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Use CATCH prefix to avoid name conflicts with Caffe2.

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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/11780

Differential Revision: D9889925

Pulled By: gchanan

fbshipit-source-id: 5eca849c36ced00b8ae7482b7945b445a3e1687e
This commit is contained in:
Gregory Chanan
2018-09-18 07:59:41 -07:00
committed by Facebook Github Bot
parent 4ee0a78ee6
commit e00fb69b25
41 changed files with 1689 additions and 1665 deletions
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146
test/cpp/api/tensor.cpp
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@ -1,4 +1,4 @@
#include <catch.hpp>
#include "catch_utils.hpp"
#include <torch/tensor.h>
@ -19,12 +19,12 @@ bool almost_equal(at::Tensor left, T right, T tolerance = 1e-4) {
}
#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
REQUIRE(tensor.device().type() == at::Device((device_), (index_)).type()); \
REQUIRE(tensor.device().index() == at::Device((device_), (index_)).index()); \
REQUIRE(tensor.dtype() == (type_)); \
REQUIRE(tensor.layout() == (layout_))
CATCH_REQUIRE(tensor.device().type() == at::Device((device_), (index_)).type()); \
CATCH_REQUIRE(tensor.device().index() == at::Device((device_), (index_)).index()); \
CATCH_REQUIRE(tensor.dtype() == (type_)); \
CATCH_REQUIRE(tensor.layout() == (layout_))
TEST_CASE("Tensor/ToDtype") {
CATCH_TEST_CASE("Tensor/ToDtype") {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
@ -39,7 +39,7 @@ TEST_CASE("Tensor/ToDtype") {
}
// Not currently supported.
// TEST_CASE("Tensor/ToLayout") {
// CATCH_TEST_CASE("Tensor/ToLayout") {
// auto tensor = at::empty({3, 4});
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
//
@ -50,7 +50,7 @@ TEST_CASE("Tensor/ToDtype") {
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
// }
TEST_CASE("Tensor/ToDevice", "[cuda]") {
CATCH_TEST_CASE("Tensor/ToDevice", "[cuda]") {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
@ -67,7 +67,7 @@ TEST_CASE("Tensor/ToDevice", "[cuda]") {
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
}
TEST_CASE("Tensor/ToDeviceAndDtype", "[cuda]") {
CATCH_TEST_CASE("Tensor/ToDeviceAndDtype", "[cuda]") {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
@ -75,119 +75,119 @@ TEST_CASE("Tensor/ToDeviceAndDtype", "[cuda]") {
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
}
TEST_CASE("Tensor/ToOptionsRespectsRequiresGrad") {
CATCH_TEST_CASE("Tensor/ToOptionsRespectsRequiresGrad") {
{
auto tensor = torch::empty({3, 4}, at::requires_grad());
REQUIRE(tensor.requires_grad());
CATCH_REQUIRE(tensor.requires_grad());
tensor = tensor.to(at::kDouble);
REQUIRE(tensor.requires_grad());
CATCH_REQUIRE(tensor.requires_grad());
}
{
auto tensor = torch::empty({3, 4});
REQUIRE(!tensor.requires_grad());
CATCH_REQUIRE(!tensor.requires_grad());
tensor = tensor.to(at::kDouble);
REQUIRE(!tensor.requires_grad());
CATCH_REQUIRE(!tensor.requires_grad());
}
}
TEST_CASE("Tensor/ToDoesNotCopyWhenOptionsAreAllTheSame") {
CATCH_TEST_CASE("Tensor/ToDoesNotCopyWhenOptionsAreAllTheSame") {
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(at::kFloat);
REQUIRE(hopefully_not_copy.data<float>() == tensor.data<float>());
CATCH_REQUIRE(hopefully_not_copy.data<float>() == tensor.data<float>());
}
TEST_CASE("Tensor/ContainsCorrectValueForSingleValue") {
CATCH_TEST_CASE("Tensor/ContainsCorrectValueForSingleValue") {
auto tensor = at::tensor(123);
REQUIRE(tensor.numel() == 1);
REQUIRE(tensor.dtype() == at::kInt);
REQUIRE(tensor[0].toCInt() == 123);
CATCH_REQUIRE(tensor.numel() == 1);
CATCH_REQUIRE(tensor.dtype() == at::kInt);
CATCH_REQUIRE(tensor[0].toCInt() == 123);
tensor = at::tensor(123.456f);
REQUIRE(tensor.numel() == 1);
REQUIRE(tensor.dtype() == at::kFloat);
REQUIRE(almost_equal(tensor[0], 123.456f));
CATCH_REQUIRE(tensor.numel() == 1);
CATCH_REQUIRE(tensor.dtype() == at::kFloat);
CATCH_REQUIRE(almost_equal(tensor[0], 123.456f));
tensor = at::tensor(123.456);
REQUIRE(tensor.numel() == 1);
REQUIRE(tensor.dtype() == at::kDouble);
REQUIRE(almost_equal(tensor[0], 123.456));
CATCH_REQUIRE(tensor.numel() == 1);
CATCH_REQUIRE(tensor.dtype() == at::kDouble);
CATCH_REQUIRE(almost_equal(tensor[0], 123.456));
}
TEST_CASE("Tensor/ContainsCorrectValuesForManyValues") {
CATCH_TEST_CASE("Tensor/ContainsCorrectValuesForManyValues") {
auto tensor = at::tensor({1, 2, 3});
REQUIRE(tensor.numel() == 3);
REQUIRE(tensor.dtype() == at::kInt);
REQUIRE(exactly_equal(tensor[0], 1));
REQUIRE(exactly_equal(tensor[1], 2));
REQUIRE(exactly_equal(tensor[2], 3));
CATCH_REQUIRE(tensor.numel() == 3);
CATCH_REQUIRE(tensor.dtype() == at::kInt);
CATCH_REQUIRE(exactly_equal(tensor[0], 1));
CATCH_REQUIRE(exactly_equal(tensor[1], 2));
CATCH_REQUIRE(exactly_equal(tensor[2], 3));
tensor = at::tensor({1.5, 2.25, 3.125});
REQUIRE(tensor.numel() == 3);
REQUIRE(tensor.dtype() == at::kDouble);
REQUIRE(almost_equal(tensor[0], 1.5));
REQUIRE(almost_equal(tensor[1], 2.25));
REQUIRE(almost_equal(tensor[2], 3.125));
CATCH_REQUIRE(tensor.numel() == 3);
CATCH_REQUIRE(tensor.dtype() == at::kDouble);
CATCH_REQUIRE(almost_equal(tensor[0], 1.5));
CATCH_REQUIRE(almost_equal(tensor[1], 2.25));
CATCH_REQUIRE(almost_equal(tensor[2], 3.125));
}
TEST_CASE("Tensor/ContainsCorrectValuesForManyValuesVariable") {
CATCH_TEST_CASE("Tensor/ContainsCorrectValuesForManyValuesVariable") {
auto tensor = torch::tensor({1, 2, 3});
REQUIRE(tensor.is_variable());
REQUIRE(tensor.numel() == 3);
REQUIRE(tensor.dtype() == at::kInt);
REQUIRE(exactly_equal(tensor[0], 1));
REQUIRE(exactly_equal(tensor[1], 2));
REQUIRE(exactly_equal(tensor[2], 3));
CATCH_REQUIRE(tensor.is_variable());
CATCH_REQUIRE(tensor.numel() == 3);
CATCH_REQUIRE(tensor.dtype() == at::kInt);
CATCH_REQUIRE(exactly_equal(tensor[0], 1));
CATCH_REQUIRE(exactly_equal(tensor[1], 2));
CATCH_REQUIRE(exactly_equal(tensor[2], 3));
tensor = torch::tensor({1.5, 2.25, 3.125});
REQUIRE(tensor.is_variable());
REQUIRE(tensor.numel() == 3);
REQUIRE(tensor.dtype() == at::kDouble);
REQUIRE(almost_equal(tensor[0], 1.5));
REQUIRE(almost_equal(tensor[1], 2.25));
REQUIRE(almost_equal(tensor[2], 3.125));
CATCH_REQUIRE(tensor.is_variable());
CATCH_REQUIRE(tensor.numel() == 3);
CATCH_REQUIRE(tensor.dtype() == at::kDouble);
CATCH_REQUIRE(almost_equal(tensor[0], 1.5));
CATCH_REQUIRE(almost_equal(tensor[1], 2.25));
CATCH_REQUIRE(almost_equal(tensor[2], 3.125));
}
TEST_CASE("Tensor/ContainsCorrectValuesWhenConstructedFromVector") {
CATCH_TEST_CASE("Tensor/ContainsCorrectValuesWhenConstructedFromVector") {
std::vector<int> v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
auto tensor = at::tensor(v);
REQUIRE(tensor.numel() == v.size());
REQUIRE(tensor.dtype() == at::kInt);
CATCH_REQUIRE(tensor.numel() == v.size());
CATCH_REQUIRE(tensor.dtype() == at::kInt);
for (size_t i = 0; i < v.size(); ++i) {
REQUIRE(exactly_equal(tensor[i], v.at(i)));
CATCH_REQUIRE(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);
REQUIRE(tensor.numel() == w.size());
REQUIRE(tensor.dtype() == at::kFloat);
CATCH_REQUIRE(tensor.numel() == w.size());
CATCH_REQUIRE(tensor.dtype() == at::kFloat);
for (size_t i = 0; i < w.size(); ++i) {
REQUIRE(almost_equal(tensor[i], w.at(i)));
CATCH_REQUIRE(almost_equal(tensor[i], w.at(i)));
}
}
TEST_CASE("Tensor/UsesOptionsThatAreSupplied") {
CATCH_TEST_CASE("Tensor/UsesOptionsThatAreSupplied") {
auto tensor = at::tensor(123, dtype(at::kFloat)) + 0.5;
REQUIRE(tensor.numel() == 1);
REQUIRE(tensor.dtype() == at::kFloat);
REQUIRE(almost_equal(tensor[0], 123.5));
CATCH_REQUIRE(tensor.numel() == 1);
CATCH_REQUIRE(tensor.dtype() == at::kFloat);
CATCH_REQUIRE(almost_equal(tensor[0], 123.5));
tensor = at::tensor({1.1, 2.2, 3.3}, dtype(at::kInt));
REQUIRE(tensor.numel() == 3);
REQUIRE(tensor.dtype() == at::kInt);
REQUIRE(tensor.layout() == at::kStrided);
REQUIRE(exactly_equal(tensor[0], 1));
REQUIRE(exactly_equal(tensor[1], 2));
REQUIRE(exactly_equal(tensor[2], 3));
CATCH_REQUIRE(tensor.numel() == 3);
CATCH_REQUIRE(tensor.dtype() == at::kInt);
CATCH_REQUIRE(tensor.layout() == at::kStrided);
CATCH_REQUIRE(exactly_equal(tensor[0], 1));
CATCH_REQUIRE(exactly_equal(tensor[1], 2));
CATCH_REQUIRE(exactly_equal(tensor[2], 3));
}
TEST_CASE("FromBlob") {
CATCH_TEST_CASE("FromBlob") {
std::vector<int32_t> v = {1, 2, 3};
auto tensor = torch::from_blob(v.data(), v.size(), torch::kInt32);
REQUIRE(tensor.is_variable());
REQUIRE(tensor.numel() == 3);
REQUIRE(tensor[0].toCInt() == 1);
REQUIRE(tensor[1].toCInt() == 2);
REQUIRE(tensor[2].toCInt() == 3);
CATCH_REQUIRE(tensor.is_variable());
CATCH_REQUIRE(tensor.numel() == 3);
CATCH_REQUIRE(tensor[0].toCInt() == 1);
CATCH_REQUIRE(tensor[1].toCInt() == 2);
CATCH_REQUIRE(tensor[2].toCInt() == 3);
}