Merge branch 'dev'
This commit is contained in:
commit
e371955cc7
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@ -63,7 +63,24 @@ local function crop_if_large_pair(x, y, max_size)
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return x, y
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end
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end
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local function padding_x(x, pad)
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if pad > 0 then
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x = iproc.padding(x, pad, pad, pad, pad)
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end
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return x
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end
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local function padding_xy(x, y, pad, y_zero)
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local scale = y:size(2) / x:size(2)
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if pad > 0 then
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x = iproc.padding(x, pad, pad, pad, pad)
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if y_zero then
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y = iproc.zero_padding(y, pad * scale, pad * scale, pad * scale, pad * scale)
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else
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y = iproc.padding(y, pad * scale, pad * scale, pad * scale, pad * scale)
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end
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end
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return x, y
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end
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local function load_images(list)
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local MARGIN = 32
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local csv = csvigo.load({path = list, verbose = false, mode = "raw"})
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@ -105,6 +122,11 @@ local function load_images(list)
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xx = alpha_util.fill(xx, meta2.alpha, alpha_color)
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end
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xx, yy = crop_if_large_pair(xx, yy, settings.max_training_image_size)
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xx, yy = padding_xy(xx, yy, settings.padding, settings.padding_y_zero)
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if settings.grayscale then
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xx = iproc.rgb2y(xx)
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yy = iproc.rgb2y(yy)
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end
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table.insert(x, {{y = compression.compress(yy), x = compression.compress(xx)},
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{data = {filters = filters, has_x = true}}})
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else
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@ -113,11 +135,15 @@ local function load_images(list)
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else
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im = crop_if_large(im, settings.max_training_image_size)
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im = iproc.crop_mod4(im)
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im = padding_x(im, settings.padding)
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local scale = 1.0
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if settings.random_half_rate > 0.0 then
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scale = 2.0
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end
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if im:size(2) > (settings.crop_size * scale + MARGIN) and im:size(3) > (settings.crop_size * scale + MARGIN) then
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if settings.grayscale then
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im = iproc.rgb2y(im)
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end
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table.insert(x, {compression.compress(im), {data = {filters = filters}}})
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else
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io.stderr:write(string.format("\n%s: skip: image is too small (%d > size).\n", filename, settings.crop_size * scale + MARGIN))
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42
lib/ShakeShakeTable.lua
Normal file
42
lib/ShakeShakeTable.lua
Normal file
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@ -0,0 +1,42 @@
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local ShakeShakeTable, parent = torch.class('w2nn.ShakeShakeTable','nn.Module')
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function ShakeShakeTable:__init()
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parent.__init(self)
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self.alpha = torch.Tensor()
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self.beta = torch.Tensor()
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self.first = torch.Tensor()
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self.second = torch.Tensor()
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self.train = true
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end
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function ShakeShakeTable:updateOutput(input)
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local batch_size = input[1]:size(1)
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if self.train then
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self.alpha:resize(batch_size):uniform()
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self.beta:resize(batch_size):uniform()
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self.second:resizeAs(input[1]):copy(input[2])
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for i = 1, batch_size do
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self.second[i]:mul(self.alpha[i])
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end
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self.output:resizeAs(input[1]):copy(input[1])
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for i = 1, batch_size do
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self.output[i]:mul(1.0 - self.alpha[i])
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end
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self.output:add(self.second):mul(2)
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else
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self.output:resizeAs(input[1]):copy(input[1]):add(input[2])
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end
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return self.output
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end
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function ShakeShakeTable:updateGradInput(input, gradOutput)
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local batch_size = input[1]:size(1)
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self.first:resizeAs(gradOutput):copy(gradOutput)
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for i = 1, batch_size do
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self.first[i]:mul(self.beta[i])
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end
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self.second:resizeAs(gradOutput):copy(gradOutput)
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for i = 1, batch_size do
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self.second[i]:mul(1.0 - self.beta[i])
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end
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self.gradOutput = {self.first, self.second}
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return self.gradOutput
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end
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@ -80,6 +80,8 @@ function iproc.scale_with_gamma22(src, width, height, filter, blur)
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return dest
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end
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function iproc.padding(img, w1, w2, h1, h2)
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local conversion
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img, conversion = iproc.byte2float(img)
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image = image or require 'image'
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local dst_height = img:size(2) + h1 + h2
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local dst_width = img:size(3) + w1 + w2
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@ -88,9 +90,15 @@ function iproc.padding(img, w1, w2, h1, h2)
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flow[2] = torch.ger(torch.ones(dst_height), torch.linspace(0, dst_width - 1, dst_width))
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flow[1]:add(-h1)
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flow[2]:add(-w1)
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return image.warp(img, flow, "simple", false, "clamp")
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local dest = image.warp(img, flow, "simple", false, "clamp")
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if conversion then
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dest = iproc.float2byte(dest)
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end
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return dest
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end
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function iproc.zero_padding(img, w1, w2, h1, h2)
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local conversion
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img, conversion = iproc.byte2float(img)
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image = image or require 'image'
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local dst_height = img:size(2) + h1 + h2
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local dst_width = img:size(3) + w1 + w2
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@ -99,7 +107,11 @@ function iproc.zero_padding(img, w1, w2, h1, h2)
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flow[2] = torch.ger(torch.ones(dst_height), torch.linspace(0, dst_width - 1, dst_width))
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flow[1]:add(-h1)
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flow[2]:add(-w1)
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return image.warp(img, flow, "simple", false, "pad", 0)
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local dest = image.warp(img, flow, "simple", false, "pad", 0)
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if conversion then
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dest = iproc.float2byte(dest)
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end
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return dest
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end
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function iproc.white_noise(src, std, rgb_weights, gamma)
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gamma = gamma or 0.454545
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@ -217,6 +229,7 @@ function iproc.rgb2y(src)
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src, conversion = iproc.byte2float(src)
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local dest = torch.FloatTensor(1, src:size(2), src:size(3)):zero()
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dest:add(0.299, src[1]):add(0.587, src[2]):add(0.114, src[3])
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dest:clamp(0, 1)
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if conversion then
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dest = iproc.float2byte(dest)
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end
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@ -43,8 +43,10 @@ function pairwise_transform.jpeg_(src, quality, size, offset, n, options)
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yc = iproc.byte2float(yc)
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if options.rgb then
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else
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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if xc:size(1) > 1 then
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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end
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end
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if torch.uniform() < options.nr_rate then
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-- reducing noise
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@ -51,8 +51,10 @@ function pairwise_transform.scale(src, scale, size, offset, n, options)
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yc = iproc.byte2float(yc)
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if options.rgb then
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else
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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if xc:size(1) > 1 then
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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end
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end
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table.insert(batch, {xc, iproc.crop(yc, offset, offset, size - offset, size - offset)})
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end
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@ -38,8 +38,10 @@ function pairwise_transform.user(x, y, size, offset, n, options)
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yc = iproc.byte2float(yc)
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if options.rgb then
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else
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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if xc:size(1) > 1 then
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yc = iproc.rgb2y(yc)
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xc = iproc.rgb2y(xc)
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end
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end
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if options.gcn then
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local mean = xc:mean()
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@ -279,10 +279,17 @@ function pairwise_transform_utils.low_resolution(src)
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toTensor("byte", "RGB", "DHW")
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end
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--]]
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return gm.Image(src, "RGB", "DHW"):
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size(src:size(3) * 0.5, src:size(2) * 0.5, "Box"):
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size(src:size(3), src:size(2), "Box"):
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toTensor("byte", "RGB", "DHW")
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if src:size(1) == 1 then
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return gm.Image(src, "I", "DHW"):
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size(src:size(3) * 0.5, src:size(2) * 0.5, "Box"):
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size(src:size(3), src:size(2), "Box"):
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toTensor("byte", "I", "DHW")
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else
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return gm.Image(src, "RGB", "DHW"):
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size(src:size(3) * 0.5, src:size(2) * 0.5, "Box"):
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size(src:size(3), src:size(2), "Box"):
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toTensor("byte", "RGB", "DHW")
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end
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end
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return pairwise_transform_utils
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@ -76,6 +76,9 @@ cmd:option("-name", "user", 'model name for user method')
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cmd:option("-gpu", "", 'GPU Device ID or ID lists (comma seprated)')
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cmd:option("-loss", "huber", 'loss function (huber|l1|mse|bce)')
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cmd:option("-update_criterion", "mse", 'mse|loss')
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cmd:option("-padding", 0, 'replication padding size')
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cmd:option("-padding_y_zero", 0, 'zero padding y for segmentation (0|1)')
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cmd:option("-grayscale", 0, 'grayscale x&y (0|1)')
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local function to_bool(settings, name)
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if settings[name] == 1 then
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@ -94,6 +97,8 @@ to_bool(settings, "save_history")
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to_bool(settings, "use_transparent_png")
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to_bool(settings, "pairwise_y_binary")
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to_bool(settings, "pairwise_flip")
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to_bool(settings, "padding_y_zero")
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to_bool(settings, "grayscale")
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if settings.plot then
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require 'gnuplot'
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@ -74,5 +74,6 @@ else
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require 'SSIMCriterion'
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require 'InplaceClip01'
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require 'L1Criterion'
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require 'ShakeShakeTable'
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return w2nn
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end
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