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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.
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
import torch
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from .Container import Container
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class Concat(Container):
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def __init__(self, dimension):
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super(Concat, self).__init__()
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self.outputSize = torch.Size()
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self.dimension = dimension
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def updateOutput(self, input):
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outs = []
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for i in range(len(self.modules)):
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currentOutput = self.modules[i].updateOutput(input)
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outs.append(currentOutput)
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if i == 0:
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size = list(currentOutput.size())
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else:
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size[self.dimension] += currentOutput.size(self.dimension)
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self.outputSize = torch.Size(size)
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self.output.resize_(self.outputSize)
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offset = 0
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for i, module in enumerate(self.modules):
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currentOutput = outs[i]
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self.output.narrow(self.dimension, offset, currentOutput.size(self.dimension)).copy_(currentOutput)
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offset = offset + currentOutput.size(self.dimension)
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return self.output
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def updateGradInput(self, input, gradOutput):
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self.gradInput.resize_as_(input)
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offset = 0
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for i, module in enumerate(self.modules):
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currentOutput = module.output
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currentGradInput = module.updateGradInput(input, gradOutput.narrow(
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self.dimension, offset, currentOutput.size(self.dimension)))
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# if the module does not produce a gradInput (for example first layer),: ignore it and move on.
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if currentGradInput:
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if i == 0:
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self.gradInput.copy_(currentGradInput)
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else:
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self.gradInput.add_(currentGradInput)
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offset = offset + currentOutput.size(self.dimension)
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return self.gradInput
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def accGradParameters(self, input, gradOutput, scale=1):
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offset = 0
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for i, module in enumerate(self.modules):
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currentOutput = module.output
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module.accGradParameters(
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input,
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gradOutput.narrow(self.dimension, offset, currentOutput.size(self.dimension)),
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scale)
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offset = offset + currentOutput.size(self.dimension)
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def backward(self, input, gradOutput, scale=1):
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self.gradInput.resize_as_(input)
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offset = 0
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for i, module in enumerate(self.modules):
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currentOutput = module.output
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currentGradInput = module.backward(input, gradOutput.narrow(
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self.dimension, offset, currentOutput.size(self.dimension)), scale)
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# if the module.es not produce a gradInput (for example first layer),: ignore it and move on.
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if currentGradInput is not None:
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if i == 0:
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self.gradInput.copy_(currentGradInput)
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else:
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self.gradInput.add_(currentGradInput)
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offset = offset + currentOutput.size(self.dimension)
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return self.gradInput
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def accUpdateGradParameters(self, input, gradOutput, lr):
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offset = 0
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for i, module in enumerate(self.modules):
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currentOutput = module.output
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module.accUpdateGradParameters(
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input,
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gradOutput.narrow(self.dimension, offset, currentOutput.size(self.dimension)),
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lr)
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offset = offset + currentOutput.size(self.dimension)
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def __tostring__(self):
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tab = ' '
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line = '\n'
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next = ' |`-> '
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ext = ' | '
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extlast = ' '
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last = ' +. -> '
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res = torch.type(self)
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res += ' {' + line + tab + 'input'
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for i in range(len(self.modules)):
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if i == len(self.modules) - 1:
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res += line + tab + next + '(' + i + '): ' + str(self.modules[i]).replace(line, line + tab + extlast)
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else:
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res += line + tab + next + '(' + i + '): ' + str(self.modules[i]).replace(line, line + tab + ext)
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res += line + tab + last + 'output'
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res += line + '}'
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return res
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