92 lines
3.2 KiB
Lua
92 lines
3.2 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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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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end
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function nn.SpatialConvolutionMM:clearState()
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if self.gradWeight then
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self.gradWeight = torch.Tensor(self.nOutputPlane, self.nInputPlane * self.kH * self.kW):typeAs(self.gradWeight):zero()
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end
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if self.gradBias then
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self.gradBias = torch.Tensor(self.nOutputPlane):typeAs(self.gradBias):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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return model:get(model:size() - 1).weight:size(1)
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end
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function srcnn.waifu2x_cunn(ch)
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local model = nn.Sequential()
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model:add(nn.SpatialConvolutionMM(ch, 32, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(nn.SpatialConvolutionMM(32, 32, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(nn.SpatialConvolutionMM(32, 64, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(nn.SpatialConvolutionMM(64, 64, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(nn.SpatialConvolutionMM(64, 128, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(nn.SpatialConvolutionMM(128, 128, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(nn.SpatialConvolutionMM(128, ch, 3, 3, 1, 1, 0, 0))
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model:add(nn.View(-1):setNumInputDims(3))
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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.waifu2x_cudnn(ch)
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local model = nn.Sequential()
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model:add(cudnn.SpatialConvolution(ch, 32, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(cudnn.SpatialConvolution(32, 32, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(cudnn.SpatialConvolution(32, 64, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(cudnn.SpatialConvolution(64, 64, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(cudnn.SpatialConvolution(64, 128, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(cudnn.SpatialConvolution(128, 128, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.LeakyReLU(0.1))
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model:add(cudnn.SpatialConvolution(128, ch, 3, 3, 1, 1, 0, 0))
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model:add(nn.View(-1):setNumInputDims(3))
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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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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 backend == "cunn" then
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return srcnn.waifu2x_cunn(ch)
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elseif backend == "cudnn" then
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return srcnn.waifu2x_cudnn(ch)
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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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return srcnn
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