mirror of
https://github.com/zebrajr/pytorch.git
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5cac738713
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26915 Test Plan: Imported from OSS Differential Revision: D17627826 Pulled By: pbelevich fbshipit-source-id: 04a5a7e7d19b1610cafaaa0bd329d4d228ab4be5
219 lines
7.2 KiB
C++
219 lines
7.2 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, PDist) {
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{
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auto input = torch::tensor({{-1.0, -5.0, -1.0}, {2.0, 4.0, 6.0}});
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auto output = F::pdist(input);
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auto expected = torch::tensor({11.7898});
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ASSERT_TRUE(output.allclose(expected));
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}
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{
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auto input = torch::tensor({{1.0, -1.0}, {1.0, 3.0}, {3.0, 3.0}});
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auto output = F::pdist(input, 1.5);
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auto expected = torch::tensor({4.0, 4.8945, 2.0});
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ASSERT_TRUE(output.allclose(expected));
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}
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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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TEST_F(FunctionalTest, AdaptiveMaxPool2d) {
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auto x = torch::ones({2, 5, 5});
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auto y = F::adaptive_max_pool2d(x, AdaptiveMaxPool2dOptions(3));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 3, 3}));
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}
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TEST_F(FunctionalTest, AdaptiveMaxPool3d) {
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auto x = torch::ones({2, 5, 5, 5});
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auto y = F::adaptive_max_pool3d(x, AdaptiveMaxPool3dOptions(3));
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ASSERT_EQ(y.ndimension(), 4);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3, 3})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 3, 3, 3}));
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}
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TEST_F(FunctionalTest, AdaptiveAvgPool1d) {
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auto x = torch::ones({1, 1, 5});
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auto y = F::adaptive_avg_pool1d(x, AdaptiveAvgPool1dOptions(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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TEST_F(FunctionalTest, AdaptiveAvgPool2d) {
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auto x = torch::ones({2, 5, 5});
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auto y = F::adaptive_avg_pool2d(x, AdaptiveAvgPool2dOptions(3));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 3, 3}));
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}
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TEST_F(FunctionalTest, AdaptiveAvgPool3d) {
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auto x = torch::ones({2, 5, 5, 5});
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auto y = F::adaptive_avg_pool3d(x, AdaptiveAvgPool3dOptions(3));
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ASSERT_EQ(y.ndimension(), 4);
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ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3, 3})));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 3, 3, 3}));
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}
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TEST_F(FunctionalTest, HingeEmbeddingLoss) {
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auto input = torch::tensor({{2, 22, 4}, {20, 10, 0}}, torch::kFloat);
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auto target = torch::tensor({{2, 6, 4}, {1, 10, 0}}, torch::kFloat);
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auto output = F::hinge_embedding_loss(
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input, target, HingeEmbeddingLossOptions().margin(2));
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auto expected = torch::tensor({10}, torch::kFloat);
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ASSERT_TRUE(output.allclose(expected));
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}
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TEST_F(FunctionalTest, MaxUnpool1d) {
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auto x = torch::tensor({{{2, 4, 5}}}, torch::requires_grad());
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auto indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
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auto y = F::max_unpool1d(x, indices, MaxUnpool1dOptions(3));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::tensor({{{0, 2, 0, 4, 5, 0, 0, 0, 0}}}, torch::kFloat)));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 9}));
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x = torch::tensor({{{2, 4, 5}}}, torch::requires_grad());
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indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
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y = F::max_unpool1d(x, indices, MaxUnpool1dOptions(3), c10::IntArrayRef({1, 1, 9}));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::tensor({{{0, 2, 0, 4, 5, 0, 0, 0, 0}}}, torch::kFloat)));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 9}));
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x = torch::tensor({{{2, 4, 5}}}, torch::requires_grad());
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indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
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y = F::max_unpool1d(x, indices, MaxUnpool1dOptions(3).stride(2).padding(1));
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_TRUE(torch::allclose(y, torch::tensor({{{0, 2, 0, 4, 5}}}, torch::kFloat)));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 5}));
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}
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TEST_F(FunctionalTest, MaxUnpool2d) {
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auto indices = torch::tensor({
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{{{ 6, 8, 9},
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{16, 18, 19},
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{21, 23, 24}}},
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{{{ 6, 8, 9},
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{16, 18, 19},
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{21, 23, 24}}}}, torch::kLong);
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auto x = torch::tensor({
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{{{ 6, 8, 9},
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{16, 18, 19},
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{21, 23, 24}}},
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{{{31, 33, 34},
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{41, 43, 44},
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{46, 48, 49}}}}, torch::requires_grad());
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auto y = F::max_unpool2d(x, indices, MaxUnpool2dOptions(3).stride(2).padding(1));
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ASSERT_EQ(y.dim(), 4);
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ASSERT_TRUE(torch::allclose(y, torch::tensor(
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{{{{ 0, 0, 0, 0, 0},
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{ 0, 6, 0, 8, 9},
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{ 0, 0, 0, 0, 0},
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{ 0, 16, 0, 18, 19},
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{ 0, 21, 0, 23, 24}}},
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{{{ 0, 0, 0, 0, 0},
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{ 0, 31, 0, 33, 34},
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{ 0, 0, 0, 0, 0},
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{ 0, 41, 0, 43, 44},
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{ 0, 46, 0, 48, 49}}}} , torch::kFloat)));
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({2, 1, 5, 5}));
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}
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