chengduoZH
8 years ago
parent
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commit
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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 <gtest/gtest.h>
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#include "paddle/operators/math/pooling.h"
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#include "paddle/memory/memcpy.h"
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#include "paddle/platform/enforce.h"
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#include <stdlib.h>
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#include <time.h>
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#ifndef PADDLE_ONLY_CPU
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template <typename PoolType, typename PoolGradType>
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void testPool2d(paddle::platform::DeviceContext& context, PoolType pool_process,
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PoolGradType poolGrad_process, paddle::framework::Tensor& input,
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paddle::framework::Tensor& input_grad,
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paddle::framework::Tensor& output,
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paddle::framework::Tensor& output_grad, std::vector<int>& ksize,
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std::vector<int>& strides, std::vector<int>& paddings) {
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paddle::operators::math::Pool2dForwardFunctor<paddle::platform::GPUPlace,
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PoolType, float>
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pool2d_forward;
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pool2d_forward(context, input, output, ksize, strides, paddings,
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pool_process);
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int times = 50;
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clock_t start, finish;
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double totaltime;
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// Pool2dBackwardFunctor
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start = clock();
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for (int i = 0; i < times; ++i) {
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paddle::operators::math::Pool2dBackwardFunctor<paddle::platform::GPUPlace,
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PoolGradType, float>
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pool2d_backward;
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pool2d_backward(context, input, input_grad, output, output_grad, ksize,
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strides, paddings, poolGrad_process);
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PADDLE_ENFORCE(cudaStreamSynchronize(0),
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"cudaStreamSynchronize failed in pool2d_backward CopyFrom");
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}
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finish = clock();
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totaltime = (double)(finish - start) / CLOCKS_PER_SEC;
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totaltime /= times;
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std::cout << "\nPool3dBackwardFunctor: " << totaltime << "s" << std::endl;
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// MaxPool3dBackwardFunctor
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start = clock();
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for (int j = 0; j < times; ++j) {
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paddle::operators::math::MaxPool2dBackwardFunctor<
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paddle::platform::GPUPlace, float>
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maxpool2d_backward;
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maxpool2d_backward(context, input, input_grad, output, output_grad, ksize,
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strides, paddings);
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PADDLE_ENFORCE(
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cudaStreamSynchronize(0),
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"cudaStreamSynchronize failed in maxpool2d_backward CopyFrom");
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}
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finish = clock();
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totaltime = (double)(finish - start) / CLOCKS_PER_SEC;
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totaltime /= times;
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std::cout << "\nMaxPool3dBackwardFunctor: " << totaltime << "s" << std::endl;
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}
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void test2dPool() {
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using paddle::platform::DeviceContext;
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using paddle::platform::CUDADeviceContext;
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using paddle::platform::GPUPlace;
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paddle::framework::Tensor input_tmp;
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paddle::framework::Tensor output_tmp;
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paddle::framework::Tensor input;
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paddle::framework::Tensor input_grad;
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paddle::framework::Tensor output;
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paddle::framework::Tensor output_grad;
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int batch = 32;
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int channel = 32;
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int input_height = 128;
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int input_width = 128;
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int in_len = batch * channel * input_height * input_width;
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std::vector<int> ksize({3, 3});
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std::vector<int> strides({1, 1});
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std::vector<int> paddings({0, 0});
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int output_height =
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(input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
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int output_width =
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(input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
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int output_len = output_height * output_width;
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input_tmp.mutable_data<float>({batch, channel, input_height, input_width},
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paddle::platform::CPUPlace());
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output_tmp.mutable_data<float>({batch, channel, output_height, output_width},
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paddle::platform::CPUPlace());
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float* arr = new float[in_len];
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auto* place = new paddle::platform::GPUPlace();
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float* input_ptr = input_tmp.data<float>();
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for (int i = 0; i < in_len; ++i) arr[i] = i; // rand() / double(RAND_MAX/2);
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memcpy(input_ptr, arr, in_len * sizeof(float));
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input.CopyFrom<float>(input_tmp, *place);
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input_ptr = input_tmp.data<float>();
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for (int i = 0; i < in_len; ++i) arr[i] = 0;
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memcpy(input_ptr, arr, in_len * sizeof(float));
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input_grad.CopyFrom<float>(input_tmp, *place);
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// output
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input_ptr = output_tmp.data<float>();
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for (int i = 0; i < output_len; ++i)
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arr[i] = 0; // rand() / double(RAND_MAX/2);
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memcpy(input_ptr, arr, output_len * sizeof(float));
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output.CopyFrom<float>(input_tmp, *place);
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// output
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input_ptr = output_tmp.data<float>();
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for (int i = 0; i < output_len; ++i)
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arr[i] = 1; // rand() / double(RAND_MAX/2);
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memcpy(input_ptr, arr, output_len * sizeof(float));
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output_grad.CopyFrom<float>(input_tmp, *place);
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paddle::platform::DeviceContext* context =
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new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace());
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paddle::operators::math::pool::maxPool<float> pool_process;
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paddle::operators::math::pool::maxPoolGrad<float> poolGrad_process;
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testPool2d<paddle::operators::math::pool::maxPool<float>,
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paddle::operators::math::pool::maxPoolGrad<float>>(
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*context, pool_process, poolGrad_process, input, input_grad, output,
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output_grad, ksize, strides, paddings);
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}
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int main() {
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// testPool3d<paddle::platform::CPUPlace>();
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test2dPool();
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// testPool3d<paddle::platform::GPUPlace>();
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}
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#endif
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@ -1,157 +0,0 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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 <gtest/gtest.h>
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#include "paddle/operators/math/pooling.h"
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#include "paddle/memory/memcpy.h"
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#include "paddle/platform/enforce.h"
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#include <stdlib.h>
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#include <time.h>
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#ifndef PADDLE_ONLY_CPU
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template <typename PoolType, typename PoolGradType>
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void testPool3d(paddle::platform::DeviceContext& context, PoolType pool_process,
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PoolGradType poolGrad_process, paddle::framework::Tensor& input,
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paddle::framework::Tensor& input_grad,
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paddle::framework::Tensor& output,
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paddle::framework::Tensor& output_grad, std::vector<int>& ksize,
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std::vector<int>& strides, std::vector<int>& paddings) {
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paddle::operators::math::Pool3dForwardFunctor<paddle::platform::GPUPlace,
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PoolType, float>
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pool3d_forward;
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pool3d_forward(context, input, output, ksize, strides, paddings,
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pool_process);
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int times = 50;
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clock_t start, finish;
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double totaltime;
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// Pool3dBackwardFunctor
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start = clock();
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for (int i = 0; i < times; ++i) {
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paddle::operators::math::Pool3dBackwardFunctor<paddle::platform::GPUPlace,
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PoolGradType, float>
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pool3d_backward;
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pool3d_backward(context, input, input_grad, output, output_grad, ksize,
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strides, paddings, poolGrad_process);
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PADDLE_ENFORCE(cudaStreamSynchronize(0),
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"cudaStreamSynchronize failed in pool3d_backward CopyFrom");
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}
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finish = clock();
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totaltime = (double)(finish - start) / CLOCKS_PER_SEC;
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totaltime /= times;
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std::cout << "\nPool3dBackwardFunctor: " << totaltime << "s" << std::endl;
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// MaxPool3dBackwardFunctor
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start = clock();
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for (int j = 0; j < times; ++j) {
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paddle::operators::math::MaxPool3dBackwardFunctor<
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paddle::platform::GPUPlace, float>
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maxpool3d_backward;
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maxpool3d_backward(context, input, input_grad, output, output_grad, ksize,
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strides, paddings);
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PADDLE_ENFORCE(
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cudaStreamSynchronize(0),
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"cudaStreamSynchronize failed in maxpool3d_backward CopyFrom");
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}
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finish = clock();
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totaltime = (double)(finish - start) / CLOCKS_PER_SEC;
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totaltime /= times;
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std::cout << "\nMaxPool3dBackwardFunctor: " << totaltime << "s" << std::endl;
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}
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void test3dPool() {
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using paddle::platform::DeviceContext;
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using paddle::platform::CUDADeviceContext;
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using paddle::platform::GPUPlace;
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paddle::framework::Tensor input_tmp;
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paddle::framework::Tensor output_tmp;
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paddle::framework::Tensor input;
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paddle::framework::Tensor input_grad;
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paddle::framework::Tensor output;
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paddle::framework::Tensor output_grad;
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int batch = 32;
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int channel = 4;
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int input_depth = 4;
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int input_height = 128;
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int input_width = 128;
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int in_len = batch * channel * input_depth * input_height * input_width;
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std::vector<int> ksize({3, 3, 3});
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std::vector<int> strides({2, 2, 2});
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std::vector<int> paddings({1, 1, 1});
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int output_depth =
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(input_depth - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
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int output_height =
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(input_height - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
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int output_width =
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(input_width - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
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int output_len = output_depth * output_height * output_width;
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input_tmp.mutable_data<float>(
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{batch, channel, input_depth, input_height, input_width},
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paddle::platform::CPUPlace());
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output_tmp.mutable_data<float>(
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{batch, channel, output_depth, output_height, output_width},
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paddle::platform::CPUPlace());
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float* arr = new float[in_len];
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auto* place = new paddle::platform::GPUPlace();
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// input
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float* input_ptr = input_tmp.data<float>();
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for (int i = 0; i < in_len; ++i) arr[i] = i; // rand() / double(RAND_MAX/2);
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memcpy(input_ptr, arr, in_len * sizeof(float));
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input.CopyFrom<float>(input_tmp, *place);
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// input_grad
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input_ptr = input_tmp.data<float>();
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for (int i = 0; i < in_len; ++i) arr[i] = 0;
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memcpy(input_ptr, arr, in_len * sizeof(float));
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input_grad.CopyFrom<float>(input_tmp, *place);
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// output
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input_ptr = output_tmp.data<float>();
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for (int i = 0; i < output_len; ++i)
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arr[i] = 0; // rand() / double(RAND_MAX/2);
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memcpy(input_ptr, arr, output_len * sizeof(float));
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output.CopyFrom<float>(input_tmp, *place);
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// output_grad
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input_ptr = output_tmp.data<float>();
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for (int i = 0; i < output_len; ++i)
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arr[i] = 1; // rand() / double(RAND_MAX/2);
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memcpy(input_ptr, arr, output_len * sizeof(float));
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output_grad.CopyFrom<float>(input_tmp, *place);
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paddle::platform::DeviceContext* context =
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new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace());
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paddle::operators::math::pool::maxPool<float> pool_process;
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paddle::operators::math::pool::maxPoolGrad<float> poolGrad_process;
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testPool3d<paddle::operators::math::pool::maxPool<float>,
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paddle::operators::math::pool::maxPoolGrad<float>>(
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*context, pool_process, poolGrad_process, input, input_grad, output,
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output_grad, ksize, strides, paddings);
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}
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int main() { test3dPool(); }
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#endif
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