8dea362bed
- Memory compression by snappy (lua-csnappy) - Use RGB-wise Weighted MSE(R*0.299, G*0.587, B*0.114) instead of MSE - Aggressive cropping for edge region and some change.
211 lines
7.2 KiB
Lua
211 lines
7.2 KiB
Lua
require 'image'
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local iproc = require './iproc'
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local function reconstruct_y(model, x, offset, block_size)
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if x:dim() == 2 then
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x = x:reshape(1, x:size(1), x:size(2))
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end
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local new_x = torch.Tensor():resizeAs(x):zero()
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local output_size = block_size - offset * 2
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local input = torch.CudaTensor(1, 1, block_size, block_size)
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for i = 1, x:size(2), output_size do
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for j = 1, x:size(3), output_size do
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if i + block_size - 1 <= x:size(2) and j + block_size - 1 <= x:size(3) then
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local index = {{},
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{i, i + block_size - 1},
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{j, j + block_size - 1}}
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input:copy(x[index])
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local output = model:forward(input):float():view(1, output_size, output_size)
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local output_index = {{},
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{i + offset, offset + i + output_size - 1},
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{offset + j, offset + j + output_size - 1}}
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new_x[output_index]:copy(output)
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end
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end
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end
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return new_x
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end
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local function reconstruct_rgb(model, x, offset, block_size)
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local new_x = torch.Tensor():resizeAs(x):zero()
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local output_size = block_size - offset * 2
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local input = torch.CudaTensor(1, 3, block_size, block_size)
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for i = 1, x:size(2), output_size do
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for j = 1, x:size(3), output_size do
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if i + block_size - 1 <= x:size(2) and j + block_size - 1 <= x:size(3) then
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local index = {{},
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{i, i + block_size - 1},
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{j, j + block_size - 1}}
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input:copy(x[index])
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local output = model:forward(input):float():view(3, output_size, output_size)
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local output_index = {{},
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{i + offset, offset + i + output_size - 1},
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{offset + j, offset + j + output_size - 1}}
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new_x[output_index]:copy(output)
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end
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end
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end
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return new_x
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end
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local reconstruct = {}
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function reconstruct.is_rgb(model)
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if model:get(model:size() - 1).weight:size(1) == 3 then
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-- 3ch RGB
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return true
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else
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-- 1ch Y
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return false
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end
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end
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function reconstruct.offset_size(model)
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local conv = model:findModules("nn.SpatialConvolutionMM")
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if #conv > 0 then
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local offset = 0
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for i = 1, #conv do
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offset = offset + (conv[i].kW - 1) / 2
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end
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return math.floor(offset)
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else
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conv = model:findModules("cudnn.SpatialConvolution")
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local offset = 0
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for i = 1, #conv do
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offset = offset + (conv[i].kW - 1) / 2
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end
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return math.floor(offset)
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end
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end
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function reconstruct.image_y(model, x, offset, block_size)
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block_size = block_size or 128
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local output_size = block_size - offset * 2
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local h_blocks = math.floor(x:size(2) / output_size) +
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((x:size(2) % output_size == 0 and 0) or 1)
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local w_blocks = math.floor(x:size(3) / output_size) +
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((x:size(3) % output_size == 0 and 0) or 1)
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local h = offset + h_blocks * output_size + offset
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local w = offset + w_blocks * output_size + offset
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local pad_h1 = offset
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local pad_w1 = offset
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local pad_h2 = (h - offset) - x:size(2)
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local pad_w2 = (w - offset) - x:size(3)
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local yuv = image.rgb2yuv(iproc.padding(x, pad_w1, pad_w2, pad_h1, pad_h2))
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local y = reconstruct_y(model, yuv[1], offset, block_size)
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y[torch.lt(y, 0)] = 0
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y[torch.gt(y, 1)] = 1
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yuv[1]:copy(y)
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local output = image.yuv2rgb(image.crop(yuv,
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pad_w1, pad_h1,
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yuv:size(3) - pad_w2, yuv:size(2) - pad_h2))
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output[torch.lt(output, 0)] = 0
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output[torch.gt(output, 1)] = 1
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collectgarbage()
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return output
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end
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function reconstruct.scale_y(model, scale, x, offset, block_size)
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block_size = block_size or 128
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local x_jinc = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, "Jinc")
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x = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, "Box")
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local output_size = block_size - offset * 2
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local h_blocks = math.floor(x:size(2) / output_size) +
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((x:size(2) % output_size == 0 and 0) or 1)
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local w_blocks = math.floor(x:size(3) / output_size) +
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((x:size(3) % output_size == 0 and 0) or 1)
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local h = offset + h_blocks * output_size + offset
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local w = offset + w_blocks * output_size + offset
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local pad_h1 = offset
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local pad_w1 = offset
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local pad_h2 = (h - offset) - x:size(2)
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local pad_w2 = (w - offset) - x:size(3)
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local yuv_nn = image.rgb2yuv(iproc.padding(x, pad_w1, pad_w2, pad_h1, pad_h2))
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local yuv_jinc = image.rgb2yuv(iproc.padding(x_jinc, pad_w1, pad_w2, pad_h1, pad_h2))
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local y = reconstruct_y(model, yuv_nn[1], offset, block_size)
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y[torch.lt(y, 0)] = 0
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y[torch.gt(y, 1)] = 1
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yuv_jinc[1]:copy(y)
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local output = image.yuv2rgb(image.crop(yuv_jinc,
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pad_w1, pad_h1,
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yuv_jinc:size(3) - pad_w2, yuv_jinc:size(2) - pad_h2))
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output[torch.lt(output, 0)] = 0
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output[torch.gt(output, 1)] = 1
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collectgarbage()
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return output
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end
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function reconstruct.image_rgb(model, x, offset, block_size)
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block_size = block_size or 128
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local output_size = block_size - offset * 2
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local h_blocks = math.floor(x:size(2) / output_size) +
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((x:size(2) % output_size == 0 and 0) or 1)
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local w_blocks = math.floor(x:size(3) / output_size) +
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((x:size(3) % output_size == 0 and 0) or 1)
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local h = offset + h_blocks * output_size + offset
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local w = offset + w_blocks * output_size + offset
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local pad_h1 = offset
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local pad_w1 = offset
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local pad_h2 = (h - offset) - x:size(2)
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local pad_w2 = (w - offset) - x:size(3)
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local input = iproc.padding(x, pad_w1, pad_w2, pad_h1, pad_h2)
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local y = reconstruct_rgb(model, input, offset, block_size)
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local output = image.crop(y,
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pad_w1, pad_h1,
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y:size(3) - pad_w2, y:size(2) - pad_h2)
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collectgarbage()
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output[torch.lt(output, 0)] = 0
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output[torch.gt(output, 1)] = 1
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return output
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end
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function reconstruct.scale_rgb(model, scale, x, offset, block_size)
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block_size = block_size or 128
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x = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, "Box")
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local output_size = block_size - offset * 2
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local h_blocks = math.floor(x:size(2) / output_size) +
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((x:size(2) % output_size == 0 and 0) or 1)
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local w_blocks = math.floor(x:size(3) / output_size) +
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((x:size(3) % output_size == 0 and 0) or 1)
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local h = offset + h_blocks * output_size + offset
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local w = offset + w_blocks * output_size + offset
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local pad_h1 = offset
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local pad_w1 = offset
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local pad_h2 = (h - offset) - x:size(2)
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local pad_w2 = (w - offset) - x:size(3)
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local input = iproc.padding(x, pad_w1, pad_w2, pad_h1, pad_h2)
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local y = reconstruct_rgb(model, input, offset, block_size)
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local output = image.crop(y,
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pad_w1, pad_h1,
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y:size(3) - pad_w2, y:size(2) - pad_h2)
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output[torch.lt(output, 0)] = 0
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output[torch.gt(output, 1)] = 1
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collectgarbage()
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return output
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end
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function reconstruct.image(model, x, block_size)
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if reconstruct.is_rgb(model) then
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return reconstruct.image_rgb(model, x,
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reconstruct.offset_size(model), block_size)
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else
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return reconstruct.image_y(model, x,
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reconstruct.offset_size(model), block_size)
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end
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end
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function reconstruct.scale(model, scale, x, block_size)
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if reconstruct.is_rgb(model) then
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return reconstruct.scale_rgb(model, scale, x,
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reconstruct.offset_size(model), block_size)
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else
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return reconstruct.scale_y(model, scale, x,
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reconstruct.offset_size(model), block_size)
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end
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end
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return reconstruct
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