frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-20 21:14:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

torch.tensor: add tests for list of numpy arrays case

Browse Source 
References: https://github.com/pytorch/pytorch/issues/13918

Add more test cases for list of numpy array inputs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72249
Approved by: https://github.com/mruberry
This commit is contained in:
kshitij12345
2022-04-04 01:02:31 +00:00
committed by PyTorch MergeBot
parent 631f035131
commit 0509022450
1 changed files with 22 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
test/test_numpy_interop.py
View File

@ -234,6 +234,28 @@ class TestNumPyInterop(TestCase):
with self.assertWarnsOnceRegex(UserWarning, warning_msg):
torch.tensor([np.array([0]), np.array([1])], device=device)
def test_ctor_with_invalid_numpy_array_sequence(self, device):
# Invalid list of numpy array
with self.assertRaisesRegex(ValueError, "expected sequence of length"):
torch.tensor([np.random.random(size=(3, 3)), np.random.random(size=(3, 0))], device=device)
# Invalid list of list of numpy array
with self.assertRaisesRegex(ValueError, "expected sequence of length"):
torch.tensor([[np.random.random(size=(3, 3)), np.random.random(size=(3, 2))]], device=device)
with self.assertRaisesRegex(ValueError, "expected sequence of length"):
torch.tensor([[np.random.random(size=(3, 3)), np.random.random(size=(3, 3))],
[np.random.random(size=(3, 3)), np.random.random(size=(3, 2))]], device=device)
# expected shape is `[1, 2, 3]`, hence we try to iterate over 0-D array
# leading to type error : not a sequence.
with self.assertRaisesRegex(TypeError, "not a sequence"):
torch.tensor([[np.random.random(size=(3)), np.random.random()]], device=device)
# list of list or numpy array.
with self.assertRaisesRegex(ValueError, "expected sequence of length"):
torch.tensor([[1, 2, 3], np.random.random(size=(2,)), ], device=device)
@onlyCPU
def test_ctor_with_numpy_scalar_ctor(self, device) -> None:
dtypes = [