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Copy pathmodel_dual.lua
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208 lines (179 loc) · 7.52 KB
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require 'nn'
torch.setdefaulttensortype('torch.FloatTensor')
-- toolJointNames = {'LeftClasperPoint', 'RightClasperPoint',
-- 'HeadPoint', 'ShaftPoint',
-- 'TrackedPoint', 'EndPoint' } -- joint number = 6
-- output size is 4 times smaller
-- 2 as base
local function Tool_sep4_v1(input_channels, first_layer_channels, joint_num, comp_num)
local m = nn.Sequential()
-- conv1
m:add(nn.SpatialConvolution(input_channels, first_layer_channels, 3, 3, 1, 1, 1, 1))
m:add(nn.SpatialBatchNormalization(first_layer_channels))
m:add(nn.ReLU())
m:add(nn.ConcatTable():add(nn.Identity()))
-- down sample
local input_c, output_c, dual_c
for i=1, 4 do
-- select first element
input_c = first_layer_channels * math.pow(2, i-1)
dual_c = input_c
local dual = nn.ConcatTable()
dual:add(nn.Sequential()
:add(nn.SelectTable(1))
:add(nn.SpatialMaxPooling(2,2,2,2))
:add(nn.SpatialConvolution(input_c, dual_c,3,3,1,1,1,1))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
dual:add(nn.Sequential()
:add(nn.SelectTable(1))
:add(nn.SpatialMaxPooling(2,2,2,2))
:add(nn.SpatialConvolution(input_c, dual_c,3,3,1,1,1,1))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
local cat = nn.ConcatTable()
cat:add(nn.Sequential():add(dual):add(nn.JoinTable(2)))
for j=3, i do
cat:add(nn.SelectTable(j-2))
end
m:add(cat)
end
-- up sample
for i=4, 3, -1 do
input_c = first_layer_channels * math.pow(2,i)
output_c = first_layer_channels * math.pow(2,i-1)
dual_c = first_layer_channels * math.pow(2, i-2)
local dual = nn.ConcatTable()
dual:add(nn.Sequential()
-- :add(nn.SpatialUpSamplingBilinear(2))
-- :add(nn.SpatialConvolution(input_c, dual_c, 3,3,1,1,1,1))
:add(nn.SpatialFullConvolution(input_c, dual_c, 2,2,2,2))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
dual:add(nn.Sequential()
-- :add(nn.SpatialUpSamplingBilinear(2))
-- :add(nn.SpatialConvolution(input_c, dual_c, 3,3,1,1,1,1))
:add(nn.SpatialFullConvolution(input_c, dual_c, 2,2,2,2))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
local p = nn.ParallelTable()
p:add(nn.Sequential():add(dual):add(nn.JoinTable(2)))
p:add(nn.Identity())
local cat = nn.ConcatTable()
cat:add(nn.Sequential()
:add(nn.NarrowTable(1,2))
:add(p):add(nn.JoinTable(2))
:add(nn.SpatialConvolution(input_c, output_c, 3,3,1,1,1,1))
:add(nn.SpatialBatchNormalization(output_c))
:add(nn.ReLU())
)
for j=3, i+1-2 do
cat:add(nn.SelectTable(j))
end
m:add(cat)
end
m:add(nn.SelectTable(1))
-- joint and comp layer
local dual = nn.ConcatTable()
dual:add(nn.SpatialConvolution(output_c, joint_num, 1,1))
dual:add(nn.SpatialConvolution(output_c, comp_num*2, 1,1))
m:add(dual):add(nn.JoinTable(2)):add(nn.Sigmoid())
-- m:add(dual):add(nn.JoinTable(2))
return m
end
-- 2 as base
local function Tool_sep4_det(input_channels, first_layer_channels, joint_num, comp_num)
local m = nn.Sequential()
-- conv1
m:add(nn.SpatialConvolution(input_channels, first_layer_channels, 3, 3, 1, 1, 1, 1))
m:add(nn.SpatialBatchNormalization(first_layer_channels))
m:add(nn.ReLU())
m:add(nn.ConcatTable():add(nn.Identity()))
-- down sample
local input_c, output_c, dual_c
for i=1, 4 do
-- select first element
input_c = first_layer_channels * math.pow(2, i-1)
dual_c = input_c
local dual = nn.ConcatTable()
dual:add(nn.Sequential()
:add(nn.SelectTable(1))
:add(nn.SpatialMaxPooling(2,2,2,2))
:add(nn.SpatialConvolution(input_c, dual_c,3,3,1,1,1,1))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
dual:add(nn.Sequential()
:add(nn.SelectTable(1))
:add(nn.SpatialMaxPooling(2,2,2,2))
:add(nn.SpatialConvolution(input_c, dual_c,3,3,1,1,1,1))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
local cat = nn.ConcatTable()
cat:add(nn.Sequential():add(dual):add(nn.JoinTable(2)))
for j=3, i do
cat:add(nn.SelectTable(j-2))
end
m:add(cat)
end
-- up sample
for i=4, 3, -1 do
input_c = first_layer_channels * math.pow(2,i)
output_c = first_layer_channels * math.pow(2,i-1)
dual_c = first_layer_channels * math.pow(2, i-2)
local dual = nn.ConcatTable()
dual:add(nn.Sequential()
-- :add(nn.SpatialUpSamplingBilinear(2))
-- :add(nn.SpatialConvolution(input_c, dual_c, 3,3,1,1,1,1))
:add(nn.SpatialFullConvolution(input_c, dual_c, 2,2,2,2))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
dual:add(nn.Sequential()
-- :add(nn.SpatialUpSamplingBilinear(2))
-- :add(nn.SpatialConvolution(input_c, dual_c, 3,3,1,1,1,1))
:add(nn.SpatialFullConvolution(input_c, dual_c, 2,2,2,2))
:add(nn.SpatialBatchNormalization(dual_c))
:add(nn.ReLU())
)
local p = nn.ParallelTable()
p:add(nn.Sequential():add(dual):add(nn.JoinTable(2)))
p:add(nn.Identity())
local cat = nn.ConcatTable()
cat:add(nn.Sequential()
:add(nn.NarrowTable(1,2))
:add(p):add(nn.JoinTable(2))
:add(nn.SpatialConvolution(input_c, output_c, 3,3,1,1,1,1))
:add(nn.SpatialBatchNormalization(output_c))
:add(nn.ReLU())
)
for j=3, i+1-2 do
cat:add(nn.SelectTable(j))
end
m:add(cat)
end
m:add(nn.SelectTable(1))
-- joint and comp layer
local dual = nn.ConcatTable()
dual:add(nn.SpatialConvolution(output_c, joint_num, 1,1))
dual:add(nn.SpatialConvolution(output_c, comp_num, 1,1))
m:add(dual):add(nn.JoinTable(2)):add(nn.Sigmoid())
-- m:add(dual):add(nn.JoinTable(2))
return m
end
local dual_net = Tool_sep4_det(3, 64, 5, 4) -- 64,128,256,512,1024
print(dual_net)
local saveDir = '/home/xiaofei/workspace/toolPose/models'
local modelConf = {type='toolDualPoseSep', v=1 }
--
local saveID = modelConf.type .. '_v' .. modelConf.v
local initModelPath = paths.concat(saveDir, 'model.' .. saveID .. '.init.t7')
torch.save(paths.concat(saveDir, initModelPath), dual_net)
print('saved model ' .. saveID .. ' to ' .. paths.concat(saveDir, initModelPath))
-- toolPoseSep v=1: j_radius = 20, inputsize=[384,480], model_output_scale=4, with rotation augment, no flip
-- toolDualPoseSep v=1: j_radius=20, inputsize=[384, 480], model_output_scale=4, with rotation augment, no flip