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#include <gtest/gtest.h>
#include <torch/tensor.h>
#include <ATen/ATen.h>
#include <cmath>
#include <cstddef>
#include <vector>
template <typename T>
bool exactly_equal(at::Tensor left, T right) {
return at::_local_scalar(left).to<T>() == right;
}
template <typename T>
bool almost_equal(at::Tensor left, T right, T tolerance = 1e-4) {
return std::abs(at::_local_scalar(left).to<T>() - right) < tolerance;
}
#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
ASSERT_TRUE( \
tensor.device().type() == at::Device((device_), (index_)).type()); \
ASSERT_TRUE( \
tensor.device().index() == at::Device((device_), (index_)).index()); \
ASSERT_EQ(tensor.dtype(), (type_)); \
ASSERT_TRUE(tensor.layout() == (layout_))
TEST(TensorTest, ToDtype) {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
tensor = tensor.to(at::kInt);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
tensor = tensor.to(at::kChar);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kChar, at::kStrided);
tensor = tensor.to(at::kDouble);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
}
// Not currently supported.
// TEST(TensorTest, ToLayout) {
// auto tensor = at::empty({3, 4});
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
//
// tensor = tensor.to(at::kSparse);
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kSparse);
//
// tensor = tensor.to(at::kStrided);
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
// }
// TEST(TensorTest, ToDevice ", "[cuda]) {
// auto tensor = at::empty({3, 4});
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
//
// tensor = tensor.to({at::kCUDA, 1});
// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
//
// tensor = tensor.to({at::kCUDA, 0});
// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
//
// tensor = tensor.to({at::kCUDA, 1});
// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
//
// tensor = tensor.to(at::Device(at::kCPU));
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
// }
//
// TEST(TensorTest, ToDeviceAndDtype ", "[cuda]) {
// auto tensor = at::empty({3, 4});
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
//
// tensor = tensor.to({at::kCUDA, 1}, at::kInt);
// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
// }
TEST(TensorTest, ToOptionsRespectsRequiresGrad) {
{
auto tensor = torch::empty({3, 4}, at::requires_grad());
ASSERT_TRUE(tensor.requires_grad());
tensor = tensor.to(at::kDouble);
ASSERT_TRUE(tensor.requires_grad());
}
{
auto tensor = torch::empty({3, 4});
ASSERT_FALSE(tensor.requires_grad());
tensor = tensor.to(at::kDouble);
ASSERT_FALSE(tensor.requires_grad());
}
}
TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame) {
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(at::kFloat);
ASSERT_EQ(hopefully_not_copy.data<float>(), tensor.data<float>());
}
TEST(TensorTest, ContainsCorrectValueForSingleValue) {
auto tensor = at::tensor(123);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_EQ(tensor[0].toCInt(), 123);
tensor = at::tensor(123.456f);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kFloat);
ASSERT_TRUE(almost_equal(tensor[0], 123.456f));
tensor = at::tensor(123.456);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 123.456));
}
TEST(TensorTest, ContainsCorrectValuesForManyValues) {
auto tensor = at::tensor({1, 2, 3});
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = at::tensor({1.5, 2.25, 3.125});
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
}
TEST(TensorTest, ContainsCorrectValuesForManyValuesVariable) {
auto tensor = torch::tensor({1, 2, 3});
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = torch::tensor({1.5, 2.25, 3.125});
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
}
TEST(TensorTest, ContainsCorrectValuesWhenConstructedFromVector) {
std::vector<int> v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
auto tensor = at::tensor(v);
ASSERT_EQ(tensor.numel(), v.size());
ASSERT_EQ(tensor.dtype(), at::kInt);
for (size_t i = 0; i < v.size(); ++i) {
ASSERT_TRUE(exactly_equal(tensor[i], v.at(i)));
}
std::vector<float> w = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.0};
tensor = at::tensor(w);
ASSERT_EQ(tensor.numel(), w.size());
ASSERT_EQ(tensor.dtype(), at::kFloat);
for (size_t i = 0; i < w.size(); ++i) {
ASSERT_TRUE(almost_equal(tensor[i], w.at(i)));
}
}
TEST(TensorTest, UsesOptionsThatAreSupplied) {
auto tensor = at::tensor(123, dtype(at::kFloat)) + 0.5;
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kFloat);
ASSERT_TRUE(almost_equal(tensor[0], 123.5));
tensor = at::tensor({1.1, 2.2, 3.3}, dtype(at::kInt));
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_EQ(tensor.layout(), at::kStrided);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
}
TEST(TensorTest, FromBlob) {
std::vector<int32_t> v = {1, 2, 3};
auto tensor = torch::from_blob(v.data(), v.size(), torch::kInt32);
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor[0].toCInt(), 1);
ASSERT_EQ(tensor[1].toCInt(), 2);
ASSERT_EQ(tensor[2].toCInt(), 3);
}
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