| import random |
| import math |
| import torch |
| from .Module import Module |
| |
| |
| class SpatialFullConvolutionMap(Module): |
| |
| def __init__(self, conMatrix, kW, kH, dW=1, dH=1): |
| super(SpatialFullConvolutionMap, self).__init__() |
| |
| self.kW = kW |
| self.kH = kH |
| self.dW = dW |
| self.dH = dH |
| self.connTable = conMatrix |
| self.nInputPlane = int(self.connTable.select(1, 0).max()) + 1 |
| self.nOutputPlane = int(self.connTable.select(1, 1).max()) + 1 |
| |
| self.weight = torch.Tensor(self.connTable.size(0), kH, kW) |
| self.gradWeight = torch.Tensor(self.connTable.size(0), kH, kW) |
| |
| self.bias = torch.Tensor(self.nOutputPlane) |
| self.gradBias = torch.Tensor(self.nOutputPlane) |
| |
| self.reset() |
| |
| def reset(self, stdv=None): |
| if stdv is not None: |
| stdv = stdv * math.sqrt(3) |
| self.weight.uniform_(-stdv, stdv) |
| self.bias.uniform_(-stdv, stdv) |
| else: |
| ninp = torch.Tensor(self.nOutputPlane).zero_() |
| for i in range(self.connTable.size(0)): |
| idx = int(self.connTable[i][1]) |
| ninp[idx] += 1 |
| for k in range(self.connTable.size(0)): |
| idx = int(self.connTable[k][1]) |
| stdv = 1. / math.sqrt(self.kW * self.kH * ninp[idx]) |
| self.weight[k].uniform_(-stdv, stdv) |
| for k in range(self.bias.size(0)): |
| stdv = 1. / math.sqrt(self.kW * self.kH * ninp[k]) |
| # TODO: torch.uniform |
| self.bias[k] = random.uniform(-stdv, stdv) |
| |
| def updateOutput(self, input): |
| self._backend.SpatialFullConvolutionMap_updateOutput( |
| self._backend.library_state, |
| input, |
| self.output, |
| self.weight, |
| self.bias, |
| self.connTable, |
| self.nInputPlane, |
| self.nOutputPlane, |
| self.dW, self.dH |
| ) |
| return self.output |
| |
| def updateGradInput(self, input, gradOutput): |
| self._backend.SpatialFullConvolutionMap_updateGradInput( |
| self._backend.library_state, |
| input, |
| gradOutput, |
| self.gradInput, |
| self.weight, |
| self.bias, |
| self.connTable, |
| self.nInputPlane, |
| self.nOutputPlane, |
| self.dW, self.dH |
| ) |
| return self.gradInput |
| |
| def accGradParameters(self, input, gradOutput, scale=1): |
| self._backend.SpatialFullConvolutionMap_accGradParameters( |
| self._backend.library_state, |
| input, |
| gradOutput, |
| self.gradWeight, |
| self.gradBias, |
| self.connTable, |
| self.nInputPlane, |
| self.nOutputPlane, |
| self.dW, self.dH, |
| scale |
| ) |
