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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.
106 lines
3.7 KiB
Python
106 lines
3.7 KiB
Python
import torch
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from .Container import Container
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class Parallel(Container):
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def __init__(self, inputDimension, outputDimension):
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super(Parallel, self).__init__()
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self.inputDimension = inputDimension
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self.outputDimension = outputDimension
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self.totalOutputSize = None
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def updateOutput(self, input):
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nModule = input.size(self.inputDimension)
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outputs = []
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for i in range(nModule):
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currentInput = input.select(self.inputDimension, i)
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currentOutput = self.modules[i].updateOutput(currentInput)
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outputs.append(currentOutput)
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outputSize = currentOutput.size(self.outputDimension)
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if i == 0:
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totalOutputSize = list(currentOutput.size())
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else:
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totalOutputSize[self.outputDimension] += outputSize
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self.totalOutputSize = torch.Size(totalOutputSize)
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self.output.resize_(self.totalOutputSize)
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offset = 0
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for i in range(nModule):
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currentOutput = outputs[i]
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outputSize = currentOutput.size(self.outputDimension)
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self.output.narrow(self.outputDimension, offset, outputSize).copy_(currentOutput)
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offset = offset + currentOutput.size(self.outputDimension)
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return self.output
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def updateGradInput(self, input, gradOutput):
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nModule = input.size(self.inputDimension)
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self.gradInput.resize_as_(input)
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offset = 0
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for i in range(nModule):
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module = self.modules[i]
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currentInput = input.select(self.inputDimension, i)
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currentOutput = module.output
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outputSize = currentOutput.size(self.outputDimension)
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currentGradOutput = gradOutput.narrow(self.outputDimension, offset, outputSize)
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currentGradInput = module.updateGradInput(currentInput, currentGradOutput)
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self.gradInput.select(self.inputDimension, i).copy_(currentGradInput)
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offset = offset + outputSize
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return self.gradInput
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def accGradParameters(self, input, gradOutput, scale=1):
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nModule = input.size(self.inputDimension)
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offset = 0
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for i in range(nModule):
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module = self.modules[i]
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currentOutput = module.output
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outputSize = currentOutput.size(self.outputDimension)
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module.accGradParameters(
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input.select(self.inputDimension, i),
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gradOutput.narrow(self.outputDimension, offset, outputSize),
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scale)
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offset += outputSize
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def accUpdateGradParameters(self, input, gradOutput, lr):
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nModule = input.size(self.inputDimension)
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offset = 0
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for i in range(nModule):
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module = self.modules[i]
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currentOutput = module.output
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module.accupdateGradParameters(
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input.select(self.inputDimension, i),
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gradOutput.narrow(self.outputDimension, offset, currentOutput.size(self.outputDimension)),
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lr)
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offset = offset + currentOutput.size(self.outputDimension)
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def __repr__(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.typename(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 + '(' + str(i) + '): ' + \
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str(self.modules[i]).replace(line, line + tab + extlast)
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else:
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res += line + tab + next + '(' + str(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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