Temporary added experimental models
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404
lib/srcnn.lua
404
lib/srcnn.lua
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@ -226,6 +226,110 @@ local function GlobalAveragePooling(n_output)
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end
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srcnn.GlobalAveragePooling = GlobalAveragePooling
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-- Squeeze and Excitation Block
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local function SEBlock(backend, n_output, r)
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local con = nn.ConcatTable(2)
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local attention = nn.Sequential()
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local n_mid = math.floor(n_output / r)
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attention:add(GlobalAveragePooling(n_output))
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attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true)) -- don't use cudnn sigmoid
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con:add(nn.Identity())
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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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local n_mid = math.floor(n_output / r)
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attention:add(SpatialAveragePooling(backend, ave_size, ave_size, ave_size, ave_size))
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attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true))
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attention:add(nn.SpatialUpSamplingNearest(ave_size, ave_size))
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con:add(nn.Identity())
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con:add(attention)
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return con
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end
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local function ResBlock(backend, i, o)
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local seq = nn.Sequential()
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local con = nn.ConcatTable()
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local conv = nn.Sequential()
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conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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conv:add(SpatialConvolution(backend, o, o, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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con:add(conv)
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if i == o then
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con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
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else
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local seq = nn.Sequential()
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seq:add(SpatialConvolution(backend, i, o, 1, 1, 1, 1, 0, 0))
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seq:add(nn.SpatialZeroPadding(-2, -2, -2, -2))
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con:add(seq)
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end
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seq:add(con)
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seq:add(nn.CAddTable())
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return seq
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end
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local function ResBlockSE(backend, i, o)
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local seq = nn.Sequential()
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local con = nn.ConcatTable()
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local conv = nn.Sequential()
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conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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conv:add(SpatialConvolution(backend, o, o, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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conv:add(SEBlock(backend, o, 8))
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conv:add(w2nn.ScaleTable())
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con:add(conv)
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if i == o then
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con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
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else
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local seq = nn.Sequential()
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seq:add(SpatialConvolution(backend, i, o, 1, 1, 1, 1, 0, 0))
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seq:add(nn.SpatialZeroPadding(-2, -2, -2, -2))
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con:add(seq)
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end
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seq:add(con)
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seq:add(nn.CAddTable())
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return seq
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end
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local function ResGroup(backend, n, n_output)
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local seq = nn.Sequential()
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local res = nn.Sequential()
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local con = nn.ConcatTable(2)
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local depad = -2 * n
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for i = 1, n do
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res:add(ResBlock(backend, n_output, n_output))
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end
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con:add(res)
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con:add(nn.SpatialZeroPadding(depad, depad, depad, depad))
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seq:add(con)
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seq:add(nn.CAddTable())
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return seq
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end
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local function ResGroupSE(backend, n, n_output)
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local seq = nn.Sequential()
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local res = nn.Sequential()
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local con = nn.ConcatTable(2)
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local depad = -2 * n
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for i = 1, n do
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res:add(ResBlockSE(backend, n_output, n_output))
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end
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con:add(res)
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con:add(nn.SpatialZeroPadding(depad, depad, depad, depad))
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seq:add(con)
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seq:add(nn.CAddTable())
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return seq
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end
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-- VGG style net(7 layers)
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function srcnn.vgg_7(backend, ch)
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local model = nn.Sequential()
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@ -317,36 +421,15 @@ function srcnn.upconv_7l(backend, ch)
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end
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function srcnn.resnet_14l(backend, ch)
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local function resblock(backend, i, o)
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local seq = nn.Sequential()
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local con = nn.ConcatTable()
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local conv = nn.Sequential()
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conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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conv:add(SpatialConvolution(backend, o, o, 3, 3, 1, 1, 0, 0))
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conv:add(nn.LeakyReLU(0.1, true))
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con:add(conv)
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if i == o then
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con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
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else
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local seq = nn.Sequential()
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seq:add(SpatialConvolution(backend, i, o, 1, 1, 1, 1, 0, 0))
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seq:add(nn.SpatialZeroPadding(-2, -2, -2, -2))
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con:add(seq)
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end
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seq:add(con)
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seq:add(nn.CAddTable())
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return seq
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end
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(resblock(backend, 32, 64))
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model:add(resblock(backend, 64, 64))
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model:add(resblock(backend, 64, 128))
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model:add(resblock(backend, 128, 128))
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model:add(resblock(backend, 128, 256))
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model:add(resblock(backend, 256, 256))
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model:add(ResBlock(backend, 32, 64))
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model:add(ResBlock(backend, 64, 64))
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model:add(ResBlock(backend, 64, 128))
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model:add(ResBlock(backend, 128, 128))
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model:add(ResBlock(backend, 128, 256))
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model:add(ResBlock(backend, 256, 256))
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model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
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model:add(w2nn.InplaceClip01())
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model:add(nn.View(-1):setNumInputDims(3))
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@ -362,6 +445,65 @@ function srcnn.resnet_14l(backend, ch)
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return model
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end
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-- ResNet_with SEBlock for fast conversion
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function srcnn.upresnet_s(backend, ch)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(ResGroupSE(backend, 3, 64))
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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, true))
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model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3):noBias())
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model:add(w2nn.InplaceClip01())
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model.w2nn_arch_name = "upresnet_s"
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model.w2nn_offset = 18
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model.w2nn_scale_factor = 2
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model.w2nn_resize = true
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model.w2nn_channels = ch
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return model
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end
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-- Cascaded ResNet with SEBlock
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function srcnn.upcresnet(backend, ch)
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local function resnet(backend, ch, deconv)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(ResGroupSE(backend, 2, 64))
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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, true))
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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(resnet(backend, ch, true))
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con:add(nn.Sequential():add(resnet(backend, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- output is odd
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con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
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aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
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aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01())) -- single unet output
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model:add(con)
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model:add(aux_con)
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model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
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model.w2nn_arch_name = "upcresnet"
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model.w2nn_offset = 22
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model.w2nn_scale_factor = 2
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model.w2nn_resize = true
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model.w2nn_channels = ch
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return model
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end
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-- for segmentation
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function srcnn.fcn_v1(backend, ch)
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-- input_size = 120
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@ -416,37 +558,6 @@ function srcnn.fcn_v1(backend, ch)
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return model
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end
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-- Squeeze and Excitation Block
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local function SEBlock(backend, n_output, r)
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local con = nn.ConcatTable(2)
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local attention = nn.Sequential()
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local n_mid = math.floor(n_output / r)
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attention:add(GlobalAveragePooling(n_output))
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attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true)) -- don't use cudnn sigmoid
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con:add(nn.Identity())
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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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local n_mid = math.floor(n_output / r)
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attention:add(SpatialAveragePooling(backend, ave_size, ave_size, ave_size, ave_size))
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attention:add(SpatialConvolution(backend, n_output, n_mid, 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, n_mid, n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true))
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attention:add(nn.SpatialUpSamplingNearest(ave_size, ave_size))
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con:add(nn.Identity())
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con:add(attention)
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return con
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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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@ -525,7 +636,6 @@ 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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function srcnn.cunet(backend, ch)
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local function unet(backend, ch)
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@ -573,19 +683,14 @@ function srcnn.cunet(backend, ch)
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return model
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end
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-- small version of cunet
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function srcnn.upcunet_s(backend, ch)
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function srcnn.upcunet_s_p0(backend, ch)
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-- Residual U-Net
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local function unet(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, 32, 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, 32, true))
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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, 32, false))
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model:add(unet_branch(backend, block2, backend, 32, 32, 16))
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model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
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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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@ -599,8 +704,72 @@ function srcnn.upcunet_s(backend, ch)
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local aux_con = nn.ConcatTable()
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-- 2 cascade
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model:add(unet(backend, ch, true))
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con:add(unet(backend, ch, false))
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model:add(unet1(backend, ch, true))
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con:add(unet1(backend, ch, false))
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con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
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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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aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01())) -- single unet output
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model:add(con)
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model:add(aux_con)
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model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
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model.w2nn_arch_name = "upcunet_s_p0"
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model.w2nn_offset = 24
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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model.w2nn_valid_input_size = {}
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for i = 76, 512, 4 do
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table.insert(model.w2nn_valid_input_size, i)
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end
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return model
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end
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function srcnn.upcunet_s_p1(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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--con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
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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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@ -610,8 +779,72 @@ function srcnn.upcunet_s(backend, ch)
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model:add(aux_con)
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model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
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model.w2nn_arch_name = "upcunet_s"
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model.w2nn_offset = 60
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model.w2nn_arch_name = "upcunet_s_p1"
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model.w2nn_offset = 36
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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model.w2nn_valid_input_size = {}
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for i = 76, 512, 4 do
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table.insert(model.w2nn_valid_input_size, i)
|
||||
end
|
||||
|
||||
return model
|
||||
end
|
||||
|
||||
function srcnn.upcunet_s_p2(backend, ch)
|
||||
-- Residual U-Net
|
||||
local function unet1(backend, ch, deconv)
|
||||
local block1 = unet_conv(backend, 64, 128, 64, true)
|
||||
local model = nn.Sequential()
|
||||
model:add(unet_conv(backend, ch, 32, 64, false))
|
||||
model:add(unet_branch(backend, block1, backend, 64, 64, 4))
|
||||
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
|
||||
return model
|
||||
end
|
||||
local function unet2(backend, ch, deconv)
|
||||
local block1 = unet_conv(backend, 128, 256, 128, true)
|
||||
local block2 = nn.Sequential()
|
||||
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(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
|
||||
return model
|
||||
end
|
||||
local model = nn.Sequential()
|
||||
local con = nn.ConcatTable()
|
||||
local aux_con = nn.ConcatTable()
|
||||
|
||||
-- 2 cascade
|
||||
model:add(unet2(backend, ch, true))
|
||||
con:add(unet1(backend, ch, false))
|
||||
con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
|
||||
--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_s_p2"
|
||||
model.w2nn_offset = 48
|
||||
model.w2nn_scale_factor = 2
|
||||
model.w2nn_channels = ch
|
||||
model.w2nn_resize = true
|
||||
|
@ -672,29 +905,30 @@ local function bench()
|
|||
local sys = require 'sys'
|
||||
cudnn.benchmark = true
|
||||
local model = nil
|
||||
local arch = {"upconv_7", "upcunet","upcunet_s", "vgg_7", "cunet", "cunet_s"}
|
||||
local arch = {"upconv_7", "upresnet_s","upcresnet", "resnet_14l", "upcunet", "upcunet_s_p0", "upcunet_s_p1", "upcunet_s_p2"}
|
||||
--local arch = {"upconv_7", "upcunet","upcunet_v0", "upcunet_s", "vgg_7", "cunet", "cunet_s"}
|
||||
local backend = "cudnn"
|
||||
local ch = 3
|
||||
local batch_size = 1
|
||||
local crop_size = 512
|
||||
local output_size = 320
|
||||
for k = 1, #arch do
|
||||
model = srcnn[arch[k]](backend, ch):cuda()
|
||||
model:evaluate()
|
||||
local dummy = nil
|
||||
local crop_size = (output_size + model.w2nn_offset * 2) / 2
|
||||
local dummy = torch.Tensor(batch_size, ch, output_size, output_size):zero():cuda()
|
||||
|
||||
print(arch[k], output_size, crop_size)
|
||||
-- warn
|
||||
for i = 1, 20 do
|
||||
for i = 1, 4 do
|
||||
local x = torch.Tensor(batch_size, ch, crop_size, crop_size):uniform():cuda()
|
||||
model:forward(x)
|
||||
end
|
||||
t = sys.clock()
|
||||
for i = 1, 20 do
|
||||
for i = 1, 100 do
|
||||
local x = torch.Tensor(batch_size, ch, crop_size, crop_size):uniform():cuda()
|
||||
local z = model:forward(x)
|
||||
if dummy == nil then
|
||||
dummy = z:clone()
|
||||
else
|
||||
dummy:add(z)
|
||||
end
|
||||
dummy:add(z)
|
||||
end
|
||||
print(arch[k], sys.clock() - t)
|
||||
model:clearState()
|
||||
|
@ -721,10 +955,10 @@ function srcnn.create(model_name, backend, color)
|
|||
end
|
||||
end
|
||||
--[[
|
||||
local model = srcnn.upcunet_s("cunn", 3):cuda()
|
||||
local model = srcnn.resnet_s("cunn", 3):cuda()
|
||||
print(model)
|
||||
model:training()
|
||||
print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()))
|
||||
print(model:forward(torch.Tensor(1, 3, 128, 128):zero():cuda()):size())
|
||||
bench()
|
||||
os.exit()
|
||||
--]]
|
||||
|
|
Loading…
Reference in a new issue