Add cunet for 1x; Remove unused code

lib/srcnn.lua

@@ -255,77 +255,6 @@ function srcnn.vgg_7(backend, ch)
    return model
 end
 
--- VGG style net(12 layers)
-function srcnn.vgg_12(backend, ch)
-   local model = nn.Sequential()
-   model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, 128, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0))
-   model:add(w2nn.InplaceClip01())
-   model:add(nn.View(-1):setNumInputDims(3))
-
-   model.w2nn_arch_name = "vgg_12"
-   model.w2nn_offset = 12
-   model.w2nn_scale_factor = 1
-   model.w2nn_resize = false
-   model.w2nn_channels = ch
-   --model:cuda()
-   --print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
-   
-   return model
-end
-
--- Dilated Convolution (7 layers)
-function srcnn.dilated_7(backend, ch)
-   local model = nn.Sequential()
-   model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(nn.SpatialDilatedConvolution(32, 64, 3, 3, 1, 1, 0, 0, 2, 2))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(nn.SpatialDilatedConvolution(64, 64, 3, 3, 1, 1, 0, 0, 2, 2))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(nn.SpatialDilatedConvolution(64, 128, 3, 3, 1, 1, 0, 0, 4, 4))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, 128, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0))
-   model:add(w2nn.InplaceClip01())
-   model:add(nn.View(-1):setNumInputDims(3))
-
-   model.w2nn_arch_name = "dilated_7"
-   model.w2nn_offset = 12
-   model.w2nn_scale_factor = 1
-   model.w2nn_resize = false
-   model.w2nn_channels = ch
-
-   --model:cuda()
-   --print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
-   
-   return model
-end
-
 -- Upconvolution
 function srcnn.upconv_7(backend, ch)
    local model = nn.Sequential()
@@ -387,121 +316,6 @@ function srcnn.upconv_7l(backend, ch)
    return model
 end
 
--- layerwise linear blending with skip connections
--- Note: PSNR: upconv_7 < skiplb_7 < upconv_7l
-function srcnn.skiplb_7(backend, ch)
-   local function skip(backend, i, o)
-      local con = nn.Concat(2)
-      local conv = nn.Sequential()
-      conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 1, 1))
-      conv:add(nn.LeakyReLU(0.1, true))
-
-      -- depth concat
-      con:add(conv)
-      con:add(nn.Identity()) -- skip
-      return con
-   end
-   local model = nn.Sequential()
-   model:add(skip(backend, ch, 16))
-   model:add(skip(backend, 16+ch, 32))
-   model:add(skip(backend, 32+16+ch, 64))
-   model:add(skip(backend, 64+32+16+ch, 128))
-   model:add(skip(backend, 128+64+32+16+ch, 128))
-   model:add(skip(backend, 128+128+64+32+16+ch, 256))
-   -- input of last layer = [all layerwise output(contains input layer)].flatten
-   model:add(SpatialFullConvolution(backend, 256+128+128+64+32+16+ch, ch, 4, 4, 2, 2, 3, 3):noBias()) -- linear blend
-   model:add(w2nn.InplaceClip01())
-   model:add(nn.View(-1):setNumInputDims(3))
-   model.w2nn_arch_name = "skiplb_7"
-   model.w2nn_offset = 14
-   model.w2nn_scale_factor = 2
-   model.w2nn_resize = true
-   model.w2nn_channels = ch
-
-   --model:cuda()
-   --print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
-
-   return model
-end
-
--- dilated convolution + deconvolution
--- Note: This model is not better than upconv_7. Maybe becuase of under-fitting.
-function srcnn.dilated_upconv_7(backend, ch)
-   local model = nn.Sequential()
-   model:add(SpatialConvolution(backend, ch, 16, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 16, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(nn.SpatialDilatedConvolution(32, 64, 3, 3, 1, 1, 0, 0, 2, 2))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(nn.SpatialDilatedConvolution(64, 128, 3, 3, 1, 1, 0, 0, 2, 2))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(nn.SpatialDilatedConvolution(128, 128, 3, 3, 1, 1, 0, 0, 2, 2))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, 256, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
-   model:add(w2nn.InplaceClip01())
-   model:add(nn.View(-1):setNumInputDims(3))
-
-   model.w2nn_arch_name = "dilated_upconv_7"
-   model.w2nn_offset = 20
-   model.w2nn_scale_factor = 2
-   model.w2nn_resize = true
-   model.w2nn_channels = ch
-
-   --model:cuda()
-   --print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
-
-   return model
-end
-
--- ref: https://arxiv.org/abs/1609.04802
--- note: no batch-norm, no zero-paading
-function srcnn.srresnet_2x(backend, ch)
-   local function resblock(backend)
-      local seq = nn.Sequential()
-      local con = nn.ConcatTable()
-      local conv = nn.Sequential()
-      conv:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-      conv:add(ReLU(backend))
-      conv:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
-      conv:add(ReLU(backend))
-      con:add(conv)
-      con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
-      seq:add(con)
-      seq:add(nn.CAddTable())
-      return seq
-   end
-   local model = nn.Sequential()
-   --model:add(skip(backend, ch, 64 - ch))
-   model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(resblock(backend))
-   model:add(resblock(backend))
-   model:add(resblock(backend))
-   model:add(resblock(backend))
-   model:add(resblock(backend))
-   model:add(resblock(backend))
-   model:add(SpatialFullConvolution(backend, 64, 64, 4, 4, 2, 2, 2, 2))
-   model:add(ReLU(backend))
-   model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
-
-   model:add(w2nn.InplaceClip01())
-   --model:add(nn.View(-1):setNumInputDims(3))
-   model.w2nn_arch_name = "srresnet_2x"
-   model.w2nn_offset = 28
-   model.w2nn_scale_factor = 2
-   model.w2nn_resize = true
-   model.w2nn_channels = ch
-
-   --model:cuda()
-   --print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
-
-   return model
-end
-
--- large version of srresnet_2x. It's current best model but slow.
 function srcnn.resnet_14l(backend, ch)
    local function resblock(backend, i, o)
       local seq = nn.Sequential()
@@ -601,204 +415,79 @@ function srcnn.fcn_v1(backend, ch)
    
    return model
 end
-function srcnn.cupconv_14(backend, ch)
-   local function skip(backend, n_input, n_output, pad)
-      local con = nn.ConcatTable()
-      local conv = nn.Sequential()
-      local depad = nn.Sequential()
-      conv:add(nn.SelectTable(1))
-      conv:add(SpatialConvolution(backend, n_input, n_output, 3, 3, 1, 1, 0, 0))
-      conv:add(nn.LeakyReLU(0.1, true))
-      con:add(conv)
-      con:add(nn.Identity())
-      return con
-   end
-   local function concat(backend, n, ch, n_middle)
-      local con = nn.ConcatTable()
-      for i = 1, n do
-	 local pad = i - 1
-	 if i == 1 then
-	    con:add(nn.Sequential():add(nn.SelectTable(i)))
-	 else
-	    local seq = nn.Sequential()
-	    seq:add(nn.SelectTable(i))
-	    if pad > 0 then
-	       seq:add(nn.SpatialZeroPadding(-pad, -pad, -pad, -pad))
-	    end
-	    if i == n then
-	       --seq:add(SpatialConvolution(backend, ch, n_middle, 1, 1, 1, 1, 0, 0))
-	    else
-	       seq:add(w2nn.GradWeight(0.025))
-	       seq:add(SpatialConvolution(backend, n_middle, n_middle, 1, 1, 1, 1, 0, 0))
-	    end
-	    seq:add(nn.LeakyReLU(0.1, true))
-	    con:add(seq)
-	 end
-      end
-      return nn.Sequential():add(con):add(nn.JoinTable(2))
-   end
-   local model = nn.Sequential()
-   local m = 64
-   local n = 14
 
-   model:add(nn.ConcatTable():add(nn.Identity()))
-   for i = 1, n - 1 do
-      if i == 1 then
-	 model:add(skip(backend, ch, m))
-      else
-	 model:add(skip(backend, m, m))
-      end
-   end
-   model:add(nn.FlattenTable())
-   model:add(concat(backend, n, ch, m))
-   model:add(SpatialFullConvolution(backend, m * (n - 1) + 3, ch, 4, 4, 2, 2, 3, 3):noBias())
-   model:add(w2nn.InplaceClip01())
-   model:add(nn.View(-1):setNumInputDims(3))
-
-   model.w2nn_arch_name = "cupconv_14"
-   model.w2nn_offset = 28
-   model.w2nn_scale_factor = 2
-   model.w2nn_channels = ch
-   model.w2nn_resize = true
-
-   return model
-end
-
-function srcnn.upconv_refine(backend, ch)
-   local function block(backend, ch)
-      local seq = nn.Sequential()
-      local con = nn.ConcatTable()
-      local res = nn.Sequential()
-      local base = nn.Sequential()
-      local refine = nn.Sequential()
-      local aux_con = nn.ConcatTable()
-
-      res:add(w2nn.GradWeight(0.1))
-      res:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
-      res:add(nn.LeakyReLU(0.1, true))
-      res:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
-      res:add(nn.LeakyReLU(0.1, true))
-      res:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
-      res:add(nn.LeakyReLU(0.1, true))
-      res:add(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0):noBias())
-      res:add(w2nn.InplaceClip01())
-      res:add(nn.MulConstant(0.5))
-
-      con:add(res)
-      con:add(nn.Sequential():add(nn.SpatialZeroPadding(-4, -4, -4, -4)):add(nn.MulConstant(0.5)))
-
-      -- main output
-      refine:add(nn.CAddTable()) -- averaging
-      refine:add(nn.View(-1):setNumInputDims(3))
-      -- aux output
-      base:add(nn.SelectTable(2))
-      base:add(nn.MulConstant(2)) -- revert mul 0.5
-      base:add(nn.View(-1):setNumInputDims(3))
-
-      aux_con:add(refine)
-      aux_con:add(base)
-
-      seq:add(con)
-      seq:add(aux_con)
-      seq:add(w2nn.AuxiliaryLossTable(1))
-      return seq
-   end
-   local model = nn.Sequential()
-   model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, 128, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialConvolution(backend, 128, 256, 3, 3, 1, 1, 0, 0))
-   model:add(nn.LeakyReLU(0.1, true))
-   model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
-   model:add(w2nn.InplaceClip01())
-   model:add(block(backend, ch))
-
-   model.w2nn_arch_name = "upconv_refine"
-   model.w2nn_offset = 18
-   model.w2nn_scale_factor = 2
-   model.w2nn_resize = true
-   model.w2nn_channels = ch
-
-   return model
-end
-
--- I devised this arch because of the block size and global average pooling problem,
--- but SEBlock may possibly learn multi-scale input and no problems occur.
-local function SpatialSEBlock(backend, ave_size, n_output, r)
+-- Squeeze and Excitation Block
+local function SEBlock(backend, n_output, r)
    local con = nn.ConcatTable(2)
    local attention = nn.Sequential()
    local n_mid = math.floor(n_output / r)
-   attention:add(SpatialAveragePooling(backend, ave_size, ave_size, ave_size, ave_size))
+   attention:add(GlobalAveragePooling(n_output))
    attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
    attention:add(nn.ReLU(true))
    attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
-   attention:add(nn.Sigmoid(true))
-   attention:add(nn.SpatialUpSamplingNearest(ave_size, ave_size))
+   attention:add(nn.Sigmoid(true)) -- don't use cudnn sigmoid 
    con:add(nn.Identity())
    con:add(attention)
    return con
 end
-
--- Squeeze and Excitation Block
-local function SEBlock(backend, n_output, r)
+-- I devised this arch for the block size and global average pooling problem,
+-- but SEBlock may possibly learn multi-scale input or just a normalization. No problems occur.
+-- So this arch is not used.
+local function SpatialSEBlock(backend, ave_size, n_output, r)
    local con = nn.ConcatTable(2)
    local attention = nn.Sequential()
    local n_mid = math.floor(n_output / r)
-   attention:add(GlobalAveragePooling(n_output))
+   attention:add(SpatialAveragePooling(backend, ave_size, ave_size, ave_size, ave_size))
    attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
    attention:add(nn.ReLU(true))
    attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
-   attention:add(nn.Sigmoid(true)) -- don't use cudnn sigmoid 
+   attention:add(nn.Sigmoid(true))
+   attention:add(nn.SpatialUpSamplingNearest(ave_size, ave_size))
    con:add(nn.Identity())
    con:add(attention)
    return con
 end
+local function unet_branch(backend, 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(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))
-      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()
+      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))
 
-   -- 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

tools/find_unet.py

@@ -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()