| import torch |
| from .Module import Module |
| from .Sequential import Sequential |
| from .Square import Square |
| from .Power import Power |
| from .SpatialAveragePooling import SpatialAveragePooling |
| from .MulConstant import MulConstant |
| from .Sqrt import Sqrt |
| |
| |
| class SpatialLPPooling(Sequential): |
| |
| def __init__(self, nInputPlane, pnorm, kW, kH, dW=None, dH=None): |
| super(SpatialLPPooling, self).__init__() |
| |
| dW = dW or kW |
| dH = dH or kH |
| |
| self.kW = kW |
| self.kH = kH |
| self.dW = dW |
| self.dH = dH |
| |
| if pnorm == 2: |
| self.add(Square()) |
| else: |
| self.add(Power(pnorm)) |
| |
| self.add(SpatialAveragePooling(kW, kH, dW, dH)) |
| self.add(MulConstant(kW * kH)) |
| if pnorm == 2: |
| self.add(Sqrt()) |
| else: |
| self.add(Power(1. / pnorm)) |
| |
| # the module is a Sequential: by default, it'll try to learn the parameters |
| # of the sub sampler: we avoid that by redefining its methods. |
| def reset(self, stdev=None): |
| pass |
| |
| def accGradParameters(self, input, gradOutput): |
| pass |
| |
| def accUpdateGradParameters(self, input, gradOutput, lr): |
| pass |
| |
| def zeroGradParameters(self): |
| pass |
| |
| def updateParameters(self, learningRate): |
| pass |
