| import torch |
| from .Module import Module |
| |
| |
| class SpatialReplicationPadding(Module): |
| |
| def __init__(self, pad_l, pad_r=None, pad_t=None, pad_b=None): |
| super(SpatialReplicationPadding, self).__init__() |
| self.pad_l = pad_l |
| self.pad_r = pad_r if pad_r is not None else pad_l |
| self.pad_t = pad_t if pad_t is not None else pad_l |
| self.pad_b = pad_b if pad_b is not None else pad_l |
| |
| def updateOutput(self, input): |
| assert input.dim() == 4 |
| self._backend.SpatialReplicationPadding_updateOutput( |
| self._backend.library_state, |
| input, |
| self.output, |
| self.pad_l, self.pad_r, self.pad_t, self.pad_b |
| ) |
| |
| return self.output |
| |
| def updateGradInput(self, input, gradOutput): |
| assert input.dim() == 4 and gradOutput.dim() == 4 |
| assert input.size(0) == gradOutput.size(0) and \ |
| input.size(1) == gradOutput.size(1) and \ |
| input.size(2) + self.pad_t + self.pad_b == gradOutput.size(2) and \ |
| input.size(3) + self.pad_l + self.pad_r == gradOutput.size(3) |
| |
| self._backend.SpatialReplicationPadding_updateGradInput( |
| self._backend.library_state, |
| input, |
| gradOutput, |
| self.gradInput, |
| self.pad_l, self.pad_r, self.pad_t, self.pad_b |
| ) |
| |
| return self.gradInput |
| |
| def __repr__(self): |
| s = super(SpatialReplicationPadding, self).__repr__() |
| s += '({}, {}, {}, {})'.format(self.pad_l, self.pad_r, self.pad_t, self.pad_b) |
| return s |
