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@@ -439,46 +439,60 @@ end
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-- for segmentation
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function srcnn.fcn_v1(backend, ch)
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- -- input size = 128
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+ -- input_size = 120
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local model = nn.Sequential()
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+ --i = 120
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+ --model:cuda()
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+ --print(model:forward(torch.Tensor(32, ch, i, i):uniform():cuda()):size())
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model:add(SpatialConvolution(backend, ch, 32, 5, 5, 2, 2, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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+ model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
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+ model:add(nn.LeakyReLU(0.1, true))
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+ model:add(SpatialMaxPooling(backend, 2, 2, 2, 2))
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+
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model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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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(SpatialMaxPooling(backend, 2, 2, 2, 2))
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model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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+ model:add(SpatialConvolution(backend, 128, 128, 3, 3, 1, 1, 0, 0))
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+ model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialMaxPooling(backend, 2, 2, 2, 2))
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- model:add(SpatialConvolution(backend, 128, 256, 3, 3, 1, 1, 0, 0))
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+ model:add(SpatialConvolution(backend, 128, 256, 1, 1, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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- model:add(SpatialConvolution(backend, 256, 256, 3, 3, 1, 1, 0, 0))
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+ model:add(nn.Dropout(0.5, false, true))
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+ model:add(SpatialConvolution(backend, 256, 256, 1, 1, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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- model:add(SpatialMaxPooling(backend, 2, 2, 2, 2))
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+ model:add(nn.Dropout(0.5, false, true))
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- model:add(SpatialFullConvolution(backend, 256, 128, 4, 4, 2, 2, 2, 2))
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+ model:add(SpatialFullConvolution(backend, 256, 128, 2, 2, 2, 2, 0, 0))
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+ model:add(nn.LeakyReLU(0.1, true))
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+ model:add(SpatialFullConvolution(backend, 128, 128, 2, 2, 2, 2, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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- model:add(SpatialFullConvolution(backend, 128, 64, 4, 4, 2, 2, 2, 2))
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+ model:add(SpatialConvolution(backend, 128, 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, 32, 4, 4, 2, 2, 2, 2))
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+ model:add(SpatialFullConvolution(backend, 64, 64, 2, 2, 2, 2, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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- model:add(SpatialFullConvolution(backend, 32, ch, 4, 4, 2, 2, 2, 2))
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+ model:add(SpatialConvolution(backend, 64, 32, 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, 32, ch, 4, 4, 2, 2, 3, 3))
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model:add(w2nn.InplaceClip01())
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model:add(nn.View(-1):setNumInputDims(3))
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model.w2nn_arch_name = "fcn_v1"
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- model.w2nn_offset = 39
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+ model.w2nn_offset = 36
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model.w2nn_scale_factor = 1
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model.w2nn_channels = ch
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- --model:cuda()
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- --print(model:forward(torch.Tensor(32, ch, 128, 128):uniform():cuda()):size())
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+ model.w2nn_input_size = 120
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return model
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end
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-
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function srcnn.create(model_name, backend, color)
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model_name = model_name or "vgg_7"
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backend = backend or "cunn"
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@@ -500,9 +514,8 @@ function srcnn.create(model_name, backend, color)
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end
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end
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--[[
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-local model = srcnn.srresnet_2x("cunn", 3):cuda()
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+local model = srcnn.fcn_v1("cunn", 3):cuda()
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+print(model:forward(torch.Tensor(1, 3, 108, 108):zero():cuda()):size())
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print(model)
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-print(model:forward(torch.Tensor(1, 3, 128, 128):zero():cuda()):size())
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--]]
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-
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return srcnn
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nagadomi