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273 lines
9.4 KiB
273 lines
9.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/math/im2col.h"
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#include <gtest/gtest.h>
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#include <vector>
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#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
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#include "paddle/fluid/platform/port.h"
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template <typename DeviceContext, typename Place>
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void testIm2col() {
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paddle::framework::Tensor input_tmp;
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paddle::framework::Tensor input;
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paddle::framework::Tensor output_cfo;
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paddle::framework::Tensor output_ocf;
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paddle::framework::Tensor output_tmp;
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/**
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* input = [0, 1, 2,
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* 3, 4, 5]
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*
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* output_cfo = [0, 1
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* 1, 2
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* 3, 4
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* 4, 5]
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*
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* output_ocf = [0, 1, 3, 4
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* 1, 2, 4, 5]
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*
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* col2im_cfo = [0, 2, 2
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* 3, 4, 5]
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*
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* col2im_ocf = [0, 2, 2
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* 3, 4, 5]
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*/
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int input_height = 2;
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int input_width = 3;
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int filter_size = 2;
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std::vector<int> stride({1, 1}); // stride_y, stride_x
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std::vector<int> padding(
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{0, 0, 0, 0}); // up_pad, left_pad, down_pad, right_pad
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std::vector<int> dilation({1, 1}); // dilation_y, dilation_x
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int output_height =
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(input_height - filter_size + padding[0] + padding[1]) / stride[0] + 1;
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int output_width =
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(input_width - filter_size + padding[2] + padding[3]) / stride[1] + 1;
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float* input_ptr = input_tmp.mutable_data<float>(
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{1, input_height, input_width}, paddle::platform::CPUPlace());
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float arr[6] = {0, 1, 2, 3, 4, 5};
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memcpy(input_ptr, arr, 6 * sizeof(float));
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auto* place = new Place();
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DeviceContext* context = new DeviceContext(*place);
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if (paddle::platform::is_cpu_place(*place)) {
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input = input_tmp;
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} else {
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TensorCopySync(input_tmp, *place, &input);
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}
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output_cfo.mutable_data<float>(
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{1, filter_size, filter_size, output_height, output_width}, *place);
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output_ocf.mutable_data<float>(
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{output_height, output_width, 1, filter_size, filter_size}, *place);
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// Im2Col
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paddle::operators::math::Im2ColFunctor<
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paddle::operators::math::ColFormat::kCFO, DeviceContext, float>
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im2col;
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paddle::operators::math::Im2ColFunctor<
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paddle::operators::math::ColFormat::kOCF, DeviceContext, float>
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im2col_ocf;
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im2col(*context, input, dilation, stride, padding, &output_cfo);
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im2col_ocf(*context, input, dilation, stride, padding, &output_ocf);
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float out_cfo_data[] = {0, 1, 1, 2, 3, 4, 4, 5};
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float out_ocf_data[] = {0, 1, 3, 4, 1, 2, 4, 5};
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float* out_cfo_ptr;
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if (paddle::platform::is_cpu_place(*place)) {
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out_cfo_ptr = output_cfo.data<float>();
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} else {
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TensorCopySync(output_cfo, paddle::platform::CPUPlace(), &output_tmp);
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out_cfo_ptr = output_tmp.data<float>();
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}
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for (int i = 0; i < 6; ++i) {
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EXPECT_EQ(out_cfo_ptr[i], out_cfo_data[i]);
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}
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float* out_ocf_ptr;
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if (paddle::platform::is_cpu_place(*place)) {
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out_ocf_ptr = output_ocf.data<float>();
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} else {
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TensorCopySync(output_ocf, paddle::platform::CPUPlace(), &output_tmp);
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out_ocf_ptr = output_tmp.data<float>();
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}
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for (int i = 0; i < 6; ++i) {
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EXPECT_EQ(out_ocf_ptr[i], out_ocf_data[i]);
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}
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// Col2Im: kCFO
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paddle::operators::math::Col2ImFunctor<
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paddle::operators::math::ColFormat::kCFO, DeviceContext, float>
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col2im;
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paddle::operators::math::Col2ImFunctor<
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paddle::operators::math::ColFormat::kOCF, DeviceContext, float>
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col2im_ocf;
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float col2im_data[] = {0, 2, 2, 3, 8, 5};
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memset(input_ptr, 0, 6 * sizeof(float));
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if (paddle::platform::is_cpu_place(*place)) {
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input = input_tmp;
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} else {
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TensorCopySync(input_tmp, *place, &input);
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}
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col2im(*context, output_cfo, dilation, stride, padding, &input);
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float* in_ptr;
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if (paddle::platform::is_cpu_place(*place)) {
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in_ptr = input.data<float>();
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} else {
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TensorCopySync(input, paddle::platform::CPUPlace(), &input_tmp);
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in_ptr = input_tmp.data<float>();
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}
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for (int i = 0; i < 6; ++i) {
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EXPECT_EQ(in_ptr[i], col2im_data[i]);
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}
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// Col2Im: kOCF
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memset(input_ptr, 0, 6 * sizeof(float));
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if (paddle::platform::is_cpu_place(*place)) {
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input = input_tmp;
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} else {
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TensorCopySync(input_tmp, *place, &input);
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}
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col2im_ocf(*context, output_ocf, dilation, stride, padding, &input);
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if (paddle::platform::is_cpu_place(*place)) {
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in_ptr = input.data<float>();
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} else {
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TensorCopySync(input, paddle::platform::CPUPlace(), &input_tmp);
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in_ptr = input_tmp.data<float>();
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}
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for (int i = 0; i < 6; ++i) {
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EXPECT_EQ(in_ptr[i], col2im_data[i]);
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}
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delete place;
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delete context;
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}
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TEST(math, im2col) {
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testIm2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
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#ifdef PADDLE_WITH_CUDA
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testIm2col<paddle::platform::CUDADeviceContext,
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paddle::platform::CUDAPlace>();
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#endif
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}
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#define PREPARE_IM2COL_CPU \
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paddle::platform::CPUPlace place; \
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paddle::platform::CPUDeviceContext context(place); \
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paddle::framework::Tensor input; \
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paddle::framework::Tensor out; \
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paddle::framework::Tensor ref; \
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std::vector<int> padding({ph, pw}); \
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std::vector<int> stride({1, 1}); \
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std::vector<int> dilation({1, 1}); \
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float* input_ptr = input.mutable_data<float>({ic, ih, iw}, place); \
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for (int i = 0; i < input.numel(); ++i) { \
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input_ptr[i] = static_cast<float>(i + 1); \
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} \
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int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1; \
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int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1; \
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out.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
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ref.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
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paddle::operators::math::Im2ColFunctor< \
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paddle::operators::math::ColFormat::kCFO, \
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paddle::platform::CPUDeviceContext, float> \
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im2col
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void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
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PREPARE_IM2COL_CPU;
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im2col(context, input, dilation, stride, padding, &out);
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paddle::operators::math::im2col_common<float>(input, dilation, stride,
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padding, &ref);
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float* ref_data = ref.data<float>();
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float* out_data = out.data<float>();
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for (int i = 0; i < out.numel(); ++i) {
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EXPECT_EQ(out_data[i], ref_data[i]);
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}
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}
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void benchIm2col(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
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PREPARE_IM2COL_CPU;
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constexpr int repeat = 100;
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auto GetCurrentMs = []() -> double {
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struct timeval time;
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gettimeofday(&time, NULL);
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return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
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};
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auto t1 = GetCurrentMs();
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for (int i = 0; i < repeat; ++i) {
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im2col(context, input, dilation, stride, padding, &out);
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}
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auto t2 = GetCurrentMs();
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for (int i = 0; i < repeat; ++i) {
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paddle::operators::math::im2col_common<float>(input, dilation, stride,
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padding, &ref);
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}
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auto t3 = GetCurrentMs();
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LOG(INFO) << "before: " << (t3 - t2) / repeat
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<< ",after: " << (t2 - t1) / repeat
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<< ",boost: " << ((t3 - t2) / (t2 - t1) - 1) * 100 << "%";
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}
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TEST(math, im2col_cputest) {
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// padding_h == padding_w
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for (int p = 0; p < 4; ++p) {
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// width == height
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testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 5, /*fh*/ 4, /*fw*/ 4, /*ph*/ p,
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/*pw*/ p);
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testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ p,
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/*pw*/ p);
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testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 2, /*ph*/ p,
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/*pw*/ p);
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// height != width
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testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ p,
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/*pw*/ p);
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testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 1, /*fw*/ 3, /*ph*/ p,
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/*pw*/ p);
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testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 5, /*fh*/ 3, /*fw*/ 1, /*ph*/ p,
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/*pw*/ p);
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// filter == 1
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testIm2colCPU(/*ic*/ 3, /*ih*/ 4, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
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/*pw*/ p);
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testIm2colCPU(/*ic*/ 3, /*ih*/ 3, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
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/*pw*/ p);
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}
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// padding_h != padding_w
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testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ 1,
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/*pw*/ 2);
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// benchmark
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for (int p : {0, 1}) {
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for (int k : {1, 3, 5}) {
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LOG(INFO) << "padding == " << p << ", filter == " << k;
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benchIm2col(/*ic*/ 3, /*ih*/ 224, /*iw*/ 224, /*fh*/ k, /*fw*/ k,
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/*ph*/ p, /*pw*/ p);
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}
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}
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}
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