Brian Broll
3426a07096
WIP #488 Added logs for graph plotting WIP #488 Added graph creation from cmdline logs WIP #488 Fixed JobEditor bugs exposed by metadata WIP #488 Removed old metadata on ExecuteJob WIP #488 Moved graph points to graph attribute WIP #488 Removed extra whitespace in points WIP #488 Fixed graph id collisions between jobs WIP #488 Created OutputViewer WIP #488 Added filtering for the metadata nodes WIP #488 Fixed visibility WIP #488 Adding auto hide when not relevant WIP #488 remove entries from pagination on delete WIP #488 Added initialization to the LineGraph WIP #488 Added LineGraph viz boilerplate WIP #488 Added basic linegraph WIP #488 Set LineGraph to use the 'Graph' node WIP #488 Fixed double-playing commands WIP #488 Fixed graph sizing and showing in the outputviewer WIP #488 Adding metadata updating support WIP #488 Updating graph w/ new data WIP #488 Added some multi-line support WIP #488 Added mult-line support for LineGraph WIP #488 Added Graph metadata type WIP #488 Fixed 'No Data Available' on quick graph open WIP #488 revert to console if active graph deleted WIP #488 Fixed graph update error WIP #488 Fixed JobEditor nodeId checking WIP #488 Removed 'points' from Graph WIP #488 Filtered out deepforge commands from stdout WIP #488 Fixed filtering deepforge cmds WIP #488 Fixed ExecutePipeline WIP #488 Added nvd3 to codeclimate ignore WIP #488. better error handling for incorrect node types WIP #488 Removed extra files and fixed code climate issues
86 linhas
3.4 KiB
Lua
86 linhas
3.4 KiB
Lua
-- thanks to https://github.com/soumith/imagenet-multiGPU.torch for this example
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require 'nn'
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nClasses = 1000
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local function inception(input_size, config)
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local concat = nn.Concat(2)
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if config[1][1] ~= 0 then
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local conv1 = nn.Sequential()
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conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)):add(nn.ReLU(true))
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concat:add(conv1)
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end
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local conv3 = nn.Sequential()
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conv3:add(nn.SpatialConvolution( input_size, config[2][1],1,1,1,1)):add(nn.ReLU(true))
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conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)):add(nn.ReLU(true))
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concat:add(conv3)
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local conv3xx = nn.Sequential()
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conv3xx:add(nn.SpatialConvolution( input_size, config[3][1],1,1,1,1)):add(nn.ReLU(true))
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conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)):add(nn.ReLU(true))
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conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)):add(nn.ReLU(true))
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concat:add(conv3xx)
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local pool = nn.Sequential()
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pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode
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if config[4][1] == 'max' then
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pool:add(nn.SpatialMaxPooling(3,3,1,1))
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elseif config[4][1] == 'avg' then
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pool:add(nn.SpatialAveragePooling(3,3,1,1))
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else
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error('Unknown pooling')
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end
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if config[4][2] ~= 0 then
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pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)):add(nn.ReLU(true))
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end
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concat:add(pool)
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return concat
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end
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local features = nn.Sequential()
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features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)):add(nn.ReLU(true))
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features:add(nn.SpatialMaxPooling(3,3,2,2))
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features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true))
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features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)):add(nn.ReLU(true))
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features:add(nn.SpatialMaxPooling(3,3,2,2))
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features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a)
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features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b)
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features:add(inception( 320, {{ 0},{128,160},{ 64, 96},{'max', 0}})) -- 3(c)
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features:add(nn.SpatialConvolution(576,576,2,2,2,2))
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features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a)
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features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b)
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features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c)
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features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d)
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local main_branch = nn.Sequential()
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main_branch:add(inception( 576, {{ 0},{128,192},{192,256},{'max', 0}})) -- 4(e)
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main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2))
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main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a)
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main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b)
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main_branch:add(nn.SpatialAveragePooling(7,7,1,1))
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main_branch:add(nn.View(1024))
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main_branch:add(nn.Linear(1024,nClasses))
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main_branch:add(nn.LogSoftMax())
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-- add auxillary classifier here (thanks to Christian Szegedy for the details)
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local aux_classifier = nn.Sequential()
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aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3))
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aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1))
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aux_classifier:add(nn.View(128*4*4))
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aux_classifier:add(nn.Linear(128*4*4,768))
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aux_classifier:add(nn.ReLU())
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aux_classifier:add(nn.Linear(768,nClasses))
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aux_classifier:add(nn.LogSoftMax())
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local splitter = nn.Concat(2)
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splitter:add(main_branch):add(aux_classifier)
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local model = nn.Sequential():add(features):add(splitter)
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-- model:cuda()
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-- model = makeDataParallel(model, nGPU) -- defined in util.lua
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model.imageSize = 256
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model.imageCrop = 224
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return model
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