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functional.cpp
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#include <gtest/gtest.h>
#include <torch/torch.h>
#include <test/cpp/api/support.h>
namespace F = torch::nn::functional;
using namespace torch::nn;
struct FunctionalTest : torch::test::SeedingFixture {};
TEST_F(FunctionalTest, Conv1d) {
auto x = torch::arange(30, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 5});
auto weight = torch::arange(18, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 3});
auto y = F::conv1d(x, weight, F::Conv1dFuncOptions().stride(1));
auto expected = torch::tensor({{{ 312., 348., 384.},
{ 798., 915., 1032.}},
{{ 852., 888., 924.},
{2553., 2670., 2787.}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
auto y_no_options = F::conv1d(x, weight);
ASSERT_TRUE(torch::allclose(y_no_options, expected));
}
TEST_F(FunctionalTest, Conv2dEven) {
auto x = torch::arange(75, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 5});
auto weight = torch::arange(54, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 3, 3});
auto y = F::conv2d(x, weight, F::Conv2dFuncOptions().stride(1));
auto expected = torch::tensor({{{{15219., 15570., 15921.},
{16974., 17325., 17676.},
{18729., 19080., 19431.}},
{{37818., 38898., 39978.},
{43218., 44298., 45378.},
{48618., 49698., 50778.}}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
auto y_no_options = F::conv2d(x, weight);
ASSERT_TRUE(torch::allclose(y_no_options, expected));
}
TEST_F(FunctionalTest, Conv2dUneven) {
auto x = torch::arange(60, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 4});
auto weight = torch::arange(36, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 3, 2});
auto y = F::conv2d(x, weight, F::Conv2dFuncOptions().stride(1));
auto expected = torch::tensor({{{{ 5289., 5442., 5595.},
{ 5901., 6054., 6207.},
{ 6513., 6666., 6819.}},
{{13227., 13704., 14181.},
{15135., 15612., 16089.},
{17043., 17520., 17997.}}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
auto y_no_options = F::conv2d(x, weight);
ASSERT_TRUE(torch::allclose(y_no_options, expected));
}
TEST_F(FunctionalTest, Conv3d) {
auto x = torch::arange(375, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 5, 5});
auto weight = torch::arange(162, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 3, 3, 3});
auto y = F::conv3d(x, weight, F::Conv3dFuncOptions().stride(1));
auto expected = torch::tensor({{{{{ 700704., 703944., 707184.},
{ 716904., 720144., 723384.},
{ 733104., 736344., 739584.}},
{{ 781704., 784944., 788184.},
{ 797904., 801144., 804384.},
{ 814104., 817344., 820584.}},
{{ 862704., 865944., 869184.},
{ 878904., 882144., 885384.},
{ 895104., 898344., 901584.}}},
{{{1724220., 1734021., 1743822.},
{1773225., 1783026., 1792827.},
{1822230., 1832031., 1841832.}},
{{1969245., 1979046., 1988847.},
{2018250., 2028051., 2037852.},
{2067255., 2077056., 2086857.}},
{{2214270., 2224071., 2233872.},
{2263275., 2273076., 2282877.},
{2312280., 2322081., 2331882.}}}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
auto y_no_options = F::conv3d(x, weight);
ASSERT_TRUE(torch::allclose(y_no_options, expected));
}
TEST_F(FunctionalTest, MaxPool1d) {
auto x = torch::ones({1, 1, 5});
auto y = F::max_pool1d(x, F::MaxPool1dFuncOptions(3).stride(2));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
}
TEST_F(FunctionalTest, MaxPool2d) {
auto x = torch::ones({2, 5, 5});
auto y = F::max_pool2d(x, F::MaxPool2dFuncOptions(3).stride(2));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(FunctionalTest, MaxPool2dBackward) {
auto input = torch::rand({1, 2, 4, 4}, torch::dtype(torch::kFloat).requires_grad(true));
auto output = F::max_pool2d(input, F::MaxPool2dFuncOptions(2));
auto s = output.sum();
s.backward();
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, MaxPool3d) {
auto x = torch::ones({2, 5, 5, 5});
auto y = F::max_pool3d(x, F::MaxPool3dFuncOptions(3).stride(2));
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(FunctionalTest, AvgPool1d) {
auto x = torch::ones({1, 1, 5});
auto y = F::avg_pool1d(x, F::AvgPool1dFuncOptions(3).stride(2));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
}
TEST_F(FunctionalTest, AvgPool2d) {
auto x = torch::ones({2, 5, 5});
auto y = F::avg_pool2d(x, F::AvgPool2dFuncOptions(3).stride(2));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(FunctionalTest, AvgPool3d) {
auto x = torch::ones({2, 5, 5, 5});
auto y = F::avg_pool3d(x, F::AvgPool3dFuncOptions(3).stride(2));
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(FunctionalTest, FractionalMaxPool2d) {
auto x = torch::ones({2, 5, 5});
auto y = F::fractional_max_pool2d(x, F::FractionalMaxPool2dFuncOptions(3).output_size(2));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
auto y_with_indices = F::fractional_max_pool2d_with_indices(x, F::FractionalMaxPool2dFuncOptions(3).output_size(2));
ASSERT_TRUE(torch::equal(y, std::get<0>(y_with_indices)));
ASSERT_TRUE(torch::allclose(
std::get<1>(y_with_indices),
torch::tensor({{{ 0, 2},
{10, 12}},
{{ 0, 2},
{10, 12}}})));
ASSERT_EQ(std::get<1>(y_with_indices).sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(FunctionalTest, FractionalMaxPool3d) {
auto x = torch::ones({2, 5, 5, 5});
auto y = F::fractional_max_pool3d(x, F::FractionalMaxPool3dFuncOptions(3).output_size(2));
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
auto y_with_indices = F::fractional_max_pool3d_with_indices(x, F::FractionalMaxPool3dFuncOptions(3).output_size(2));
ASSERT_TRUE(torch::equal(y, std::get<0>(y_with_indices)));
ASSERT_TRUE(torch::allclose(
std::get<1>(y_with_indices),
torch::tensor({{{{ 0, 2},
{10, 12}},
{{50, 52},
{60, 62}}},
{{{ 0, 2},
{10, 12}},
{{50, 52},
{60, 62}}}})));
ASSERT_EQ(std::get<1>(y_with_indices).sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(FunctionalTest, LPPool1d) {
int norm_type = 2;
int stride = 2;
int kernel_size = 3;
auto x = torch::ones({1, 1, 5});
auto y = F::lp_pool1d(x, F::LPPool1dFuncOptions(norm_type, kernel_size).stride(stride));
auto expected = (torch::pow(torch::tensor({{{1, 1}}}, torch::kFloat), norm_type) * kernel_size).pow(1. / norm_type);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, expected));
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
}
TEST_F(FunctionalTest, LPPool2d) {
int norm_type = 2;
int stride = 2;
std::vector<int64_t> kernel_size({2, 3});
auto x = torch::ones({1, 2, 5});
auto y = F::lp_pool2d(x, F::LPPool2dFuncOptions(norm_type, kernel_size).stride(stride));
auto expected = (torch::pow(torch::tensor({{{1, 1}}}, torch::kFloat), norm_type) * (kernel_size[0] * kernel_size[1])).pow(1. / norm_type);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, expected));
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
}
TEST_F(FunctionalTest, CosineSimilarity) {
auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::kFloat);
auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::kFloat);
auto output =
F::cosine_similarity(input1, input2, F::CosineSimilarityFuncOptions().dim(1));
auto expected = torch::tensor({0.8078, 0.8721}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
TEST_F(FunctionalTest, SmoothL1LossDefaultOptions) {
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output =
F::smooth_l1_loss(input, target);
auto expected = torch::tensor(0.0233335, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, SmoothL1LossBeta) {
auto input = torch::tensor({0.1, 1.5, 10.0}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output =
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,bugprone-argument-comment)
F::smooth_l1_loss(input, target, /*reduction=*/torch::kMean, /*beta=*/0.5);
auto expected = torch::tensor(1.67, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, SmoothL1LossNoReduction) {
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output =
// NOLINTNEXTLINE(bugprone-argument-comment)
F::smooth_l1_loss(input, target, /*reduction=*/torch::kNone);
auto expected = torch::tensor({0.005, 0.02, 0.045}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, HuberLossDefaultOptions) {
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output =
F::huber_loss(input, target);
auto expected = torch::tensor(0.0233335, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, HuberLossDelta) {
auto input = torch::tensor({0.1, 1.5, 10.0}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto options = F::HuberLossFuncOptions().reduction(torch::kMean).delta(0.5);
auto output = F::huber_loss(input, target, options);
auto expected = torch::tensor(1.67 * 0.5, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, HuberLossNoReduction) {
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto options = F::HuberLossFuncOptions().reduction(torch::kNone);
auto output = F::huber_loss(input, target, options);
auto expected = torch::tensor({0.005, 0.02, 0.045}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(input.sizes() == input.grad().sizes());
}
TEST_F(FunctionalTest, SoftMarginLossDefaultOptions) {
auto input = torch::tensor({2., 4., 1., 3.}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
auto output =
F::soft_margin_loss(input, target);
auto expected = torch::tensor({1.3767317}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, MultiLabelSoftMarginLossDefaultOptions) {
auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
auto output =
F::multilabel_soft_margin_loss(input, target);
auto expected = torch::tensor({0.7608436}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, SoftMarginLossNoReduction) {
auto input = torch::tensor({2., 4., 1., 3.}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
auto output =
F::soft_margin_loss(input, target, torch::kNone);
auto expected = torch::tensor({2.1269281, 0.01814993, 0.3132617, 3.0485873}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, MultiLabelSoftMarginLossWeightedNoReduction) {
auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
auto weight = torch::tensor({0.1, 0.6, 0.4, 0.8}, torch::kFloat);
auto options = F::MultilabelSoftMarginLossFuncOptions().reduction(torch::kNone).weight(weight);
auto output =
F::multilabel_soft_margin_loss(input, target, options);
auto expected = torch::tensor({0.4876902, 0.3321295}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, PairwiseDistance) {
auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::kFloat);
auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::kFloat);
auto output =
F::pairwise_distance(input1, input2, F::PairwiseDistanceFuncOptions().p(1));
auto expected = torch::tensor({6, 6}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
TEST_F(FunctionalTest, PDist) {
{
auto input = torch::tensor({{-1.0, -5.0, -1.0}, {2.0, 4.0, 6.0}});
auto output = F::pdist(input);
auto expected = torch::tensor({11.7898});
ASSERT_TRUE(output.allclose(expected));
}
{
auto input = torch::tensor({{1.0, -1.0}, {1.0, 3.0}, {3.0, 3.0}});
auto output = F::pdist(input, 1.5);
auto expected = torch::tensor({4.0, 4.8945, 2.0});
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(FunctionalTest, AdaptiveMaxPool1d) {
auto x = torch::ones({1, 1, 5});
auto y = F::adaptive_max_pool1d(x, F::AdaptiveMaxPool1dFuncOptions(3));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 3})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
}
TEST_F(FunctionalTest, AdaptiveMaxPool2d) {
auto x = torch::ones({2, 5, 5});
auto y = F::adaptive_max_pool2d(x, F::AdaptiveMaxPool2dFuncOptions(3));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
}
TEST_F(FunctionalTest, AdaptiveMaxPool3d) {
auto x = torch::ones({2, 5, 5, 5});
auto y = F::adaptive_max_pool3d(x, F::AdaptiveMaxPool3dFuncOptions(3));
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3, 3})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3, 3}));
}
TEST_F(FunctionalTest, AdaptiveAvgPool1d) {
auto x = torch::ones({1, 1, 5});
auto y = F::adaptive_avg_pool1d(x, F::AdaptiveAvgPool1dFuncOptions(3));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 3})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
}
TEST_F(FunctionalTest, AdaptiveAvgPool2d) {
auto x = torch::ones({2, 5, 5});
auto y = F::adaptive_avg_pool2d(x, F::AdaptiveAvgPool2dFuncOptions(3));
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
}
TEST_F(FunctionalTest, AdaptiveAvgPool3d) {
auto x = torch::ones({2, 5, 5, 5});
auto y = F::adaptive_avg_pool3d(x, F::AdaptiveAvgPool3dFuncOptions(3));
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 3, 3, 3})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3, 3}));
}
TEST_F(FunctionalTest, L1Loss) {
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = F::l1_loss(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, MSELoss) {
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = F::mse_loss(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, BCELoss) {
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = F::binary_cross_entropy(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, KLDivLoss) {
KLDivLoss loss;
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = F::kl_div(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, HingeEmbeddingLoss) {
auto input = torch::tensor({{2, 22, 4}, {20, 10, 0}}, torch::kFloat);
auto target = torch::tensor({{2, 6, 4}, {1, 10, 0}}, torch::kFloat);
auto output = F::hinge_embedding_loss(
input, target, F::HingeEmbeddingLossFuncOptions().margin(2));
auto expected = torch::tensor({10}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
TEST_F(FunctionalTest, GridSample) {
auto input = torch::arange(9, torch::kFloat).view(std::vector<int64_t>({1, 1, 3, 3}));
auto grid = torch::tensor({{
{{-2., -1.}, {-1., -1.}, {0., -1.}},
{{-1., 0.}, {0., 0.}, {1., 0.}},
{{0., 1.}, {1., 1.}, {2., 1.}}
}}, torch::kFloat);
// bilinear, zeros, true
auto options = F::GridSampleFuncOptions()
.mode(torch::kBilinear)
.padding_mode(torch::kZeros)
.align_corners(true);
auto output = F::grid_sample(input, grid, options);
auto expected = torch::tensor({{{{0., 0., 1.}, {3., 4., 5.}, {7., 8., 0.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
// bilinear, zeros, false
options = F::GridSampleFuncOptions()
.mode(torch::kBilinear)
.padding_mode(torch::kZeros)
.align_corners(false);
output = F::grid_sample(input, grid, options);
expected = torch::tensor({{{{0., 0., 0.5}, {1.5, 4., 2.5}, {3.5, 2., 0.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
// default options (bilinear, zeros, false) same result as above
output = F::grid_sample(input, grid);
ASSERT_TRUE(output.allclose(expected));
// nearest, zeros, true
options = F::GridSampleFuncOptions()
.mode(torch::kNearest)
.padding_mode(torch::kZeros)
.align_corners(true);
output = F::grid_sample(input, grid, options);
expected = torch::tensor({{{{0., 0., 1.}, {3., 4., 5.}, {7., 8., 0.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
// bilinear, border, true
options = F::GridSampleFuncOptions()
.mode(torch::kBilinear)
.padding_mode(torch::kBorder)
.align_corners(true);
output = F::grid_sample(input, grid, options);
expected = torch::tensor({{{{0., 0., 1.}, {3., 4., 5.}, {7., 8., 8.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
// bilinear, reflection, true
options = F::GridSampleFuncOptions()
.mode(torch::kBilinear)
.padding_mode(torch::kReflection)
.align_corners(true);
output = F::grid_sample(input, grid, options);
expected = torch::tensor({{{{1., 0., 1.}, {3., 4., 5.}, {7., 8., 7.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
TEST_F(FunctionalTest, AffineGrid) {
{
// 2D affine.
auto theta = torch::arange(1., 13)
.view(std::vector<int64_t>({2, 2, 3}));
auto size = std::vector<int64_t>({2, 3, 2, 2});
auto align_corners = true;
auto output = F::affine_grid(theta, size, !align_corners);
auto expected = torch::tensor(
{{{{1.50, 1.50}, {2.50, 5.50}}, {{3.50, 6.50}, {4.50, 10.50}}},
{{{1.50, 1.50}, {8.50, 11.50}}, {{9.50, 12.50}, {16.50, 22.50}}}});
auto output_aligned = F::affine_grid(theta, size, align_corners);
auto expected_aligned = torch::tensor(
{{{{0.0, -3.0}, {2.0, 5.0}}, {{4.0, 7.0}, {6.0, 15.0}}},
{{{-6.0, -9.0}, {8.0, 11.0}}, {{10.0, 13.0}, {24.0, 33.0}}}});
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(output_aligned.allclose(expected_aligned));
}
{
// 3D affine.
auto theta = torch::arange(1., 13)
.view(std::vector<int64_t>({1, 3, 4}));
auto size = std::vector<int64_t>({1, 1, 3, 2, 2});
auto align_corners = true;
auto output = F::affine_grid(theta, size, !align_corners);
auto expected = torch::tensor(
{{{{{0.5000, -2.1667, -4.8333}, {1.5000, 2.8333, 4.1667}},
{{2.5000, 3.8333, 5.1667}, {3.5000, 8.8333, 14.1667}}},
{{{2.5000, 2.5000, 2.5000}, {3.5000, 7.5000, 11.5000}},
{{4.5000, 8.5000, 12.5000}, {5.5000, 13.5000, 21.5000}}},
{{{4.5000, 7.1667, 9.8333}, {5.5000, 12.1667, 18.8333}},
{{6.5000, 13.1667, 19.8333}, {7.5000, 18.1667, 28.8333}}}}});
auto output_aligned = F::affine_grid(theta, size, align_corners);
auto expected_aligned =
torch::tensor({{{{{-2.0, -10.0, -18.0}, {0.0, 0.0, 0.0}},
{{2.0, 2.0, 2.0}, {4.0, 12.0, 20.0}}},
{{{1.0, -3.0, -7.0}, {3.0, 7.0, 11.0}},
{{5.0, 9.0, 13.0}, {7.0, 19.0, 31.0}}},
{{{4.0, 4.0, 4.0}, {6.0, 14.0, 22.0}},
{{8.0, 16.0, 24.0}, {10.0, 26.0, 42.0}}}}});
ASSERT_TRUE(output.allclose(expected, 1e-2));
ASSERT_TRUE(output_aligned.allclose(expected_aligned));
}
{
auto theta = torch::empty({1, 2, 3}, torch::kDouble);
auto size = std::vector<int64_t>({1, 1, 2, 2});
ASSERT_THROWS_WITH(
F::affine_grid(torch::empty({2, 2, 3}), {-1, 1, 2, 2}),
"Expected non-zero, positive output size. Got [-1, 1, 2, 2]");
ASSERT_THROWS_WITH(
F::affine_grid(torch::empty({2, 2, 3}, torch::kInt), size),
"Expected theta to have floating point type, but got int");
ASSERT_THROWS_WITH(
F::affine_grid(theta[0], size),
"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
"[1, 1, 2, 2]. Got [2, 3].");
ASSERT_THROWS_WITH(
F::affine_grid(theta.unsqueeze(0), size),
"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
"[1, 1, 2, 2]. Got [1, 1, 2, 3].");
ASSERT_THROWS_WITH(
F::affine_grid(theta.repeat({1, 2, 1}), size),
"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
"[1, 1, 2, 2]. Got [1, 4, 3].");
ASSERT_THROWS_WITH(
F::affine_grid(theta.repeat({1, 1, 2}), size),
"Expected a batch of 2D affine matrices of shape Nx2x3 for size "
"[1, 1, 2, 2]. Got [1, 2, 6].");
}
{
auto theta = torch::empty({1, 3, 4}, torch::kDouble);
auto size = std::vector<int64_t>({1, 1, 2, 2, 3});
ASSERT_THROWS_WITH(
F::affine_grid(theta[0], size),
"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
"[1, 1, 2, 2, 3]. Got [3, 4].");
ASSERT_THROWS_WITH(
F::affine_grid(theta.unsqueeze(0), size),
"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
"[1, 1, 2, 2, 3]. Got [1, 1, 3, 4].");
ASSERT_THROWS_WITH(
F::affine_grid(theta.repeat({1, 2, 1}), size),
"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
"[1, 1, 2, 2, 3]. Got [1, 6, 4].");
ASSERT_THROWS_WITH(
F::affine_grid(theta.repeat({1, 1, 2}), size),
"Expected a batch of 3D affine matrices of shape Nx3x4 for size "
"[1, 1, 2, 2, 3]. Got [1, 3, 8].");
ASSERT_THROWS_WITH(
F::affine_grid(theta, {1, 1, 1, 2, 2, 3}),
"affine_grid only supports 4D and 5D sizes, for 2D and 3D affine "
"transforms, respectively. Got size [1, 1, 1, 2, 2, 3]");
ASSERT_THROWS_WITH(
F::affine_grid(theta, {1, 1}),
"affine_grid only supports 4D and 5D sizes, for 2D and 3D affine "
"transforms, respectively. Got size [1, 1]");
}
}
TEST_F(FunctionalTest, MultiMarginLoss) {
auto weight = torch::tensor({0.3, 0.3, 0.4}, torch::kFloat);
auto input = torch::tensor(
{{0.2, 0.2, 0.6}, {0.1, 0.8, 0.1}, {0.9, 0.09, 0.01}},
torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({2, 1, 0}, torch::kLong);
auto output = F::multi_margin_loss(
input, target, F::MultiMarginLossFuncOptions().margin(2).weight(weight));
auto expected = torch::tensor({0.305556}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
TEST_F(FunctionalTest, CosineEmbeddingLoss) {
auto input1 = torch::tensor({{2, 3, 4}, {6, 2, 4}});
auto input2 = torch::tensor({{2, 3, 5}, {9, 12, 0}});
auto target = torch::tensor({1, -1});
auto output = F::cosine_embedding_loss(
input1, input2, target, F::CosineEmbeddingLossFuncOptions().margin(0.5));
auto expected = torch::tensor({0.1004}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-4));
}
TEST_F(FunctionalTest, MultiLabelMarginLossDefaultOptions) {
auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
auto output = F::multilabel_margin_loss(input, target);
auto expected = torch::tensor({0.8500}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, MultiLabelMarginLossNoReduction) {
auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
auto output = F::multilabel_margin_loss(
input, target, torch::kNone);
auto expected = torch::tensor({0.8500}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(FunctionalTest, TripletMarginLoss) {
auto anchor = torch::tensor({{3., 3.}}, torch::kFloat);
auto positive = torch::tensor({{2., 2.}}, torch::kFloat);
auto negative = torch::tensor({{0., 0.}}, torch::kFloat);
auto output = F::triplet_margin_loss(
anchor, positive, negative, F::TripletMarginLossFuncOptions().margin(1.0));
auto expected = torch::tensor({0.}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
TEST_F(FunctionalTest, TripletMarginWithDistanceLossDefaultParity) {
// Check that if we use torch::pairwise_distance with the default
// TripletMarginLoss options as our distance function, the outputs
// are equal (i.e., equal under defaults).
std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
reductions = {torch::kSum, torch::kMean, torch::kNone};
std::vector<float> margins = {0.5, 1.0, 1.5};
std::vector<bool> swaps = {true, false};
for (auto& reduction : reductions) {
for (auto& margin : margins) {
for (const auto& swap : swaps) {
auto anchor =
torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto positive =
torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto negative =
torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto basicOptions = F::TripletMarginLossFuncOptions()
.reduction(reduction)
.margin(margin)
.swap(swap);
auto distanceOptions =
F::TripletMarginWithDistanceLossFuncOptions()
.reduction(reduction)
.margin(margin)
.swap(swap);
TripletMarginLoss basicLoss(basicOptions);
TripletMarginWithDistanceLoss distanceLoss(distanceOptions);
auto basicOutput =
F::triplet_margin_loss(anchor, positive, negative, basicOptions);
auto distanceOutput = F::triplet_margin_with_distance_loss(
anchor, positive, negative, distanceOptions);
ASSERT_TRUE(distanceOutput.allclose(basicOutput, 1e-6, 1e-6));
// handle for torch::kNone reduction
auto sum = distanceOutput.sum();
sum.backward();
ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
ASSERT_EQ(positive.sizes(), positive.grad().sizes());
ASSERT_EQ(negative.sizes(), negative.grad().sizes());
}
}
}
}
TEST_F(FunctionalTest, NLLLoss) {
auto input = torch::tensor({{-0.1315, -3.1315, -2.5315},
{-3.7038, -0.1038, -2.6038},
{-2.3422, -1.3422, -0.4422}},
torch::kFloat);
auto target = torch::tensor({1, 0, 2}, torch::kLong);
auto output = F::nll_loss(
input, target, F::NLLLossFuncOptions().ignore_index(-100).reduction(torch::kMean));
auto expected = torch::tensor(2.4258, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_TRUE(F::nll_loss(input, target).allclose(expected, 1e-04));
}
TEST_F(FunctionalTest, CrossEntropy) {
auto input = torch::tensor({{3., 3.}, {2., 2.}}, torch::kFloat);
auto target = torch::tensor({0, 1}, torch::kLong);
auto output = F::cross_entropy(
input, target, F::CrossEntropyFuncOptions().ignore_index(-100).reduction(torch::kMean));
auto expected = torch::tensor(0.6931, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_TRUE(F::cross_entropy(input, target).allclose(expected, 1e-04));
// label smoothing with class indices
input = torch::tensor({{3., 1.}, {1., 2.}}, torch::kFloat);
output = F::cross_entropy(
input, target, F::CrossEntropyFuncOptions().label_smoothing(0.15).reduction(torch::kMean));
expected = torch::tensor(0.3326, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
// label smoothing with target probabilities
target = torch::tensor({{0.8, 0.2}, {0.1, 0.9}}, torch::kFloat);
output = F::cross_entropy(
input, target, F::CrossEntropyFuncOptions().label_smoothing(0.2).reduction(torch::kMean));
expected = torch::tensor(0.5701, torch::kFloat);
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
TEST_F(FunctionalTest, MaxUnpool1d) {
auto x = torch::tensor({{{2, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
auto y = F::max_unpool1d(x, indices, F::MaxUnpool1dFuncOptions(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::dtype(torch::kFloat).requires_grad(true));
indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
y = F::max_unpool1d(
x, indices, F::MaxUnpool1dFuncOptions(3).output_size(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::dtype(torch::kFloat).requires_grad(true));
indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
y = F::max_unpool1d(x, indices, F::MaxUnpool1dFuncOptions(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::dtype(torch::kFloat).requires_grad(true));
auto y = F::max_unpool2d(x, indices, F::MaxUnpool2dFuncOptions(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, MaxUnpool3d) {
auto indices = torch::tensor({{{{{26}}}}}, torch::kLong);
auto x = torch::tensor({{{{{26}}}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = F::max_unpool3d(x, indices, F::MaxUnpool3dFuncOptions(3));
ASSERT_EQ(y.dim(), 5);
ASSERT_TRUE(torch::allclose(y, torch::tensor(
{{{{{ 0, 0, 0},
{ 0, 0, 0},
{ 0, 0, 0}},
{{ 0, 0, 0},
{ 0, 0, 0},
{ 0, 0, 0}},
{{ 0, 0, 0},
{ 0, 0, 0},
{ 0, 0, 26}}}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3, 3, 3}));
}
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, F::ELUFuncOptions().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));
}
}
}
ASSERT_TRUE(F::elu(torch::tensor(1.)).defined());
}
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));
}
ASSERT_TRUE(F::selu(torch::tensor(1.)).defined());
}
TEST_F(FunctionalTest, GLU) {
int64_t dim = 1;
auto input = torch::randn({4, 2}, torch::requires_grad());
auto output = F::glu(input, dim);
auto input_size = input.sizes()[dim] / 2;
auto first_half = input.narrow(dim, 0, input_size);
auto second_half = input.narrow(dim, input_size, input_size);
auto expected = first_half * torch::sigmoid(second_half);
ASSERT_TRUE(output.allclose(expected));
ASSERT_TRUE(F::glu(input).allclose(expected));
}
TEST_F(FunctionalTest, GELU) {
GELU model;
const auto x = torch::linspace(-3.0, 3.0, 100);
const auto y_exp = x * 0.5 * (1.0 + torch::erf(x / std::sqrt(2.0)));
const auto y = F::gelu(x);
ASSERT_TRUE(torch::allclose(y, y_exp, 1.4e-06, 1e-05));
}
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, F::HardshrinkFuncOptions().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);