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require 'nn.THNN'
local UpSampling, parent =
torch.class('nn.UpSampling', 'nn.Module')
--[[
Upsamples a given 2D (spatial) or 3D (volumetric) input using either nearest neighbor, or linear
interpolation.
The input data is assumed to be of the form `minibatch x channels x [depth] x height x width`.
Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.
The input parameter scale_factor specifies the amount of upsampling, and is assumed to be a positive
integer. An optional mode parameter specifies either 'nearest' (the default) or 'linear'. Linear refers
to either bilinear for spatial (4D) tensors, or trilinear for volumetric (5D) tensors.
For nearest neighbour, output size will be:
odepth = depth*scale_factor
owidth = width*scale_factor
oheight = height*scale_factor
For linear interpolation:
owidth = (width-1)*(scale_factor-1) + width
owidth = (width-1)*(scale_factor-1) + width
oheight = (height-1)*(scale_factor-1) + height
Alternatively for bilinear or trilinear, [odepth], owidth and oheight can be directly provided as input
--]]
function UpSampling:__init(params, mode)
parent.__init(self)
-- Any ambigious mode will default to nearest
if mode ~= nil and (mode == 'linear' or mode == 'bilinear' or mode == 'trilinear') then
self.mode = 'linear'
else
self.mode = 'nearest'
end
self.odepth, self.owidth, self.oheight, self.scale_factor = nil, nil, nil, nil
if torch.type(params) == 'table' then
if self.mode == 'nearest' then
error ('Nearest neighbour upsampling requires a scale_factor')
end
self.odepth, self.owidth, self.oheight = params.odepth, params.owidth, params.oheight
if self.owidth == nil or self.oheight == nil then
error('Output height and width parameters are required')
end
else
self.scale_factor = params
if self.scale_factor < 1 then
error('scale_factor must be greater than 1')
end
if math.floor(self.scale_factor) ~= self.scale_factor then
error('scale_factor must be integer')
end
end
self.inputSize = torch.LongStorage(5):fill(0)
self.outputSize = torch.LongStorage(5):fill(0)
end
function UpSampling:setSize(input)
local xdim = input:dim()
local ydim = xdim - 1
local zdim = nil
if xdim > 4 then
zdim = xdim - 2
end
for i = 1, input:dim() do
self.inputSize[i] = input:size(i)
self.outputSize[i] = input:size(i)
end
if self.scale_factor ~= nil then
if zdim ~= nil then
self.outputSize[zdim] = self.outputSize[zdim] * self.scale_factor
end
self.outputSize[ydim] = self.outputSize[ydim] * self.scale_factor
self.outputSize[xdim] = self.outputSize[xdim] * self.scale_factor
else
if zdim ~= nil then
-- Runtime chech that depth was supplied given received 5D input
if self.odepth == nil then
error ('No output depth dimension was supplied for volumetric upsampling')
end
self.outputSize[zdim] = self.odepth
end
self.outputSize[ydim] = self.oheight
self.outputSize[xdim] = self.owidth
end
end
function UpSampling:updateOutput(input)
local nDim = input:dim()
if nDim < 4 or nDim > 5 then
error('UpSampling only supports 4D or 5D tensors')
end
local xdim = nDim
local ydim = xdim - 1
local zdim
if nDim == 5 then
zdim = xdim - 2
end
self:setSize(input)
if nDim == 4 then
if self.mode == 'nearest' then
input.THNN.SpatialUpSamplingNearest_updateOutput(
input:cdata(),
self.output:cdata(),
self.scale_factor
)
else
input.THNN.SpatialUpSamplingBilinear_updateOutput(
input:cdata(),
self.output:cdata(),
self.outputSize[ydim],
self.outputSize[xdim]
)
end
else
if self.mode == 'nearest' then
input.THNN.VolumetricUpSamplingNearest_updateOutput(
input:cdata(),
self.output:cdata(),
self.scale_factor
)
else
input.THNN.VolumetricUpSamplingTrilinear_updateOutput(
input:cdata(),
self.output:cdata(),
self.outputSize[zdim],
self.outputSize[ydim],
self.outputSize[xdim]
)
end
end
return self.output
end
function UpSampling:updateGradInput(input, gradOutput)
local nDim = input:dim()
if nDim < 4 or nDim > 5 then
error('UpSampling only supports 4D or 5D tensors')
end
if nDim ~= gradOutput:dim() then
error('Input and gradOutput should be of same dimension')
end
local xdim = nDim
local ydim = xdim - 1
local zdim
if nDim == 5 then
zdim = xdim - 2
end
self.gradInput:resizeAs(input)
if nDim == 4 then
if self.mode == 'nearest' then
input.THNN.SpatialUpSamplingNearest_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.scale_factor
)
else
input.THNN.SpatialUpSamplingBilinear_updateGradInput(
gradOutput:cdata(),
self.gradInput:cdata(),
input:size(1),
input:size(2),
input:size(3),
input:size(4),
self.outputSize[ydim],
self.outputSize[xdim]
)
end
else
if self.mode == 'nearest' then
input.THNN.VolumetricUpSamplingNearest_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.scale_factor
)
else
input.THNN.VolumetricUpSamplingTrilinear_updateGradInput(
gradOutput:cdata(),
self.gradInput:cdata(),
input:size(1),
input:size(2),
input:size(3),
input:size(4),
input:size(5),
self.outputSize[zdim],
self.outputSize[ydim],
self.outputSize[xdim]
)
end
end
return self.gradInput
end
function UpSampling:__tostring__()
local s
if self.scale_factor ~= nil then
s = string.format('%s(%dx, %s)', torch.type(self), self.scale_factor, self.mode)
else
if self.odepth ~= nil then
s = string.format('%s(%dx%dx%d, %s)', torch.type(self), self.odepth, self.oheight, self.owidth, self.mode)
else
s = string.format('%s(%dx%d, %s)', torch.type(self), self.oheight, self.owidth, self.mode)
end
end
return s
end
|
