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function torch.CudaTensor.apply(self, func)
local x = torch.FloatTensor(self:size()):copy(self)
x:apply(func)
self:copy(x)
return self
end
local function Tensor__type(self,type)
local current = torch.typename(self)
if not type then return current end
if type ~= current then
local new = torch.getmetatable(type).new()
if self:nElement() > 0 then
new:resize(self:size()):copy(self)
end
return new
else
return self
end
end
local function Tensor__typeAs(self,tensor)
return self:type(tensor:type())
end
local TensorTypes = {
float = 'torch.FloatTensor',
half = 'torch.HalfTensor',
double = 'torch.DoubleTensor',
byte = 'torch.ByteTensor',
char = 'torch.CharTensor',
int = 'torch.IntTensor',
short = 'torch.ShortTensor',
long = 'torch.LongTensor',
cuda = 'torch.CudaTensor',
cudaDouble = 'torch.CudaDoubleTensor',
cudaByte = 'torch.CudaByteTensor',
cudaChar = 'torch.CudaCharTensor',
cudaInt = 'torch.CudaIntTensor',
cudaShort = 'torch.CudaShortTensor',
cudaLong = 'torch.CudaLongTensor'
}
if cutorch.hasHalf then
TensorTypes['cudaHalf'] = 'torch.CudaHalfTensor'
end
local function Tensor__converter(type)
return function(self)
return self:type(type)
end
end
for _, SrcType in pairs(TensorTypes) do
for FuncName, DstType in pairs(TensorTypes) do
rawset(torch.getmetatable(SrcType), FuncName, Tensor__converter(DstType))
end
end
for _, CudaTensorType in pairs(TensorTypes) do
local metatable = torch.getmetatable(CudaTensorType)
rawset(metatable, 'type', Tensor__type)
rawset(metatable, 'typeAs', Tensor__typeAs)
rawset(metatable, 'view', torch['view'])
for _,func in pairs{'expand', 'expandAs', 'viewAs', 'repeatTensor',
'permute', 'split', 'chunk'} do
rawset(metatable, func, torch[func])
end
end
local CudaTensorTypes = {
float = 'torch.CudaTensor',
double = 'torch.CudaDoubleTensor',
byte = 'torch.CudaByteTensor',
char = 'torch.CudaCharTensor',
int = 'torch.CudaIntTensor',
short = 'torch.CudaShortTensor',
long = 'torch.CudaLongTensor'
}
if cutorch.hasHalf then
CudaTensorTypes['half'] = 'torch.CudaHalfTensor'
end
for ValueType, CudaTensorType in pairs(CudaTensorTypes) do
local function Tensor__totable(self)
local host_tensor = self[ValueType](self)
return host_tensor:totable()
end
rawset(torch.getmetatable(CudaTensorType), 'totable', Tensor__totable)
end
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