| import torch |
| from .Module import Module |
| from .utils import clear |
| |
| |
| class SpatialAdaptiveMaxPooling(Module): |
| |
| def __init__(self, w, h): |
| super(SpatialAdaptiveMaxPooling, self).__init__() |
| self.w = w |
| self.h = h |
| self.indices = None |
| |
| def updateOutput(self, input): |
| if self.indices is None: |
| self.indices = input.new() |
| self.indices = self.indices.long() |
| self._backend.SpatialAdaptiveMaxPooling_updateOutput( |
| self._backend.library_state, |
| input, |
| self.output, |
| self.indices, |
| self.w, |
| self.h |
| ) |
| return self.output |
| |
| def updateGradInput(self, input, gradOutput): |
| self._backend.SpatialAdaptiveMaxPooling_updateGradInput( |
| self._backend.library_state, |
| input, |
| gradOutput, |
| self.gradInput, |
| self.indices |
| ) |
| return self.gradInput |
| |
| def clearState(self): |
| clear(self, 'indices') |
| return super(SpatialAdaptiveMaxPooling, self).clearState() |
