a210090033
- Add new pretrained model to ./models/upconv_7
- Move old models to ./models/vgg_7
- Use nn.LeakyReLU instead of w2nn.LeakyReLU
- Add useful attribute to .json
New JSON attribute:
The first layer has `model_config` attribute.
It contains:
model_arch: architecture name of model. see `lib/srcnn.lua`
scale_factor: if scale_factor > 1, model:forward() changes image resolution with scale_factor.
channels: input/output channels. if channels == 3, model is RGB model.
offset: pixel size that is to be removed from output.
for example:
(scale_factor=1, offset=7, input=100x100) => output=(100-7)x(100-7)
(scale_factor=2, offset=12, input=100x100) => output=(100*2-12)x(100*2-12)
And each layer has `class_name` attribute.
87 lines
3.1 KiB
Lua
87 lines
3.1 KiB
Lua
local pairwise_utils = require 'pairwise_transform_utils'
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local iproc = require 'iproc'
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local pairwise_transform = {}
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function pairwise_transform.scale(src, scale, size, offset, n, options)
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local filters = options.downsampling_filters
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local unstable_region_offset = 8
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local downsampling_filter = filters[torch.random(1, #filters)]
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local y = pairwise_utils.preprocess(src, size, options)
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assert(y:size(2) % 4 == 0 and y:size(3) % 4 == 0)
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local down_scale = 1.0 / scale
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local x
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if options.gamma_correction then
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local small = iproc.scale_with_gamma22(y, y:size(3) * down_scale,
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y:size(2) * down_scale, downsampling_filter)
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if options.x_upsampling then
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x = iproc.scale(small, y:size(3), y:size(2), options.upsampling_filter)
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else
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x = small
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end
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else
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local small = iproc.scale(y, y:size(3) * down_scale,
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y:size(2) * down_scale, downsampling_filter)
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if options.x_upsampling then
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x = iproc.scale(small, y:size(3), y:size(2), options.upsampling_filter)
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else
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x = small
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end
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end
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if options.x_upsampling then
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x = iproc.crop(x, unstable_region_offset, unstable_region_offset,
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x:size(3) - unstable_region_offset, x:size(2) - unstable_region_offset)
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y = iproc.crop(y, unstable_region_offset, unstable_region_offset,
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y:size(3) - unstable_region_offset, y:size(2) - unstable_region_offset)
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assert(x:size(2) % 4 == 0 and x:size(3) % 4 == 0)
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assert(x:size(1) == y:size(1) and x:size(2) == y:size(2) and x:size(3) == y:size(3))
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else
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assert(x:size(1) == y:size(1) and x:size(2) * scale == y:size(2) and x:size(3) * scale == y:size(3))
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end
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local scale_inner = scale
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if options.x_upsampling then
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scale_inner = 1
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end
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local batch = {}
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for i = 1, n do
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local xc, yc = pairwise_utils.active_cropping(x, y,
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size,
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scale_inner,
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options.active_cropping_rate,
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options.active_cropping_tries)
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xc = iproc.byte2float(xc)
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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 = image.rgb2yuv(yc)[1]:reshape(1, yc:size(2), yc:size(3))
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xc = image.rgb2yuv(xc)[1]:reshape(1, xc:size(2), xc:size(3))
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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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return batch
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end
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function pairwise_transform.test_scale(src)
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torch.setdefaulttensortype("torch.FloatTensor")
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local options = {random_color_noise_rate = 0.5,
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random_half_rate = 0.5,
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random_overlay_rate = 0.5,
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random_unsharp_mask_rate = 0.5,
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active_cropping_rate = 0.5,
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active_cropping_tries = 10,
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max_size = 256,
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x_upsampling = false,
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downsampling_filters = "Box",
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rgb = true
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}
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local image = require 'image'
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local src = image.lena()
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for i = 1, 10 do
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local xy = pairwise_transform.scale(src, 2.0, 128, 7, 1, options)
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image.display({image = xy[1][1], legend = "y:" .. (i * 10), min = 0, max = 1})
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image.display({image = xy[1][2], legend = "x:" .. (i * 10), min = 0, max = 1})
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end
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end
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return pairwise_transform
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