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108 lines
4.1 KiB
108 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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 <gtest/gtest.h>
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#include "FunctionTest.h"
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namespace paddle {
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TEST(BlockExpandForward, real) {
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for (size_t batchSize : {5}) {
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for (size_t channels : {1, 5}) {
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for (size_t inputHeight : {5, 33}) {
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for (size_t inputWidth : {5, 32}) {
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for (size_t block : {1, 3, 5}) {
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for (size_t stride : {1, 2}) {
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for (size_t padding : {0, 1}) {
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// init Test object
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std::vector<size_t> strides = {stride, stride};
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std::vector<size_t> paddings = {padding, padding};
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std::vector<size_t> blocks = {block, block};
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CpuGpuFuncCompare test("BlockExpand",
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FuncConfig()
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.set("strides", strides)
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.set("paddings", paddings)
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.set("blocks", blocks));
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size_t outputHeight =
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1 +
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(inputHeight + 2 * padding - block + stride - 1) / stride;
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size_t outputWidth =
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1 +
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(inputWidth + 2 * padding - block + stride - 1) / stride;
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TensorShape inputShape =
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TensorShape({batchSize, channels, inputHeight, inputWidth});
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TensorShape outputShape =
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TensorShape({batchSize,
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outputHeight * outputWidth,
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channels * block * block});
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, inputShape));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, outputShape));
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// run Function
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test.run();
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}
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}
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}
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}
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}
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}
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}
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}
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TEST(BlockExpandBackward, real) {
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for (size_t batchSize : {5}) {
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for (size_t channels : {1, 5}) {
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for (size_t inputHeight : {5, 33}) {
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for (size_t inputWidth : {5, 32}) {
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for (size_t block : {1, 3, 5}) {
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for (size_t stride : {1, 2}) {
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for (size_t padding : {0, 1}) {
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// init Test object
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std::vector<size_t> strides = {stride, stride};
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std::vector<size_t> paddings = {padding, padding};
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std::vector<size_t> blocks = {block, block};
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CpuGpuFuncCompare test("BlockExpandGrad",
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FuncConfig()
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.set("strides", strides)
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.set("paddings", paddings)
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.set("blocks", blocks));
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size_t outputHeight =
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1 +
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(inputHeight + 2 * padding - block + stride - 1) / stride;
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size_t outputWidth =
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1 +
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(inputWidth + 2 * padding - block + stride - 1) / stride;
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TensorShape inputShape =
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TensorShape({batchSize, channels, inputHeight, inputWidth});
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TensorShape outputShape =
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TensorShape({batchSize,
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outputHeight * outputWidth,
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channels * block * block});
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, outputShape));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, inputShape),
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ADD_TO);
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// run Function
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test.run();
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}
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}
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}
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}
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}
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}
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}
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}
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} // namespace paddle
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