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@@ -470,7 +470,7 @@ local function unet_conv(backend, n_input, n_middle, n_output, se)
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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, 4))
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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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@@ -486,7 +486,7 @@ function srcnn.upcunet(backend, ch)
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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_conv(backend, ch, 32, 64, true))
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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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@@ -536,7 +536,7 @@ function srcnn.cunet(backend, ch)
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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_conv(backend, ch, 32, 64, true))
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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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nagadomi