mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
7d277b0670
Summary: In accordance with https://github.com/pytorch/pytorch/issues/25883, I added the `MultiLabelMarginLoss` module and `multilabel_margin_loss` functional. Pull Request resolved: https://github.com/pytorch/pytorch/pull/27659 Differential Revision: D17931905 Pulled By: yf225 fbshipit-source-id: 3642f75c79843dda55ac38de9f6f970f3e237847
965 lines
33 KiB
C++
965 lines
33 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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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 =
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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, SoftMarginLossDefaultOptions) {
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auto input = torch::tensor({2., 4., 1., 3.}, torch::requires_grad());
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auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
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auto output =
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F::soft_margin_loss(input, target);
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auto expected = torch::tensor({1.3767317}, torch::kFloat);
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auto s = output.sum();
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s.backward();
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_EQ(input.sizes(), input.grad().sizes());
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}
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TEST_F(FunctionalTest, MultiLabelSoftMarginLossDefaultOptions) {
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auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::requires_grad());
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auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
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auto output =
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F::multilabel_soft_margin_loss(input, target);
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auto expected = torch::tensor({0.7608436}, torch::kFloat);
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auto s = output.sum();
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s.backward();
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_EQ(input.sizes(), input.grad().sizes());
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}
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TEST_F(FunctionalTest, SoftMarginLossNoReduction) {
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auto input = torch::tensor({2., 4., 1., 3.}, torch::requires_grad());
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auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
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auto output =
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F::soft_margin_loss(input, target, torch::Reduction::None);
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auto expected = torch::tensor({2.1269281, 0.01814993, 0.3132617, 3.0485873}, torch::kFloat);
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auto s = output.sum();
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s.backward();
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_EQ(input.sizes(), input.grad().sizes());
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}
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TEST_F(FunctionalTest, MultiLabelSoftMarginLossWeightedNoReduction) {
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auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::requires_grad());
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auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
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auto weight = torch::tensor({0.1, 0.6, 0.4, 0.8}, torch::kFloat);
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auto options = MultiLabelSoftMarginLossOptions().reduction(torch::Reduction::None).weight(weight);
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auto output =
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F::multilabel_soft_margin_loss(input, target, options);
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auto expected = torch::tensor({0.4876902, 0.3321295}, torch::kFloat);
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auto s = output.sum();
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s.backward();
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_EQ(input.sizes(), input.grad().sizes());
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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 =
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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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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(), std::vector<int64_t>({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, AffineGrid) {
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{
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// 2D affine.
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auto theta = torch::arange(1, 13, torch::kDouble)
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.view(std::vector<int64_t>({2, 2, 3}));
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auto size = std::vector<int64_t>({2, 3, 2, 2});
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auto align_corners = true;
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auto output = F::affine_grid(theta, size, !align_corners);
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auto expected = torch::tensor(
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{{{{1.50, 1.50}, {2.50, 5.50}}, {{3.50, 6.50}, {4.50, 10.50}}},
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{{{1.50, 1.50}, {8.50, 11.50}}, {{9.50, 12.50}, {16.50, 22.50}}}});
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auto output_aligned = F::affine_grid(theta, size, align_corners);
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auto expected_aligned = torch::tensor(
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{{{{0.0, -3.0}, {2.0, 5.0}}, {{4.0, 7.0}, {6.0, 15.0}}},
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{{{-6.0, -9.0}, {8.0, 11.0}}, {{10.0, 13.0}, {24.0, 33.0}}}});
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_TRUE(output_aligned.allclose(expected_aligned));
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}
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{
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// 3D affine.
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auto theta = torch::arange(1, 13, torch::kDouble)
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.view(std::vector<int64_t>({1, 3, 4}));
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auto size = std::vector<int64_t>({1, 1, 3, 2, 2});
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auto align_corners = true;
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auto output = F::affine_grid(theta, size, !align_corners);
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auto expected = torch::tensor(
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{{{{{0.5000, -2.1667, -4.8333}, {1.5000, 2.8333, 4.1667}},
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{{2.5000, 3.8333, 5.1667}, {3.5000, 8.8333, 14.1667}}},
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{{{2.5000, 2.5000, 2.5000}, {3.5000, 7.5000, 11.5000}},
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{{4.5000, 8.5000, 12.5000}, {5.5000, 13.5000, 21.5000}}},
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{{{4.5000, 7.1667, 9.8333}, {5.5000, 12.1667, 18.8333}},
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{{6.5000, 13.1667, 19.8333}, {7.5000, 18.1667, 28.8333}}}}});
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auto output_aligned = F::affine_grid(theta, size, align_corners);
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auto expected_aligned =
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torch::tensor({{{{{-2.0, -10.0, -18.0}, {0.0, 0.0, 0.0}},
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{{2.0, 2.0, 2.0}, {4.0, 12.0, 20.0}}},
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{{{1.0, -3.0, -7.0}, {3.0, 7.0, 11.0}},
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{{5.0, 9.0, 13.0}, {7.0, 19.0, 31.0}}},
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{{{4.0, 4.0, 4.0}, {6.0, 14.0, 22.0}},
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{{8.0, 16.0, 24.0}, {10.0, 26.0, 42.0}}}}});
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ASSERT_TRUE(output.allclose(expected, 1e-2));
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ASSERT_TRUE(output_aligned.allclose(expected_aligned));
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}
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{
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auto theta = torch::empty({1, 2, 3}, torch::kDouble);
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auto size = std::vector<int64_t>({1, 1, 2, 2});
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ASSERT_THROWS_WITH(
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F::affine_grid(torch::empty({2, 2, 3}), {-1, 1, 2, 2}),
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"Expected non-zero, positive output size. Got [-1, 1, 2, 2]");
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ASSERT_THROWS_WITH(
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F::affine_grid(torch::empty({2, 2, 3}, torch::kInt), size),
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"Expected theta to have floating point type, but got int");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta[0], size),
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"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
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"[1, 1, 2, 2]. Got [2, 3].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta.unsqueeze(0), size),
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"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
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"[1, 1, 2, 2]. Got [1, 1, 2, 3].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta.repeat({1, 2, 1}), size),
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"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
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"[1, 1, 2, 2]. Got [1, 4, 3].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta.repeat({1, 1, 2}), size),
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"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
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"[1, 1, 2, 2]. Got [1, 2, 6].");
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}
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{
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auto theta = torch::empty({1, 3, 4}, torch::kDouble);
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auto size = std::vector<int64_t>({1, 1, 2, 2, 3});
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ASSERT_THROWS_WITH(
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F::affine_grid(theta[0], size),
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"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
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"[1, 1, 2, 2, 3]. Got [3, 4].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta.unsqueeze(0), size),
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"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
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"[1, 1, 2, 2, 3]. Got [1, 1, 3, 4].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta.repeat({1, 2, 1}), size),
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"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
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"[1, 1, 2, 2, 3]. Got [1, 6, 4].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta.repeat({1, 1, 2}), size),
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"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
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"[1, 1, 2, 2, 3]. Got [1, 3, 8].");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta, {1, 1, 1, 2, 2, 3}),
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"affine_grid only supports 4D and 5D sizes, for 2D and 3D affine "
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"transforms, respectively. Got size [1, 1, 1, 2, 2, 3]");
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ASSERT_THROWS_WITH(
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F::affine_grid(theta, {1, 1}),
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"affine_grid only supports 4D and 5D sizes, for 2D and 3D affine "
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"transforms, respectively. Got size [1, 1]");
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}
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}
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TEST_F(FunctionalTest, MultiMarginLoss) {
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auto weight = torch::tensor({0.3, 0.3, 0.4}, torch::kFloat);
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auto input = torch::tensor({{0.2, 0.2, 0.6}, {0.1, 0.8, 0.1}, {0.9, 0.09, 0.01}}, torch::requires_grad());
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auto target = torch::tensor({2, 1, 0}, torch::kLong);
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auto output = F::multi_margin_loss(
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input, target, MultiMarginLossOptions().margin(2).weight(weight));
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auto expected = torch::tensor({0.305556}, torch::kFloat);
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ASSERT_TRUE(output.allclose(expected, 1e-04));
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}
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TEST_F(FunctionalTest, CosineEmbeddingLoss) {
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auto input1 = torch::tensor({{2, 3, 4}, {6, 2, 4}});
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auto input2 = torch::tensor({{2, 3, 5}, {9, 12, 0}});
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auto target = torch::tensor({1, -1});
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auto output = F::cosine_embedding_loss(
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input1, input2, target, CosineEmbeddingLossOptions().margin(0.5));
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auto expected = torch::tensor({0.1004}, torch::kFloat);
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ASSERT_TRUE(output.allclose(expected, 1e-4));
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}
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TEST_F(FunctionalTest, MultiLabelMarginLossDefaultOptions) {
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auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::requires_grad());
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auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
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auto output = F::multilabel_margin_loss(input, target);
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auto expected = torch::tensor({0.8500}, torch::kFloat);
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auto s = output.sum();
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s.backward();
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_EQ(input.sizes(), input.grad().sizes());
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}
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TEST_F(FunctionalTest, MultiLabelMarginLossNoReduction) {
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auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::requires_grad());
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auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
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auto output = F::multilabel_margin_loss(
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input, target, torch::Reduction::None);
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auto expected = torch::tensor({0.8500}, torch::kFloat);
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auto s = output.sum();
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s.backward();
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ASSERT_TRUE(output.allclose(expected));
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ASSERT_EQ(input.sizes(), input.grad().sizes());
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}
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TEST_F(FunctionalTest, TripletMarginLoss) {
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auto anchor = torch::tensor({{3., 3.}}, torch::kFloat);
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auto positive = torch::tensor({{2., 2.}}, torch::kFloat);
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auto negative = torch::tensor({{0., 0.}}, torch::kFloat);
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auto output = F::triplet_margin_loss(
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anchor, positive, negative, TripletMarginLossOptions().margin(1.0));
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auto expected = torch::tensor({0.}, torch::kFloat);
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ASSERT_TRUE(output.allclose(expected, 1e-04));
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}
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TEST_F(FunctionalTest, MaxUnpool1d) {
|
|
auto x = torch::tensor({{{2, 4, 5}}}, torch::requires_grad());
|
|
auto indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
|
|
auto y = F::max_unpool1d(x, indices, MaxUnpool1dOptions(3));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_TRUE(torch::allclose(
|
|
y, torch::tensor({{{0, 2, 0, 4, 5, 0, 0, 0, 0}}}, torch::kFloat)));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 9}));
|
|
|
|
x = torch::tensor({{{2, 4, 5}}}, torch::requires_grad());
|
|
indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
|
|
y = F::max_unpool1d(
|
|
x, indices, MaxUnpool1dOptions(3), std::vector<int64_t>({1, 1, 9}));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_TRUE(torch::allclose(
|
|
y, torch::tensor({{{0, 2, 0, 4, 5, 0, 0, 0, 0}}}, torch::kFloat)));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 9}));
|
|
|
|
x = torch::tensor({{{2, 4, 5}}}, torch::requires_grad());
|
|
indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
|
|
y = F::max_unpool1d(x, indices, MaxUnpool1dOptions(3).stride(2).padding(1));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_TRUE(
|
|
torch::allclose(y, torch::tensor({{{0, 2, 0, 4, 5}}}, torch::kFloat)));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 5}));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, MaxUnpool2d) {
|
|
auto indices = torch::tensor({
|
|
{{{ 6, 8, 9},
|
|
{16, 18, 19},
|
|
{21, 23, 24}}},
|
|
{{{ 6, 8, 9},
|
|
{16, 18, 19},
|
|
{21, 23, 24}}}}, torch::kLong);
|
|
auto x = torch::tensor({
|
|
{{{ 6, 8, 9},
|
|
{16, 18, 19},
|
|
{21, 23, 24}}},
|
|
{{{31, 33, 34},
|
|
{41, 43, 44},
|
|
{46, 48, 49}}}}, torch::requires_grad());
|
|
auto y = F::max_unpool2d(x, indices, MaxUnpool2dOptions(3).stride(2).padding(1));
|
|
|
|
ASSERT_EQ(y.dim(), 4);
|
|
ASSERT_TRUE(torch::allclose(y, torch::tensor(
|
|
{{{{ 0, 0, 0, 0, 0},
|
|
{ 0, 6, 0, 8, 9},
|
|
{ 0, 0, 0, 0, 0},
|
|
{ 0, 16, 0, 18, 19},
|
|
{ 0, 21, 0, 23, 24}}},
|
|
{{{ 0, 0, 0, 0, 0},
|
|
{ 0, 31, 0, 33, 34},
|
|
{ 0, 0, 0, 0, 0},
|
|
{ 0, 41, 0, 43, 44},
|
|
{ 0, 46, 0, 48, 49}}}} , torch::kFloat)));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 1, 5, 5}));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, ELU) {
|
|
const auto size = 3;
|
|
for (const auto inplace : {false, true}) {
|
|
for (const auto alpha : {0.0, 0.42, 1.0, 4.2, 42.42}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = torch::max(torch::zeros_like(x), x) +
|
|
torch::min(torch::zeros_like(x), alpha * (torch::exp(x) - 1.0));
|
|
auto y = F::elu(x, ELUOptions().alpha(alpha).inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, SELU) {
|
|
{
|
|
const double scale = 1.0507009873554804934193349852946;
|
|
const double alpha = 1.6732632423543772848170429916717;
|
|
for (const auto inplace : {false, true}) {
|
|
auto input = torch::randn({5, 5});
|
|
auto expected = scale *
|
|
(torch::max(torch::zeros_like(input), input) +
|
|
torch::min(
|
|
torch::zeros_like(input), alpha * (torch::exp(input) - 1)));
|
|
auto output = F::selu(input, inplace);
|
|
|
|
ASSERT_TRUE(output.allclose(expected));
|
|
if (inplace) {
|
|
ASSERT_TRUE(input.allclose(expected));
|
|
}
|
|
}
|
|
}
|
|
{
|
|
auto input = torch::arange(0, 9, torch::kDouble).view({3, 3});
|
|
auto output = F::selu(input);
|
|
auto expected = F::selu(input, false);
|
|
ASSERT_TRUE(output.allclose(expected));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Hardshrink) {
|
|
const auto size = 3;
|
|
for (const auto lambda : {-4.2, -1.0, -0.42, 0.0, 0.42, 1.0, 4.2, 42.42}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size}).set_requires_grad(true);
|
|
auto y = F::hardshrink(x, HardshrinkOptions().lambda(lambda));
|
|
torch::Tensor s = y.sum();
|
|
|
|
s.backward();
|
|
ASSERT_EQ(s.ndimension(), 0);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
auto y_exp = (x.abs() > lambda) * x;
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, OneHot) {
|
|
{ // Test #1
|
|
auto x = torch::arange(0, 5, torch::kLong);
|
|
auto y = F::one_hot(x % 3);
|
|
auto expected = torch::tensor(
|
|
{{1, 0, 0}, {0, 1, 0}, {0, 0, 1}, {1, 0, 0}, {0, 1, 0}}, torch::kLong);
|
|
|
|
ASSERT_EQ(y.ndimension(), 2);
|
|
ASSERT_TRUE(torch::allclose(y, expected));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({5, 3}));
|
|
}
|
|
|
|
{ // Test #2
|
|
auto x = torch::arange(0, 5, torch::kLong);
|
|
auto y = F::one_hot(x % 3, 5);
|
|
auto expected = torch::tensor(
|
|
{{1, 0, 0, 0, 0},
|
|
{0, 1, 0, 0, 0},
|
|
{0, 0, 1, 0, 0},
|
|
{1, 0, 0, 0, 0},
|
|
{0, 1, 0, 0, 0}},
|
|
torch::kLong);
|
|
|
|
ASSERT_EQ(y.ndimension(), 2);
|
|
ASSERT_TRUE(torch::allclose(y, expected));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({5, 5}));
|
|
}
|
|
|
|
{ // Test #3
|
|
auto x = torch::arange(0, 6, torch::kLong);
|
|
auto y = F::one_hot(x.view(std::vector<int64_t>({3, 2})) % 3);
|
|
auto expected = torch::tensor(
|
|
{{{1, 0, 0}, {0, 1, 0}},
|
|
{{0, 0, 1}, {1, 0, 0}},
|
|
{{0, 1, 0}, {0, 0, 1}}},
|
|
torch::kLong);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_TRUE(torch::allclose(y, expected));
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({3, 2, 3}));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Hardtanh) {
|
|
const auto size = 3;
|
|
for (const auto min_val : {-4.2, -1.0, -0.42, 0.0}) {
|
|
for (const auto max_val : {0.0, 0.42, 1.0, 4.2}) {
|
|
for (const auto inplace : {false, true}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x < min_val) * min_val +
|
|
((x >= min_val) * (x <= max_val)) * x +
|
|
(x > max_val) * max_val;
|
|
auto y = F::hardtanh(x,HardtanhOptions().min_val(min_val)
|
|
.max_val(max_val).inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, LeakyReLU) {
|
|
const auto size = 3;
|
|
for (const auto negative_slope : {0.0, 0.42, 1.0}) {
|
|
for (const auto inplace : {false, true}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x < 0) * x * negative_slope + (x >= 0) * x;
|
|
auto y = F::leaky_relu(x, LeakyReLUOptions()
|
|
.negative_slope(negative_slope).inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, LogSigmoid) {
|
|
const auto size = 3;
|
|
LogSigmoid model;
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y = F::logsigmoid(x);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
auto y_exp = torch::log(torch::ones_like(x)/(torch::ones_like(x) + torch::exp(torch::neg(x))));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Softmax) {
|
|
auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
|
|
auto output = F::softmax(input, /*dim=*/1);
|
|
auto sum = torch::sum(torch::exp(input), 1);
|
|
|
|
for (int i = 0; i < 2; i++) {
|
|
auto expected = torch::exp(input[i]) / sum[i];
|
|
ASSERT_TRUE(torch::allclose(output[i], expected));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Softmin) {
|
|
auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
|
|
auto output = F::softmin(input, /*dim=*/1);
|
|
auto sum = torch::sum(torch::exp(-input), 1);
|
|
|
|
for (int i = 0; i < 2; i++) {
|
|
auto expected = torch::exp(-input[i]) / sum[i];
|
|
ASSERT_TRUE(torch::allclose(output[i], expected));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, LogSoftmax) {
|
|
auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
|
|
auto output = F::log_softmax(input, /*dim=*/1);
|
|
auto sum = torch::sum(torch::exp(input), 1);
|
|
|
|
for (int i = 0; i < 2; i++) {
|
|
auto expected = torch::log(torch::exp(input[i]) / sum[i]);
|
|
ASSERT_TRUE(torch::allclose(output[i], expected));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, PReLU) {
|
|
const auto x = torch::rand({42, 24}) * 200 - 100;
|
|
const auto w = torch::rand(24) * 200 - 100;
|
|
const auto y = F::prelu(x, w);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({42, 24}));
|
|
const auto y_exp = (x < 0) * w * x + (x >= 0) * x;
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Bilinear) {
|
|
auto input1 = torch::tensor({{1, 2, 3}, {7, 6, 5}});
|
|
auto input2 = torch::tensor({{7, 4}, {8 ,9}});
|
|
auto weight = torch::tensor({{{2, 3}, {9, 7}, {8, 6}}});
|
|
auto bias = torch::tensor({1});
|
|
|
|
auto y_with_bias = F::bilinear(input1, input2, weight, bias);
|
|
ASSERT_EQ(y_with_bias.ndimension(), 2);
|
|
ASSERT_EQ(y_with_bias.sizes(), torch::IntArrayRef({2, 1}));
|
|
auto y_with_bias_exp = torch::tensor({{449}, {1702}}).reshape({2, 1});
|
|
ASSERT_TRUE(torch::allclose(y_with_bias, y_with_bias_exp, 1e-4, 1e-7));
|
|
|
|
auto y_no_bias = F::bilinear(input1, input2, weight);
|
|
ASSERT_EQ(y_no_bias.ndimension(), 2);
|
|
ASSERT_EQ(y_no_bias.sizes(), torch::IntArrayRef({2, 1}));
|
|
auto y_no_bias_exp = torch::tensor({{448, 1701}}).reshape({2, 1});
|
|
ASSERT_TRUE(torch::allclose(y_no_bias, y_no_bias_exp, 1e-4, 1e-7));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Normalize) {
|
|
const auto expected = torch::tensor(
|
|
{{{0.00000000, 0.10000000, 0.2000, 0.30000000, 0.40000000},
|
|
{0.14285715, 0.17142858, 0.2000, 0.22857143, 0.25714287}}}, torch::requires_grad().dtype(torch::kFloat));
|
|
{ // Test #1
|
|
auto input = torch::tensor({{{0, 1, 2, 3, 4}, {5, 6, 7, 8, 9}}}, torch::dtype(torch::kFloat).requires_grad(true));
|
|
auto norm = F::normalize(input, NormalizeOptions().p(1).dim(-1));
|
|
|
|
// reduce to scalar to call .backward()
|
|
torch::Tensor s = norm.sum();
|
|
s.backward();
|
|
|
|
ASSERT_EQ(s.ndimension(), 0);
|
|
ASSERT_EQ(input.grad().numel(), 10);
|
|
ASSERT_TRUE(torch::allclose(norm, expected));
|
|
}
|
|
|
|
{ // Test #2 Check variations of optional arguments
|
|
auto input = torch::tensor({{{0, 1, 2, 3, 4}, {5, 6, 7, 8, 9}}}, torch::dtype(torch::kFloat));
|
|
auto output = torch::randn({1,2,5}, torch::dtype(torch::kFloat));
|
|
// non-null output argument
|
|
F::normalize(input, NormalizeOptions().p(1).dim(-1), output);
|
|
// default options
|
|
F::normalize(input);
|
|
|
|
ASSERT_TRUE(torch::allclose(output, expected));
|
|
}
|
|
|
|
{ // Test #3 Base case of scalar tensor
|
|
auto input = torch::randn({}, torch::requires_grad());
|
|
torch::Tensor norm = F::normalize(input, NormalizeOptions().p(1).dim(-1));
|
|
norm.backward();
|
|
|
|
ASSERT_EQ(input.grad().numel(), 1);
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, ReLU) {
|
|
const auto size = 3;
|
|
for (const auto inplace : {false, true}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x < 0) * 0 + (x >= 0) * x;
|
|
auto y = F::relu(x, ReLUOptions().inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
|
|
y = F::relu(x, /*inplace=*/inplace);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, ReLUDefaultOptions) {
|
|
const auto size = 3;
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x < 0) * 0 + (x >= 0) * x;
|
|
auto y = F::relu(x);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, ReLU6) {
|
|
const auto size = 3;
|
|
for (const auto inplace : {false, true}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x < 0) * 0 + ((x >= 0) * (x <= 6)) * x + (x > 6) * 6;
|
|
auto y = F::relu6(x, ReLU6Options().inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
|
|
y = F::relu6(x, /*inplace=*/inplace);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, ReLU6DefaultOptions) {
|
|
const auto size = 3;
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x < 0) * 0 + ((x >= 0) * (x <= 6)) * x + (x > 6) * 6;
|
|
auto y = F::relu6(x);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, RReLU) {
|
|
const auto size = 3;
|
|
for (const auto lower : {0.01, 0.1, 0.2}) {
|
|
for (const auto upper : {0.3, 0.4, 0.5}) {
|
|
for (const auto inplace : {false, true}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto x_copy = x.clone();
|
|
auto y = F::rrelu(x, RReLUOptions().lower(lower)
|
|
.upper(upper).inplace(inplace));
|
|
auto z = ((x_copy >= 0) * (x_copy == y) +
|
|
(x_copy < 0) * (y >= x_copy * upper) * (y <= lower * x_copy)) * 1.0;
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
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|
ASSERT_TRUE(torch::allclose(z, torch::ones_like(z)));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y));
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|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, RReLUDefaultOptions) {
|
|
const auto size = 3;
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|
const auto lower = 1.0 / 8.0;
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|
const auto upper = 1.0 / 3.0;
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|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
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|
x.resize_({size, size, size});
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|
auto x_copy = x.clone();
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|
auto y = F::rrelu(x);
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|
auto z = ((x_copy >= 0) * (x_copy == y) +
|
|
(x_copy < 0) * (y >= x_copy * upper) * (y <= lower * x_copy)) * 1.0;
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
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|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
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|
ASSERT_TRUE(torch::allclose(z, torch::ones_like(z)));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, CELU) {
|
|
const auto size = 3;
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|
for (const auto inplace : {false, true}) {
|
|
for (const auto alpha : {0.42, 1.0, 4.2, 42.42}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = torch::max(torch::zeros_like(x), x) +
|
|
torch::min(torch::zeros_like(x), alpha * (torch::exp(x / alpha) - 1.0));
|
|
auto y = F::celu(x, CELUOptions().alpha(alpha).inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, CELUDefaultOptions) {
|
|
const auto size = 3;
|
|
const auto alpha = 1.0;
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = torch::max(torch::zeros_like(x), x) +
|
|
torch::min(torch::zeros_like(x), alpha * (torch::exp(x / alpha) - 1.0));
|
|
auto y = F::celu(x);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Softplus) {
|
|
const auto size = 3;
|
|
for (const auto beta : {0.5, 1.0, 2.0}) {
|
|
for (const auto threshold : {1.0, 3.0, 5.0}) {
|
|
auto x = torch::linspace(-3.0, 3.0, 61);
|
|
x.resize_({size, size, size});
|
|
auto y_exp =
|
|
(x <= threshold) * torch::log(1 + torch::exp(x * beta)) / beta +
|
|
(x > threshold) * x;
|
|
auto y = F::softplus(x,
|
|
SoftplusOptions().beta(beta).threshold(threshold));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, SoftplusDefaultOptions) {
|
|
const auto size = 3;
|
|
const auto beta = 1.0;
|
|
const auto threshold = 20.0;
|
|
auto x = torch::linspace(-3.0, 3.0, 61);
|
|
x.resize_({size, size, size});
|
|
auto y_exp =
|
|
(x <= threshold) * torch::log(1 + torch::exp(x * beta)) / beta +
|
|
(x > threshold) * x;
|
|
auto y = F::softplus(x);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Unfold) {
|
|
auto input = torch::randn({2, 2, 4, 4}, torch::requires_grad());
|
|
auto output = F::unfold(input, UnfoldOptions({2, 4}).padding(1).stride(2));
|
|
auto expected_sizes = std::vector<int64_t>({2, 16, 6});
|
|
|
|
ASSERT_EQ(output.sizes(), expected_sizes);
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Softshrink) {
|
|
const auto size = 3;
|
|
for (const auto lambda : {0.0, 0.42, 1.0, 4.2, 42.42}) {
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size}).set_requires_grad(true);
|
|
auto y = F::softshrink(x, /*lambda=*/lambda);
|
|
torch::Tensor s = y.sum();
|
|
|
|
s.backward();
|
|
ASSERT_EQ(s.ndimension(), 0);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
auto y_exp = (x < -lambda) * (x + lambda) + (x > lambda) * (x - lambda);
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
}
|
|
|
|
TEST_F(FunctionalTest, SoftshrinkDefaultOptions) {
|
|
const auto size = 3;
|
|
const auto lambda = 0.5;
|
|
auto x = torch::linspace(-10.0, 10.0, size * size * size);
|
|
x.resize_({size, size, size}).set_requires_grad(true);
|
|
auto y = F::softshrink(x);
|
|
torch::Tensor s = y.sum();
|
|
|
|
s.backward();
|
|
ASSERT_EQ(s.ndimension(), 0);
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
auto y_exp = (x < -lambda) * (x + lambda) + (x > lambda) * (x - lambda);
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Softsign) {
|
|
auto x = torch::randn(100) * 10;
|
|
auto y_exp = x / (1 + x.abs());
|
|
auto y = F::softsign(x);
|
|
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Tanhshrink) {
|
|
auto x = torch::randn(100) * 10;
|
|
auto y_exp = x - x.tanh();
|
|
auto y = F::tanhshrink(x);
|
|
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
}
|
|
|
|
TEST_F(FunctionalTest, Threshold) {
|
|
const auto size = 3;
|
|
for (const auto threshold : {0.5, 1.0, 2.0}) {
|
|
for (const auto value : {0.5, 1.0, 2.0}) {
|
|
for (const auto inplace : {false, true}) {
|
|
auto x = torch::linspace(-3.0, 3.0, 61);
|
|
x.resize_({size, size, size});
|
|
auto y_exp = (x <= threshold) * value + (x > threshold) * x;
|
|
auto y = F::threshold(x,
|
|
ThresholdOptions(threshold, value).inplace(inplace));
|
|
|
|
ASSERT_EQ(y.ndimension(), 3);
|
|
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
|
|
ASSERT_TRUE(torch::allclose(y, y_exp));
|
|
if (inplace) {
|
|
ASSERT_TRUE(torch::allclose(x, y_exp));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|