mirror of
https://github.com/zebrajr/pytorch.git
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a9e6a673ae
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11876 Modern C++ api instead of macros, item() is aligned with Python frontend. caffe2::Tensor::capacity_nbytes is effecitvely unused and confusing w.r.t. caffe2::Tensor::nbytes(). codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCByte "item<uint8_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCLong "item<int64_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCInt "item<int32_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCDouble "item<double>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat "item<float>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toByteData "data<uint8_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toLongData "data<int64_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toIntData "data<int32_t>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toDoubleData "data<double>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toFloatData "data<float>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCByte "item<uint8_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCLong "item<int64_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCInt "item<int32_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCDouble "item<double>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat "item<float>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toByteData "data<uint8_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toLongData "data<int64_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toIntData "data<int32_t>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toDoubleData "data<double>" codemod -d hphp --extensions cc,cpp,cu,cuh,h,py,hpp,mm toFloatData "data<float>" codemod -d caffe2 --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCComplexDouble "item<std::complex<double>>" codemod -d tc --extensions cc,cpp,cu,cuh,h,py,hpp,mm toCFloat "item<float>" Reviewed By: ezyang Differential Revision: D9948572 fbshipit-source-id: 70c9f5390d92b82c85fdd5f8a5aebca338ab413c
196 lines
6.3 KiB
C++
196 lines
6.3 KiB
C++
#include <gtest/gtest.h>
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#include <torch/tensor.h>
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#include <ATen/ATen.h>
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#include <cmath>
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#include <cstddef>
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#include <vector>
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template <typename T>
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bool exactly_equal(at::Tensor left, T right) {
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return at::_local_scalar(left).to<T>() == right;
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}
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template <typename T>
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bool almost_equal(at::Tensor left, T right, T tolerance = 1e-4) {
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return std::abs(at::_local_scalar(left).to<T>() - right) < tolerance;
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}
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#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
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ASSERT_TRUE( \
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tensor.device().type() == at::Device((device_), (index_)).type()); \
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ASSERT_TRUE( \
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tensor.device().index() == at::Device((device_), (index_)).index()); \
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ASSERT_EQ(tensor.dtype(), (type_)); \
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ASSERT_TRUE(tensor.layout() == (layout_))
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TEST(TensorTest, ToDtype) {
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auto tensor = at::empty({3, 4});
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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tensor = tensor.to(at::kInt);
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
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tensor = tensor.to(at::kChar);
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kChar, at::kStrided);
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tensor = tensor.to(at::kDouble);
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REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
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}
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// Not currently supported.
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// TEST(TensorTest, ToLayout) {
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// auto tensor = at::empty({3, 4});
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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//
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// tensor = tensor.to(at::kSparse);
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kSparse);
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//
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// tensor = tensor.to(at::kStrided);
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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// }
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// TEST(TensorTest, ToDevice ", "[cuda]) {
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// auto tensor = at::empty({3, 4});
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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//
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// tensor = tensor.to({at::kCUDA, 1});
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// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
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//
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// tensor = tensor.to({at::kCUDA, 0});
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// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
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//
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// tensor = tensor.to({at::kCUDA, 1});
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// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
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//
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// tensor = tensor.to(at::Device(at::kCPU));
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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// }
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//
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// TEST(TensorTest, ToDeviceAndDtype ", "[cuda]) {
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// auto tensor = at::empty({3, 4});
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// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
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//
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// tensor = tensor.to({at::kCUDA, 1}, at::kInt);
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// REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
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// }
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TEST(TensorTest, ToOptionsRespectsRequiresGrad) {
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{
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auto tensor = torch::empty({3, 4}, at::requires_grad());
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ASSERT_TRUE(tensor.requires_grad());
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tensor = tensor.to(at::kDouble);
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ASSERT_TRUE(tensor.requires_grad());
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}
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{
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auto tensor = torch::empty({3, 4});
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ASSERT_FALSE(tensor.requires_grad());
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tensor = tensor.to(at::kDouble);
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ASSERT_FALSE(tensor.requires_grad());
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}
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}
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TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame) {
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auto tensor = at::empty({3, 4}, at::kFloat);
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auto hopefully_not_copy = tensor.to(at::kFloat);
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ASSERT_EQ(hopefully_not_copy.data<float>(), tensor.data<float>());
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}
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TEST(TensorTest, ContainsCorrectValueForSingleValue) {
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auto tensor = at::tensor(123);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kInt);
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ASSERT_EQ(tensor[0].item<int32_t>(), 123);
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tensor = at::tensor(123.456f);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kFloat);
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ASSERT_TRUE(almost_equal(tensor[0], 123.456f));
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tensor = at::tensor(123.456);
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kDouble);
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ASSERT_TRUE(almost_equal(tensor[0], 123.456));
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}
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TEST(TensorTest, ContainsCorrectValuesForManyValues) {
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auto tensor = at::tensor({1, 2, 3});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kInt);
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ASSERT_TRUE(exactly_equal(tensor[0], 1));
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ASSERT_TRUE(exactly_equal(tensor[1], 2));
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ASSERT_TRUE(exactly_equal(tensor[2], 3));
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tensor = at::tensor({1.5, 2.25, 3.125});
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kDouble);
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ASSERT_TRUE(almost_equal(tensor[0], 1.5));
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ASSERT_TRUE(almost_equal(tensor[1], 2.25));
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ASSERT_TRUE(almost_equal(tensor[2], 3.125));
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}
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TEST(TensorTest, ContainsCorrectValuesForManyValuesVariable) {
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auto tensor = torch::tensor({1, 2, 3});
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ASSERT_TRUE(tensor.is_variable());
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kInt);
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ASSERT_TRUE(exactly_equal(tensor[0], 1));
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ASSERT_TRUE(exactly_equal(tensor[1], 2));
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ASSERT_TRUE(exactly_equal(tensor[2], 3));
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tensor = torch::tensor({1.5, 2.25, 3.125});
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ASSERT_TRUE(tensor.is_variable());
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kDouble);
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ASSERT_TRUE(almost_equal(tensor[0], 1.5));
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ASSERT_TRUE(almost_equal(tensor[1], 2.25));
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ASSERT_TRUE(almost_equal(tensor[2], 3.125));
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}
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TEST(TensorTest, ContainsCorrectValuesWhenConstructedFromVector) {
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std::vector<int> v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
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auto tensor = at::tensor(v);
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ASSERT_EQ(tensor.numel(), v.size());
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ASSERT_EQ(tensor.dtype(), at::kInt);
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for (size_t i = 0; i < v.size(); ++i) {
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ASSERT_TRUE(exactly_equal(tensor[i], v.at(i)));
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}
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std::vector<float> w = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.0};
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tensor = at::tensor(w);
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ASSERT_EQ(tensor.numel(), w.size());
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ASSERT_EQ(tensor.dtype(), at::kFloat);
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for (size_t i = 0; i < w.size(); ++i) {
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ASSERT_TRUE(almost_equal(tensor[i], w.at(i)));
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}
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}
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TEST(TensorTest, UsesOptionsThatAreSupplied) {
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auto tensor = at::tensor(123, dtype(at::kFloat)) + 0.5;
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ASSERT_EQ(tensor.numel(), 1);
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ASSERT_EQ(tensor.dtype(), at::kFloat);
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ASSERT_TRUE(almost_equal(tensor[0], 123.5));
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tensor = at::tensor({1.1, 2.2, 3.3}, dtype(at::kInt));
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor.dtype(), at::kInt);
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ASSERT_EQ(tensor.layout(), at::kStrided);
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ASSERT_TRUE(exactly_equal(tensor[0], 1));
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ASSERT_TRUE(exactly_equal(tensor[1], 2));
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ASSERT_TRUE(exactly_equal(tensor[2], 3));
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}
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TEST(TensorTest, FromBlob) {
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std::vector<int32_t> v = {1, 2, 3};
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auto tensor = torch::from_blob(v.data(), v.size(), torch::kInt32);
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ASSERT_TRUE(tensor.is_variable());
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ASSERT_EQ(tensor.numel(), 3);
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ASSERT_EQ(tensor[0].item<int32_t>(), 1);
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ASSERT_EQ(tensor[1].item<int32_t>(), 2);
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ASSERT_EQ(tensor[2].item<int32_t>(), 3);
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}
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