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Run mypy on more test files (#49658)

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Summary:
Improves one annotation for `augment_model_with_bundled_inputs`

Also add a comment to not work on caffe2 type annotations, that's not worth the effort - those ignores can stay as they are.

xref gh-16574

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49658

Reviewed By: heitorschueroff

Differential Revision: D25757721

Pulled By: ezyang

fbshipit-source-id: 44c396d8da9ef3f41b97f9c46a528f0431c4b463
This commit is contained in:
Ralf Gommers
2021-01-05 09:22:01 -08:00
committed by Facebook GitHub Bot
parent e35b822d7d
commit b7bfc723d3
6 changed files with 30 additions and 14 deletions
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18
test/test_numpy_interop.py
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@ -47,10 +47,8 @@ class TestNumPyInterop(TestCase):
else:
# can't directly use min and max, because for int64_t, max - min
# is greater than int64_t range and triggers UB.
dtype_info = torch.iinfo(dtype)
low = max(dtype_info.min, int(-1e10))
high = min(dtype_info.max, int(1e10))
dtype_info = torch.iinfo(dtype)
low = max(torch.iinfo(dtype).min, int(-1e10))
high = min(torch.iinfo(dtype).max, int(1e10))
t = torch.empty(shape, dtype=torch.int64).random_(low, high)
return t.to(dtype)
@ -272,10 +270,12 @@ class TestNumPyInterop(TestCase):
]
for tp, dtype in zip(types, dtypes):
if np.dtype(dtype).kind == 'u':
x = torch.Tensor([1, 2, 3, 4]).type(tp)
# .type expects a XxxTensor, which have no type hints on
# purpose, so ignore during mypy type checking
x = torch.Tensor([1, 2, 3, 4]).type(tp) # type: ignore
array = np.array([1, 2, 3, 4], dtype=dtype)
else:
x = torch.Tensor([1, -2, 3, -4]).type(tp)
x = torch.Tensor([1, -2, 3, -4]).type(tp) # type: ignore
array = np.array([1, -2, 3, -4], dtype=dtype)
# Test __array__ w/o dtype argument
@ -309,7 +309,7 @@ class TestNumPyInterop(TestCase):
float_types = [torch.DoubleTensor, torch.FloatTensor]
float_dtypes = [np.float64, np.float32]
for tp, dtype in zip(float_types, float_dtypes):
x = torch.Tensor([1, 2, 3, 4]).type(tp)
x = torch.Tensor([1, 2, 3, 4]).type(tp) # type: ignore
array = np.array([1, 2, 3, 4], dtype=dtype)
for func in ['sin', 'sqrt', 'ceil']:
ufunc = getattr(np, func)
@ -321,7 +321,7 @@ class TestNumPyInterop(TestCase):
# Test functions with boolean return value
for tp, dtype in zip(types, dtypes):
x = torch.Tensor([1, 2, 3, 4]).type(tp)
x = torch.Tensor([1, 2, 3, 4]).type(tp) # type: ignore
array = np.array([1, 2, 3, 4], dtype=dtype)
geq2_x = np.greater_equal(x, 2)
geq2_array = np.greater_equal(array, 2).astype('uint8')
@ -360,7 +360,7 @@ class TestNumPyInterop(TestCase):
self.assertEqual(torch.ones([2, 2, 2, 2]).mean(scalar), torch.ones([2, 2, 2, 2]).mean(np_val))
# numpy integral type parses like a python int in custom python bindings:
self.assertEqual(torch.Storage(np_val).size(), scalar)
self.assertEqual(torch.Storage(np_val).size(), scalar) # type: ignore
tensor = torch.tensor([2], dtype=torch.int)
tensor[0] = np_val