Ignore F401 in all __init__.py without putting noqa (#25823)

Summary:
By adding `per-file-ignores = __init__.py: F401` into `.flake8` with `flake8>=3.7`, we can ignore F410 in all `__init__.py` without putting `# noqa: F401` line by line.

http://flake8.pycqa.org/en/latest/user/options.html?highlight=per-file-ignores#cmdoption-flake8-per-file-ignores
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25823

Differential Revision: D17252182

Pulled By: soumith

fbshipit-source-id: 87b174075b79e4078953a7521bd1a8f82405646b

.flake8

@@ -9,4 +9,5 @@ ignore =
     B007,B008,
     # these ignores are from flake8-comprehensions; please fix!
     C400,C401,C402,C403,C404,C405,C407,C411,
+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

benchmarks/fastrnns/__init__.py

@@ -1,5 +1,5 @@
-from .cells import *  # noqa: F401
-from .factory import *  # noqa: F401
+from .cells import *
+from .factory import *
 
 # (output, next_state) = cell(input, state)
 seqLength = 100

test/onnx/model_defs/__init__.py

@@ -1,4 +1,4 @@
-from .squeezenet import *  # noqa: F401
-from .super_resolution import *  # noqa: F401
-from .op_test import *  # noqa: F401
-from .srresnet import *  # noqa: F401
+from .squeezenet import *
+from .super_resolution import *
+from .op_test import *
+from .srresnet import *

tools/shared/__init__.py

@@ -1,2 +1,2 @@
-from .module_loader import import_module  # noqa: F401
-from .cwrap_common import set_declaration_defaults, sort_by_number_of_args  # noqa: F401
+from .module_loader import import_module
+from .cwrap_common import set_declaration_defaults, sort_by_number_of_args

torch/__init__.py

@@ -15,7 +15,7 @@ import sys
 import platform
 from ._utils import _import_dotted_name
 from ._utils_internal import get_file_path, prepare_multiprocessing_environment
-from .version import __version__  # noqa: F401
+from .version import __version__
 from ._six import string_classes as _string_classes
 
 __all__ = [
@@ -41,7 +41,7 @@ import os as _dl_flags
 # if we have numpy, it *must* be imported before the call to setdlopenflags()
 # or there is risk that later c modules will segfault when importing numpy
 try:
-    import numpy as _np  # noqa: F401
+    import numpy as _np
 except ImportError:
     pass
 
@@ -301,7 +301,7 @@ del BFloat16StorageBase
 
 import torch.cuda
 import torch.autograd
-from torch.autograd import no_grad, enable_grad, set_grad_enabled  # noqa: F401
+from torch.autograd import no_grad, enable_grad, set_grad_enabled
 import torch.nn
 import torch.nn.intrinsic
 import torch.nn.quantized
@@ -337,8 +337,8 @@ def compiled_with_cxx11_abi():
 
 
 # Import the ops "namespace"
-from torch._ops import ops  # noqa: F401
-from torch._classes import classes  # noqa: F401
+from torch._ops import ops
+from torch._classes import classes
 
 # Import the quasi random sampler
 import torch.quasirandom

torch/autograd/__init__.py

@@ -8,11 +8,11 @@ import torch
 import warnings
 
 from .variable import Variable
-from .function import Function, NestedIOFunction  # noqa: F401
-from .gradcheck import gradcheck, gradgradcheck  # noqa: F401
-from .grad_mode import no_grad, enable_grad, set_grad_enabled  # noqa: F401
-from .anomaly_mode import detect_anomaly, set_detect_anomaly  # noqa: F401
-from . import profiler  # noqa: F401
+from .function import Function, NestedIOFunction
+from .gradcheck import gradcheck, gradgradcheck
+from .grad_mode import no_grad, enable_grad, set_grad_enabled
+from .anomaly_mode import detect_anomaly, set_detect_anomaly
+from . import profiler
 
 __all__ = ['Variable', 'Function', 'backward', 'grad_mode']
 

torch/autograd/_functions/__init__.py

@@ -1 +1 @@
-from .tensor import *  # noqa: F401
+from .tensor import *

torch/cuda/__init__.py

@@ -555,7 +555,7 @@ torch._storage_classes.add(HalfStorage)
 torch._storage_classes.add(BoolStorage)
 torch._storage_classes.add(BFloat16Storage)
 
-from . import sparse  # noqa: F401
-from . import profiler  # noqa: F401
-from . import nvtx  # noqa: F401
-from .streams import Stream, Event  # noqa: F401
+from . import sparse
+from . import profiler
+from . import nvtx
+from .streams import Stream, Event

torch/distributed/__init__.py

@@ -13,8 +13,9 @@ if is_available() and not (torch._C._c10d_init() and torch._C._rpc_init() and to
 
 
 if is_available():
-    from .distributed_c10d import *  # noqa: F401
+    from .distributed_c10d import *
     # Variables prefixed with underscore are not auto imported
     # See the comment in `distributed_c10d.py` above `_backend` on why we expose
     # this.
-    from .distributed_c10d import _backend  # noqa: F401
+
+    from .distributed_c10d import _backend

torch/for_onnx/__init__.py

@@ -1 +1 @@
-from .onnx import *  # noqa: F401
+from .onnx import *

torch/jit/__init__.py

@@ -32,8 +32,8 @@ import warnings
 from collections import OrderedDict
 
 # These are imported so users can access them from the `torch.jit` module
-from torch._jit_internal import Final, _overload, _overload_method  # noqa: F401
-from torch._jit_internal import ignore, export, unused  # noqa: F401
+from torch._jit_internal import Final, _overload, _overload_method
+from torch._jit_internal import ignore, export, unused
 
 if sys.version_info[0] > 2:
     import pathlib

torch/multiprocessing/__init__.py

@@ -22,7 +22,7 @@ __all__ = ['set_sharing_strategy', 'get_sharing_strategy',
            'get_all_sharing_strategies']
 
 
-from multiprocessing import *  # noqa: F401
+from multiprocessing import *
 
 
 __all__ += multiprocessing.__all__
@@ -36,13 +36,13 @@ torch._C._multiprocessing_init()
 if sys.version_info < (3, 3):
     """Override basic classes in Python 2.7 and Python 3.3 to use ForkingPickler
     for serialization. Later versions of Python already use ForkingPickler."""
-    from .queue import Queue, SimpleQueue  # noqa: F401
-    from .pool import Pool  # noqa: F401
+    from .queue import Queue, SimpleQueue
+    from .pool import Pool
 
 
 """Add helper function to spawn N processes and wait for completion of any of
 them. This depends `mp.get_context` which was added in Python 3.4."""
-from .spawn import spawn, SpawnContext, _supports_context  # noqa: F401
+from .spawn import spawn, SpawnContext, _supports_context
 
 
 if sys.platform == 'darwin' or sys.platform == 'win32':

torch/nn/__init__.py

@@ -1,5 +1,5 @@
-from .modules import *  # noqa: F401
-from .parameter import Parameter  # noqa: F401
-from .parallel import DataParallel  # noqa: F401
-from . import init  # noqa: F401
-from . import utils  # noqa: F401
+from .modules import *
+from .parameter import Parameter
+from .parallel import DataParallel
+from . import init
+from . import utils

torch/nn/qat/__init__.py

@@ -1,2 +1,2 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
-from .modules import *  # noqa: F401
+from .modules import *

torch/nn/quantized/__init__.py

@@ -3,4 +3,4 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-from .modules import *  # noqa: F401
+from .modules import *

torch/nn/quantized/dynamic/__init__.py

@@ -1,4 +1,4 @@
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-from .modules import *  # noqa: F401
+from .modules import *

torch/nn/utils/__init__.py

@@ -1,6 +1,6 @@
-from . import rnn  # noqa: F401
-from .clip_grad import clip_grad_norm, clip_grad_norm_, clip_grad_value_  # noqa: F401
-from .weight_norm import weight_norm, remove_weight_norm  # noqa: F401
-from .convert_parameters import parameters_to_vector, vector_to_parameters  # noqa: F401
-from .spectral_norm import spectral_norm, remove_spectral_norm  # noqa: F401
-from .fusion import fuse_conv_bn_eval, fuse_conv_bn_weights  # noqa: F401
+from . import rnn
+from .clip_grad import clip_grad_norm, clip_grad_norm_, clip_grad_value_
+from .weight_norm import weight_norm, remove_weight_norm
+from .convert_parameters import parameters_to_vector, vector_to_parameters
+from .spectral_norm import spectral_norm, remove_spectral_norm
+from .fusion import fuse_conv_bn_eval, fuse_conv_bn_weights

torch/optim/__init__.py

@@ -5,19 +5,19 @@ enough, so that more sophisticated ones can be also easily integrated in the
 future.
 """
 
-from .adadelta import Adadelta  # noqa: F401
-from .adagrad import Adagrad  # noqa: F401
-from .adam import Adam  # noqa: F401
-from .adamw import AdamW  # noqa: F401
-from .sparse_adam import SparseAdam  # noqa: F401
-from .adamax import Adamax  # noqa: F401
-from .asgd import ASGD  # noqa: F401
-from .sgd import SGD  # noqa: F401
-from .rprop import Rprop  # noqa: F401
-from .rmsprop import RMSprop  # noqa: F401
-from .optimizer import Optimizer  # noqa: F401
-from .lbfgs import LBFGS  # noqa: F401
-from . import lr_scheduler  # noqa: F401
+from .adadelta import Adadelta
+from .adagrad import Adagrad
+from .adam import Adam
+from .adamw import AdamW
+from .sparse_adam import SparseAdam
+from .adamax import Adamax
+from .asgd import ASGD
+from .sgd import SGD
+from .rprop import Rprop
+from .rmsprop import RMSprop
+from .optimizer import Optimizer
+from .lbfgs import LBFGS
+from . import lr_scheduler
 
 del adadelta
 del adagrad

torch/quantization/__init__.py

@@ -1,10 +1,10 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
-from .quantize import *  # noqa: F401
-from .observer import *  # noqa: F401
-from .qconfig import *  # noqa: F401
-from .fake_quantize import *  # noqa: F401
-from .fuse_modules import fuse_modules  # noqa: F401
-from .stubs import *  # noqa: F401
+from .quantize import *
+from .observer import *
+from .qconfig import *
+from .fake_quantize import *
+from .fuse_modules import fuse_modules
+from .stubs import *
 
 def default_eval_fn(model, calib_data):
     r"""

torch/utils/__init__.py

@@ -1,6 +1,6 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-from .throughput_benchmark import ThroughputBenchmark  # noqa: F401
+from .throughput_benchmark import ThroughputBenchmark
 
 # Set the module for a given object for nicer printing
 def set_module(obj, mod):

torch/utils/data/__init__.py

@@ -1,4 +1,4 @@
-from .sampler import Sampler, SequentialSampler, RandomSampler, SubsetRandomSampler, WeightedRandomSampler, BatchSampler  # noqa: F401
-from .distributed import DistributedSampler  # noqa: F401
-from .dataset import Dataset, IterableDataset, TensorDataset, ConcatDataset, ChainDataset, Subset, random_split  # noqa: F401
-from .dataloader import DataLoader, _DatasetKind, get_worker_info  # noqa: F401
+from .sampler import Sampler, SequentialSampler, RandomSampler, SubsetRandomSampler, WeightedRandomSampler, BatchSampler
+from .distributed import DistributedSampler
+from .dataset import Dataset, IterableDataset, TensorDataset, ConcatDataset, ChainDataset, Subset, random_split
+from .dataloader import DataLoader, _DatasetKind, get_worker_info

torch/utils/data/_utils/__init__.py

@@ -11,7 +11,7 @@ import sys
 import atexit
 
 # old private location of the ExceptionWrapper that some users rely on:
-from torch._utils import ExceptionWrapper  # noqa: F401
+from torch._utils import ExceptionWrapper
 
 
 IS_WINDOWS = sys.platform == "win32"
@@ -42,4 +42,4 @@ def _set_python_exit_flag():
 atexit.register(_set_python_exit_flag)
 
 
-from . import worker, signal_handling, pin_memory, collate, fetch  # noqa: F401
+from . import worker, signal_handling, pin_memory, collate, fetch