Brian Broll
09b6ca0ad0
WIP #3. Added import tests WIP #3 Added more test-cases WIP #3 Added more tests WIP #3. Fixed concat.lua test WIP #3 minor changes WIP #3 Fixed concat-parallel.lua WIP #3 Added check-model helper WIP #3 Added more tests for model checker WIP #3 Added extra tests WIP #3 Changed check-model to GraphChecker WIP #3. multiple cases fail for ImportTorch... WIP #3 Fixed ImportTorch batch test case running WIP #3 Changed graph checker to use gme path for id WIP #3 Updated tests WIP #3. Tweaked to get all examples working locally w/ 'th' WIP #3 Fixed tests
56 linhas
1.8 KiB
Lua
56 linhas
1.8 KiB
Lua
-- thanks to https://github.com/soumith/imagenet-multiGPU.torch for this example
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require 'nn'
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local modelType = 'A' -- on a titan black, B/D/E run out of memory even for batch-size 32
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-- Create tables describing VGG configurations A, B, D, E
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local cfg = {}
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if modelType == 'A' then
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cfg = {64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'}
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elseif modelType == 'B' then
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cfg = {64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'}
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elseif modelType == 'D' then
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cfg = {64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'}
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elseif modelType == 'E' then
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cfg = {64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'}
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else
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error('Unknown model type: ' .. modelType .. ' | Please specify a modelType A or B or D or E')
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end
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local features = nn.Sequential()
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do
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local iChannels = 3;
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for k,v in ipairs(cfg) do
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if v == 'M' then
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features:add(nn.SpatialMaxPooling(2,2,2,2))
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else
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local oChannels = v;
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local conv3 = nn.SpatialConvolution(iChannels,oChannels,3,3,1,1,1,1);
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features:add(conv3)
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features:add(nn.ReLU(true))
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iChannels = oChannels;
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end
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end
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end
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-- features:cuda()
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-- features = makeDataParallel(features, nGPU) -- defined in util.lua
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local classifier = nn.Sequential()
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classifier:add(nn.View(512*7*7))
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classifier:add(nn.Linear(512*7*7, 4096))
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classifier:add(nn.Threshold(0, 0.000001))
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classifier:add(nn.BatchNormalization(4096, 0.001))
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classifier:add(nn.Dropout(0.5))
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classifier:add(nn.Linear(4096, 4096))
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classifier:add(nn.Threshold(0, 0.000001))
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classifier:add(nn.BatchNormalization(4096, 0.001))
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classifier:add(nn.Dropout(0.5))
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classifier:add(nn.Linear(4096, 1000))
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classifier:add(nn.LogSoftMax())
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-- classifier:cuda()
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local model = nn.Sequential()
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model:add(features):add(classifier)
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return model
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