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https://github.com/zebrajr/pytorch.git
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e7c1e6a8e3
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
from .backend import FunctionBackend
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class THNNFunctionBackend(FunctionBackend):
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def __reduce__(self):
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return (_get_thnn_function_backend, ())
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def __deepcopy__(self, memo):
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memo[id(self)] = self
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return self
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def __copy__(self):
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return self
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def _get_thnn_function_backend():
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return backend
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def _initialize_backend():
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from .._functions.thnn import _all_functions as _thnn_functions
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from .._functions.linear import Linear
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from .._functions.batchnorm import BatchNorm
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from .._functions.conv import ConvNd
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from .._functions.rnn import RNN, \
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RNNTanhCell, RNNReLUCell, GRUCell, LSTMCell
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from .._functions.dropout import Dropout, FeatureDropout
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from .._functions.activation import Softsign
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from .._functions.loss import CosineEmbeddingLoss, \
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HingeEmbeddingLoss, MarginRankingLoss
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backend.register_function('Linear', Linear)
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backend.register_function('BatchNorm', BatchNorm)
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backend.register_function('ConvNd', ConvNd)
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backend.register_function('RNN', RNN)
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backend.register_function('RNNTanhCell', RNNTanhCell)
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backend.register_function('RNNReLUCell', RNNReLUCell)
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backend.register_function('LSTMCell', LSTMCell)
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backend.register_function('GRUCell', GRUCell)
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backend.register_function('Dropout', Dropout)
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backend.register_function('Dropout2d', FeatureDropout)
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backend.register_function('Dropout3d', FeatureDropout)
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backend.register_function('CosineEmbeddingLoss', CosineEmbeddingLoss)
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backend.register_function('HingeEmbeddingLoss', HingeEmbeddingLoss)
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backend.register_function('MarginRankingLoss', MarginRankingLoss)
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backend.register_function('Softsign', Softsign)
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for cls in _thnn_functions:
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name = cls.__name__
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backend.register_function(name, cls)
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backend = THNNFunctionBackend()
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_initialize_backend()
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