| import torch |
| from .Module import Module |
| from .SpatialMaxPooling import SpatialMaxPooling |
| |
| class SpatialMaxUnpooling(Module): |
| |
| def __init__(self, poolingModule): |
| super(SpatialMaxUnpooling, self).__init__() |
| assert isinstance(poolingModule, SpatialMaxPooling) |
| assert poolingModule.kH == poolingModule.dH |
| assert poolingModule.kW == poolingModule.dW |
| self.pooling = poolingModule |
| |
| def _setParams(self): |
| self.indices = self.pooling.indices |
| self.oheight = self.pooling.iheight |
| self.owidth = self.pooling.iwidth |
| |
| def updateOutput(self, input): |
| self._setParams() |
| self._backend.SpatialMaxUnpooling_updateOutput( |
| self._backend.library_state, |
| input, |
| self.output, |
| self.indices, |
| self.owidth, self.oheight |
| ) |
| return self.output |
| |
| def updateGradInput(self, input, gradOutput): |
| self._setParams() |
| self._backend.SpatialMaxUnpooling_updateGradInput( |
| self._backend.library_state, |
| input, |
| gradOutput, |
| self.gradInput, |
| self.indices, |
| self.owidth, self.oheight |
| ) |
| return self.gradInput |
| |
| def __repr__(self): |
| return 'nn.SpatialMaxUnpooling associated to ' + self.pooling.__repr__() |
| |
