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
46 linhas
1.6 KiB
Lua
46 linhas
1.6 KiB
Lua
require 'nn'
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-- thanks to https://github.com/soumith/imagenet-multiGPU.torch for this example
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nGPU = 4
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nClasses = 5
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-- from https://code.google.com/p/cuda-convnet2/source/browse/layers/layers-imagenet-1gpu.cfg
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-- this is AlexNet that was presented in the One Weird Trick paper. http://arxiv.org/abs/1404.5997
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local features = nn.Sequential()
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features:add(nn.SpatialConvolution(3,64,11,11,4,4,2,2)) -- 224 -> 55
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features:add(nn.ReLU(true))
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features:add(nn.SpatialMaxPooling(3,3,2,2)) -- 55 -> 27
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features:add(nn.SpatialConvolution(64,192,5,5,1,1,2,2)) -- 27 -> 27
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features:add(nn.ReLU(true))
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features:add(nn.SpatialMaxPooling(3,3,2,2)) -- 27 -> 13
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features:add(nn.SpatialConvolution(192,384,3,3,1,1,1,1)) -- 13 -> 13
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features:add(nn.ReLU(true))
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features:add(nn.SpatialConvolution(384,256,3,3,1,1,1,1)) -- 13 -> 13
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features:add(nn.ReLU(true))
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features:add(nn.SpatialConvolution(256,256,3,3,1,1,1,1)) -- 13 -> 13
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features:add(nn.ReLU(true))
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features:add(nn.SpatialMaxPooling(3,3,2,2)) -- 13 -> 6
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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(256*6*6))
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classifier:add(nn.Dropout(0.5))
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classifier:add(nn.Linear(256*6*6, 4096))
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classifier:add(nn.ReLU())
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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.ReLU())
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classifier:add(nn.Linear(4096, nClasses))
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classifier:add(nn.LogSoftMax())
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-- classifier:cuda()
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local model = nn.Sequential():add(features):add(classifier)
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model.imageSize = 256
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model.imageCrop = 224
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return model
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