Add some experimental model
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lib/srcnn.lua
108
lib/srcnn.lua
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@ -266,6 +266,107 @@ function srcnn.upconv_7(backend, ch)
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return model
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end
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-- large version of upconv_7
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-- This model able to beat upconv_7 (PSNR: +0.3 ~ +0.8) but this model is 2x slower than upconv_7.
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function srcnn.upconv_7l(backend, ch)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 128, 192, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 192, 256, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 256, 512, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialFullConvolution(backend, 512, ch, 4, 4, 2, 2, 3, 3):noBias())
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model:add(w2nn.InplaceClip01())
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model:add(nn.View(-1):setNumInputDims(3))
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model.w2nn_arch_name = "upconv_7l"
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model.w2nn_offset = 14
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model.w2nn_scale_factor = 2
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model.w2nn_resize = true
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model.w2nn_channels = ch
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--model:cuda()
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--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
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return model
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end
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-- layerwise linear blending with skip connections
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-- Note: PSNR: upconv_7 < skiplb_7 < upconv_7l
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function srcnn.skiplb_7(backend, ch)
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local function skip(backend, i, o)
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local con = nn.Concat(2)
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local conv = nn.Sequential()
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conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 1, 1))
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conv:add(nn.LeakyReLU(0.1, true))
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-- depth concat
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con:add(conv)
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con:add(nn.Identify()) -- skip
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return con
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end
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local model = nn.Sequential()
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model:add(skip(backend, ch, 16))
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model:add(skip(backend, 16+ch, 32))
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model:add(skip(backend, 32+16+ch, 64))
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model:add(skip(backend, 64+32+16+ch, 128))
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model:add(skip(backend, 128+64+32+16+ch, 128))
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model:add(skip(backend, 128+128+64+32+16+ch, 256))
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-- input of last layer = [all layerwise output(contains input layer)].flatten
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model:add(SpatialFullConvolution(backend, 256+128+128+64+32+16+ch, ch, 4, 4, 2, 2, 3, 3):noBias()) -- linear blend
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model:add(w2nn.InplaceClip01())
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model:add(nn.View(-1):setNumInputDims(3))
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model.w2nn_arch_name = "skiplb_7"
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model.w2nn_offset = 14
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model.w2nn_scale_factor = 2
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model.w2nn_resize = true
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model.w2nn_channels = ch
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--model:cuda()
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--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
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return model
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end
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-- dilated convolution + deconvolution
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-- Note: This model is not better than upconv_7. Maybe becuase of under-fitting.
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function srcnn.dilated_upconv_7(backend, ch)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 16, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 16, 32, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(nn.SpatialDilatedConvolution(32, 64, 3, 3, 1, 1, 0, 0, 2, 2))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(nn.SpatialDilatedConvolution(64, 128, 3, 3, 1, 1, 0, 0, 2, 2))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(nn.SpatialDilatedConvolution(128, 128, 3, 3, 1, 1, 0, 0, 2, 2))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 128, 256, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
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model:add(w2nn.InplaceClip01())
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--model:add(nn.View(-1):setNumInputDims(3))
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model.w2nn_arch_name = "dilated_upconv_7"
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model.w2nn_offset = 20
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model.w2nn_scale_factor = 2
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model.w2nn_resize = true
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model.w2nn_channels = ch
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--model:cuda()
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--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
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return model
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end
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function srcnn.create(model_name, backend, color)
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model_name = model_name or "vgg_7"
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backend = backend or "cunn"
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@ -287,7 +388,10 @@ function srcnn.create(model_name, backend, color)
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end
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end
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--local model = srcnn.upconv_6("cunn", 3):cuda()
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--print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
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--[[
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local model = srcnn.upconv_7l("cunn", 3):cuda()
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print(model)
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print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
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--]]
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return srcnn
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