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[cker] Introduce cker for avgpool (#14086)
This commit adds kernel for average pooling ONE-DCO-1.0-Signed-off-by: JuYoung Lee <[email protected]>
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/* | ||
* Copyright (c) 2024 Samsung Electronics Co., Ltd. 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. | ||
*/ | ||
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#ifndef __NNFW_CKER_TRAIN_OPERATION_AVERAGEPOOL_H__ | ||
#define __NNFW_CKER_TRAIN_OPERATION_AVERAGEPOOL_H__ | ||
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#include "cker/Shape.h" | ||
#include "cker/Utils.h" | ||
#include "cker/eigen/Utils.h" | ||
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#include <Eigen/Core> | ||
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namespace nnfw | ||
{ | ||
namespace cker | ||
{ | ||
namespace train | ||
{ | ||
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inline void AveragePool2DGrad(const PoolParams ¶ms, const Shape &incoming_shape, | ||
const float *incoming_data, const Shape &grad_shape, float *grad_data) | ||
{ | ||
assert(grad_shape.DimensionsCount() == 4); | ||
assert(incoming_shape.DimensionsCount() == 4); | ||
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const int batches = MatchingDim(incoming_shape, 0, grad_shape, 0); | ||
const int grad_height = grad_shape.Dims(1); | ||
const int grad_width = grad_shape.Dims(2); | ||
const int incoming_height = incoming_shape.Dims(1); | ||
const int incoming_width = incoming_shape.Dims(2); | ||
const int stride_height = params.stride_height; | ||
const int stride_width = params.stride_width; | ||
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// initialize grad_data | ||
std::fill(grad_data, grad_data + grad_shape.FlatSize(), 0.0); | ||
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const auto incoming_mat = MapAsMatrixWithLastDimAsRows(incoming_data, incoming_shape); | ||
auto grad_mat = MapAsMatrixWithLastDimAsRows(grad_data, grad_shape); | ||
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for (int b = 0; b < batches; ++b) | ||
{ | ||
for (int h = 0; h < incoming_height; ++h) | ||
{ | ||
for (int w = 0; w < incoming_width; ++w) | ||
{ | ||
// (h_start, h_end) * (w_start, w_end) is input range | ||
// that output is projected from. | ||
int h_start = h * stride_height - params.padding_values.height; | ||
int h_end = std::min(h_start + params.filter_height, grad_height); | ||
h_start = h_start < 0 ? 0 : h_start; | ||
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int w_start = w * stride_width - params.padding_values.width; | ||
int w_end = std::min(w_start + params.filter_width, grad_width); | ||
w_start = w_start < 0 ? 0 : w_start; | ||
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int count = (h_end - h_start) * (w_end - w_start); | ||
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if (h_end <= 0 || w_end <= 0 || count <= 0 || h_start >= grad_height || | ||
w_start >= grad_width) | ||
continue; | ||
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int incoming_offset = NodeOffset(b, h, w, incoming_height, incoming_width); | ||
for (int ph = h_start; ph < h_end; ++ph) | ||
{ | ||
for (int pw = w_start; pw < w_end; ++pw) | ||
{ | ||
int grad_offset = NodeOffset(b, ph, pw, grad_height, grad_width); | ||
grad_mat.col(grad_offset) += incoming_mat.col(incoming_offset) / count; | ||
} | ||
} | ||
} | ||
} | ||
} | ||
} | ||
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} // namespace train | ||
} // namespace cker | ||
} // namespace nnfw | ||
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#endif // __NNFW_CKER_TRAIN_OPERATION_AVERAGEPOOL_H__ |
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/* | ||
* Copyright (c) 2024 Samsung Electronics Co., Ltd. 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. | ||
*/ | ||
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#include <cker/eigen/Utils.h> | ||
#include <cker/operation/AveragePool.h> | ||
#include <cker/train/operation/AveragePool.h> | ||
#include <cker/Shape.h> | ||
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#include <gtest/gtest.h> | ||
#include <vector> | ||
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namespace | ||
{ | ||
using namespace nnfw::cker; | ||
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template <typename T> class AvgPoolOpVerifier | ||
{ | ||
private: | ||
const PoolParams _op_params; | ||
const Shape _in_shape; | ||
const Shape _out_shape; | ||
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public: | ||
AvgPoolOpVerifier(const nnfw::cker::PoolParams &op_params, const Shape &in_shape, | ||
const Shape &out_shape) | ||
: _op_params(op_params), _in_shape(in_shape), _out_shape(out_shape) | ||
{ | ||
} | ||
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public: | ||
void verifyForward(const std::vector<T> input, const std::vector<T> expected_output, | ||
bool expect_eq = true) | ||
{ | ||
assert(input.size() == _in_shape.FlatSize()); | ||
assert(expected_output.size() == _out_shape.FlatSize()); | ||
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std::vector<T> cacluated_output(_out_shape.FlatSize()); | ||
nnfw::cker::AveragePool<float>(_op_params, _in_shape, input.data(), _out_shape, | ||
cacluated_output.data()); | ||
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if (expect_eq) | ||
EXPECT_EQ(expected_output, cacluated_output); | ||
else | ||
EXPECT_NE(expected_output, cacluated_output); | ||
} | ||
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void verifyBackward(const std::vector<T> incoming_data, const std::vector<T> expected_grad_data, | ||
bool expect_eq = true) | ||
{ | ||
assert(incoming_data.size() == _out_shape.FlatSize()); | ||
assert(expected_grad_data.size() == _in_shape.FlatSize()); | ||
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std::vector<T> calcuated_grad(_in_shape.FlatSize()); | ||
nnfw::cker::train::AveragePool2DGrad(_op_params, _out_shape, incoming_data.data(), _in_shape, | ||
calcuated_grad.data()); | ||
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if (expect_eq) | ||
{ | ||
for (size_t i = 0; i < expected_grad_data.size(); i++) | ||
{ | ||
EXPECT_FLOAT_EQ(expected_grad_data[i], calcuated_grad[i]); | ||
} | ||
} | ||
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else | ||
EXPECT_NE(expected_grad_data, calcuated_grad); | ||
} | ||
}; | ||
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} // namespace | ||
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TEST(CKer_Operation, AveragePool2D) | ||
{ | ||
// Depth 1 case | ||
{ | ||
nnfw::cker::PoolParams op_param; | ||
{ | ||
op_param.stride_height = 1; | ||
op_param.stride_width = 1; | ||
op_param.filter_height = 2; | ||
op_param.filter_width = 2; | ||
op_param.padding_values.height = 0; | ||
op_param.padding_values.width = 0; | ||
op_param.float_activation_max = std::numeric_limits<float>::max(); | ||
op_param.float_activation_min = std::numeric_limits<float>::lowest(); | ||
} | ||
nnfw::cker::Shape in = {1, 3, 3, 1}; | ||
nnfw::cker::Shape out = {1, 2, 2, 1}; | ||
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AvgPoolOpVerifier<float> verifier(op_param, in, out); | ||
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/** | ||
* input : output: | ||
* | ||
* 10(0) 15(1) 2(2) | ||
* 7(3) 8(4) 9(5) - (forward) -> 10(4) 8.5(4) | ||
* 10(6) 1(7) 0(8) 6.5(4) 4.5(4) | ||
*/ | ||
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std::vector<float> input = {10, 15, 2, 7, 8, 9, 10, 1, 0}; | ||
std::vector<float> expected_output = {10, 8.5, 6.5, 4.5}; | ||
verifier.verifyForward(input, expected_output); | ||
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/** | ||
* output_deriv: input_deriv: | ||
* | ||
* | ||
* 0.4 0.4 0.1 0.2 0.1 | ||
* 0.4 0.4 - (backward) -> 0.2 0.4 0.2 | ||
* 0.1 0.2 0.1 | ||
*/ | ||
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std::vector<float> output_deriv = {0.4, 0.4, 0.4, 0.4}; | ||
std::vector<float> expected_input_deriv = {0.1, 0.2, 0.1, 0.2, 0.4, 0.2, 0.1, 0.2, 0.1}; | ||
verifier.verifyBackward(output_deriv, expected_input_deriv); | ||
} | ||
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// Depth 2 case | ||
{ | ||
nnfw::cker::PoolParams op_param; | ||
{ | ||
op_param.stride_height = 1; | ||
op_param.stride_width = 1; | ||
op_param.filter_height = 3; | ||
op_param.filter_width = 3; | ||
op_param.padding_values.height = 0; | ||
op_param.padding_values.width = 0; | ||
op_param.float_activation_max = std::numeric_limits<float>::max(); | ||
op_param.float_activation_min = std::numeric_limits<float>::lowest(); | ||
} | ||
nnfw::cker::Shape in = {1, 3, 3, 2}; | ||
nnfw::cker::Shape out = {1, 1, 1, 2}; | ||
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AvgPoolOpVerifier<float> verifier(op_param, in, out); | ||
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/** | ||
* depth[0] | ||
* input : output: | ||
* | ||
* 10(0) 15(1) 2(2) | ||
* 10(3) 12(4) 17(5) -(forward)-> 16(0) | ||
* 50(6) 30(7) -2(8) | ||
* | ||
* | ||
* depth[1] | ||
* input: output: | ||
* | ||
* -1(0) 2(1) 3(2) | ||
* 8(3) 9(4) 2(5) -(forward)-> 4(0) | ||
* 4(6) 2(7) 7(8) | ||
*/ | ||
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std::vector<float> input(in.FlatSize()); | ||
auto input_mat = MapAsMatrixWithLastDimAsRows(input.data(), in); | ||
input_mat << /* depth0 */ 10, 15, 2, 10, 12, 17, 50, 30, -2, | ||
/* depth1 */ -1, 2, 3, 8, 9, 2, 4, 2, 7; | ||
std::vector<float> expected_output = {16, 4}; | ||
verifier.verifyForward(input, expected_output); | ||
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/** | ||
* depth[0] | ||
* ouput_deriv: input_deriv: | ||
* | ||
* 0.02 0.02 0.02 | ||
* 0.18 -(backward)-> 0.02 0.02 0.02 | ||
* 0.02 0.02 0.02 | ||
* | ||
* | ||
* depth[1] | ||
* output_deriv: input_deriv: | ||
* 0.04 0.04 0.04 | ||
* 0.36 -(backward)-> 0.04 0.04 0.04 | ||
* 0.04 0.04 0.04 | ||
*/ | ||
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std::vector<float> output_deriv = {0.18, 0.36}; | ||
std::vector<float> expected_input_deriv(in.FlatSize()); | ||
auto input_deriv_mat = MapAsMatrixWithLastDimAsRows(expected_input_deriv.data(), in); | ||
input_deriv_mat << /* depth0 */ 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, | ||
/* depth1 */ 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04; | ||
verifier.verifyBackward(output_deriv, expected_input_deriv); | ||
} | ||
} | ||
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TEST(CKer_Operation, neg_AveragePoolInvalidExpectedValue) | ||
{ | ||
// Invalid expected value | ||
{ | ||
nnfw::cker::PoolParams op_param; | ||
{ | ||
op_param.stride_height = 1; | ||
op_param.stride_width = 1; | ||
op_param.filter_height = 2; | ||
op_param.filter_width = 2; | ||
op_param.padding_values.height = 0; | ||
op_param.padding_values.width = 0; | ||
op_param.float_activation_max = std::numeric_limits<float>::max(); | ||
op_param.float_activation_min = std::numeric_limits<float>::lowest(); | ||
} | ||
nnfw::cker::Shape in = {1, 2, 2, 1}; | ||
nnfw::cker::Shape out = {1, 1, 1, 1}; | ||
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AvgPoolOpVerifier<float> verifier(op_param, in, out); | ||
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std::vector<float> input = {0, 0, 0, 0}; | ||
std::vector<float> expected_output = {-1}; | ||
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verifier.verifyForward(input, expected_output, false); | ||
} | ||
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// Invalid expected value | ||
{ | ||
nnfw::cker::PoolParams op_param; | ||
{ | ||
op_param.stride_height = 2; | ||
op_param.stride_width = 2; | ||
op_param.filter_height = 2; | ||
op_param.filter_width = 2; | ||
op_param.padding_values.height = 1; | ||
op_param.padding_values.width = 1; | ||
op_param.float_activation_max = std::numeric_limits<float>::max(); | ||
op_param.float_activation_min = std::numeric_limits<float>::lowest(); | ||
} | ||
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nnfw::cker::Shape in = {1, 2, 2, 1}; | ||
nnfw::cker::Shape out = {1, 2, 2, 1}; | ||
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AvgPoolOpVerifier<float> verifier(op_param, in, out); | ||
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std::vector<float> input = {0, 0, 0, 0}; | ||
std::vector<float> expected_output = {0, 0, 0, 0}; | ||
verifier.verifyForward(input, expected_output); | ||
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std::vector<float> output_deriv = {0.1, 0.1, 0.1, 0.2}; | ||
std::vector<float> expected_input_deriv = {0.1, 0.1, 0.1, 0.1}; | ||
verifier.verifyBackward(output_deriv, expected_input_deriv, false); | ||
} | ||
} |