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local BatchNormalization, parent = torch.class('cudnn.BatchNormalization', 'nn.Module')
local ffi = require 'ffi'
local errcheck = cudnn.errcheck
BatchNormalization.mode = 'CUDNN_BATCHNORM_PER_ACTIVATION'
BatchNormalization.nDim = 2
BatchNormalization.__version = 2
function BatchNormalization:__init(nFeature, eps, momentum, affine)
parent.__init(self)
assert(nFeature and type(nFeature) == 'number',
'Missing argument #1: Number of feature planes. ')
assert(nFeature ~= 0, 'To set affine=false call BatchNormalization'
.. '(nFeature, eps, momentum, false) ')
assert(affine == true or affine == nil, 'only affine supported')
self.affine = true
self.eps = eps or 1e-5
self.train = true
self.momentum = momentum or 0.1
self.running_mean = torch.zeros(nFeature)
self.running_var = torch.ones(nFeature)
if self.affine then
self.weight = torch.Tensor(nFeature)
self.bias = torch.Tensor(nFeature)
self.gradWeight = torch.Tensor(nFeature)
self.gradBias = torch.Tensor(nFeature)
self:reset()
end
end
function BatchNormalization:reset()
if self.weight then
self.weight:uniform()
end
if self.bias then
self.bias:zero()
end
self.running_mean:zero()
self.running_var:fill(1)
end
function BatchNormalization:createIODescriptors(input)
assert(input:dim() == self.nDim)
assert(cudnn.typemap[torch.typename(self.weight)], 'Only Cuda supported duh!')
assert(cudnn.typemap[torch.typename(self.bias)] or not self.bias, 'Only Cuda supported duh!')
if not self.iDesc or not self.oDesc or not input:isSize(self.iSize) then
local nFeature = self.running_mean:numel()
self.iSize = input:size()
self.output:resizeAs(input)
self.iDesc = cudnn.toDescriptor(input)
self.oDesc = cudnn.toDescriptor(self.output)
local biasSize = torch.ones(self.nDim):totable()
biasSize[2] = nFeature
self.sDesc = cudnn.toDescriptor(self.bias:view(table.unpack(biasSize)))
end
end
local one = torch.FloatTensor({1});
local zero = torch.FloatTensor({0});
local scaleTens = torch.FloatTensor(1);
function BatchNormalization:updateOutput(input)
self:createIODescriptors(input)
self.save_mean = self.save_mean or self.running_mean.new()
self.save_mean:resizeAs(self.running_mean)
self.save_std = self.save_std or self.running_mean.new()
self.save_std:resizeAs(self.running_var)
if self.train then
errcheck('cudnnBatchNormalizationForwardTraining',
cudnn.getHandle(), self.mode, one:data(), zero:data(),
self.iDesc[0], input:data(), self.oDesc[0], self.output:data(),
self.sDesc[0], self.weight:data(), self.bias:data(),
self.momentum, self.running_mean:data(), self.running_var:data(), self.eps, self.save_mean:data(), self.save_std:data());
else
errcheck('cudnnBatchNormalizationForwardInference',
cudnn.getHandle(), self.mode, one:data(), zero:data(),
self.iDesc[0], input:data(), self.oDesc[0], self.output:data(),
self.sDesc[0], self.weight:data(), self.bias:data(),
self.running_mean:data(), self.running_var:data(), self.eps);
end
return self.output
end
local function backward(self,input,gradOutput, scale)
assert(gradOutput:isContiguous())
self:createIODescriptors(input)
self.gradInput:resizeAs(input)
scale = scale or 1
scaleTens:fill(scale)
errcheck('cudnnBatchNormalizationBackward',
cudnn.getHandle(), self.mode, one:data(), zero:data(), scaleTens:data(), one:data(),
self.iDesc[0], input:data(), self.iDesc[0], gradOutput:data(), self.iDesc[0], self.gradInput:data(),
-- input is bottom, gradOutput is topDiff, self.gradInput is resultBottomDiff
self.sDesc[0], self.weight:data(), self.gradWeight:data(), self.gradBias:data(),
self.eps, self.save_mean:data(), self.save_std:data());
return self.gradInput
end
function BatchNormalization:updateGradInput(input, gradOutput, scale)
-- will in fact update gradWeight and gradBias too, accGradParameters call is empty
return backward(self, input, gradOutput, scale)
end
function BatchNormalization:backward(input, gradOutput, scale)
return backward(self, input, gradOutput, scale)
end
function BatchNormalization:accGradParameters(input, gradOutput, scale)
end
function BatchNormalization:clearDesc()
self.iDesc = nil
self.oDesc = nil
self.sDesc = nil
end
function BatchNormalization:read(file, version)
parent.read(self, file)
if version < 2 then
if self.running_std then
self.running_var = self.running_std:pow(-2):add(-self.eps)
self.running_std = nil
end
end
end
function BatchNormalization:write(f)
self:clearDesc()
local var = {}
for k,v in pairs(self) do
var[k] = v
end
f:writeObject(var)
end
function BatchNormalization:type(type, tensorCache)
local _type = type == 'torch.CudaHalfTensor' and 'torch.CudaTensor' or type
parent.type(self, _type, tensorCache)
self.output = self.output:type(type)
self.gradInput = self.gradInput:type(type)
return self
end
function BatchNormalization:clearState()
self:clearDesc()
nn.utils.clear(self, 'save_mean', 'save_std')
return parent.clearState(self)
end
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