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@@ -987,16 +987,15 @@ end
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function srcnn.cunet_v6(backend, ch)
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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- local pooling = SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0) -- downsampling
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- --block:add(w2nn.Print())
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- block:add(pooling)
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+ local con = nn.ConcatTable(2)
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+ local model = nn.Sequential()
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+
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+ block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
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block:add(insert)
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block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
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- local parallel = nn.ConcatTable(2)
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- parallel:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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- parallel:add(block)
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- local model = nn.Sequential()
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- model:add(parallel)
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+ con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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+ con:add(block)
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+ model:add(con)
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model:add(nn.CAddTable())
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return model
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end
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@@ -1015,7 +1014,7 @@ function srcnn.cunet_v6(backend, ch)
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, math.floor(n_output / 4), n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true))
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- con:add(nn.Identity())
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+ con:add(nn.Identity())
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con:add(attention)
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model:add(con)
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model:add(w2nn.ScaleTable())
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@@ -1046,7 +1045,6 @@ function srcnn.cunet_v6(backend, ch)
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local aux_con = nn.ConcatTable()
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model:add(unet(backend, ch, true))
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-
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con:add(unet(backend, ch, false))
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con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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nagadomi