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nagadomi
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152
lib/srcnn.lua
152
lib/srcnn.lua
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@ -1,7 +1,9 @@
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require 'w2nn'
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-- ref: http://arxiv.org/abs/1502.01852
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-- ref: http://arxiv.org/abs/1501.00092
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-- ref: https://arxiv.org/abs/1502.01852
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-- ref: https://arxiv.org/abs/1501.00092
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-- ref: https://arxiv.org/abs/1709.01507
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-- ref: https://arxiv.org/abs/1505.04597
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local srcnn = {}
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local function msra_filler(mod)
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@ -240,9 +242,6 @@ 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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@ -353,8 +352,6 @@ function srcnn.vgg_7(backend, ch)
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model.w2nn_offset = 7
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model.w2nn_scale_factor = 1
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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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@ -378,7 +375,6 @@ function srcnn.upconv_7(backend, ch)
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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_7"
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model.w2nn_offset = 14
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model.w2nn_scale_factor = 2
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@ -414,9 +410,6 @@ function srcnn.upconv_7l(backend, ch)
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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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@ -439,9 +432,6 @@ function srcnn.resnet_14l(backend, ch)
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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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@ -557,6 +547,21 @@ function srcnn.fcn_v1(backend, ch)
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return model
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end
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-- Cascaded Residual U-Net with SEBlock
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local function unet_conv(backend, n_input, n_middle, n_output, se)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 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, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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if se then
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model:add(SEBlock(backend, n_output, 8))
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model:add(w2nn.ScaleTable())
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end
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return model
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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)
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@ -573,61 +578,47 @@ local function unet_branch(backend, insert, backend, n_input, n_output, depad)
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model:add(nn.CAddTable())
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return model
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end
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local function unet_conv(backend, n_input, n_middle, n_output, se)
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local function cunet_unet1(backend, ch, deconv)
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local block1 = unet_conv(backend, 64, 128, 64, true)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 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, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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if se then
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model:add(SEBlock(backend, n_output, 8))
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model:add(w2nn.ScaleTable())
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model:add(unet_conv(backend, ch, 32, 64, false))
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model:add(unet_branch(backend, block1, backend, 64, 64, 4))
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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))
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if deconv then
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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-- Cascaded Residual Channel Attention U-Net
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local function cunet_unet2(backend, ch, deconv)
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local block1 = unet_conv(backend, 128, 256, 128, true)
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local block2 = nn.Sequential()
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block2:add(unet_conv(backend, 64, 64, 128, true))
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block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
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block2:add(unet_conv(backend, 128, 64, 64, true))
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 64, false))
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model:add(unet_branch(backend, block2, backend, 64, 64, 16))
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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))
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if deconv then
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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-- 2x
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function srcnn.upcunet(backend, ch)
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-- Residual U-Net
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local function unet1(backend, ch, deconv)
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local block1 = unet_conv(backend, 64, 128, 64, true)
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 64, false))
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model:add(unet_branch(backend, block1, backend, 64, 64, 4))
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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))
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if deconv then
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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local function unet2(backend, ch, deconv)
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local block1 = unet_conv(backend, 128, 256, 128, true)
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local block2 = nn.Sequential()
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block2:add(unet_conv(backend, 64, 64, 128, true))
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block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
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block2:add(unet_conv(backend, 128, 64, 64, true))
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 64, false))
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model:add(unet_branch(backend, block2, backend, 64, 64, 16))
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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))
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if deconv then
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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local model = nn.Sequential()
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local con = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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-- 2 cascade
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model:add(unet1(backend, ch, true))
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con:add(unet2(backend, ch, false))
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model:add(cunet_unet1(backend, ch, true))
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con:add(cunet_unet2(backend, ch, false))
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con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
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@ -649,47 +640,15 @@ function srcnn.upcunet(backend, ch)
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return model
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end
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-- cunet for 1x
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-- 1x
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function srcnn.cunet(backend, ch)
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local function unet1(backend, ch, deconv)
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local block1 = unet_conv(backend, 64, 128, 64, true)
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 64, false))
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model:add(unet_branch(backend, block1, backend, 64, 64, 4))
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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))
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if deconv then
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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local function unet2(backend, ch, deconv)
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local block1 = unet_conv(backend, 128, 256, 128, true)
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local block2 = nn.Sequential()
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block2:add(unet_conv(backend, 64, 64, 128, true))
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block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
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block2:add(unet_conv(backend, 128, 64, 64, true))
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 64, false))
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model:add(unet_branch(backend, block2, backend, 64, 64, 16))
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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))
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if deconv then
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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local model = nn.Sequential()
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local con = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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-- 2 cascade
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model:add(unet1(backend, ch))
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con:add(unet2(backend, ch))
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model:add(cunet_unet1(backend, ch, false))
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con:add(cunet_unet2(backend, ch, false))
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con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
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@ -711,6 +670,7 @@ function srcnn.cunet(backend, ch)
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return model
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end
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local function bench()
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local sys = require 'sys'
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cudnn.benchmark = true
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@ -719,7 +679,7 @@ local function bench()
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local backend = "cudnn"
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local ch = 3
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local batch_size = 1
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local output_size = 320
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local output_size = 256
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for k = 1, #arch do
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model = srcnn[arch[k]](backend, ch):cuda()
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model:evaluate()
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@ -739,7 +699,7 @@ local function bench()
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model:forward(x)
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end
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t = sys.clock()
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for i = 1, 100 do
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for i = 1, 10 do
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local x = torch.Tensor(batch_size, ch, crop_size, crop_size):uniform():cuda()
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local z = model:forward(x)
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dummy:add(z)
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