Merge pull request #3753 from hedaoyuan/conv_op

Add im2col functor
Adaptive_data_structure_for_SwitchOrderLayer
hedaoyuan 8 years ago committed by GitHub
commit 0140f3f9d0

@ -1,8 +1,10 @@
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc
im2col.cu DEPS cblas device_context)
else()
cc_library(math_function SRCS math_function.cc DEPS cblas device_context)
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
endif()
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor)

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

@ -0,0 +1,90 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace operators {
namespace math {
/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
enum class ColFormat { kCFO = 0, kOCF = 1 };
/*
* \brief Converts the image data of three dimensions(CHW) into a colData of
* five dimensions in the Im2ColFunctor calculation,
* And in the Col2ImFunctor calculation, it is reversed.
*
* \param imData Image data.
* \param imShape The shape of imData,
* [input_channels, input_height, input_width].
* \param colData Column data.
* \param colShape The shape of colData.
*
* If the template argument Format is kCFO, the shape of colData is:
* [input_channels, filter_height, filter_width, output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution
* calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the height is equal
* input_channels * filter_height * filter_width, and the width is equal
* output_height * output_width.
*
* Reshape:
* shape of colData shape of convolution matrix
* [input_channels,
* filter_height,
* filter_width, ======> [height, width]
* output_height,
* output_width]
*
* If the template argument Format is kOCF, the shape of colData is:
* [output_height, output_width, input_channels, filter_height, filter_width]
* So, it is easy to reshape into a sequence matrix for rnn calculation.
* The shape of sequence matrix is [seq_length, step_size], where the seq_length
* is equal output_height * output_width, and the step_size is equal
* input_channels * filter_height * filter_width.
*
* Reshape:
* shape of colData shape of sequence matrix
* [output_height,
* output_width,
* input_channels, ======> [seqLength, stepSize]
* filter_height,
* filter_width]
*
* \note The caller needs to ensure that imShape.inputChannels is equal to
* colShape.inputChannels.
*/
template <ColFormat Format, typename Place, typename T>
class Im2ColFunctor {
public:
void operator()(const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width, platform::DeviceContext* context);
};
template <ColFormat Format, typename Place, typename T>
class Col2ImFunctor {
public:
void operator()(framework::Tensor& im, const framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width, platform::DeviceContext* context);
};
} // namespace math
} // namespace operators
} // namespace paddle

@ -0,0 +1,118 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "paddle/operators/math/im2col.h"
#include <gtest/gtest.h>
#include <iostream>
template <typename Place>
void testIm2col() {
paddle::framework::Tensor input_tmp;
paddle::framework::Tensor input;
paddle::framework::Tensor output_cfo;
paddle::framework::Tensor output_ocf;
paddle::framework::Tensor output_tmp;
/**
* input = [0, 1, 2,
* 3, 4, 5]
*
* output_cfo = [0, 1
* 1, 2
* 3, 4
* 4, 5]
*
* output_ocf = [0, 1, 3, 4
* 1, 2, 4, 5]
*/
int input_height = 2;
int input_width = 3;
int filter_size = 2;
int stride = 1;
int padding = 0;
int output_height = (input_height - filter_size + 2 * padding) / stride + 1;
int output_width = (input_width - filter_size + 2 * padding) / stride + 1;
float* input_ptr = input_tmp.mutable_data<float>(
{1, input_height, input_width}, paddle::platform::CPUPlace());
float arr[6] = {0, 1, 2, 3, 4, 5};
memcpy(input_ptr, arr, 6 * sizeof(float));
auto* place = new Place();
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
} else {
input.CopyFrom<float>(input_tmp, *place);
}
output_cfo.mutable_data<float>(
{1, filter_size, filter_size, output_height, output_width}, *place);
output_ocf.mutable_data<float>(
{output_height, output_width, 1, filter_size, filter_size}, *place);
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO, Place, float>
im2col;
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
im2col_ocf;
paddle::platform::DeviceContext* context;
if (paddle::platform::is_cpu_place(*place)) {
context =
new paddle::platform::CPUDeviceContext(paddle::platform::CPUPlace());
} else {
context =
new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace());
}
im2col(input, output_cfo, stride, stride, padding, padding, context);
im2col_ocf(input, output_ocf, stride, stride, padding, padding, context);
float* out_cfo_ptr;
if (paddle::platform::is_cpu_place(*place)) {
out_cfo_ptr = output_cfo.data<float>();
} else {
output_tmp.CopyFrom<float>(output_cfo, paddle::platform::CPUPlace());
out_cfo_ptr = output_tmp.data<float>();
}
EXPECT_EQ(out_cfo_ptr[0], 0);
EXPECT_EQ(out_cfo_ptr[1], 1);
EXPECT_EQ(out_cfo_ptr[2], 1);
EXPECT_EQ(out_cfo_ptr[3], 2);
EXPECT_EQ(out_cfo_ptr[4], 3);
EXPECT_EQ(out_cfo_ptr[5], 4);
EXPECT_EQ(out_cfo_ptr[6], 4);
EXPECT_EQ(out_cfo_ptr[7], 5);
float* out_ocf_ptr;
if (paddle::platform::is_cpu_place(*place)) {
out_ocf_ptr = output_ocf.data<float>();
} else {
output_tmp.CopyFrom<float>(output_ocf, paddle::platform::CPUPlace());
out_ocf_ptr = output_tmp.data<float>();
}
EXPECT_EQ(out_ocf_ptr[0], 0);
EXPECT_EQ(out_ocf_ptr[1], 1);
EXPECT_EQ(out_ocf_ptr[2], 3);
EXPECT_EQ(out_ocf_ptr[3], 4);
EXPECT_EQ(out_ocf_ptr[4], 1);
EXPECT_EQ(out_ocf_ptr[5], 2);
EXPECT_EQ(out_ocf_ptr[6], 4);
EXPECT_EQ(out_ocf_ptr[7], 5);
}
TEST(math, im2col) {
testIm2col<paddle::platform::CPUPlace>();
#ifndef PADDLE_ONLY_CPU
testIm2col<paddle::platform::GPUPlace>();
#endif
}
Loading…
Cancel
Save