Brian Broll
383eda1f2b
WIP #541 Refactored nn-parser for better reusability WIP #541 Added setter support to the parser script WIP #541 Added check for class method match WIP #541 Added default detection WIP #541 Added setter support in CreateTorchMeta WIP #541 Added setters to layer-args.js WIP #541 Added setter support in ImportTorch WIP #541 Updated ImportTorch tests WIP setPointer -> setBase WIP #541 Updated ImportTorch examples WIP #541 added setter attributes WIP #541 Added setter support for GenArch WIP #541 Updated the GenArch tests WIP #541 Fixed utils tests WIP #541 Updated nn library WIP #541 Removed 'const' setters w/ only one value WIP #541 Added setter creation test WIP #541 Updated to use torch from deepforge config, if exists WIP #541 Fixed code climate issues WIP #541 skipping broken tests until webgme error is resolved WIP #541 Updated nn seed after removing meaningless 'const' setters
85 linhas
3.5 KiB
Lua
85 linhas
3.5 KiB
Lua
-- Copy of googlenet.lua which uses setters (the other googlenet has them removed)
|
|
require 'nn'
|
|
nGPU = 10
|
|
nClasses = 1000
|
|
local function inception(input_size, config)
|
|
local concat = nn.Concat(2)
|
|
if config[1][1] ~= 0 then
|
|
local conv1 = nn.Sequential()
|
|
conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)):add(nn.ReLU(true))
|
|
concat:add(conv1)
|
|
end
|
|
|
|
local conv3 = nn.Sequential()
|
|
conv3:add(nn.SpatialConvolution( input_size, config[2][1],1,1,1,1)):add(nn.ReLU(true))
|
|
conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)):add(nn.ReLU(true))
|
|
concat:add(conv3)
|
|
|
|
local conv3xx = nn.Sequential()
|
|
conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)):add(nn.ReLU(true))
|
|
conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)):add(nn.ReLU(true))
|
|
conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)):add(nn.ReLU(true))
|
|
concat:add(conv3xx)
|
|
|
|
local pool = nn.Sequential()
|
|
pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode
|
|
if config[4][1] == 'max' then
|
|
pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil())
|
|
elseif config[4][1] == 'avg' then
|
|
pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil())
|
|
else
|
|
error('Unknown pooling')
|
|
end
|
|
if config[4][2] ~= 0 then
|
|
pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)):add(nn.ReLU(true))
|
|
end
|
|
concat:add(pool)
|
|
|
|
return concat
|
|
end
|
|
|
|
local features = nn.Sequential()
|
|
features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)):add(nn.ReLU(true))
|
|
features:add(nn.SpatialMaxPooling(3,3,2,2):ceil())
|
|
features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true))
|
|
features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)):add(nn.ReLU(true))
|
|
features:add(nn.SpatialMaxPooling(3,3,2,2):ceil())
|
|
features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a)
|
|
features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b)
|
|
features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c)
|
|
features:add(nn.SpatialConvolution(576,576,2,2,2,2))
|
|
features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a)
|
|
features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b)
|
|
features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c)
|
|
features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d)
|
|
|
|
local main_branch = nn.Sequential()
|
|
main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e)
|
|
main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2))
|
|
main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a)
|
|
main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b)
|
|
main_branch:add(nn.SpatialAveragePooling(7,7,1,1))
|
|
main_branch:add(nn.View(1024):setNumInputDims(3))
|
|
main_branch:add(nn.Linear(1024,nClasses))
|
|
main_branch:add(nn.LogSoftMax())
|
|
|
|
-- add auxillary classifier here (thanks to Christian Szegedy for the details)
|
|
local aux_classifier = nn.Sequential()
|
|
aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil())
|
|
aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1))
|
|
aux_classifier:add(nn.View(128*4*4):setNumInputDims(3))
|
|
aux_classifier:add(nn.Linear(128*4*4,768))
|
|
aux_classifier:add(nn.ReLU())
|
|
aux_classifier:add(nn.Linear(768,nClasses))
|
|
aux_classifier:add(nn.LogSoftMax())
|
|
|
|
local splitter = nn.Concat(2)
|
|
splitter:add(main_branch):add(aux_classifier)
|
|
local model = nn.Sequential():add(features):add(splitter)
|
|
|
|
model.imageSize = 256
|
|
model.imageCrop = 224
|
|
|
|
|
|
return model
|