| import torch |
| from .Module import Module |
| |
| class SpatialUpSamplingNearest(Module): |
| """ |
| Applies a 2D up-sampling over an input image composed of several input planes. |
| |
| The upsampling is.ne using the simple nearest neighbor technique. |
| |
| The Y and X dimensions are assumed to be the last 2 tensor dimensions. For |
| instance, if the tensor is 4D,: dim 3 is the y dimension and dim 4 is the x. |
| |
| owidth = width*scale_factor |
| oheight = height*scale_factor |
| """ |
| |
| def __init__(self, scale): |
| super(SpatialUpSamplingNearest, self).__init__() |
| |
| self.scale_factor = scale |
| if self.scale_factor < 1: |
| raise ValueError('scale_factor must be greater than 1') |
| if self.scale_factor % 1 != 0: |
| raise ValueError('scale_factor must be integer') |
| |
| self.inputSize = torch.LongStorage(4) |
| self.outputSize = torch.LongStorage(4) |
| |
| def updateOutput(self, input): |
| assert input.dim() == 4 |
| |
| # Copy the input size |
| xdim = input.dim() - 1 |
| ydim = input.dim() - 2 |
| for i in range(input.dim()): |
| self.inputSize[i] = input.size(i) |
| self.outputSize[i] = input.size(i) |
| |
| self.outputSize[ydim] = self.outputSize[ydim] * self.scale_factor |
| self.outputSize[xdim] = self.outputSize[xdim] * self.scale_factor |
| self.output.resize_(self.outputSize) |
| self._backend.SpatialUpSamplingNearest_updateOutput( |
| self._backend.library_state, |
| input, |
| self.output, |
| self.scale_factor |
| ) |
| return self.output |
| |
| |
| def updateGradInput(self, input, gradOutput): |
| self.gradInput.resizeAs_(input) |
| self._backend.SpatialUpSamplingNearest_updateGradInput( |
| self._backend.library_state, |
| input, |
| gradOutput, |
| self.gradInput, |
| self.scale_factor |
| ) |
| return self.gradInput |
| |
