Add cunet for 1x; Remove unused code
This commit is contained in:
nagadomi
parent
b28b6172ca
commit
6efd7f890e
570
lib/srcnn.lua
570
lib/srcnn.lua
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@ -255,77 +255,6 @@ function srcnn.vgg_7(backend, ch)
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return model
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end
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-- VGG style net(12 layers)
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function srcnn.vgg_12(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, 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, 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, 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, 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, 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, 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, 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, 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, ch, 3, 3, 1, 1, 0, 0))
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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 = "vgg_12"
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model.w2nn_offset = 12
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model.w2nn_scale_factor = 1
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model.w2nn_resize = false
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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 (7 layers)
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function srcnn.dilated_7(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, 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, 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, 4, 4))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 128, 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, ch, 3, 3, 1, 1, 0, 0))
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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_7"
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model.w2nn_offset = 12
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model.w2nn_scale_factor = 1
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model.w2nn_resize = false
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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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-- Upconvolution
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function srcnn.upconv_7(backend, ch)
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local model = nn.Sequential()
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@ -387,121 +316,6 @@ function srcnn.upconv_7l(backend, ch)
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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.Identity()) -- 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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-- ref: https://arxiv.org/abs/1609.04802
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-- note: no batch-norm, no zero-paading
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function srcnn.srresnet_2x(backend, ch)
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local function resblock(backend)
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local seq = nn.Sequential()
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local con = nn.ConcatTable()
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local conv = nn.Sequential()
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conv:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
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conv:add(ReLU(backend))
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conv:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
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conv:add(ReLU(backend))
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con:add(conv)
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con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
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seq:add(con)
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seq:add(nn.CAddTable())
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return seq
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end
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local model = nn.Sequential()
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--model:add(skip(backend, ch, 64 - ch))
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model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(resblock(backend))
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model:add(resblock(backend))
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model:add(resblock(backend))
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model:add(resblock(backend))
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model:add(resblock(backend))
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model:add(resblock(backend))
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model:add(SpatialFullConvolution(backend, 64, 64, 4, 4, 2, 2, 2, 2))
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model:add(ReLU(backend))
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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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 = "srresnet_2x"
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model.w2nn_offset = 28
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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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-- large version of srresnet_2x. It's current best model but slow.
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function srcnn.resnet_14l(backend, ch)
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local function resblock(backend, i, o)
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local seq = nn.Sequential()
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@ -601,150 +415,6 @@ function srcnn.fcn_v1(backend, ch)
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return model
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end
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function srcnn.cupconv_14(backend, ch)
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local function skip(backend, n_input, n_output, pad)
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local con = nn.ConcatTable()
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local conv = nn.Sequential()
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local depad = nn.Sequential()
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conv:add(nn.SelectTable(1))
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conv:add(SpatialConvolution(backend, n_input, n_output, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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con:add(conv)
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con:add(nn.Identity())
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return con
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end
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local function concat(backend, n, ch, n_middle)
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local con = nn.ConcatTable()
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for i = 1, n do
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local pad = i - 1
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if i == 1 then
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con:add(nn.Sequential():add(nn.SelectTable(i)))
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else
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local seq = nn.Sequential()
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seq:add(nn.SelectTable(i))
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if pad > 0 then
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seq:add(nn.SpatialZeroPadding(-pad, -pad, -pad, -pad))
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end
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if i == n then
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--seq:add(SpatialConvolution(backend, ch, n_middle, 1, 1, 1, 1, 0, 0))
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else
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seq:add(w2nn.GradWeight(0.025))
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seq:add(SpatialConvolution(backend, n_middle, n_middle, 1, 1, 1, 1, 0, 0))
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end
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seq:add(nn.LeakyReLU(0.1, true))
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con:add(seq)
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end
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end
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return nn.Sequential():add(con):add(nn.JoinTable(2))
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end
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local model = nn.Sequential()
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local m = 64
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local n = 14
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model:add(nn.ConcatTable():add(nn.Identity()))
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for i = 1, n - 1 do
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if i == 1 then
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model:add(skip(backend, ch, m))
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else
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model:add(skip(backend, m, m))
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end
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end
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model:add(nn.FlattenTable())
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model:add(concat(backend, n, ch, m))
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model:add(SpatialFullConvolution(backend, m * (n - 1) + 3, 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 = "cupconv_14"
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model.w2nn_offset = 28
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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return model
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end
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function srcnn.upconv_refine(backend, ch)
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local function block(backend, ch)
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local seq = nn.Sequential()
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local con = nn.ConcatTable()
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local res = nn.Sequential()
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local base = nn.Sequential()
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local refine = nn.Sequential()
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local aux_con = nn.ConcatTable()
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res:add(w2nn.GradWeight(0.1))
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res:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
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res:add(nn.LeakyReLU(0.1, true))
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res:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
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res:add(nn.LeakyReLU(0.1, true))
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res:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
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res:add(nn.LeakyReLU(0.1, true))
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res:add(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0):noBias())
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res:add(w2nn.InplaceClip01())
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res:add(nn.MulConstant(0.5))
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con:add(res)
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con:add(nn.Sequential():add(nn.SpatialZeroPadding(-4, -4, -4, -4)):add(nn.MulConstant(0.5)))
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-- main output
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refine:add(nn.CAddTable()) -- averaging
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refine:add(nn.View(-1):setNumInputDims(3))
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-- aux output
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base:add(nn.SelectTable(2))
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base:add(nn.MulConstant(2)) -- revert mul 0.5
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base:add(nn.View(-1):setNumInputDims(3))
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aux_con:add(refine)
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aux_con:add(base)
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seq:add(con)
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seq:add(aux_con)
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seq:add(w2nn.AuxiliaryLossTable(1))
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return seq
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end
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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, 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, 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, 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(block(backend, ch))
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model.w2nn_arch_name = "upconv_refine"
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model.w2nn_offset = 18
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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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return model
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end
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-- I devised this arch because of the block size and global average pooling problem,
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-- but SEBlock may possibly learn multi-scale input and no problems occur.
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local function SpatialSEBlock(backend, ave_size, n_output, r)
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local con = nn.ConcatTable(2)
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local attention = nn.Sequential()
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local n_mid = math.floor(n_output / r)
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attention:add(SpatialAveragePooling(backend, ave_size, ave_size, ave_size, ave_size))
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attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true))
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attention:add(nn.SpatialUpSamplingNearest(ave_size, ave_size))
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con:add(nn.Identity())
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con:add(attention)
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return con
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end
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-- Squeeze and Excitation Block
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local function SEBlock(backend, n_output, r)
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@ -760,45 +430,64 @@ local function SEBlock(backend, n_output, r)
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con:add(attention)
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return con
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end
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-- I devised this arch for the block size and global average pooling problem,
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-- but SEBlock may possibly learn multi-scale input or just a normalization. No problems occur.
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-- So this arch is not used.
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local function SpatialSEBlock(backend, ave_size, n_output, r)
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local con = nn.ConcatTable(2)
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local attention = nn.Sequential()
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local n_mid = math.floor(n_output / r)
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attention:add(SpatialAveragePooling(backend, ave_size, ave_size, ave_size, ave_size))
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attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true))
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attention:add(nn.SpatialUpSamplingNearest(ave_size, ave_size))
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con:add(nn.Identity())
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con:add(attention)
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return con
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end
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local function unet_branch(backend, insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local con = nn.ConcatTable(2)
|
||||
local model = nn.Sequential()
|
||||
|
||||
block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
|
||||
block:add(nn.LeakyReLU(0.1, true))
|
||||
block:add(insert)
|
||||
block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
|
||||
block:add(nn.LeakyReLU(0.1, true))
|
||||
con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
|
||||
con:add(block)
|
||||
model:add(con)
|
||||
model:add(nn.CAddTable())
|
||||
return model
|
||||
end
|
||||
local function unet_conv(backend, n_input, n_middle, n_output, se)
|
||||
local model = nn.Sequential()
|
||||
model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
if se then
|
||||
model:add(SEBlock(backend, n_output, 4))
|
||||
model:add(w2nn.ScaleTable())
|
||||
end
|
||||
return model
|
||||
end
|
||||
|
||||
-- cascaded residual channel attention unet
|
||||
-- Cascaded Residual Channel Attention U-Net
|
||||
function srcnn.upcunet(backend, ch)
|
||||
function unet_branch(insert, backend, n_input, n_output, depad)
|
||||
local block = nn.Sequential()
|
||||
local con = nn.ConcatTable(2)
|
||||
local model = nn.Sequential()
|
||||
|
||||
block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
|
||||
block:add(insert)
|
||||
block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
|
||||
con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
|
||||
con:add(block)
|
||||
model:add(con)
|
||||
model:add(nn.CAddTable())
|
||||
return model
|
||||
end
|
||||
function unet_conv(n_input, n_middle, n_output, se)
|
||||
local model = nn.Sequential()
|
||||
model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
if se then
|
||||
model:add(SEBlock(backend, n_output, 4))
|
||||
model:add(w2nn.ScaleTable())
|
||||
end
|
||||
return model
|
||||
end
|
||||
-- Residual U-Net
|
||||
function unet(backend, ch, deconv)
|
||||
local block1 = unet_conv(128, 256, 128, true)
|
||||
local function unet(backend, ch, deconv)
|
||||
local block1 = unet_conv(backend, 128, 256, 128, true)
|
||||
local block2 = nn.Sequential()
|
||||
block2:add(unet_conv(64, 64, 128, true))
|
||||
block2:add(unet_branch(block1, backend, 128, 128, 4))
|
||||
block2:add(unet_conv(128, 64, 64, true))
|
||||
block2:add(unet_conv(backend, 64, 64, 128, true))
|
||||
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
|
||||
block2:add(unet_conv(backend, 128, 64, 64, true))
|
||||
local model = nn.Sequential()
|
||||
model:add(unet_conv(ch, 32, 64, false))
|
||||
model:add(unet_branch(block2, backend, 64, 64, 16))
|
||||
model:add(unet_conv(backend, ch, 32, 64, false))
|
||||
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
|
||||
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1))
|
||||
if deconv then
|
||||
|
|
@ -837,124 +526,22 @@ function srcnn.upcunet(backend, ch)
|
|||
return model
|
||||
end
|
||||
|
||||
-- cascaded residual spatial channel attention unet
|
||||
function srcnn.upcunet_v2(backend, ch)
|
||||
function unet_branch(insert, backend, n_input, n_output, depad)
|
||||
local block = nn.Sequential()
|
||||
local con = nn.ConcatTable(2)
|
||||
local model = nn.Sequential()
|
||||
|
||||
block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
|
||||
block:add(insert)
|
||||
block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
|
||||
con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
|
||||
con:add(block)
|
||||
model:add(con)
|
||||
model:add(nn.CAddTable())
|
||||
return model
|
||||
end
|
||||
function unet_conv(n_input, n_middle, n_output, se)
|
||||
local model = nn.Sequential()
|
||||
model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
if se then
|
||||
model:add(SpatialSEBlock(backend, 4, n_output, 4))
|
||||
model:add(nn.CMulTable())
|
||||
end
|
||||
return model
|
||||
end
|
||||
-- Residual U-Net
|
||||
function unet(backend, in_ch, out_ch, deconv)
|
||||
local block1 = unet_conv(128, 256, 128, true)
|
||||
-- cunet for 1x
|
||||
function srcnn.cunet(backend, ch)
|
||||
local function unet(backend, ch)
|
||||
local block1 = unet_conv(backend, 128, 256, 128, true)
|
||||
local block2 = nn.Sequential()
|
||||
block2:add(unet_conv(64, 64, 128, true))
|
||||
block2:add(unet_branch(block1, backend, 128, 128, 4))
|
||||
block2:add(unet_conv(128, 64, 64, true))
|
||||
block2:add(unet_conv(backend, 64, 64, 128, true))
|
||||
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
|
||||
block2:add(unet_conv(backend, 128, 64, 64, true))
|
||||
|
||||
local model = nn.Sequential()
|
||||
model:add(unet_conv(in_ch, 32, 64, false))
|
||||
model:add(unet_branch(block2, backend, 64, 64, 16))
|
||||
if deconv then
|
||||
model:add(SpatialFullConvolution(backend, 64, out_ch, 4, 4, 2, 2, 3, 3):noBias())
|
||||
else
|
||||
model:add(SpatialConvolution(backend, 64, out_ch, 3, 3, 1, 1, 0, 0):noBias())
|
||||
end
|
||||
return model
|
||||
end
|
||||
local model = nn.Sequential()
|
||||
local con = nn.ConcatTable()
|
||||
local aux_con = nn.ConcatTable()
|
||||
|
||||
-- 2 cascade
|
||||
model:add(unet(backend, ch, ch, true))
|
||||
con:add(nn.Sequential():add(unet(backend, ch, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- -1 for odd output size
|
||||
con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
|
||||
|
||||
aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
|
||||
aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01())) -- single unet output
|
||||
|
||||
model:add(con)
|
||||
model:add(aux_con)
|
||||
model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
|
||||
|
||||
model.w2nn_arch_name = "upcunet_v2"
|
||||
model.w2nn_offset = 58
|
||||
model.w2nn_scale_factor = 2
|
||||
model.w2nn_channels = ch
|
||||
model.w2nn_resize = true
|
||||
-- {76,92,108,140} are also valid size but it is too small
|
||||
model.w2nn_valid_input_size = {156,172,188,204,220,236,252,268,284,300,316,332,348,364,380,396,412,428,444,460,476,492,508}
|
||||
|
||||
return model
|
||||
end
|
||||
-- cascaded residual channel attention unet
|
||||
function srcnn.upcunet_v3(backend, ch)
|
||||
local function unet_branch(insert, backend, n_input, n_output, depad)
|
||||
local block = nn.Sequential()
|
||||
local con = nn.ConcatTable(2)
|
||||
local model = nn.Sequential()
|
||||
|
||||
block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
|
||||
block:add(nn.LeakyReLU(0.1, true))
|
||||
block:add(insert)
|
||||
block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
|
||||
block:add(nn.LeakyReLU(0.1, true))
|
||||
con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
|
||||
con:add(block)
|
||||
model:add(con)
|
||||
model:add(nn.CAddTable())
|
||||
return model
|
||||
end
|
||||
local function unet_conv(n_input, n_middle, n_output, se)
|
||||
local model = nn.Sequential()
|
||||
model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1, true))
|
||||
if se then
|
||||
model:add(SEBlock(backend, n_output, 4))
|
||||
model:add(w2nn.ScaleTable())
|
||||
end
|
||||
return model
|
||||
end
|
||||
-- Residual U-Net
|
||||
local function unet(backend, ch, deconv)
|
||||
local block1 = unet_conv(128, 256, 128, true)
|
||||
local block2 = nn.Sequential()
|
||||
block2:add(unet_conv(64, 64, 128, true))
|
||||
block2:add(unet_branch(block1, backend, 128, 128, 4))
|
||||
block2:add(unet_conv(128, 64, 64, true))
|
||||
local model = nn.Sequential()
|
||||
model:add(unet_conv(ch, 32, 64, false))
|
||||
model:add(unet_branch(block2, backend, 64, 64, 16))
|
||||
model:add(unet_conv(backend, ch, 32, 64, false))
|
||||
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
|
||||
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
||||
model:add(nn.LeakyReLU(0.1))
|
||||
if deconv then
|
||||
model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
|
||||
else
|
||||
model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
|
||||
end
|
||||
model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
|
||||
|
||||
return model
|
||||
end
|
||||
local model = nn.Sequential()
|
||||
|
|
@ -962,8 +549,8 @@ function srcnn.upcunet_v3(backend, ch)
|
|||
local aux_con = nn.ConcatTable()
|
||||
|
||||
-- 2 cascade
|
||||
model:add(unet(backend, ch, true))
|
||||
con:add(unet(backend, ch, false))
|
||||
model:add(unet(backend, ch))
|
||||
con:add(unet(backend, ch))
|
||||
con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
|
||||
|
||||
aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
|
||||
|
|
@ -973,13 +560,13 @@ function srcnn.upcunet_v3(backend, ch)
|
|||
model:add(aux_con)
|
||||
model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
|
||||
|
||||
model.w2nn_arch_name = "upcunet_v3"
|
||||
model.w2nn_offset = 60
|
||||
model.w2nn_scale_factor = 2
|
||||
model.w2nn_arch_name = "cunet"
|
||||
model.w2nn_offset = 40
|
||||
model.w2nn_scale_factor = 1
|
||||
model.w2nn_channels = ch
|
||||
model.w2nn_resize = true
|
||||
model.w2nn_resize = false
|
||||
model.w2nn_valid_input_size = {}
|
||||
for i = 76, 512, 4 do
|
||||
for i = 100, 512, 4 do
|
||||
table.insert(model.w2nn_valid_input_size, i)
|
||||
end
|
||||
|
||||
|
|
@ -990,7 +577,7 @@ local function bench()
|
|||
local sys = require 'sys'
|
||||
cudnn.benchmark = true
|
||||
local model = nil
|
||||
local arch = {"upconv_7", "upcunet", "upcunet_v3"}
|
||||
local arch = {"upconv_7", "upcunet","vgg_7", "cunet"}
|
||||
local backend = "cudnn"
|
||||
for k = 1, #arch do
|
||||
model = srcnn[arch[k]](backend, 3):cuda()
|
||||
|
|
@ -1040,17 +627,8 @@ local model = srcnn.cunet_v3("cunn", 3):cuda()
|
|||
print(model)
|
||||
model:training()
|
||||
print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()):size())
|
||||
local model = srcnn.upcunet_v2("cunn", 3):cuda()
|
||||
print(model)
|
||||
model:training()
|
||||
print(model:forward(torch.Tensor(1, 3, 76, 76):zero():cuda()))
|
||||
os.exit()
|
||||
local model = srcnn.upcunet_v3("cunn", 3):cuda()
|
||||
print(model)
|
||||
model:training()
|
||||
print(model:forward(torch.Tensor(1, 3, 76, 76):zero():cuda()))
|
||||
os.exit()
|
||||
bench()
|
||||
os.exit()
|
||||
--]]
|
||||
|
||||
return srcnn
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
def find_unet_v2():
|
||||
def find_upcunet_v2():
|
||||
avg_pool=4
|
||||
print_mod = False
|
||||
check_mod = True
|
||||
|
|
@ -57,12 +57,12 @@ def find_unet_v2():
|
|||
# continue
|
||||
print("ok", i, s)
|
||||
|
||||
def find_unet():
|
||||
def find_upcunet():
|
||||
check_mod = True
|
||||
print_size = False
|
||||
print("cascade")
|
||||
|
||||
for i in range(76, 512):
|
||||
for i in range(72, 512):
|
||||
print_buf = []
|
||||
s = i
|
||||
# unet 1
|
||||
|
|
@ -110,7 +110,61 @@ def find_unet():
|
|||
s = s - 2 # conv3x3 last
|
||||
#if s % avg_pool != 0:
|
||||
# continue
|
||||
print("ok", i, s)
|
||||
print("ok", i, s, s/ i)
|
||||
|
||||
find_unet()
|
||||
def find_cunet():
|
||||
check_mod = True
|
||||
print_size = False
|
||||
print("cascade")
|
||||
|
||||
for i in range(72, 512):
|
||||
print_buf = []
|
||||
s = i
|
||||
# unet 1
|
||||
|
||||
s = s - 4 # conv3x3x2
|
||||
if print_size: print("1/2", s)
|
||||
if check_mod and s % 2 != 0:
|
||||
continue
|
||||
s = s / 2 # down2x2
|
||||
s = s - 4 # conv3x3x2
|
||||
if print_size: print("1/2",s)
|
||||
if check_mod and s % 2 != 0:
|
||||
continue
|
||||
s = s / 2 # down2x2
|
||||
s = s - 4 # conv3x3x2
|
||||
|
||||
s = s * 2 # up2x2
|
||||
if print_size: print("2x",s)
|
||||
s = s - 4 # conv3x3x2
|
||||
s = s * 2 # up2x2
|
||||
if print_size: print("2x",s)
|
||||
|
||||
s = s - 4
|
||||
#s = s * 2 - 4
|
||||
|
||||
# unet 2
|
||||
s = s - 4 # conv3x3x2
|
||||
if print_size: print("1/2",s)
|
||||
if check_mod and s % 2 != 0:
|
||||
continue
|
||||
s = s / 2 # down2x2
|
||||
s = s - 4 # conv3x3x2
|
||||
if print_size: print("1/2",s)
|
||||
if check_mod and s % 2 != 0:
|
||||
continue
|
||||
s = s / 2 # down2x2
|
||||
s = s - 4 # conv3x3x2
|
||||
s = s * 2 # up2x2
|
||||
if print_size: print("2x",s)
|
||||
s = s - 4 # conv3x3x2
|
||||
s = s * 2 # up2x2
|
||||
if print_size: print("2x",s)
|
||||
s = s - 2 # conv3x3
|
||||
s = s - 2 # conv3x3 last
|
||||
#if s % avg_pool != 0:
|
||||
# continue
|
||||
print("ok", i, s, s / i)
|
||||
|
||||
#find_upcunet()
|
||||
find_cunet()
|
||||
|
|
|
|||
Loading…
Reference in a new issue