|
@@ -486,7 +486,7 @@ function srcnn.upcunet(backend, ch)
|
|
|
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
|
|
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
|
|
|
block2:add(unet_conv(backend, 128, 64, 64, true))
|
|
block2:add(unet_conv(backend, 128, 64, 64, true))
|
|
|
local model = nn.Sequential()
|
|
local model = nn.Sequential()
|
|
|
- model:add(unet_conv(backend, ch, 32, 64, true))
|
|
|
|
|
|
|
+ model:add(unet_conv(backend, ch, 32, 64, false))
|
|
|
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
|
|
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
|
|
|
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
|
model:add(nn.LeakyReLU(0.1))
|
|
model:add(nn.LeakyReLU(0.1))
|
|
@@ -536,7 +536,7 @@ function srcnn.cunet(backend, ch)
|
|
|
block2:add(unet_conv(backend, 128, 64, 64, true))
|
|
block2:add(unet_conv(backend, 128, 64, 64, true))
|
|
|
|
|
|
|
|
local model = nn.Sequential()
|
|
local model = nn.Sequential()
|
|
|
- model:add(unet_conv(backend, ch, 32, 64, true))
|
|
|
|
|
|
|
+ model:add(unet_conv(backend, ch, 32, 64, false))
|
|
|
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
|
|
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
|
|
|
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
|
model:add(nn.LeakyReLU(0.1))
|
|
model:add(nn.LeakyReLU(0.1))
|
|
@@ -583,7 +583,7 @@ function srcnn.upcunet_s(backend, ch)
|
|
|
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
|
|
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
|
|
|
block2:add(unet_conv(backend, 128, 64, 32, true))
|
|
block2:add(unet_conv(backend, 128, 64, 32, true))
|
|
|
local model = nn.Sequential()
|
|
local model = nn.Sequential()
|
|
|
- model:add(unet_conv(backend, ch, 32, 32, true))
|
|
|
|
|
|
|
+ model:add(unet_conv(backend, ch, 32, 32, false))
|
|
|
model:add(unet_branch(backend, block2, backend, 32, 32, 16))
|
|
model:add(unet_branch(backend, block2, backend, 32, 32, 16))
|
|
|
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
|
model:add(nn.LeakyReLU(0.1))
|
|
model:add(nn.LeakyReLU(0.1))
|
|
@@ -631,7 +631,7 @@ function srcnn.cunet_s(backend, ch)
|
|
|
block2:add(unet_conv(backend, 128, 64, 32, true))
|
|
block2:add(unet_conv(backend, 128, 64, 32, true))
|
|
|
|
|
|
|
|
local model = nn.Sequential()
|
|
local model = nn.Sequential()
|
|
|
- model:add(unet_conv(backend, ch, 32, 32, true))
|
|
|
|
|
|
|
+ model:add(unet_conv(backend, ch, 32, 32, false))
|
|
|
model:add(unet_branch(backend, block2, backend, 32, 32, 16))
|
|
model:add(unet_branch(backend, block2, backend, 32, 32, 16))
|
|
|
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
|
model:add(nn.LeakyReLU(0.1))
|
|
model:add(nn.LeakyReLU(0.1))
|
nagadomi