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107 lines
3.4 KiB
107 lines
3.4 KiB
/*
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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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*/
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#include "paddle/framework/lod_tensor.h"
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <memory>
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namespace paddle {
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namespace framework {
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class LoDTensorTester : public ::testing::Test {
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public:
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virtual void SetUp() override {
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// tensor's batch_size: 30
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// 3 levels
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// 0 10 20
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// 0 5 10 15 20
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// 0 2 5 7 10 12 15 20
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LoD lod;
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lod.push_back(std::vector<size_t>{0, 10, 20});
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lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
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lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
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ASSERT_EQ(lod.size(), 3UL);
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lod_tensor_.Resize({20 /*batch size*/, 128 /*dim*/});
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// malloc memory
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lod_tensor_.mutable_data<float>(place);
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lod_tensor_.set_lod(lod);
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}
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protected:
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platform::CPUPlace place;
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LoDTensor lod_tensor_;
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};
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TEST_F(LoDTensorTester, NumLevels) { ASSERT_EQ(lod_tensor_.NumLevels(), 3UL); }
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TEST_F(LoDTensorTester, NumElements) {
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ASSERT_EQ(lod_tensor_.NumElements(0), 2UL);
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ASSERT_EQ(lod_tensor_.NumElements(1), 4UL);
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ASSERT_EQ(lod_tensor_.NumElements(2), 8UL);
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}
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TEST_F(LoDTensorTester, NumElements2) {
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ASSERT_EQ(lod_tensor_.NumElements(0, 0), 2UL);
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ASSERT_EQ(lod_tensor_.NumElements(0, 1), 2UL);
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ASSERT_EQ(lod_tensor_.NumElements(1, 1), 2UL);
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}
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TEST_F(LoDTensorTester, ShrinkLevels) {
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// slice 1 level
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for (size_t level = 0; level < 3UL; ++level) {
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LoDTensor new_lod_tensor = lod_tensor_;
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new_lod_tensor.ShrinkLevels(level, level + 1);
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ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
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ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor_.NumElements(level));
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ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
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}
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// shrink 2 level
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for (size_t level = 0; level < 2UL; ++level) {
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LoDTensor new_lod_tensor = lod_tensor_;
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new_lod_tensor.ShrinkLevels(level, level + 2);
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ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
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ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor_.NumElements(level));
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ASSERT_EQ(new_lod_tensor.NumElements(1),
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lod_tensor_.NumElements(level + 1));
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ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
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}
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}
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TEST_F(LoDTensorTester, ShrinkInLevel) {
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size_t level = 0;
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LoDTensor new_lod_tensor = lod_tensor_;
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new_lod_tensor.ShrinkInLevel(level, 0, 2);
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EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
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EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
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EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
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EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
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ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
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level = 1;
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new_lod_tensor = lod_tensor_;
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new_lod_tensor.ShrinkInLevel(level, 0, 2);
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ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
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ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
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ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
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ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
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}
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} // namespace framework
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} // namespace paddle
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