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frame_level_models.py
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# Copyright 2017 Antoine Miech All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains a collection of models which operate on variable-length sequences.
"""
import math
import models
import video_level_models
import tensorflow as tf
import model_utils as utils
import tensorflow.contrib.slim as slim
from tensorflow import flags
import tensorflow.contrib as tf_contrib
weight_init = tf_contrib.layers.xavier_initializer()
weight_regularizer = None
weight_regularizer_fully = None
import scipy.io as sio
import numpy as np
def spectral_norm(w, iteration=1):
w_shape = w.shape.as_list()
w = tf.reshape(w, [-1, w_shape[-1]])
u = tf.get_variable("u", [1, w_shape[-1]], initializer=tf.random_normal_initializer(), trainable=False)
u_hat = u
v_hat = None
for i in range(iteration):
"""
power iteration
Usually iteration = 1 will be enough
"""
v_ = tf.matmul(u_hat, tf.transpose(w))
v_hat = tf.nn.l2_normalize(v_)
u_ = tf.matmul(v_hat, w)
u_hat = tf.nn.l2_normalize(u_)
u_hat = tf.stop_gradient(u_hat)
v_hat = tf.stop_gradient(v_hat)
sigma = tf.matmul(tf.matmul(v_hat, w), tf.transpose(u_hat))
with tf.control_dependencies([u.assign(u_hat)]):
w_norm = w / sigma
w_norm = tf.reshape(w_norm, w_shape)
return w_norm
FLAGS = flags.FLAGS
flags.DEFINE_bool("gating_remove_diag", False,
"Remove diag for self gating")
flags.DEFINE_bool("lightvlad", False,
"Light or full NetVLAD")
flags.DEFINE_bool("vlagd", False,
"vlagd of vlad")
flags.DEFINE_integer("iterations", 40,
"Number of frames per batch for DBoF.")
flags.DEFINE_bool("dbof_add_batch_norm", True,
"Adds batch normalization to the DBoF model.")
flags.DEFINE_bool(
"sample_random_frames", False,
"If true samples random frames (for frame level models). If false, a random"
"sequence of frames is sampled instead.")
flags.DEFINE_integer("dbof_cluster_size", 16384,
"Number of units in the DBoF cluster layer.")
flags.DEFINE_integer("dbof_hidden_size", 2048,
"Number of units in the DBoF hidden layer.")
flags.DEFINE_bool("dbof_relu", True, 'add ReLU to hidden layer')
flags.DEFINE_integer("dbof_var_features", 0,
"Variance features on top of Dbof cluster layer.")
flags.DEFINE_string("dbof_activation", "relu", 'dbof activation')
flags.DEFINE_bool("softdbof_maxpool", False, 'add max pool to soft dbof')
flags.DEFINE_integer("netvlad_cluster_size", 64,
"Number of units in the NetVLAD cluster layer.")
flags.DEFINE_bool("netvlad_relu", True, 'add ReLU to hidden layer')
flags.DEFINE_integer("netvlad_dimred", -1,
"NetVLAD output dimension reduction")
flags.DEFINE_integer("gatednetvlad_dimred", 1024,
"GatedNetVLAD output dimension reduction")
flags.DEFINE_bool("gating", False,
"Gating for NetVLAD")
flags.DEFINE_integer("hidden_size", 1024,
"size of hidden layer for BasicStatModel.")
flags.DEFINE_integer("netvlad_hidden_size", 1024,
"Number of units in the NetVLAD hidden layer.")
flags.DEFINE_integer("netvlad_hidden_size_video", 1024,
"Number of units in the NetVLAD video hidden layer.")
flags.DEFINE_integer("netvlad_hidden_size_audio", 64,
"Number of units in the NetVLAD audio hidden layer.")
flags.DEFINE_bool("netvlad_add_batch_norm", True,
"Adds batch normalization to the DBoF model.")
flags.DEFINE_integer("fv_cluster_size", 64,
"Number of units in the NetVLAD cluster layer.")
flags.DEFINE_integer("fv_hidden_size", 2048,
"Number of units in the NetVLAD hidden layer.")
flags.DEFINE_bool("fv_relu", True,
"ReLU after the NetFV hidden layer.")
flags.DEFINE_bool("fv_couple_weights", True,
"Coupling cluster weights or not")
flags.DEFINE_float("fv_coupling_factor", 0.01,
"Coupling factor")
flags.DEFINE_string("dbof_pooling_method", "max",
"The pooling method used in the DBoF cluster layer. "
"Choices are 'average' and 'max'.")
flags.DEFINE_string("video_level_classifier_model", "MoeModel",
"Some Frame-Level models can be decomposed into a "
"generalized pooling operation followed by a "
"classifier layer")
flags.DEFINE_integer("lstm_cells", 1024, "Number of LSTM cells.")
flags.DEFINE_integer("lstm_layers", 2, "Number of LSTM layers.")
flags.DEFINE_integer("lstm_cells_video", 1024, "Number of LSTM cells (video).")
flags.DEFINE_integer("lstm_cells_audio", 128, "Number of LSTM cells (audio).")
flags.DEFINE_integer("gru_cells", 1024, "Number of GRU cells.")
flags.DEFINE_integer("gru_cells_video", 1024, "Number of GRU cells (video).")
flags.DEFINE_integer("gru_cells_audio", 128, "Number of GRU cells (audio).")
flags.DEFINE_integer("gru_layers", 2, "Number of GRU layers.")
flags.DEFINE_bool("lstm_random_sequence", False,
"Random sequence input for lstm.")
flags.DEFINE_bool("gru_random_sequence", False,
"Random sequence input for gru.")
flags.DEFINE_bool("gru_backward", False, "BW reading for GRU")
flags.DEFINE_bool("lstm_backward", False, "BW reading for LSTM")
flags.DEFINE_bool("fc_dimred", True, "Adding FC dimred after pooling")
class LightVLAD():
def __init__(self, feature_size, max_frames, cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self, reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
activation = tf.transpose(activation, perm=[0, 2, 1])
reshaped_input = tf.reshape(reshaped_input, [-1, self.max_frames, self.feature_size])
vlad = tf.matmul(activation, reshaped_input)
vlad = tf.transpose(vlad, perm=[0, 2, 1])
vlad = tf.nn.l2_normalize(vlad, 1)
vlad = tf.reshape(vlad, [-1, self.cluster_size * self.feature_size])
vlad = tf.nn.l2_normalize(vlad, 1)
return vlad
class NetVLAD():
def __init__(self, feature_size, max_frames, cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self, reshaped_input):
#print("====================NetVLAD===================")
#print("input:{}".format(reshaped_input.shape.as_list()))
#input:[40,1024]
#cluster_w:[1024,256]
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
#print("cluster_w:{}".format(cluster_weights.shape.as_list()))
#tensorboard可视化
tf.summary.histogram("cluster_weights", cluster_weights)
#40x256
activation = tf.matmul(reshaped_input, cluster_weights)
#print("active1:{}".format(activation.shape.as_list()))
#print("add_batch_norm:{}".format(self.add_batch_norm))
#true
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
#[40,256]
activation = tf.nn.softmax(activation)
#print("softmax:{}".format(activation.shape.as_list()))
tf.summary.histogram("cluster_output", activation)
#[1,40,256]
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
#print("reshape:{}".format(activation.shape.as_list()))
#[1,1,256]
a_sum = tf.reduce_sum(activation, -2, keep_dims=True)
#print("a_sum:{}".format(a_sum.shape.as_list()))
#[1,1024,256]
cluster_weights2 = tf.get_variable("cluster_weights2",
[1, self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
#print("cluster_w2:{}".format(cluster_weights2.shape.as_list()))
#[1,1024,256] multiply元素级别相乘
a = tf.multiply(a_sum, cluster_weights2)
#print("a:{}".format(a.shape.as_list()))
#[1,256,40]
activation = tf.transpose(activation, perm=[0, 2, 1])
#print("transpose:{}".format(activation.shape.as_list()))
#[1,40,1024]
reshaped_input = tf.reshape(reshaped_input, [-1, self.max_frames, self.feature_size])
#1,256,1024
vlad = tf.matmul(activation, reshaped_input)
#print("vlad1::{}".format(vlad.shape.as_list()))
#1,1024,256
vlad = tf.transpose(vlad, perm=[0, 2, 1])
#print("vld_trans:{}".format(vlad.shape.as_list()))
#1.11024,256
vlad = tf.subtract(vlad, a)
#print("vlad_subtract:{}".format(vlad.shape.as_list()))
#1,1024,256
vlad = tf.nn.l2_normalize(vlad, 1)
#print("vlad_l2_norm:{}".format(vlad.shape.as_list()))
#1,1024*256
vlad = tf.reshape(vlad, [-1, self.cluster_size * self.feature_size])
#print("vlad_reshape:{}".format(vlad.shape.as_list()))
vlad = tf.nn.l2_normalize(vlad, 1)
# print("output:{}".format(vlad.shape.as_list()))
# print("=====================================")
#output:[1, 262144]
return vlad
class NetVLAGD():
def __init__(self, feature_size, max_frames, cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self, reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
gate_weights = tf.get_variable("gate_weights",
[1, self.cluster_size, self.feature_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
gate_weights = tf.sigmoid(gate_weights)
activation = tf.transpose(activation, perm=[0, 2, 1])
reshaped_input = tf.reshape(reshaped_input, [-1, self.max_frames, self.feature_size])
vlagd = tf.matmul(activation, reshaped_input)
vlagd = tf.multiply(vlagd, gate_weights)
vlagd = tf.transpose(vlagd, perm=[0, 2, 1])
vlagd = tf.nn.l2_normalize(vlagd, 1)
vlagd = tf.reshape(vlagd, [-1, self.cluster_size * self.feature_size])
vlagd = tf.nn.l2_normalize(vlagd, 1)
return vlagd
class GatedDBoF():
def __init__(self, feature_size, max_frames, cluster_size, max_pool, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
self.max_pool = max_pool
def forward(self, reshaped_input):
feature_size = self.feature_size
cluster_size = self.cluster_size
add_batch_norm = self.add_batch_norm
max_frames = self.max_frames
is_training = self.is_training
max_pool = self.max_pool
cluster_weights = tf.get_variable("cluster_weights",
[feature_size, cluster_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer=tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
activation_sum = tf.reduce_sum(activation, 1)
activation_max = tf.reduce_max(activation, 1)
activation_max = tf.nn.l2_normalize(activation_max, 1)
dim_red = tf.get_variable("dim_red",
[cluster_size, feature_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
cluster_weights_2 = tf.get_variable("cluster_weights_2",
[feature_size, cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights_2", cluster_weights_2)
activation = tf.matmul(activation_max, dim_red)
activation = tf.matmul(activation, cluster_weights_2)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn_2")
else:
cluster_biases = tf.get_variable("cluster_biases_2",
[cluster_size],
initializer=tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases_2", cluster_biases)
activation += cluster_biases
activation = tf.sigmoid(activation)
activation = tf.multiply(activation, activation_sum)
activation = tf.nn.l2_normalize(activation, 1)
return activation
class SoftDBoF():
def __init__(self, feature_size, max_frames, cluster_size, max_pool, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
self.max_pool = max_pool
def forward(self, reshaped_input):
feature_size = self.feature_size
cluster_size = self.cluster_size
add_batch_norm = self.add_batch_norm
max_frames = self.max_frames
is_training = self.is_training
max_pool = self.max_pool
cluster_weights = tf.get_variable("cluster_weights",
[feature_size, cluster_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer=tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
activation_sum = tf.reduce_sum(activation, 1)
activation_sum = tf.nn.l2_normalize(activation_sum, 1)
if max_pool:
activation_max = tf.reduce_max(activation, 1)
activation_max = tf.nn.l2_normalize(activation_max, 1)
activation = tf.concat([activation_sum, activation_max], 1)
else:
activation = activation_sum
return activation
class DBoF():
def __init__(self, feature_size, max_frames, cluster_size, activation, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
self.activation = activation
def forward(self, reshaped_input):
feature_size = self.feature_size
cluster_size = self.cluster_size
add_batch_norm = self.add_batch_norm
max_frames = self.max_frames
is_training = self.is_training
cluster_weights = tf.get_variable("cluster_weights",
[feature_size, cluster_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer=tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
if activation == 'glu':
space_ind = range(cluster_size / 2)
gate_ind = range(cluster_size / 2, cluster_size)
gates = tf.sigmoid(activation[:, gate_ind])
activation = tf.multiply(activation[:, space_ind], gates)
elif activation == 'relu':
activation = tf.nn.relu6(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
avg_activation = utils.FramePooling(activation, 'average')
avg_activation = tf.nn.l2_normalize(avg_activation, 1)
max_activation = utils.FramePooling(activation, 'max')
max_activation = tf.nn.l2_normalize(max_activation, 1)
return tf.concat([avg_activation, max_activation], 1)
class NetFV():
def __init__(self, feature_size, max_frames, cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self, reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
covar_weights = tf.get_variable("covar_weights",
[self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(mean=1.0, stddev=1 / math.sqrt(
self.feature_size)))
covar_weights = tf.square(covar_weights)
eps = tf.constant([1e-6])
covar_weights = tf.add(covar_weights, eps)
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[self.cluster_size],
initializer=tf.random_normal(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation, -2, keep_dims=True)
if not FLAGS.fv_couple_weights:
cluster_weights2 = tf.get_variable("cluster_weights2",
[1, self.feature_size, self.cluster_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(self.feature_size)))
else:
cluster_weights2 = tf.scalar_mul(FLAGS.fv_coupling_factor, cluster_weights)
a = tf.multiply(a_sum, cluster_weights2)
activation = tf.transpose(activation, perm=[0, 2, 1])
reshaped_input = tf.reshape(reshaped_input, [-1, self.max_frames, self.feature_size])
fv1 = tf.matmul(activation, reshaped_input)
fv1 = tf.transpose(fv1, perm=[0, 2, 1])
# computing second order FV
a2 = tf.multiply(a_sum, tf.square(cluster_weights2))
b2 = tf.multiply(fv1, cluster_weights2)
fv2 = tf.matmul(activation, tf.square(reshaped_input))
fv2 = tf.transpose(fv2, perm=[0, 2, 1])
fv2 = tf.add_n([a2, fv2, tf.scalar_mul(-2, b2)])
fv2 = tf.divide(fv2, tf.square(covar_weights))
fv2 = tf.subtract(fv2, a_sum)
fv2 = tf.reshape(fv2, [-1, self.cluster_size * self.feature_size])
fv2 = tf.nn.l2_normalize(fv2, 1)
fv2 = tf.reshape(fv2, [-1, self.cluster_size * self.feature_size])
fv2 = tf.nn.l2_normalize(fv2, 1)
fv1 = tf.subtract(fv1, a)
fv1 = tf.divide(fv1, covar_weights)
fv1 = tf.nn.l2_normalize(fv1, 1)
fv1 = tf.reshape(fv1, [-1, self.cluster_size * self.feature_size])
fv1 = tf.nn.l2_normalize(fv1, 1)
return tf.concat([fv1, fv2], 1)
def conv(x, channels, kernel=4, stride=2, pad=0, pad_type='zero', use_bias=True, sn=False, scope='conv_0'):
with tf.variable_scope(scope):
if pad > 0:
h = x.get_shape().as_list()[1]
if h % stride == 0:
pad = pad * 2
else:
pad = max(kernel - (h % stride), 0)
pad_top = pad // 2
pad_bottom = pad - pad_top
pad_left = pad // 2
pad_right = pad - pad_left
if pad_type == 'zero':
x = tf.pad(x, [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]])
if pad_type == 'reflect':
x = tf.pad(x, [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]], mode='REFLECT')
if sn:
w = tf.get_variable("kernel", shape=[kernel, kernel, x.get_shape()[-1], channels], initializer=weight_init,
regularizer=weight_regularizer)
x = tf.nn.conv2d(input=x, filter=spectral_norm(w),
strides=[1, stride, stride, 1], padding='VALID')
if use_bias:
bias = tf.get_variable("bias", [channels], initializer=tf.constant_initializer(0.0))
x = tf.nn.bias_add(x, bias)
else:
x = tf.layers.conv2d(inputs=x, filters=channels,
kernel_size=kernel, kernel_initializer=weight_init,
kernel_regularizer=weight_regularizer,
strides=stride, use_bias=use_bias)
return x
def hw_flatten(x) :
print("hw_flattn:{}".format(type(x)))
print("hw_flatten:{}".format(x.shape.as_list()))
return tf.reshape(x, [x.shape[0], x.shape[1]*x.shape[2], x.shape[-1]])
class Attn():
def __init__(self, channels, scope='attention', sn=False):
self.channels = channels
self.scope = scope
self.sn = sn
def forward( self, x):
#tf_variable_scope 共享变量/模型封装
with tf.variable_scope(self.scope):
print("x-shape:{}".format(x.shape.as_list()))
#x = tf.reshape(x, [1, 1, 40, x.shape[1]])
# f = conv(x, self.channels // 8, kernel=1, stride=1, sn=self.sn, scope='f_conv') # [bs, h, w, c']
# g = conv(x, self.channels // 8, kernel=1, stride=1, sn=self.sn, scope='g_conv') # [bs, h, w, c']
# h = conv(x, self.channels, kernel=1, stride=1, sn=self.sn, scope='h_conv') # [bs, h, w, c]
f = tf.transpose(x, perm=[1, 0])
g = x
h = x
# N = h * w
s = tf.matmul(g, f) # # [bs, N, N]
beta = tf.nn.softmax(s) # attention map
o = tf.matmul(beta, h) # [bs, N, C]
print("!!oo:{}".format(o.shape.as_list()))
gamma = tf.get_variable("gamma", [1], initializer=tf.constant_initializer(0.0))
#o = tf.reshape(o, shape=x.shape) # [bs, h, w, C]
print("!!o:{}".format(o.shape.as_list()))
o_w = tf.get_variable("o_w",[1024, 1024],
initializer=tf.random_normal_initializer(mean=1.0, stddev=1 / math.sqrt(
1024)))
#o = conv(o, self.channels, kernel=1, stride=1, sn=self.sn, scope='attn_conv')
o = tf.matmul(o,o_w)
x = gamma * o + x
print("out:{}".format(x.shape.as_list()))
return x
class NetVLADModelLF(models.BaseModel):
"""Creates a NetVLAD based model.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
imp=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.netvlad_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.netvlad_cluster_size
hidden1_size = hidden_size or FLAGS.netvlad_hidden_size
relu = FLAGS.netvlad_relu
dimred = FLAGS.netvlad_dimred
gating = FLAGS.gating
remove_diag = FLAGS.gating_remove_diag
lightvlad = FLAGS.lightvlad
vlagd = FLAGS.vlagd
print("=============")
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
print("model_input(before):", model_input)
print("ranndom_frames:{}".format(random_frames))
#random_frames=false
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
print("model_input(After):", model_input)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
print("model_input:{}".format(model_input.shape.as_list()))
reshaped_input = tf.reshape(model_input, [-1, feature_size])
print("model_input reshape:{}".format(reshaped_input.shape.as_list()))
if lightvlad:
video_NetVLAD = LightVLAD(1024, max_frames, cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = LightVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif vlagd:
video_NetVLAD = NetVLAGD(1024, max_frames, cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAGD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
else:
video_NetVLAD = NetVLAD(1024, max_frames, cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
if add_batch_norm: # and not lightvlad:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
##-------------------------------improve part--------------------------------
print("-----------------NetVLAD improve---------------------")
print("reshaped_input:{}".format(reshaped_input.shape.as_list()))
src_input = reshaped_input[:, 0:1024]
print("src_input:{}".format(src_input.shape.as_list()))
#temporal relation
#[40,1024]
attention = Attn(1024)
tem_rla = attention.forward(src_input)
print("-----------------------------------------------")
#---------------------------------------------------------------------------
with tf.variable_scope("video_VLAD"):
#reshaped_input[:, 0:1024]
vlad_video = video_NetVLAD.forward(tem_rla)
# with tf.variable_scope("audio_VLAD"):
# vlad_audio = audio_NetVLAD.forward(reshaped_input[:,1024:])
# vlad = tf.concat([vlad_video, vlad_audio],1)
vlad = tf.concat([vlad_video], 1)
vlad_dim = vlad.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[vlad_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
activation = tf.matmul(vlad, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer=tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[hidden1_size, hidden1_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(hidden1_size)))
gates = tf.matmul(activation, gating_weights)
if remove_diag:
# removes diagonals coefficients
diagonals = tf.matrix_diag_part(gating_weights)
gates = gates - tf.multiply(diagonals, activation)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
else:
gating_biases = tf.get_variable("gating_biases",
[cluster_size],
initializer=tf.random_normal(stddev=1 / math.sqrt(feature_size)))
gates += gating_biases
gates = tf.sigmoid(gates)
activation = tf.multiply(activation, gates)
#multi-label prediction
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class DbofModelLF(models.BaseModel):
"""Creates a Deep Bag of Frames model.
The model projects the features for each frame into a higher dimensional
'clustering' space, pools across frames in that space, and then
uses a configurable video-level model to classify the now aggregated features.
The model will randomly sample either frames or sequences of frames during
training to speed up convergence.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.dbof_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.dbof_cluster_size
hidden1_size = hidden_size or FLAGS.dbof_hidden_size
relu = FLAGS.dbof_relu
cluster_activation = FLAGS.dbof_activation
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
tf.summary.histogram("input_hist", reshaped_input)
if cluster_activation == 'glu':
cluster_size = 2 * cluster_size
video_Dbof = DBoF(1024, max_frames, cluster_size, cluster_activation, add_batch_norm, is_training)
audio_Dbof = DBoF(128, max_frames, cluster_size / 8, cluster_activation, add_batch_norm, is_training)
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
with tf.variable_scope("video_DBOF"):
dbof_video = video_Dbof.forward(reshaped_input[:, 0:1024])
with tf.variable_scope("audio_DBOF"):
dbof_audio = audio_Dbof.forward(reshaped_input[:, 1024:])
dbof = tf.concat([dbof_video, dbof_audio], 1)
dbof_dim = dbof.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[dbof_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
tf.summary.histogram("hidden1_weights", hidden1_weights)
activation = tf.matmul(dbof, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer=tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
tf.summary.histogram("hidden1_output", activation)