module NN.Examples.GoogLeNet(googLeNet, main) where
import Gen.Caffe.FillerParameter as FP
import Gen.Caffe.InnerProductParameter as IP
import Gen.Caffe.LayerParameter as LP
import Control.Lens
import Control.Monad
import Data.Sequence (singleton)
import Data.Word
import NN
import NN.Examples.ImageNet
googleTrain = train & mirror' True & batchSize' 32 & cropSize' 224
googleTest = test & mirror' False & batchSize' 50 & cropSize' 224
googleMult = [def & lrMult' 1 & decayMult' 1,
def & lrMult' 2 & decayMult' 0]
googleConv = conv & param' googleMult & biasFillerC' (constant 0.2)
googleLRN = lrn & localSize' 5 & alphaLRN' 0.0001 & betaLRN' 0.75
googlePool = maxPool & sizeP' 3 & strideP' 2
googleIP n = ip n & param' googleMult
conv1 = googleConv & numOutputC' 64 & padC' 3 & kernelSizeC' 7 & strideC' 2 & weightFillerC' (xavier 0.1)
conv2 = googleConv & numOutputC' 192 & padC' 1 & kernelSizeC' 3 & weightFillerC' (xavier 0.03)
topPool = avgPool & sizeP' 7 & strideP' 1
topFc = googleIP 1000 & biasFillerIP' (constant 0) & weightFillerIP' (xavier 0.0)
& _inner_product_param._Just.IP._weight_filler._Just._std .~ Nothing
data Inception = Inception {_1x1, _3x3reduce, _3x3, _5x5reduce, _5x5, _poolProj :: Word32}
inception :: Node -> Inception -> G LayerParameter Node
inception input Inception{..} = do
columns' <- mapM sequential columns
concat'' <- layer' concat'
forM_ columns' $ \(bottom, top) -> input >-> bottom >> top >-> concat''
return concat''
where
columns = [
[googleConv & numOutputC' _1x1 & kernelSizeC' 1 & weightFillerC' (xavier 0.03), relu],
[googleConv & numOutputC' _3x3reduce & kernelSizeC' 1 & weightFillerC' (xavier 0.09), relu, googleConv & numOutputC' _3x3 & kernelSizeC' 3 & weightFillerC' (xavier 0.03) & padC' 1, relu],
[googleConv & numOutputC' _5x5reduce & kernelSizeC' 1 & weightFillerC' (xavier 0.2), relu, googleConv & numOutputC' _5x5 & kernelSizeC' 5 & weightFillerC' (xavier 0.03) & padC' 2, relu],
[maxPool& sizeP' 3 & strideP' 3 & padP' 1, googleConv & numOutputC' _poolProj & kernelSizeC' 1 & weightFillerC' (xavier 0.1), relu]]
intermediateClassifier :: Node -> NetBuilder
intermediateClassifier source = do
(input, representation) <- sequential [pool1, conv1', relu, fc1, relu, dropout 0.7, fc2]
source >-> input
forM_ [accuracy 1, accuracy 5, softmax & _loss_weight <>~ singleton 0.3] $ attach (From representation)
where
pool1 = avgPool & sizeP' 5 & strideP' 3
conv1' = googleConv & numOutputC' 128 & kernelSizeC' 1 & weightFillerC' (xavier 0.08)
fc1 = googleIP 1024 & weightFillerIP' (xavier 0.02) & biasFillerIP' (constant 0.2)
fc2 = googleIP 1000 & weightFillerIP' (xavier 0.0009765625) & biasFillerIP' (constant 0)
data ColumnStep = I Inception | Classifier | MaxPool
googLeNet :: NetBuilder
googLeNet = do
(input, initial) <- sequential [conv1, relu, googlePool, googleLRN, conv2, relu, googleLRN, googlePool]
incepted <- foldM inceptionClassifier initial [
I $ Inception 64 96 128 16 32 32,
I $ Inception 128 128 192 32 96 64,
MaxPool,
I $ Inception 192 96 208 16 48 64,
Classifier,
I $ Inception 150 112 224 24 64 64,
I $ Inception 128 128 256 24 64 64,
I $ Inception 112 144 288 32 64 64,
Classifier,
I $ Inception 256 160 320 32 128 128,
MaxPool,
I $ Inception 256 160 320 32 128 128,
I $ Inception 384 192 384 48 128 128]
(_, representation) <- return (incepted, incepted) >- sequential [topPool, dropout 0.4, topFc]
forM_ [accuracy 1, accuracy 5, softmax] $ attach (From representation)
forM_ [googleTrain, googleTest] $ attach (To input)
where
inceptionClassifier input (I inceptor) = inception input inceptor
inceptionClassifier input Classifier = intermediateClassifier input >> return input
inceptionClassifier input MaxPool = do {node <- layer' googlePool; input >-> node; return node}
main :: IO ()
main = cli googLeNet