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waifu2x/lib/srcnn.lua
nagadomi 9563d84302 No bias at FullConvolution
2016-07-12 11:15:12 +09:00

290 lines
9.7 KiB
Lua
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require 'w2nn'
-- ref: http://arxiv.org/abs/1502.01852
-- ref: http://arxiv.org/abs/1501.00092
local srcnn = {}
function nn.SpatialConvolutionMM:reset(stdv)
local fin = self.kW * self.kH * self.nInputPlane
local fout = self.kW * self.kH * self.nOutputPlane
stdv = math.sqrt(4 / ((1.0 + 0.1 * 0.1) * (fin + fout)))
self.weight:normal(0, stdv)
self.bias:zero()
end
function nn.SpatialFullConvolution:reset(stdv)
local fin = self.kW * self.kH * self.nInputPlane
local fout = self.kW * self.kH * self.nOutputPlane
stdv = math.sqrt(4 / ((1.0 + 0.1 * 0.1) * (fin + fout)))
self.weight:normal(0, stdv)
self.bias:zero()
end
if cudnn and cudnn.SpatialConvolution then
function cudnn.SpatialConvolution:reset(stdv)
local fin = self.kW * self.kH * self.nInputPlane
local fout = self.kW * self.kH * self.nOutputPlane
stdv = math.sqrt(4 / ((1.0 + 0.1 * 0.1) * (fin + fout)))
self.weight:normal(0, stdv)
self.bias:zero()
end
function cudnn.SpatialFullConvolution:reset(stdv)
local fin = self.kW * self.kH * self.nInputPlane
local fout = self.kW * self.kH * self.nOutputPlane
stdv = math.sqrt(4 / ((1.0 + 0.1 * 0.1) * (fin + fout)))
self.weight:normal(0, stdv)
self.bias:zero()
end
end
function nn.SpatialConvolutionMM:clearState()
if self.gradWeight then
self.gradWeight:resize(self.nOutputPlane, self.nInputPlane * self.kH * self.kW):zero()
end
if self.gradBias then
self.gradBias:resize(self.nOutputPlane):zero()
end
return nn.utils.clear(self, 'finput', 'fgradInput', '_input', '_gradOutput', 'output', 'gradInput')
end
function srcnn.channels(model)
if model.w2nn_channels ~= nil then
return model.w2nn_channels
else
return model:get(model:size() - 1).weight:size(1)
end
end
function srcnn.backend(model)
local conv = model:findModules("cudnn.SpatialConvolution")
local fullconv = model:findModules("cudnn.SpatialFullConvolution")
if #conv > 0 or #fullconv > 0 then
return "cudnn"
else
return "cunn"
end
end
function srcnn.color(model)
local ch = srcnn.channels(model)
if ch == 3 then
return "rgb"
else
return "y"
end
end
function srcnn.name(model)
if model.w2nn_arch_name ~= nil then
return model.w2nn_arch_name
else
local conv = model:findModules("nn.SpatialConvolutionMM")
if #conv == 0 then
conv = model:findModules("cudnn.SpatialConvolution")
end
if #conv == 7 then
return "vgg_7"
elseif #conv == 12 then
return "vgg_12"
else
error("unsupported model")
end
end
end
function srcnn.offset_size(model)
if model.w2nn_offset ~= nil then
return model.w2nn_offset
else
local name = srcnn.name(model)
if name:match("vgg_") then
local conv = model:findModules("nn.SpatialConvolutionMM")
if #conv == 0 then
conv = model:findModules("cudnn.SpatialConvolution")
end
local offset = 0
for i = 1, #conv do
offset = offset + (conv[i].kW - 1) / 2
end
return math.floor(offset)
else
error("unsupported model")
end
end
end
function srcnn.scale_factor(model)
if model.w2nn_scale_factor ~= nil then
return model.w2nn_scale_factor
else
local name = srcnn.name(model)
if name == "upconv_7" then
return 2
elseif name == "upconv_8_4x" then
return 4
else
return 1
end
end
end
local function SpatialConvolution(backend, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
if backend == "cunn" then
return nn.SpatialConvolutionMM(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
elseif backend == "cudnn" then
return cudnn.SpatialConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
else
error("unsupported backend:" .. backend)
end
end
local function SpatialFullConvolution(backend, nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, adjW, adjH)
if backend == "cunn" then
return nn.SpatialFullConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH, adjW, adjH)
elseif backend == "cudnn" then
return cudnn.SpatialFullConvolution(nInputPlane, nOutputPlane, kW, kH, dW, dH, padW, padH)
else
error("unsupported backend:" .. backend)
end
end
-- VGG style net(7 layers)
function srcnn.vgg_7(backend, ch)
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, 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, ch, 3, 3, 1, 1, 0, 0))
model:add(nn.View(-1):setNumInputDims(3))
model.w2nn_arch_name = "vgg_7"
model.w2nn_offset = 7
model.w2nn_scale_factor = 1
model.w2nn_channels = ch
--model:cuda()
--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
return model
end
-- VGG style net(12 layers)
function srcnn.vgg_12(backend, ch)
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, 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(SpatialConvolution(backend, 64, 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(SpatialConvolution(backend, 64, 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(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, ch, 3, 3, 1, 1, 0, 0))
model:add(nn.View(-1):setNumInputDims(3))
model.w2nn_arch_name = "vgg_12"
model.w2nn_offset = 12
model.w2nn_scale_factor = 1
model.w2nn_resize = false
model.w2nn_channels = ch
--model:cuda()
--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
return model
end
-- Dilated Convolution (7 layers)
function srcnn.dilated_7(backend, ch)
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(nn.SpatialDilatedConvolution(32, 64, 3, 3, 1, 1, 0, 0, 2, 2))
model:add(nn.LeakyReLU(0.1, true))
model:add(nn.SpatialDilatedConvolution(64, 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, 4, 4))
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, ch, 3, 3, 1, 1, 0, 0))
model:add(nn.View(-1):setNumInputDims(3))
model.w2nn_arch_name = "dilated_7"
model.w2nn_offset = 12
model.w2nn_scale_factor = 1
model.w2nn_resize = false
model.w2nn_channels = ch
--model:cuda()
--print(model:forward(torch.Tensor(32, ch, 92, 92):uniform():cuda()):size())
return model
end
-- Upconvolution
function srcnn.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(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(nn.View(-1):setNumInputDims(3))
model.w2nn_arch_name = "upconv_7"
model.w2nn_offset = 14
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
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.upconv_6("cunn", 3):cuda()
--print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
return srcnn
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