927 lines
32 KiB
Lua
927 lines
32 KiB
Lua
require 'w2nn'
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-- ref: http://arxiv.org/abs/1502.01852
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-- ref: http://arxiv.org/abs/1501.00092
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local srcnn = {}
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local function msra_filler(mod)
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local fin = mod.kW * mod.kH * mod.nInputPlane
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local fout = mod.kW * mod.kH * mod.nOutputPlane
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stdv = math.sqrt(4 / ((1.0 + 0.1 * 0.1) * (fin + fout)))
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mod.weight:normal(0, stdv)
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mod.bias:zero()
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end
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local function identity_filler(mod)
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assert(mod.nInputPlane <= mod.nOutputPlane)
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mod.weight:normal(0, 0.01)
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mod.bias:zero()
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local num_groups = mod.nInputPlane -- fixed
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local filler_value = num_groups / mod.nOutputPlane
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local in_group_size = math.floor(mod.nInputPlane / num_groups)
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local out_group_size = math.floor(mod.nOutputPlane / num_groups)
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local x = math.floor(mod.kW / 2)
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local y = math.floor(mod.kH / 2)
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for i = 0, num_groups - 1 do
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for j = i * out_group_size, (i + 1) * out_group_size - 1 do
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for k = i * in_group_size, (i + 1) * in_group_size - 1 do
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mod.weight[j+1][k+1][y+1][x+1] = filler_value
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end
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end
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end
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end
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function nn.SpatialConvolutionMM:reset(stdv)
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msra_filler(self)
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end
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function nn.SpatialFullConvolution:reset(stdv)
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msra_filler(self)
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end
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function nn.SpatialDilatedConvolution:reset(stdv)
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identity_filler(self)
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end
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if cudnn and cudnn.SpatialConvolution then
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function cudnn.SpatialConvolution:reset(stdv)
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msra_filler(self)
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end
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function cudnn.SpatialFullConvolution:reset(stdv)
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msra_filler(self)
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end
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if cudnn.SpatialDilatedConvolution then
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function cudnn.SpatialDilatedConvolution:reset(stdv)
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identity_filler(self)
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end
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end
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end
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function nn.SpatialConvolutionMM:clearState()
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if self.gradWeight then
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self.gradWeight:resize(self.nOutputPlane, self.nInputPlane * self.kH * self.kW):zero()
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end
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if self.gradBias then
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self.gradBias:resize(self.nOutputPlane):zero()
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end
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return nn.utils.clear(self, 'finput', 'fgradInput', '_input', '_gradOutput', 'output', 'gradInput')
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end
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function srcnn.channels(model)
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if model.w2nn_channels ~= nil then
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return model.w2nn_channels
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else
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return model:get(model:size() - 1).weight:size(1)
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end
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end
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function srcnn.backend(model)
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local conv = model:findModules("cudnn.SpatialConvolution")
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local fullconv = model:findModules("cudnn.SpatialFullConvolution")
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if #conv > 0 or #fullconv > 0 then
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return "cudnn"
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else
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return "cunn"
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end
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end
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function srcnn.color(model)
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local ch = srcnn.channels(model)
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if ch == 3 then
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return "rgb"
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else
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return "y"
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end
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end
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function srcnn.name(model)
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if model.w2nn_arch_name ~= nil then
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return model.w2nn_arch_name
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else
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local conv = model:findModules("nn.SpatialConvolutionMM")
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if #conv == 0 then
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conv = model:findModules("cudnn.SpatialConvolution")
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end
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if #conv == 7 then
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return "vgg_7"
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elseif #conv == 12 then
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return "vgg_12"
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else
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error("unsupported model")
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end
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end
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end
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function srcnn.offset_size(model)
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if model.w2nn_offset ~= nil then
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return model.w2nn_offset
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else
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local name = srcnn.name(model)
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if name:match("vgg_") then
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local conv = model:findModules("nn.SpatialConvolutionMM")
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if #conv == 0 then
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conv = model:findModules("cudnn.SpatialConvolution")
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end
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local offset = 0
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for i = 1, #conv do
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offset = offset + (conv[i].kW - 1) / 2
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end
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return math.floor(offset)
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else
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error("unsupported model")
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end
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end
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end
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function srcnn.scale_factor(model)
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if model.w2nn_scale_factor ~= nil then
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return model.w2nn_scale_factor
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else
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local name = srcnn.name(model)
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if name == "upconv_7" then
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return 2
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elseif name == "upconv_8_4x" then
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return 4
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else
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return 1
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end
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end
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end
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local function SpatialConvolution(backend, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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if backend == "cunn" then
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return nn.SpatialConvolutionMM(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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elseif backend == "cudnn" then
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return cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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else
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error("unsupported backend:" .. backend)
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end
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end
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srcnn.SpatialConvolution = SpatialConvolution
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local function SpatialFullConvolution(backend, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, adjW, adjH)
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if backend == "cunn" then
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return nn.SpatialFullConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, adjW, adjH)
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elseif backend == "cudnn" then
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return cudnn.SpatialFullConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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else
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error("unsupported backend:" .. backend)
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end
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end
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srcnn.SpatialFullConvolution = SpatialFullConvolution
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local function ReLU(backend)
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if backend == "cunn" then
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return nn.ReLU(true)
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elseif backend == "cudnn" then
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return cudnn.ReLU(true)
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else
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error("unsupported backend:" .. backend)
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end
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end
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srcnn.ReLU = ReLU
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local function SpatialMaxPooling(backend, kW, kH, dW, dH, padW, padH)
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if backend == "cunn" then
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return nn.SpatialMaxPooling(kW, kH, dW, dH, padW, padH)
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elseif backend == "cudnn" then
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return cudnn.SpatialMaxPooling(kW, kH, dW, dH, padW, padH)
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else
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error("unsupported backend:" .. backend)
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end
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end
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srcnn.SpatialMaxPooling = SpatialMaxPooling
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local function SpatialAveragePooling(backend, kW, kH, dW, dH, padW, padH)
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if backend == "cunn" then
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return nn.SpatialAveragePooling(kW, kH, dW, dH, padW, padH)
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elseif backend == "cudnn" then
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return cudnn.SpatialAveragePooling(kW, kH, dW, dH, padW, padH)
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else
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error("unsupported backend:" .. backend)
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end
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end
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srcnn.SpatialAveragePooling = SpatialAveragePooling
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local function SpatialDilatedConvolution(backend, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, dilationW, dilationH)
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if backend == "cunn" then
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return nn.SpatialDilatedConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, dilationW, dilationH)
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elseif backend == "cudnn" then
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if cudnn.SpatialDilatedConvolution then
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-- cudnn v 6
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return cudnn.SpatialDilatedConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, dilationW, dilationH)
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else
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return nn.SpatialDilatedConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, dilationW, dilationH)
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end
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else
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error("unsupported backend:" .. backend)
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end
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end
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srcnn.SpatialDilatedConvolution = SpatialDilatedConvolution
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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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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(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(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(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(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0))
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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 = "vgg_7"
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model.w2nn_offset = 7
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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, 92, 92):uniform():cuda()):size())
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return model
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end
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-- VGG style net(12 layers)
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function srcnn.vgg_12(backend, ch)
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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(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(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(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(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(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(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(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(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(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0))
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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 = "vgg_12"
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model.w2nn_offset = 12
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model.w2nn_scale_factor = 1
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model.w2nn_resize = false
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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, 92, 92):uniform():cuda()):size())
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return model
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end
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-- Dilated Convolution (7 layers)
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function srcnn.dilated_7(backend, ch)
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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(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(nn.SpatialDilatedConvolution(32, 64, 3, 3, 1, 1, 0, 0, 2, 2))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(nn.SpatialDilatedConvolution(64, 64, 3, 3, 1, 1, 0, 0, 2, 2))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(nn.SpatialDilatedConvolution(64, 128, 3, 3, 1, 1, 0, 0, 4, 4))
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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(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0))
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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 = "dilated_7"
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model.w2nn_offset = 12
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model.w2nn_scale_factor = 1
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model.w2nn_resize = false
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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, 92, 92):uniform():cuda()):size())
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return model
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end
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-- Upconvolution
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function srcnn.upconv_7(backend, ch)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 16, 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, 16, 32, 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, 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, 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(SpatialConvolution(backend, 128, 256, 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, 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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model.w2nn_arch_name = "upconv_7"
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model.w2nn_offset = 14
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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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-- large version of upconv_7
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-- This model able to beat upconv_7 (PSNR: +0.3 ~ +0.8) but this model is 2x slower than upconv_7.
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function srcnn.upconv_7l(backend, ch)
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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(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, 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, 192, 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, 192, 256, 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, 256, 512, 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, 512, 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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model.w2nn_arch_name = "upconv_7l"
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model.w2nn_offset = 14
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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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--model:cuda()
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--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
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return model
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end
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-- layerwise linear blending with skip connections
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-- Note: PSNR: upconv_7 < skiplb_7 < upconv_7l
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function srcnn.skiplb_7(backend, ch)
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local function skip(backend, i, o)
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local con = nn.Concat(2)
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local conv = nn.Sequential()
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conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 1, 1))
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conv:add(nn.LeakyReLU(0.1, true))
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-- depth concat
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con:add(conv)
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con:add(nn.Identity()) -- skip
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return con
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end
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local model = nn.Sequential()
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model:add(skip(backend, ch, 16))
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model:add(skip(backend, 16+ch, 32))
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model:add(skip(backend, 32+16+ch, 64))
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model:add(skip(backend, 64+32+16+ch, 128))
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model:add(skip(backend, 128+64+32+16+ch, 128))
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model:add(skip(backend, 128+128+64+32+16+ch, 256))
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-- input of last layer = [all layerwise output(contains input layer)].flatten
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model:add(SpatialFullConvolution(backend, 256+128+128+64+32+16+ch, ch, 4, 4, 2, 2, 3, 3):noBias()) -- linear blend
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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 = "skiplb_7"
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model.w2nn_offset = 14
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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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--model:cuda()
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--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
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return model
|
|
end
|
|
|
|
-- dilated convolution + deconvolution
|
|
-- Note: This model is not better than upconv_7. Maybe becuase of under-fitting.
|
|
function srcnn.dilated_upconv_7(backend, ch)
|
|
local model = nn.Sequential()
|
|
model:add(SpatialConvolution(backend, ch, 16, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 16, 32, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(nn.SpatialDilatedConvolution(32, 64, 3, 3, 1, 1, 0, 0, 2, 2))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(nn.SpatialDilatedConvolution(64, 128, 3, 3, 1, 1, 0, 0, 2, 2))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(nn.SpatialDilatedConvolution(128, 128, 3, 3, 1, 1, 0, 0, 2, 2))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 128, 256, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
|
|
model:add(w2nn.InplaceClip01())
|
|
model:add(nn.View(-1):setNumInputDims(3))
|
|
|
|
model.w2nn_arch_name = "dilated_upconv_7"
|
|
model.w2nn_offset = 20
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_resize = true
|
|
model.w2nn_channels = ch
|
|
|
|
--model:cuda()
|
|
--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
|
|
|
|
return model
|
|
end
|
|
|
|
-- ref: https://arxiv.org/abs/1609.04802
|
|
-- note: no batch-norm, no zero-paading
|
|
function srcnn.srresnet_2x(backend, ch)
|
|
local function resblock(backend)
|
|
local seq = nn.Sequential()
|
|
local con = nn.ConcatTable()
|
|
local conv = nn.Sequential()
|
|
conv:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
conv:add(ReLU(backend))
|
|
conv:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
conv:add(ReLU(backend))
|
|
con:add(conv)
|
|
con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
|
|
seq:add(con)
|
|
seq:add(nn.CAddTable())
|
|
return seq
|
|
end
|
|
local model = nn.Sequential()
|
|
--model:add(skip(backend, ch, 64 - ch))
|
|
model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(resblock(backend))
|
|
model:add(resblock(backend))
|
|
model:add(resblock(backend))
|
|
model:add(resblock(backend))
|
|
model:add(resblock(backend))
|
|
model:add(resblock(backend))
|
|
model:add(SpatialFullConvolution(backend, 64, 64, 4, 4, 2, 2, 2, 2))
|
|
model:add(ReLU(backend))
|
|
model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
|
|
|
|
model:add(w2nn.InplaceClip01())
|
|
--model:add(nn.View(-1):setNumInputDims(3))
|
|
model.w2nn_arch_name = "srresnet_2x"
|
|
model.w2nn_offset = 28
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_resize = true
|
|
model.w2nn_channels = ch
|
|
|
|
--model:cuda()
|
|
--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
|
|
|
|
return model
|
|
end
|
|
|
|
-- large version of srresnet_2x. It's current best model but slow.
|
|
function srcnn.resnet_14l(backend, ch)
|
|
local function resblock(backend, i, o)
|
|
local seq = nn.Sequential()
|
|
local con = nn.ConcatTable()
|
|
local conv = nn.Sequential()
|
|
conv:add(SpatialConvolution(backend, i, o, 3, 3, 1, 1, 0, 0))
|
|
conv:add(nn.LeakyReLU(0.1, true))
|
|
conv:add(SpatialConvolution(backend, o, o, 3, 3, 1, 1, 0, 0))
|
|
conv:add(nn.LeakyReLU(0.1, true))
|
|
con:add(conv)
|
|
if i == o then
|
|
con:add(nn.SpatialZeroPadding(-2, -2, -2, -2)) -- identity + de-padding
|
|
else
|
|
local seq = nn.Sequential()
|
|
seq:add(SpatialConvolution(backend, i, o, 1, 1, 1, 1, 0, 0))
|
|
seq:add(nn.SpatialZeroPadding(-2, -2, -2, -2))
|
|
con:add(seq)
|
|
end
|
|
seq:add(con)
|
|
seq:add(nn.CAddTable())
|
|
return seq
|
|
end
|
|
local model = nn.Sequential()
|
|
model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(resblock(backend, 32, 64))
|
|
model:add(resblock(backend, 64, 64))
|
|
model:add(resblock(backend, 64, 128))
|
|
model:add(resblock(backend, 128, 128))
|
|
model:add(resblock(backend, 128, 256))
|
|
model:add(resblock(backend, 256, 256))
|
|
model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
|
|
model:add(w2nn.InplaceClip01())
|
|
model:add(nn.View(-1):setNumInputDims(3))
|
|
model.w2nn_arch_name = "resnet_14l"
|
|
model.w2nn_offset = 28
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_resize = true
|
|
model.w2nn_channels = ch
|
|
|
|
--model:cuda()
|
|
--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
|
|
|
|
return model
|
|
end
|
|
|
|
-- for segmentation
|
|
function srcnn.fcn_v1(backend, ch)
|
|
-- input_size = 120
|
|
local model = nn.Sequential()
|
|
--i = 120
|
|
--model:cuda()
|
|
--print(model:forward(torch.Tensor(32, ch, i, i):uniform():cuda()):size())
|
|
|
|
model:add(SpatialConvolution(backend, ch, 32, 5, 5, 2, 2, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialMaxPooling(backend, 2, 2, 2, 2))
|
|
|
|
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialMaxPooling(backend, 2, 2, 2, 2))
|
|
|
|
model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 128, 128, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialMaxPooling(backend, 2, 2, 2, 2))
|
|
|
|
model:add(SpatialConvolution(backend, 128, 256, 1, 1, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(nn.Dropout(0.5, false, true))
|
|
|
|
model:add(SpatialFullConvolution(backend, 256, 128, 2, 2, 2, 2, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialFullConvolution(backend, 128, 128, 2, 2, 2, 2, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 128, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialFullConvolution(backend, 64, 64, 2, 2, 2, 2, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 64, 32, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialFullConvolution(backend, 32, ch, 4, 4, 2, 2, 3, 3))
|
|
|
|
model:add(w2nn.InplaceClip01())
|
|
model:add(nn.View(-1):setNumInputDims(3))
|
|
model.w2nn_arch_name = "fcn_v1"
|
|
model.w2nn_offset = 36
|
|
model.w2nn_scale_factor = 1
|
|
model.w2nn_channels = ch
|
|
model.w2nn_input_size = 120
|
|
--model.w2nn_gcn = true
|
|
|
|
return model
|
|
end
|
|
function srcnn.cupconv_14(backend, ch)
|
|
local function skip(backend, n_input, n_output, pad)
|
|
local con = nn.ConcatTable()
|
|
local conv = nn.Sequential()
|
|
local depad = nn.Sequential()
|
|
conv:add(nn.SelectTable(1))
|
|
conv:add(SpatialConvolution(backend, n_input, n_output, 3, 3, 1, 1, 0, 0))
|
|
conv:add(nn.LeakyReLU(0.1, true))
|
|
con:add(conv)
|
|
con:add(nn.Identity())
|
|
return con
|
|
end
|
|
local function concat(backend, n, ch, n_middle)
|
|
local con = nn.ConcatTable()
|
|
for i = 1, n do
|
|
local pad = i - 1
|
|
if i == 1 then
|
|
con:add(nn.Sequential():add(nn.SelectTable(i)))
|
|
else
|
|
local seq = nn.Sequential()
|
|
seq:add(nn.SelectTable(i))
|
|
if pad > 0 then
|
|
seq:add(nn.SpatialZeroPadding(-pad, -pad, -pad, -pad))
|
|
end
|
|
if i == n then
|
|
--seq:add(SpatialConvolution(backend, ch, n_middle, 1, 1, 1, 1, 0, 0))
|
|
else
|
|
seq:add(w2nn.GradWeight(0.025))
|
|
seq:add(SpatialConvolution(backend, n_middle, n_middle, 1, 1, 1, 1, 0, 0))
|
|
end
|
|
seq:add(nn.LeakyReLU(0.1, true))
|
|
con:add(seq)
|
|
end
|
|
end
|
|
return nn.Sequential():add(con):add(nn.JoinTable(2))
|
|
end
|
|
local model = nn.Sequential()
|
|
local m = 64
|
|
local n = 14
|
|
|
|
model:add(nn.ConcatTable():add(nn.Identity()))
|
|
for i = 1, n - 1 do
|
|
if i == 1 then
|
|
model:add(skip(backend, ch, m))
|
|
else
|
|
model:add(skip(backend, m, m))
|
|
end
|
|
end
|
|
model:add(nn.FlattenTable())
|
|
model:add(concat(backend, n, ch, m))
|
|
model:add(SpatialFullConvolution(backend, m * (n - 1) + 3, ch, 4, 4, 2, 2, 3, 3):noBias())
|
|
model:add(w2nn.InplaceClip01())
|
|
model:add(nn.View(-1):setNumInputDims(3))
|
|
|
|
model.w2nn_arch_name = "cupconv_14"
|
|
model.w2nn_offset = 28
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_channels = ch
|
|
model.w2nn_resize = true
|
|
|
|
return model
|
|
end
|
|
|
|
function srcnn.upconv_refine(backend, ch)
|
|
local function block(backend, ch)
|
|
local seq = nn.Sequential()
|
|
local con = nn.ConcatTable()
|
|
local res = nn.Sequential()
|
|
local base = nn.Sequential()
|
|
local refine = nn.Sequential()
|
|
local aux_con = nn.ConcatTable()
|
|
|
|
res:add(w2nn.GradWeight(0.1))
|
|
res:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
|
|
res:add(nn.LeakyReLU(0.1, true))
|
|
res:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
res:add(nn.LeakyReLU(0.1, true))
|
|
res:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
|
|
res:add(nn.LeakyReLU(0.1, true))
|
|
res:add(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0):noBias())
|
|
res:add(w2nn.InplaceClip01())
|
|
res:add(nn.MulConstant(0.5))
|
|
|
|
con:add(res)
|
|
con:add(nn.Sequential():add(nn.SpatialZeroPadding(-4, -4, -4, -4)):add(nn.MulConstant(0.5)))
|
|
|
|
-- main output
|
|
refine:add(nn.CAddTable()) -- averaging
|
|
refine:add(nn.View(-1):setNumInputDims(3))
|
|
-- aux output
|
|
base:add(nn.SelectTable(2))
|
|
base:add(nn.MulConstant(2)) -- revert mul 0.5
|
|
base:add(nn.View(-1):setNumInputDims(3))
|
|
|
|
aux_con:add(refine)
|
|
aux_con:add(base)
|
|
|
|
seq:add(con)
|
|
seq:add(aux_con)
|
|
seq:add(w2nn.AuxiliaryLossTable(1))
|
|
return seq
|
|
end
|
|
local model = nn.Sequential()
|
|
model:add(SpatialConvolution(backend, ch, 32, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 128, 128, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, 128, 256, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
|
|
model:add(w2nn.InplaceClip01())
|
|
model:add(block(backend, ch))
|
|
|
|
model.w2nn_arch_name = "upconv_refine"
|
|
model.w2nn_offset = 18
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_resize = true
|
|
model.w2nn_channels = ch
|
|
|
|
return model
|
|
end
|
|
|
|
-- cascaded residual channel attention unet
|
|
function srcnn.upcunet(backend, ch)
|
|
function unet_branch(insert, backend, n_input, n_output, depad)
|
|
local block = nn.Sequential()
|
|
local con = nn.ConcatTable(2)
|
|
local model = nn.Sequential()
|
|
|
|
block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
|
|
block:add(insert)
|
|
block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
|
|
con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
|
|
con:add(block)
|
|
model:add(con)
|
|
model:add(nn.CAddTable())
|
|
return model
|
|
end
|
|
function unet_conv(n_input, n_middle, n_output, se)
|
|
local model = nn.Sequential()
|
|
model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
if se then
|
|
-- Squeeze and Excitation Networks
|
|
local con = nn.ConcatTable(2)
|
|
local attention = nn.Sequential()
|
|
attention:add(nn.SpatialAdaptiveAveragePooling(1, 1)) -- global average pooling
|
|
attention:add(SpatialConvolution(backend, n_output, math.floor(n_output / 4), 1, 1, 1, 1, 0, 0))
|
|
attention:add(nn.ReLU(true))
|
|
attention:add(SpatialConvolution(backend, math.floor(n_output / 4), n_output, 1, 1, 1, 1, 0, 0))
|
|
attention:add(nn.Sigmoid(true))
|
|
con:add(nn.Identity())
|
|
con:add(attention)
|
|
model:add(con)
|
|
model:add(w2nn.ScaleTable())
|
|
end
|
|
return model
|
|
end
|
|
-- Residual U-Net
|
|
function unet(backend, ch, deconv)
|
|
local block1 = unet_conv(128, 256, 128, true)
|
|
local block2 = nn.Sequential()
|
|
block2:add(unet_conv(64, 64, 128, true))
|
|
block2:add(unet_branch(block1, backend, 128, 128, 4))
|
|
block2:add(unet_conv(128, 64, 64, true))
|
|
local model = nn.Sequential()
|
|
model:add(unet_conv(ch, 32, 64, false))
|
|
model:add(unet_branch(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(unet(backend, ch, true))
|
|
con:add(unet(backend, ch, false))
|
|
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"
|
|
model.w2nn_offset = 60
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_channels = ch
|
|
model.w2nn_resize = true
|
|
-- 72, 128, 256 are valid
|
|
--model.w2nn_input_size = 128
|
|
|
|
return model
|
|
end
|
|
|
|
-- cascaded residual spatial channel attention unet
|
|
function srcnn.upcunet_v2(backend, ch)
|
|
function unet_branch(insert, backend, n_input, n_output, depad)
|
|
local block = nn.Sequential()
|
|
local con = nn.ConcatTable(2)
|
|
local model = nn.Sequential()
|
|
|
|
block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
|
|
block:add(insert)
|
|
block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
|
|
con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
|
|
con:add(block)
|
|
model:add(con)
|
|
model:add(nn.CAddTable())
|
|
return model
|
|
end
|
|
function unet_conv(n_input, n_middle, n_output, se)
|
|
local model = nn.Sequential()
|
|
model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
|
|
model:add(nn.LeakyReLU(0.1, true))
|
|
if se then
|
|
-- Spatial Squeeze and Excitation Networks
|
|
local se_fac = 4
|
|
local con = nn.ConcatTable(2)
|
|
local attention = nn.Sequential()
|
|
attention:add(nn.SpatialAveragePooling(4, 4, 4, 4))
|
|
attention:add(SpatialConvolution(backend, n_output, math.floor(n_output / se_fac), 1, 1, 1, 1, 0, 0))
|
|
attention:add(nn.ReLU(true))
|
|
attention:add(SpatialConvolution(backend, math.floor(n_output / se_fac), n_output, 1, 1, 1, 1, 0, 0))
|
|
attention:add(nn.Sigmoid(true))
|
|
attention:add(nn.SpatialUpSamplingNearest(4, 4))
|
|
con:add(nn.Identity())
|
|
con:add(attention)
|
|
model:add(con)
|
|
model:add(nn.CMulTable())
|
|
end
|
|
return model
|
|
end
|
|
-- Residual U-Net
|
|
function unet(backend, in_ch, out_ch, deconv)
|
|
local block1 = unet_conv(128, 256, 128, true)
|
|
local block2 = nn.Sequential()
|
|
block2:add(unet_conv(64, 64, 128, true))
|
|
block2:add(unet_branch(block1, backend, 128, 128, 4))
|
|
block2:add(unet_conv(128, 64, 64, true))
|
|
local model = nn.Sequential()
|
|
model:add(unet_conv(in_ch, 32, 64, false))
|
|
model:add(unet_branch(block2, backend, 64, 64, 16))
|
|
if deconv then
|
|
model:add(SpatialFullConvolution(backend, 64, out_ch, 4, 4, 2, 2, 3, 3):noBias())
|
|
else
|
|
model:add(SpatialConvolution(backend, 64, out_ch, 3, 3, 1, 1, 0, 0):noBias())
|
|
end
|
|
return model
|
|
end
|
|
local model = nn.Sequential()
|
|
local con = nn.ConcatTable()
|
|
local aux_con = nn.ConcatTable()
|
|
|
|
-- 2 cascade
|
|
model:add(unet(backend, ch, ch, true))
|
|
con:add(nn.Sequential():add(unet(backend, ch, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- -1 for odd output size
|
|
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_v2"
|
|
model.w2nn_offset = 58
|
|
model.w2nn_scale_factor = 2
|
|
model.w2nn_channels = ch
|
|
model.w2nn_resize = true
|
|
model.w2nn_valid_input_size = {76,92,108,140,156,172,188,204,220,236,252,268,284,300,316,332,348,364,380,396,412,428,444,460,476,492,508}
|
|
|
|
return model
|
|
end
|
|
local function bench()
|
|
local sys = require 'sys'
|
|
cudnn.benchmark = false
|
|
local model = nil
|
|
local arch = {"upconv_7", "upcunet", "upcunet_v2"}
|
|
local backend = "cunn"
|
|
for k = 1, #arch do
|
|
model = srcnn[arch[k]](backend, 3):cuda()
|
|
model:training()
|
|
t = sys.clock()
|
|
for i = 1, 10 do
|
|
model:forward(torch.Tensor(1, 3, 172, 172):zero():cuda())
|
|
end
|
|
print(arch[k], sys.clock() - t)
|
|
end
|
|
end
|
|
function srcnn.create(model_name, backend, color)
|
|
model_name = model_name or "vgg_7"
|
|
backend = backend or "cunn"
|
|
color = color or "rgb"
|
|
local ch = 3
|
|
if color == "rgb" then
|
|
ch = 3
|
|
elseif color == "y" then
|
|
ch = 1
|
|
else
|
|
error("unsupported color: " .. color)
|
|
end
|
|
if srcnn[model_name] then
|
|
local model = srcnn[model_name](backend, ch)
|
|
assert(model.w2nn_offset % model.w2nn_scale_factor == 0)
|
|
return model
|
|
else
|
|
error("unsupported model_name: " .. model_name)
|
|
end
|
|
end
|
|
--[[
|
|
local model = srcnn.cunet_v3("cunn", 3):cuda()
|
|
print(model)
|
|
model:training()
|
|
print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()):size())
|
|
local model = srcnn.upcunet_v2("cunn", 3):cuda()
|
|
print(model)
|
|
model:training()
|
|
print(model:forward(torch.Tensor(1, 3, 76, 76):zero():cuda()))
|
|
os.exit()
|
|
--]]
|
|
return srcnn
|