 0; + int out_size(0); + map skip_idx; + double total_time(0.0); + timer.Start(); + for (int i = 0; i < FLAGS_iterations; ++i) { + float iter_loss; + const vector*>& result = caffe_net.Forward(&iter_loss); + out_size = result.size(); + LOG(INFO) << "iter_loss: " << iter_loss; + loss += iter_loss; + for (int j = 0; j < result.size(); ++j) { + if (result[j]->count() == 1) { + skip_idx[j] = 0; + continue; + } + const float* result_data = result[j]->cpu_data(); + for (int k = 0; k < result[j]->num(); ++k) { + int res_index = k * result[j]->count(1); + for (int c = 0; c < num_class; ++c) { + if (num_gts[j].find(c) == num_gts[j].end()) { + num_gts[j][c] = static_cast(result_data[res_index + c]); + } else { + num_gts[j][c] += static_cast(result_data[res_index + c]); + } + } + int all_obj_num = side * side * num_object; + int obj_index = res_index + num_class; + for (int b = 0; b < all_obj_num; ++b) { + int label = static_cast(result_data[obj_index + b * 4 + 0]); + float score = result_data[obj_index + b * 4 + 1]; + int tp = static_cast(result_data[obj_index + b * 4 + 2]); + int fp = static_cast(result_data[obj_index + b * 4 + 3]); + // LOG(INFO) << "tp: " << tp << " fp: " << fp; + true_poss[j][label].push_back(std::make_pair(score, tp)); + false_poss[j][label].push_back(std::make_pair(score, fp)); + } + } + } + LOG(INFO) << "Running Iteration " << i; + } + total_time += timer.MicroSeconds(); + LOG(INFO) << "Total time: " << total_time / 1000 << " ms."; + for (int i = 0; i < out_size; ++i) { + if (skip_idx.find(i) != skip_idx.end()) { + continue; + } + map > > true_pos = true_poss[i]; + map > > false_pos = false_poss[i]; + map num_gt = num_gts[i]; + map APs; + float mAP = 0.; + for (int j = 0; j < num_class; ++j) { + if (!num_gt[j]) { + LOG(WARNING) << "Ground trurh label number is 0: " << j; + continue; + } + if (true_pos.find(j) == true_pos.end()) { + LOG(WARNING) << "Missing true_pos for label: " << j; + continue; + } + if (false_pos.find(j) == false_pos.end()) { + LOG(WARNING) << "Missing false_pos for label: " << j; + continue; + } + string ap_version = "11point"; + vector prec, rec; + ComputeAP(true_pos[j], num_gt[j], false_pos[j], ap_version, &prec, &rec, &(APs[j])); + mAP += APs[j]; + } + mAP /= num_class; + const string& output_name = caffe_net.blob_names()[caffe_net.output_blob_indices()[i]]; + LOG(INFO) << " Test net output #" << i << ": " << output_name << " = " << mAP; + } + loss /= FLAGS_iterations; + LOG(INFO) << "Loss: " << loss; + LOG(INFO) << "Model: " << FLAGS_weights; + return 0; +} + +int main(int argc, char** argv) { +#ifdef USE_MLSL + caffe::mn::init(&argc, &argv); +#endif + + ::google::InitGoogleLogging(argv[0]); + // Print output to stderr (while still logging). + FLAGS_alsologtostderr = 1; + +#ifndef GFLAGS_GFLAGS_H_ + namespace gflags = google; +#endif + + // Usage message. + gflags::SetUsageMessage("Test a object detection model\n" + "Usage:\n" + " test_detection [FLAGS] \n"); + gflags::ParseCommandLineFlags(&argc, &argv, true); + + return test_detection(); +}