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129 lines
3.9 KiB
129 lines
3.9 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/vol2col.h"
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#include <gtest/gtest.h>
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#include <iostream>
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#include <vector>
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template <typename DeviceContext, typename Place>
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void testVol2col() {
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paddle::framework::Tensor input;
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paddle::framework::Tensor input_tmp;
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paddle::framework::Tensor output;
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paddle::framework::Tensor output_tmp;
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auto* place = new Place();
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DeviceContext* context = new DeviceContext(*place);
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/**
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* input = [[0, 1, 2,
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* 3, 4, 5]
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* [6, 7, 8,
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* 9, 10, 11]]
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*
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* output = [0, 1
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* 1, 2
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* 3, 4
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* 4, 5
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* 6, 7
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* 7, 8
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* 9, 10
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* 10, 11]
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*
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* col2vol = [[0, 2, 2,
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* 3, 8, 5]
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* [6, 14, 8,
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* 9, 20, 11]]
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*
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*/
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int input_depth = 2;
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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> strides({1, 1, 1});
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std::vector<int> paddings({0, 0, 0});
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std::vector<int> dilations({1, 1, 1});
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int output_depth =
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(input_depth - filter_size + 2 * paddings[0]) / strides[0] + 1;
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int output_height =
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(input_height - filter_size + 2 * paddings[1]) / strides[1] + 1;
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int output_width =
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(input_width - filter_size + 2 * paddings[2]) / strides[2] + 1;
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// Vol2Col test
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float* input_ptr =
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input_tmp.mutable_data<float>({1, input_depth, input_height, input_width},
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paddle::platform::CPUPlace());
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float arr[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
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memcpy(input_ptr, arr, 12 * 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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paddle::framework::TensorCopySync(input_tmp, *place, &input);
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}
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output.mutable_data<float>({1, filter_size, filter_size, filter_size,
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output_depth, output_height, output_width},
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*place);
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paddle::operators::math::Vol2ColFunctor<DeviceContext, float> vol2col;
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vol2col(*context, input, dilations, strides, paddings, &output);
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float vol_2_col[] = {0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 10, 11};
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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.data<float>();
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} else {
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TensorCopySync(output, 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 < 16; ++i) {
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EXPECT_EQ(out_cfo_ptr[i], vol_2_col[i]);
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}
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// Col2Vol test
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float col_2_vol[] = {0, 2, 2, 3, 8, 5, 6, 14, 8, 9, 20, 11};
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memset(input_ptr, 0, 12 * 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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paddle::operators::math::Col2VolFunctor<DeviceContext, float> col2vol;
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col2vol(*context, output, dilations, strides, paddings, &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 < 12; ++i) {
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EXPECT_EQ(in_ptr[i], col_2_vol[i]);
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}
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}
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TEST(math, vol2col) {
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testVol2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
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#ifdef PADDLE_WITH_CUDA
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testVol2col<paddle::platform::CUDADeviceContext,
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paddle::platform::CUDAPlace>();
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#endif // PADDLE_WITH_CUDA
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}
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