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C++ API parity: MaxUnpool3d

Browse Source 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27027

Test Plan: Imported from OSS

Differential Revision: D17682402

Pulled By: pbelevich

fbshipit-source-id: 2008ce405176c174cdba88b4f25cd77a82bb13ea
This commit is contained in:
Pavel Belevich
2019-10-02 05:39:07 -07:00
committed by Facebook Github Bot
parent f4f6d8dda5
commit 5005f7bce7
6 changed files with 104 additions and 0 deletions
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65
test/cpp/api/modules.cpp
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@ -651,6 +651,71 @@ TEST_F(ModulesTest, MaxPool2d_MaxUnpool2d) {
{ 0, 0, 0, 0, 0}}}}, torch::kFloat)));
}
TEST_F(ModulesTest, MaxUnpool3d) {
auto indices = torch::tensor({{{{{26}}}}}, torch::kLong);
auto x = torch::tensor({{{{{26}}}}}, torch::requires_grad());
auto model = MaxUnpool3d{3};
auto y = model->forward(x, indices);
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(), torch::IntArrayRef({1, 1, 3, 3, 3}));
}
TEST_F(ModulesTest, MaxUnpool3dOutputSize) {
auto indices = torch::tensor(
{{{{{21, 23},
{29, 31}},
{{53, 55},
{61, 63}}}}}, torch::kLong);
auto x = torch::tensor(
{{{{{21, 23},
{29, 31}},
{{53, 55},
{61, 63}}}}}, torch::requires_grad());
auto model = MaxUnpool3d{MaxUnpool3dOptions(3).stride(2).padding(1)};
auto y = model->forward(x, indices, torch::IntArrayRef({1, 1, 4, 4, 4}));
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, 21, 0, 23},
{ 0, 0, 0, 0},
{ 0, 29, 0, 31}},
{{ 0, 0, 0, 0},
{ 0, 0, 0, 0},
{ 0, 0, 0, 0},
{ 0, 0, 0, 0}},
{{ 0, 0, 0, 0},
{ 0, 53, 0, 55},
{ 0, 0, 0, 0},
{ 0, 61, 0, 63}}}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 4, 4, 4}));
}
TEST_F(ModulesTest, MaxPool3d_MaxUnpool3d) {
MaxPool3d pool {MaxPool3dOptions(3).stride(2)};
MaxUnpool3d unpool {MaxUnpool3dOptions(3).stride(2)};
auto input = torch::randn({20, 16, 51, 33, 15});
torch::Tensor output, indices;
std::tie(output, indices) = pool->forward_with_indices(input);
auto unpooled_output = unpool(output, indices);
ASSERT_EQ(unpooled_output.sizes(), torch::IntArrayRef({20, 16, 51, 33, 15}));
}
TEST_F(ModulesTest, Linear) {
Linear model(5, 2);
auto x = torch::randn({10, 5}, torch::requires_grad());