mirror of
https://github.com/zebrajr/pytorch.git
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0a393f6ef5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26755 Test Plan: Imported from OSS Differential Revision: D17627828 Pulled By: pbelevich fbshipit-source-id: f898a4d2c269b98eb5905291914caa25bca87ce0
91 lines
2.9 KiB
C++
91 lines
2.9 KiB
C++
#include <gtest/gtest.h>
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#include <torch/torch.h>
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#include <test/cpp/api/support.h>
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namespace F = torch::nn::functional;
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using namespace torch::nn;
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struct FunctionalTest : torch::test::SeedingFixture {};
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TEST_F(FunctionalTest, MaxPool1d) {
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auto x = torch::ones({1, 1, 5});
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auto y = F::max_pool1d(x, MaxPool1dOptions(3).stride(2));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1 ,2})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
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}
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TEST_F(FunctionalTest, MaxPool2d) {
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auto x = torch::ones({2, 5, 5});
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auto y = F::max_pool2d(x, MaxPool2dOptions(3).stride(2));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2 ,2})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 2, 2}));
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}
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TEST_F(FunctionalTest, MaxPool3d) {
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auto x = torch::ones({2, 5, 5, 5});
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auto y = F::max_pool3d(x, MaxPool3dOptions(3).stride(2));
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ASSERT_EQ(y.ndimension(), 4);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 2, 2, 2}));
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}
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TEST_F(FunctionalTest, AvgPool1d) {
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auto x = torch::ones({1, 1, 5});
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auto y = F::avg_pool1d(x, AvgPool1dOptions(3).stride(2));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 2})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
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}
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TEST_F(FunctionalTest, AvgPool2d) {
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auto x = torch::ones({2, 5, 5});
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auto y = F::avg_pool2d(x, AvgPool2dOptions(3).stride(2));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 2, 2}));
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}
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TEST_F(FunctionalTest, AvgPool3d) {
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auto x = torch::ones({2, 5, 5, 5});
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auto y = F::avg_pool3d(x, AvgPool3dOptions(3).stride(2));
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ASSERT_EQ(y.ndimension(), 4);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 2, 2, 2}));
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}
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TEST_F(FunctionalTest, CosineSimilarity) {
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auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::kFloat);
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auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::kFloat);
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auto output = F::cosine_similarity(input1, input2, CosineSimilarityOptions().dim(1));
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auto expected = torch::tensor({0.8078, 0.8721}, torch::kFloat);
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ASSERT_TRUE(output.allclose(expected, 1e-04));
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}
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TEST_F(FunctionalTest, PairwiseDistance) {
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auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::kFloat);
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auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::kFloat);
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auto output = F::pairwise_distance(input1, input2, PairwiseDistanceOptions(1));
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auto expected = torch::tensor({6, 6}, torch::kFloat);
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ASSERT_TRUE(output.allclose(expected));
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}
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TEST_F(FunctionalTest, AdaptiveMaxPool1d) {
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auto x = torch::ones({1, 1, 5});
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auto y = F::adaptive_max_pool1d(x, AdaptiveMaxPool1dOptions(3));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 3})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 3}));
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}
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