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Merged
merged 11 commits into from
Aug 2, 2021
Merged

Pytest for test_videoapi.py and test_video_reader.py #4233

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0c7ead5
test_video_reader pytest refactoring
Jul 30, 2021
c0615f0
pytest refactoring of test_videoapi.py
Jul 30, 2021
b8f4044
Merge remote-tracking branch 'upstream/master' into pytest_video
Jul 30, 2021
175536a
test_video_reader pytest refactoring
Jul 30, 2021
fc70819
pytest refactoring of test_videoapi.py
Jul 30, 2021
1588984
using pytest.approx for test_video_reader.py
Jul 30, 2021
5848316
using pytest.approx for test_videoapi.py
Jul 30, 2021
7319f81
Fixing minor comments
Aug 2, 2021
fcd37c2
Merge branch 'pytest_video' of https://github.com/vmoens/vision into …
Aug 2, 2021
b1e6194
linting fixes
Aug 2, 2021
c5372fb
minor comments
Aug 2, 2021
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126 changes: 2 additions & 124 deletions test/common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import tempfile
import contextlib
import unittest
import pytest
import argparse
import sys
import torch
Expand All @@ -20,7 +21,7 @@

IS_PY39 = sys.version_info.major == 3 and sys.version_info.minor == 9
PY39_SEGFAULT_SKIP_MSG = "Segmentation fault with Python 3.9, see https://github.com/pytorch/vision/issues/3367"
PY39_SKIP = unittest.skipIf(IS_PY39, PY39_SEGFAULT_SKIP_MSG)
PY39_SKIP = pytest.mark.skipif(IS_PY39, reason=PY39_SEGFAULT_SKIP_MSG)
IN_CIRCLE_CI = os.getenv("CIRCLECI", False) == 'true'
IN_RE_WORKER = os.environ.get("INSIDE_RE_WORKER") is not None
IN_FBCODE = os.environ.get("IN_FBCODE_TORCHVISION") == "1"
Expand Down Expand Up @@ -83,129 +84,6 @@ def is_iterable(obj):
return False


# adapted from TestCase in torch/test/common_utils to accept non-string
# inputs and set maximum binary size
class TestCase(unittest.TestCase):
precision = 1e-5

def assertEqual(self, x, y, prec=None, message='', allow_inf=False):
"""
This is copied from pytorch/test/common_utils.py's TestCase.assertEqual
"""
if isinstance(prec, str) and message == '':
message = prec
prec = None
if prec is None:
prec = self.precision

if isinstance(x, torch.Tensor) and isinstance(y, Number):
self.assertEqual(x.item(), y, prec=prec, message=message,
allow_inf=allow_inf)
elif isinstance(y, torch.Tensor) and isinstance(x, Number):
self.assertEqual(x, y.item(), prec=prec, message=message,
allow_inf=allow_inf)
elif isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor):
def assertTensorsEqual(a, b):
super(TestCase, self).assertEqual(a.size(), b.size(), message)
if a.numel() > 0:
if (a.device.type == 'cpu' and (a.dtype == torch.float16 or a.dtype == torch.bfloat16)):
# CPU half and bfloat16 tensors don't have the methods we need below
a = a.to(torch.float32)
b = b.to(a)

if (a.dtype == torch.bool) != (b.dtype == torch.bool):
raise TypeError("Was expecting both tensors to be bool type.")
else:
if a.dtype == torch.bool and b.dtype == torch.bool:
# we want to respect precision but as bool doesn't support substraction,
# boolean tensor has to be converted to int
a = a.to(torch.int)
b = b.to(torch.int)

diff = a - b
if a.is_floating_point():
# check that NaNs are in the same locations
nan_mask = torch.isnan(a)
self.assertTrue(torch.equal(nan_mask, torch.isnan(b)), message)
diff[nan_mask] = 0
# inf check if allow_inf=True
if allow_inf:
inf_mask = torch.isinf(a)
inf_sign = inf_mask.sign()
self.assertTrue(torch.equal(inf_sign, torch.isinf(b).sign()), message)
diff[inf_mask] = 0
# TODO: implement abs on CharTensor (int8)
if diff.is_signed() and diff.dtype != torch.int8:
diff = diff.abs()
max_err = diff.max()
tolerance = prec + prec * abs(a.max())
self.assertLessEqual(max_err, tolerance, message)
super(TestCase, self).assertEqual(x.is_sparse, y.is_sparse, message)
super(TestCase, self).assertEqual(x.is_quantized, y.is_quantized, message)
if x.is_sparse:
x = self.safeCoalesce(x)
y = self.safeCoalesce(y)
assertTensorsEqual(x._indices(), y._indices())
assertTensorsEqual(x._values(), y._values())
elif x.is_quantized and y.is_quantized:
self.assertEqual(x.qscheme(), y.qscheme(), prec=prec,
message=message, allow_inf=allow_inf)
if x.qscheme() == torch.per_tensor_affine:
self.assertEqual(x.q_scale(), y.q_scale(), prec=prec,
message=message, allow_inf=allow_inf)
self.assertEqual(x.q_zero_point(), y.q_zero_point(),
prec=prec, message=message,
allow_inf=allow_inf)
elif x.qscheme() == torch.per_channel_affine:
self.assertEqual(x.q_per_channel_scales(), y.q_per_channel_scales(), prec=prec,
message=message, allow_inf=allow_inf)
self.assertEqual(x.q_per_channel_zero_points(), y.q_per_channel_zero_points(),
prec=prec, message=message,
allow_inf=allow_inf)
self.assertEqual(x.q_per_channel_axis(), y.q_per_channel_axis(),
prec=prec, message=message)
self.assertEqual(x.dtype, y.dtype)
self.assertEqual(x.int_repr().to(torch.int32),
y.int_repr().to(torch.int32), prec=prec,
message=message, allow_inf=allow_inf)
else:
assertTensorsEqual(x, y)
elif isinstance(x, string_classes) and isinstance(y, string_classes):
super(TestCase, self).assertEqual(x, y, message)
elif type(x) == set and type(y) == set:
super(TestCase, self).assertEqual(x, y, message)
elif isinstance(x, dict) and isinstance(y, dict):
if isinstance(x, OrderedDict) and isinstance(y, OrderedDict):
self.assertEqual(x.items(), y.items(), prec=prec,
message=message, allow_inf=allow_inf)
else:
self.assertEqual(set(x.keys()), set(y.keys()), prec=prec,
message=message, allow_inf=allow_inf)
key_list = list(x.keys())
self.assertEqual([x[k] for k in key_list],
[y[k] for k in key_list],
prec=prec, message=message,
allow_inf=allow_inf)
elif is_iterable(x) and is_iterable(y):
super(TestCase, self).assertEqual(len(x), len(y), message)
for x_, y_ in zip(x, y):
self.assertEqual(x_, y_, prec=prec, message=message,
allow_inf=allow_inf)
elif isinstance(x, bool) and isinstance(y, bool):
super(TestCase, self).assertEqual(x, y, message)
elif isinstance(x, Number) and isinstance(y, Number):
inf = float("inf")
if abs(x) == inf or abs(y) == inf:
if allow_inf:
super(TestCase, self).assertEqual(x, y, message)
else:
self.fail("Expected finite numeric values - x={}, y={}".format(x, y))
return
super(TestCase, self).assertLessEqual(abs(x - y), prec, message)
else:
super(TestCase, self).assertEqual(x, y, message)


@contextlib.contextmanager
def freeze_rng_state():
rng_state = torch.get_rng_state()
Expand Down
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