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Upgrade lint. (#39483)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39483

I fixed all of the new errors that occurred because of the upgrade.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Differential Revision: D21884575

Pulled By: ezyang

fbshipit-source-id: 45c8e1f1ecb410c8d7c46dd3922ad70e982a0685
This commit is contained in:
Edward Yang 2020-06-04 12:53:53 -07:00 committed by Facebook GitHub Bot
parent fe684679b0
commit da2004e132
12 changed files with 54 additions and 33 deletions
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5
.flake8
View file

@ -5,9 +5,12 @@ max-line-length = 120
# E501 is not flexible enough, we're using B950 instead
ignore =
E203,E305,E402,E501,E721,E741,F403,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,
# shebang has extra meaning in fbcode lints, so I think it's not worth trying
# to line this up with executable bit
EXE001,
# these ignores are from flake8-bugbear; please fix!
B007,B008,
# these ignores are from flake8-comprehensions; please fix!
C400,C401,C402,C403,C404,C405,C407,C411,C413,C414,C415
per-file-ignores = __init__.py: F401
exclude = docs/src,venv,third_party,caffe2,scripts,docs/caffe2,torch/lib/include,torch/lib/tmp_install,build,torch/include,*.pyi,.git
exclude = docs/src,venv,third_party,caffe2,scripts,docs/caffe2,torch/lib/include,torch/lib/tmp_install,build,torch/include,*.pyi,.git,build,build_test_custom_build,build_code_analyzer

2
.github/workflows/lint.yml vendored
View file

@ -66,7 +66,7 @@ jobs:
- name: Run flake8
run: |
set -eux
pip install flake8==3.7.9 flake8-mypy flake8-bugbear flake8-comprehensions flake8-executable flake8-pyi mccabe pycodestyle==2.5.0 pyflakes==2.1.1
pip install flake8==3.8.2 flake8-mypy flake8-bugbear flake8-comprehensions flake8-executable flake8-pyi==20.5.0 mccabe pycodestyle==2.6.0 pyflakes==2.2.0
flake8 --version
flake8 --exit-zero > ${GITHUB_WORKSPACE}/flake8-output.txt
cat ${GITHUB_WORKSPACE}/flake8-output.txt

12
benchmarks/operator_benchmark/pt/qpool_test.py
View file

@ -10,12 +10,12 @@ import operator_benchmark as op_bench
# 2D pooling will have input matrix of rank 3 or 4
qpool2d_long_configs = op_bench.config_list(
attrs=(
# C H W k s p
( 1, 3, 3, (3, 3), (1, 1), (0, 0)), # dummy # noqa
( 3, 64, 64, (3, 3), (2, 2), (1, 1)), # dummy # noqa
# VGG16 pools with original input shape: (-1, 3, 224, 224)
( 64, 224, 224, (2, 2), (2, 2), (0, 0)), # MaxPool2d-4 # noqa
(256, 56, 56, (2, 2), (2, 2), (0, 0)), # MaxPool2d-16 # noqa
# C H W k s p
( 1, 3, 3, (3, 3), (1, 1), (0, 0)), # dummy # noqa
( 3, 64, 64, (3, 3), (2, 2), (1, 1)), # dummy # noqa
# VGG16 pools with original input shape: (-1, 3, 224, 224)
( 64, 224, 224, (2, 2), (2, 2), (0, 0)), # MaxPool2d-4 # noqa
(256, 56, 56, (2, 2), (2, 2), (0, 0)), # MaxPool2d-16 # noqa
),
attr_names=('C', 'H', 'W', # Input layout
'k', 's', 'p'), # Pooling parameters

3
test/jit/test_class_type.py
View file

@ -485,6 +485,7 @@ class TestClassType(JitTestCase):
def test_interface(self):
global Foo, Bar, OneTwo, OneTwoThree, OneTwoWrong, NotMember, NotMember2
@torch.jit.script
class Foo(object):
def __init__(self):
@ -647,6 +648,7 @@ class TestClassType(JitTestCase):
def test_overloaded_fn(self):
global Foo, MyClass # see [local resolution in python]
@torch.jit.script
class Foo(object):
def __init__(self, x):
@ -802,6 +804,7 @@ class TestClassType(JitTestCase):
def test_cast_overloads(self):
global Foo # see [local resolution in python]
@torch.jit.script
class Foo(object):
def __init__(self, val):

4
test/print_test_stats.py
View file

@ -47,11 +47,11 @@ class TestSuite:
print(f" avg_time: {self.total_time/test_count:.2f} seconds")
if test_count > 2:
print(f" mean_time: {sorted_tests[test_count>>1].time:.2f} seconds")
print(f" Three longest tests:")
print(" Three longest tests:")
for idx in [-1, -2, -3]:
print(f" {sorted_tests[idx].name} time: {sorted_tests[idx].time:.2f} seconds")
elif test_count > 0:
print(f" Longest test:")
print(" Longest test:")
print(f" {sorted_tests[-1].name} time: {sorted_tests[-1].time:.2f} seconds")
print("")

6
test/quantization/test_quantized_op.py
View file

@ -1401,7 +1401,8 @@ class TestQuantizedOps(TestCase):
mode=mode, align_corners=align_corners)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(X_ref, qX_hat.int_repr(), atol=1.0, rtol=0,
msg="{} results are off".format(name, qX_hat.int_repr(), X_ref))
msg="{} results are off: qX_hat={} X_ref={}"
.format(name, qX_hat.int_repr(), X_ref))
self.assertEqual(scale, qX_hat.q_scale(),
msg=error_message.format(name + '.scale', scale, qX_hat.q_scale()))
self.assertEqual(zero_point, qX_hat.q_zero_point(),
@ -1455,7 +1456,8 @@ class TestQuantizedOps(TestCase):
mode=mode, align_corners=align_corners)
# TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
self.assertEqualIgnoreType(X_ref, qX_hat.int_repr(), atol=1.0, rtol=0,
msg="{} results are off".format(name, qX_hat.int_repr(), X_ref))
msg="{} results are off: qX_hat={}, X_ref={}"
.format(name, qX_hat.int_repr(), X_ref))
self.assertEqual(scale, qX_hat.q_scale(),
msg=error_message.format(name + '.scale', scale, qX_hat.q_scale()))
self.assertEqual(zero_point, qX_hat.q_zero_point(),

1
test/test_autograd.py
View file

@ -6072,6 +6072,7 @@ class TestAutogradDeviceType(TestCase):
def test_simple_reentrant_cross_device(self, device):
class ReentrantFunc(Function):
_cpu_mode = True
@staticmethod
def forward(ctx, x):
return x * (x + 2)

9
test/test_jit.py
View file

@ -3796,6 +3796,7 @@ class TestScript(JitTestCase):
def test_bailout_loop_carried_deps_name_clash(self):
with enable_profiling_mode_for_profiling_tests():
NUM_ITERATIONS = 10
@torch.jit.script
def fct_loop(z, size):
# type: (int, int) -> Tuple[Tensor, List[int]]
@ -3817,6 +3818,7 @@ class TestScript(JitTestCase):
def test_bailout_loop_counter_transition(self):
with enable_profiling_mode_for_profiling_tests():
NUM_ITERATIONS = 10
@torch.jit.script
def fct_loop(z, size):
# type: (int, int) -> Tuple[Tensor, List[int]]
@ -7001,9 +7003,10 @@ a")
self.checkScript(test_cast_float, (-1.,))
with self.assertRaisesRegex(RuntimeError, r"Could not cast value of type Tuple\[int, int\] to bool"): # noqa: W605
@torch.jit.script
def test_bad_conditional(x):
if (1, 2):
if (1, 2): # noqa F634
return
else:
return 0
@ -7669,6 +7672,7 @@ a")
def test_error_stacktrace_interface(self):
global IFace
@torch.jit.script
def baz(c, b):
return c + b
@ -8318,6 +8322,7 @@ a")
return 4
self.assertEqual(foo(4), 7)
self.assertEqual(foo(None), 4)
@torch.jit.script
def foo2(a, b):
# type: (Optional[int], Optional[int]) -> int
@ -16082,7 +16087,7 @@ a")
def identity(x1): # noqa: F811
# type: (str) -> str
pass
#
@torch.jit._overload # noqa: F811
def identity(x1): # noqa: F811
# type: (float) -> float

5
test/test_jit_py3.py
View file

@ -16,7 +16,7 @@ class TestScriptPy3(JitTestCase):
def func(x):
hello, test = "Hello", "test"
print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
print(f"format blank")
print(f"format blank") # noqa F541
hi = 'hi'
print(f"stuff before {hi}")
print(f"{hi} stuff after")
@ -39,6 +39,7 @@ class TestScriptPy3(JitTestCase):
@unittest.skipIf(sys.version_info[:2] < (3, 7), "`dataclasses` module not present on < 3.7")
def test_dataclass_error(self):
from dataclasses import dataclass
@dataclass
class NormalizationInfo(object):
mean: float = 0.0
@ -256,6 +257,7 @@ class TestScriptPy3(JitTestCase):
return str(type(args[0]))
the_class = MyPythonClass()
@torch.jit.script
def fn(x):
return the_class(x)
@ -424,6 +426,7 @@ class TestScriptPy3(JitTestCase):
def test_export_opnames_interface(self):
global OneTwoModule
@torch.jit.interface
class OneTwoModule(nn.Module):
def one(self, x, y):

36
test/test_tensorboard.py
View file

@ -363,28 +363,32 @@ class TestTensorBoardSummary(BaseTestCase):
self))
def test_image_with_one_channel(self):
self.assertTrue(compare_image_proto(summary.image('dummy',
tensor_N(shape=(1, 8, 8)),
dataformats='CHW'),
self)) # noqa E127
self.assertTrue(compare_image_proto(
summary.image('dummy',
tensor_N(shape=(1, 8, 8)),
dataformats='CHW'),
self)) # noqa E127
def test_image_with_one_channel_batched(self):
self.assertTrue(compare_image_proto(summary.image('dummy',
tensor_N(shape=(2, 1, 8, 8)),
dataformats='NCHW'),
self)) # noqa E127
self.assertTrue(compare_image_proto(
summary.image('dummy',
tensor_N(shape=(2, 1, 8, 8)),
dataformats='NCHW'),
self)) # noqa E127
def test_image_with_3_channel_batched(self):
self.assertTrue(compare_image_proto(summary.image('dummy',
tensor_N(shape=(2, 3, 8, 8)),
dataformats='NCHW'),
self)) # noqa E127
self.assertTrue(compare_image_proto(
summary.image('dummy',
tensor_N(shape=(2, 3, 8, 8)),
dataformats='NCHW'),
self)) # noqa E127
def test_image_without_channel(self):
self.assertTrue(compare_image_proto(summary.image('dummy',
tensor_N(shape=(8, 8)),
dataformats='HW'),
self)) # noqa E127
self.assertTrue(compare_image_proto(
summary.image('dummy',
tensor_N(shape=(8, 8)),
dataformats='HW'),
self)) # noqa E127
def test_video(self):
try:

2
torch/_tensor_str.py
View file

@ -161,7 +161,7 @@ class _Formatter(object):
ret = '({{:.{}f}}{{}}{{:.{}f}}j)'.format(p, p).format(value.real, '+-'[value.imag < 0], abs(value.imag))
if not self.has_non_zero_decimal_val:
# complex tensor contains integer elements only
ret = "({{:.0f}}.{{}}{{:.0f}}.j)".format(p, p).format(value.real, '+-'[value.imag < 0], abs(value.imag))
ret = "({{:.0f}}.{{}}{{:.0f}}.j)".format(p, p).format(value.real, '+-'[value.imag < 0], abs(value.imag)) # noqa: F523
else:
ret = '{}'.format(value)
return (self.max_width - len(ret)) * ' ' + ret

2
torch/cuda/_utils.py
View file

@ -31,5 +31,5 @@ def _get_device_index(device, optional=False):
return torch.cuda.current_device()
else:
raise ValueError('Expected a cuda device with a specified index '
'or an integer, but got: '.format(device))
'or an integer, but got: {}'.format(device))
return device_idx