a210090033
- Add new pretrained model to ./models/upconv_7 - Move old models to ./models/vgg_7 - Use nn.LeakyReLU instead of w2nn.LeakyReLU - Add useful attribute to .json New JSON attribute: The first layer has `model_config` attribute. It contains: model_arch: architecture name of model. see `lib/srcnn.lua` scale_factor: if scale_factor > 1, model:forward() changes image resolution with scale_factor. channels: input/output channels. if channels == 3, model is RGB model. offset: pixel size that is to be removed from output. for example: (scale_factor=1, offset=7, input=100x100) => output=(100-7)x(100-7) (scale_factor=2, offset=12, input=100x100) => output=(100*2-12)x(100*2-12) And each layer has `class_name` attribute.
311 lines
10 KiB
Lua
311 lines
10 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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function nn.SpatialConvolutionMM:reset(stdv)
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stdv = math.sqrt(2 / ((1.0 + 0.1 * 0.1) * self.kW * self.kH * self.nOutputPlane))
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self.weight:normal(0, stdv)
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self.bias:zero()
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end
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function nn.SpatialFullConvolution:reset(stdv)
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stdv = math.sqrt(2 / ((1.0 + 0.1 * 0.1) * self.kW * self.kH * self.nOutputPlane))
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self.weight:normal(0, stdv)
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self.bias:zero()
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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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stdv = math.sqrt(2 / ((1.0 + 0.1 * 0.1) * self.kW * self.kH * self.nOutputPlane))
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self.weight:normal(0, stdv)
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self.bias:zero()
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end
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function cudnn.SpatialFullConvolution:reset(stdv)
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stdv = math.sqrt(2 / ((1.0 + 0.1 * 0.1) * self.kW * self.kH * self.nOutputPlane))
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self.weight:normal(0, stdv)
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self.bias:zero()
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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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local function SpatialFullConvolution(backend, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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if backend == "cunn" then
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return nn.SpatialFullConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
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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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-- 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(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(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(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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-- Up Convolution
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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, 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(SpatialFullConvolution(backend, 128, ch, 4, 4, 2, 2, 1, 1))
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model.w2nn_arch_name = "upconv_7"
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model.w2nn_offset = 12
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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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function srcnn.upconv_8_4x(backend, ch)
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local model = nn.Sequential()
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model:add(SpatialFullConvolution(backend, ch, 32, 4, 4, 2, 2, 1, 1))
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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, 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(SpatialFullConvolution(backend, 64, 3, 4, 4, 2, 2, 1, 1))
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model.w2nn_arch_name = "upconv_8_4x"
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model.w2nn_offset = 12
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model.w2nn_scale_factor = 4
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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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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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color = color or "rgb"
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local ch = 3
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if color == "rgb" then
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ch = 3
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elseif color == "y" then
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ch = 1
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else
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error("unsupported color: " .. color)
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end
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if srcnn[model_name] then
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local model = srcnn[model_name](backend, ch)
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assert(model.w2nn_offset == (model.w2nn_offset / model.w2nn_scale_factor) * model.w2nn_scale_factor)
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return model
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else
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error("unsupported model_name: " .. model_name)
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end
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end
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--local model = srcnn.upconv_8_4x("cunn", 3):cuda()
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--print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
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return srcnn
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