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💣 Machine learning which might blow up in your face 💣
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A deconvolution layer is in many ways a convolution layer in reverse.It learns a kernel to apply to each pixel location, spreading it outinto a larger layer.
This layer is important for image generation tasks, such as GANs onimages.-}module Grenade.Layers.Deconvolution ( Deconvolution (..) , Deconvolution' (..) , randomDeconvolution ) where
import Control.Monad.Random hiding ( fromList )import Data.Maybeimport Data.Proxyimport Data.Serialize
import GHC.TypeLitsimport Data.Kind (Type)
import Numeric.LinearAlgebra hiding ( uniformSample, konst )import qualified Numeric.LinearAlgebra as LAimport Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)
import Grenade.Coreimport Grenade.Layers.Internal.Convolutionimport Grenade.Layers.Internal.Update
-- | A Deconvolution layer for a neural network.-- This uses the im2col Convolution trick popularised by Caffe.---- The Deconvolution layer is a way of spreading out a single response-- into a larger image, and is useful in generating images.--data Deconvolution :: Nat -- Number of channels, for the first layer this could be RGB for instance. -> Nat -- Number of filters, this is the number of channels output by the layer. -> Nat -- The number of rows in the kernel filter -> Nat -- The number of column in the kernel filter -> Nat -- The row stride of the Deconvolution filter -> Nat -- The columns stride of the Deconvolution filter -> Type where Deconvolution :: ( KnownNat channels , KnownNat filters , KnownNat kernelRows , KnownNat kernelColumns , KnownNat strideRows , KnownNat strideColumns , KnownNat kernelFlattened , kernelFlattened ~ (kernelRows * kernelColumns * filters)) => !(L kernelFlattened channels) -- The kernel filter weights -> !(L kernelFlattened channels) -- The last kernel update (or momentum) -> Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns
data Deconvolution' :: Nat -- Number of channels, for the first layer this could be RGB for instance. -> Nat -- Number of filters, this is the number of channels output by the layer. -> Nat -- The number of rows in the kernel filter -> Nat -- The number of column in the kernel filter -> Nat -- The row stride of the Deconvolution filter -> Nat -- The columns stride of the Deconvolution filter -> Type where Deconvolution' :: ( KnownNat channels , KnownNat filters , KnownNat kernelRows , KnownNat kernelColumns , KnownNat strideRows , KnownNat strideColumns , KnownNat kernelFlattened , kernelFlattened ~ (kernelRows * kernelColumns * filters)) => !(L kernelFlattened channels) -- The kernel filter gradient -> Deconvolution' channels filters kernelRows kernelColumns strideRows strideColumns
instance Show (Deconvolution c f k k' s s') where show (Deconvolution a _) = renderConv a where renderConv mm = let m = extract mm ky = fromIntegral $ natVal (Proxy :: Proxy k) rs = LA.toColumns m ms = map (take ky) $ toLists . reshape ky <$> rs
render n' | n' <= 0.2 = ' ' | n' <= 0.4 = '.' | n' <= 0.6 = '-' | n' <= 0.8 = '=' | otherwise = '#'
px = (fmap . fmap . fmap) render ms in unlines $ foldl1 (zipWith (\a' b' -> a' ++ " | " ++ b')) $ px
randomDeconvolution :: ( MonadRandom m , KnownNat channels , KnownNat filters , KnownNat kernelRows , KnownNat kernelColumns , KnownNat strideRows , KnownNat strideColumns , KnownNat kernelFlattened , kernelFlattened ~ (kernelRows * kernelColumns * filters)) => m (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns)randomDeconvolution = do s <- getRandom let wN = uniformSample s (-1) 1 mm = konst 0 return $ Deconvolution wN mm
instance ( KnownNat channels , KnownNat filters , KnownNat kernelRows , KnownNat kernelColumns , KnownNat strideRows , KnownNat strideColumns , KnownNat (kernelRows * kernelColumns * filters) ) => UpdateLayer (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) where type Gradient (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) = (Deconvolution' channels filters kernelRows kernelColumns strideRows strideColumns) runUpdate lp (Deconvolution oldKernel oldMomentum) (Deconvolution' kernelGradient) = let (newKernel, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldKernel kernelGradient oldMomentum in Deconvolution newKernel newMomentum
createRandom = randomDeconvolution
instance ( KnownNat channels , KnownNat filters , KnownNat kernelRows , KnownNat kernelColumns , KnownNat strideRows , KnownNat strideColumns , KnownNat (kernelRows * kernelColumns * filters) ) => Serialize (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) where put (Deconvolution w _) = putListOf put . toList . flatten . extract $ w get = do let f = fromIntegral $ natVal (Proxy :: Proxy channels) wN <- maybe (fail "Vector of incorrect size") return . create . reshape f . LA.fromList =<< getListOf get let mm = konst 0 return $ Deconvolution wN mm
-- | A two dimentional image may have a Deconvolution filter applied to itinstance ( KnownNat kernelRows , KnownNat kernelCols , KnownNat filters , KnownNat strideRows , KnownNat strideCols , KnownNat inputRows , KnownNat inputCols , KnownNat outputRows , KnownNat outputCols , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows) , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols) , KnownNat (kernelRows * kernelCols * filters) , KnownNat (outputRows * filters) ) => Layer (Deconvolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) where type Tape (Deconvolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols 1) runForwards c (S2D input) = runForwards c (S3D input :: S ('D3 inputRows inputCols 1))
runBackwards c tape grads = case runBackwards c tape grads of (c', S3D back :: S ('D3 inputRows inputCols 1)) -> (c', S2D back)
-- | A two dimentional image may have a Deconvolution filter applied to itinstance ( KnownNat kernelRows , KnownNat kernelCols , KnownNat strideRows , KnownNat strideCols , KnownNat inputRows , KnownNat inputCols , KnownNat outputRows , KnownNat outputCols , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows) , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols) , KnownNat (kernelRows * kernelCols * 1) , KnownNat (outputRows * 1) ) => Layer (Deconvolution 1 1 kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D2 outputRows outputCols) where type Tape (Deconvolution 1 1 kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D2 outputRows outputCols) = S ('D3 inputRows inputCols 1) runForwards c (S2D input) = case runForwards c (S3D input :: S ('D3 inputRows inputCols 1)) of (tps, S3D fore :: S ('D3 outputRows outputCols 1)) -> (tps, S2D fore)
runBackwards c tape (S2D grads) = case runBackwards c tape (S3D grads :: S ('D3 outputRows outputCols 1)) of (c', S3D back :: S ('D3 inputRows inputCols 1)) -> (c', S2D back)
-- | A two dimentional image may have a Deconvolution filter applied to itinstance ( KnownNat kernelRows , KnownNat kernelCols , KnownNat strideRows , KnownNat strideCols , KnownNat inputRows , KnownNat inputCols , KnownNat outputRows , KnownNat outputCols , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows) , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols) , KnownNat (kernelRows * kernelCols * 1) , KnownNat (outputRows * 1) , KnownNat channels ) => Layer (Deconvolution channels 1 kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D2 outputRows outputCols) where type Tape (Deconvolution channels 1 kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D2 outputRows outputCols) = S ('D3 inputRows inputCols channels) runForwards c input = case runForwards c input of (tps, S3D fore :: S ('D3 outputRows outputCols 1)) -> (tps, S2D fore)
runBackwards c tape (S2D grads) = runBackwards c tape (S3D grads :: S ('D3 outputRows outputCols 1))
-- | A three dimensional image (or 2d with many channels) can have-- an appropriately sized Deconvolution filter run across it.instance ( KnownNat kernelRows , KnownNat kernelCols , KnownNat filters , KnownNat strideRows , KnownNat strideCols , KnownNat inputRows , KnownNat inputCols , KnownNat outputRows , KnownNat outputCols , KnownNat channels , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows) , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols) , KnownNat (kernelRows * kernelCols * filters) , KnownNat (outputRows * filters) ) => Layer (Deconvolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) where
type Tape (Deconvolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols channels)
runForwards (Deconvolution kernel _) (S3D input) = let ex = extract input ek = extract kernel ix = fromIntegral $ natVal (Proxy :: Proxy inputRows) iy = fromIntegral $ natVal (Proxy :: Proxy inputCols) kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows) ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols) sx = fromIntegral $ natVal (Proxy :: Proxy strideRows) sy = fromIntegral $ natVal (Proxy :: Proxy strideCols) ox = fromIntegral $ natVal (Proxy :: Proxy outputRows) oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)
c = vid2col 1 1 1 1 ix iy ex
mt = c LA.<> tr ek
r = col2vid kx ky sx sy ox oy mt rs = fromJust . create $ r in (S3D input, S3D rs) runBackwards (Deconvolution kernel _) (S3D input) (S3D dEdy) = let ex = extract input ix = fromIntegral $ natVal (Proxy :: Proxy inputRows) iy = fromIntegral $ natVal (Proxy :: Proxy inputCols) kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows) ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols) sx = fromIntegral $ natVal (Proxy :: Proxy strideRows) sy = fromIntegral $ natVal (Proxy :: Proxy strideCols) ox = fromIntegral $ natVal (Proxy :: Proxy outputRows) oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)
c = vid2col 1 1 1 1 ix iy ex
eo = extract dEdy ek = extract kernel
vs = vid2col kx ky sx sy ox oy eo
kN = fromJust . create . tr $ tr c LA.<> vs
dW = vs LA.<> ek
xW = col2vid 1 1 1 1 ix iy dW in (Deconvolution' kN, S3D . fromJust . create $ xW)