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81 lines
3.0 KiB
81 lines
3.0 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(CrossMapNormal, real) {
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for (size_t numSamples : {5, 32}) {
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for (size_t channels : {1, 5, 32}) {
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for (size_t imgSizeH : {5, 33, 100}) {
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for (size_t imgSizeW : {5, 32, 96}) {
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for (size_t size : {1, 2, 3, 5, 7}) {
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VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
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<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
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<< " size=" << size;
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// init Test object
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FunctionCompare test("CrossMapNormal",
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FuncConfig()
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.set("size", size)
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.set("scale", (real)1.5)
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.set("pow", (real)0.5));
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// prepare input arguments
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TensorShape shape{numSamples, channels, imgSizeH, imgSizeW};
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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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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TEST(CrossMapNormalGrad, real) {
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for (size_t numSamples : {5, 32}) {
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for (size_t channels : {1, 5, 32}) {
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for (size_t imgSizeH : {5, 33, 100}) {
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for (size_t imgSizeW : {5, 32, 96}) {
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for (size_t size : {1, 2, 3, 5, 7}) {
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VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
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<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW
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<< " size=" << size;
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FunctionCompare test("CrossMapNormalGrad",
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FuncConfig()
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.set("size", size)
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.set("scale", (real)1.5)
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.set("pow", (real)0.5));
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TensorShape shape{numSamples, channels, imgSizeH, imgSizeW};
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape));
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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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} // namespace paddle
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