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Update cunet arch optimized by benchmark

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nagadomi 2018-11-04 08:03:37 +09:00
parent aea254eab5
commit 62770a901a
1 changed files with 19 additions and 206 deletions
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225
lib/srcnn.lua
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@ -485,15 +485,15 @@ function srcnn.upcresnet(backend, ch)
-- 2 cascade
model:add(resnet(backend, ch, true))
con:add(nn.Sequential():add(resnet(backend, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- output is odd
con:add(nn.Sequential():add(resnet(backend, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- output size must be odd
con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
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
aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01()))
aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01()))
model:add(con)
model:add(aux_con)
model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
model:add(w2nn.AuxiliaryLossTable(1))
model.w2nn_arch_name = "upcresnet"
model.w2nn_offset = 22
@ -557,7 +557,6 @@ function srcnn.fcn_v1(backend, ch)
return model
end
local function unet_branch(backend, insert, backend, n_input, n_output, depad)
local block = nn.Sequential()
local con = nn.ConcatTable(2)
@ -589,146 +588,6 @@ end
-- Cascaded Residual Channel Attention U-Net
function srcnn.upcunet(backend, ch)
-- Residual U-Net
local function unet(backend, ch, deconv)
local block1 = unet_conv(backend, 128, 256, 128, true)
local block2 = nn.Sequential()
block2:add(unet_conv(backend, 64, 64, 128, true))
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
block2:add(unet_conv(backend, 128, 64, 64, true))
local model = nn.Sequential()
model:add(unet_conv(backend, ch, 32, 64, false))
model:add(unet_branch(backend, 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
model.w2nn_valid_input_size = {}
for i = 76, 512, 4 do
table.insert(model.w2nn_valid_input_size, i)
end
return model
end
-- cunet for 1x
function srcnn.cunet(backend, ch)
local function unet(backend, ch)
local block1 = unet_conv(backend, 128, 256, 128, true)
local block2 = nn.Sequential()
block2:add(unet_conv(backend, 64, 64, 128, true))
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
block2:add(unet_conv(backend, 128, 64, 64, true))
local model = nn.Sequential()
model:add(unet_conv(backend, ch, 32, 64, false))
model:add(unet_branch(backend, block2, backend, 64, 64, 16))
model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
model:add(nn.LeakyReLU(0.1))
model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
return model
end
local model = nn.Sequential()
local con = nn.ConcatTable()
local aux_con = nn.ConcatTable()
-- 2 cascade
model:add(unet(backend, ch))
con:add(unet(backend, ch))
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 = "cunet"
model.w2nn_offset = 40
model.w2nn_scale_factor = 1
model.w2nn_channels = ch
model.w2nn_resize = false
model.w2nn_valid_input_size = {}
for i = 100, 512, 4 do
table.insert(model.w2nn_valid_input_size, i)
end
return model
end
function srcnn.upcunet_s_p0(backend, ch)
-- Residual U-Net
local function unet1(backend, ch, deconv)
local block1 = unet_conv(backend, 64, 128, 64, true)
local model = nn.Sequential()
model:add(unet_conv(backend, ch, 32, 64, false))
model:add(unet_branch(backend, block1, backend, 64, 64, 4))
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(unet1(backend, ch, true))
con:add(unet1(backend, ch, false))
con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
--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_s_p0"
model.w2nn_offset = 24
model.w2nn_scale_factor = 2
model.w2nn_channels = ch
model.w2nn_resize = true
model.w2nn_valid_input_size = {}
for i = 76, 512, 4 do
table.insert(model.w2nn_valid_input_size, i)
end
return model
end
function srcnn.upcunet_s_p1(backend, ch)
-- Residual U-Net
local function unet1(backend, ch, deconv)
local block1 = unet_conv(backend, 64, 128, 64, true)
@ -769,7 +628,6 @@ function srcnn.upcunet_s_p1(backend, ch)
-- 2 cascade
model:add(unet1(backend, ch, true))
con:add(unet2(backend, ch, false))
--con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
@ -779,7 +637,7 @@ function srcnn.upcunet_s_p1(backend, ch)
model:add(aux_con)
model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
model.w2nn_arch_name = "upcunet_s_p1"
model.w2nn_arch_name = "upcunet"
model.w2nn_offset = 36
model.w2nn_scale_factor = 2
model.w2nn_channels = ch
@ -791,9 +649,8 @@ function srcnn.upcunet_s_p1(backend, ch)
return model
end
function srcnn.upcunet_s_p2(backend, ch)
-- Residual U-Net
-- cunet for 1x
function srcnn.cunet(backend, ch)
local function unet1(backend, ch, deconv)
local block1 = unet_conv(backend, 64, 128, 64, true)
local model = nn.Sequential()
@ -831,54 +688,8 @@ function srcnn.upcunet_s_p2(backend, ch)
local aux_con = nn.ConcatTable()
-- 2 cascade
model:add(unet2(backend, ch, true))
con:add(unet1(backend, ch, false))
con:add(nn.SpatialZeroPadding(-8, -8, -8, -8))
--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_s_p2"
model.w2nn_offset = 48
model.w2nn_scale_factor = 2
model.w2nn_channels = ch
model.w2nn_resize = true
model.w2nn_valid_input_size = {}
for i = 76, 512, 4 do
table.insert(model.w2nn_valid_input_size, i)
end
return model
end
function srcnn.cunet_s(backend, ch)
local function unet(backend, ch)
local block1 = unet_conv(backend, 128, 256, 128, true)
local block2 = nn.Sequential()
block2:add(unet_conv(backend, 32, 64, 128, true))
block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
block2:add(unet_conv(backend, 128, 64, 32, true))
local model = nn.Sequential()
model:add(unet_conv(backend, ch, 32, 32, false))
model:add(unet_branch(backend, block2, backend, 32, 32, 16))
model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
model:add(nn.LeakyReLU(0.1))
model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
return model
end
local model = nn.Sequential()
local con = nn.ConcatTable()
local aux_con = nn.ConcatTable()
-- 2 cascade
model:add(unet(backend, ch))
con:add(unet(backend, ch))
model:add(unet1(backend, ch))
con:add(unet2(backend, ch))
con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
@ -887,9 +698,9 @@ function srcnn.cunet_s(backend, ch)
model:add(con)
model:add(aux_con)
model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
model.w2nn_arch_name = "cunet_s"
model.w2nn_offset = 40
model.w2nn_arch_name = "cunet"
model.w2nn_offset = 28
model.w2nn_scale_factor = 1
model.w2nn_channels = ch
model.w2nn_resize = false
@ -900,13 +711,11 @@ function srcnn.cunet_s(backend, ch)
return model
end
local function bench()
local sys = require 'sys'
cudnn.benchmark = true
local model = nil
local arch = {"upconv_7", "upresnet_s","upcresnet", "resnet_14l", "upcunet", "upcunet_s_p0", "upcunet_s_p1", "upcunet_s_p2"}
--local arch = {"upconv_7", "upcunet","upcunet_v0", "upcunet_s", "vgg_7", "cunet", "cunet_s"}
local arch = {"upconv_7", "upcunet", "vgg_7", "cunet"}
local backend = "cudnn"
local ch = 3
local batch_size = 1
@ -915,7 +724,12 @@ local function bench()
model = srcnn[arch[k]](backend, ch):cuda()
model:evaluate()
local dummy = nil
local crop_size = (output_size + model.w2nn_offset * 2) / 2
local crop_size = nil
if model.w2nn_resize then
crop_size = (output_size + model.w2nn_offset * 2) / 2
else
crop_size = (output_size + model.w2nn_offset * 2)
end
local dummy = torch.Tensor(batch_size, ch, output_size, output_size):zero():cuda()
print(arch[k], output_size, crop_size)
@ -962,5 +776,4 @@ print(model:forward(torch.Tensor(1, 3, 128, 128):zero():cuda()):size())
bench()
os.exit()
--]]
return srcnn