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createCustomModel.R
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createCustomModel.R
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#' Implementation of the "SCFN" architecture for Brain/Gender prediction
#'
#' Creates a keras model implementation of the Simple Fully Convolutional
#' Network model from the FMRIB group:
#'
#' \url{https://github.com/ha-ha-ha-han/UKBiobank_deep_pretrain}
#'
#' @param inputImageSize Used for specifying the input tensor shape. The
#' shape (or dimension) of that tensor is the image dimensions followed by
#' the number of channels (e.g., red, green, and blue).
#' @param numberOfFiltersPerLayer number of filters for the convolutional layers
#' @param numberOfBins number of bins for final softmax output.
#' @param dropoutRate dropout rate before final convolution layer.
#'
#' @return a SCFN keras model
#' @author Tustison NJ
#' @examples
#'
#' library( ANTsRNet )
#'
#' model <- createSimpleFullyConvolutionalNeuralNetworkModel3D( list( NULL, NULL, NULL, 1 ) )
#'
#' @import keras
#' @export
createSimpleFullyConvolutionalNeuralNetworkModel3D <- function(
inputImageSize,
numberOfFiltersPerLayer = c( 32, 64, 128, 256, 256, 64 ),
numberOfBins = 40,
dropoutRate = 0.5,
doExperimentalVariant = FALSE )
{
numberOfLayers <- length( numberOfFiltersPerLayer )
input <- layer_input( shape = inputImageSize )
output <- input
for( i in seq_len( numberOfLayers ) )
{
if( i < numberOfLayers )
{
output <- output %>% layer_conv_3d( numberOfFiltersPerLayer[i],
kernel_size = c( 3L, 3L, 3L ), padding = "valid" )
output <- output %>% layer_zero_padding_3d( padding = c( 1L, 1L, 1L ) )
output <- output %>% layer_batch_normalization( momentum = 0.1, epsilon = 1e-5 )
output <- output %>% layer_max_pooling_3d( pool_size = c( 2L, 2L, 2L ),
strides = c( 2L, 2L, 2L ) )
} else {
output <- output %>% layer_conv_3d( numberOfFiltersPerLayer[i],
kernel_size = c( 1L, 1L, 1L ), padding = "valid" )
output <- output %>% layer_batch_normalization( momentum = 0.1, epsilon = 1e-5 )
}
output <- output %>% layer_activation_relu()
}
output <- output %>% layer_average_pooling_3d( pool_size = c( 5L, 6L, 5L ),
strides = c( 5L, 6L, 5L ) )
if( dropoutRate > 0.0 )
{
output <- output %>% layer_dropout( rate = dropoutRate )
}
output <- output %>% layer_conv_3d( numberOfBins,
kernel_size = c( 1L, 1L, 1L ), padding = "valid" )
if( doExperimentalVariant == TRUE )
{
output <- output %>%
layer_dense( units = 1, activation = 'linear' )
} else {
output <- output %>% layer_activation_softmax()
}
model <- keras_model( inputs = input, outputs = output )
return( model )
}
#' Implementation of the "RMNet" generator architecture for inpainting
#'
#' Creates a keras model implementation of the model:
#'
#' \url{https://github.com/Jireh-Jam/R-MNet-Inpainting-keras}
#'
#' @return a keras model
#' @author Tustison NJ
#' @examples
#'
#' library( ANTsRNet )
#'
#' model <- createRmnetGenerator()
#'
#' @import keras
#' @export
createRmnetGenerator <- function()
{
imgShape <- c( 256, 256, 3 )
imgShapeMask <- c( 256, 256, 1 )
gf <- 64
channels <- 3
# compute inputs
inputImg <- layer_input( shape = imgShape, dtype = "float32", name = "image_input" )
inputMask <- layer_input( shape = imgShapeMask, dtype = "float32", name = "mask_input" )
reversedMask <- inputMask %>% layer_lambda( f = function( x )
{
return( 1-x )
} )
maskedImage <- list( inputImg, reversedMask ) %>% layer_multiply()
# encoder
x <- maskedImage %>% layer_conv_2d( gf, c( 5, 5 ), dilation_rate = 2, input_shape = imgShape, padding = "same", name = "enc_conv_1" )
x <- x %>% layer_activation_leaky_relu( alpha = 0.2 )
x <- x %>% layer_batch_normalization( momentum = 0.8 )
pool1 <- x %>% layer_max_pooling_2d( pool_size = c( 2, 2 ) )
x <- pool1 %>% layer_conv_2d( gf, c( 5, 5 ), dilation_rate = 2, padding = "same", name = "enc_conv_2" )
x <- x %>% layer_activation_leaky_relu( alpha = 0.2 )
x <- x %>% layer_batch_normalization( momentum = 0.8 )
pool2 <- x %>% layer_max_pooling_2d( pool_size = c( 2, 2 ) )
x <- pool2 %>% layer_conv_2d( gf * 2, c( 5, 5 ), dilation_rate = 2, padding = "same", name = "enc_conv_3" )
x <- x %>% layer_activation_leaky_relu( alpha = 0.2 )
x <- x %>% layer_batch_normalization( momentum = 0.8 )
pool3 <- x %>% layer_max_pooling_2d( pool_size = c( 2, 2 ) )
x <- pool3 %>% layer_conv_2d( gf * 4, c( 5, 5 ), dilation_rate = 2, padding = "same", name = "enc_conv_4" )
x <- x %>% layer_activation_leaky_relu( alpha = 0.2 )
x <- x %>% layer_batch_normalization( momentum = 0.8 )
pool4 <- x %>% layer_max_pooling_2d( pool_size = c( 2, 2 ) )
x <- pool4 %>% layer_conv_2d( gf * 8, c( 5, 5 ), dilation_rate = 2, padding = "same", name = "enc_conv_5" )
x <- x %>% layer_activation_leaky_relu( alpha = 0.2 )
x <- x %>% layer_dropout( 0.5 )
# decoder
x <- x %>% layer_upsampling_2d( size = c( 2, 2 ), interpolation = "bilinear" )
x <- x %>% layer_conv_2d_transpose( gf * 8, c( 3, 3 ), padding = "same", name = "upsample_conv_1" )
x <- x %>% layer_lambda( f = function( x ) {
tensorflow::tf$pad( x, list( c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ) ),
'REFLECT' )
} )
x <- x %>% layer_activation_relu()
x <- x %>% layer_batch_normalization( momentum = 0.8 )
x <- x %>% layer_upsampling_2d( size = c( 2, 2 ), interpolation = "bilinear" )
x <- x %>% layer_conv_2d_transpose( gf * 4, c( 3, 3 ), padding = "same", name = "upsample_conv_2" )
x <- x %>% layer_lambda( f = function( x ) {
tensorflow::tf$pad( x, list( c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ) ),
'REFLECT' )
} )
x <- x %>% layer_activation_relu()
x <- x %>% layer_batch_normalization( momentum = 0.8 )
x <- x %>% layer_upsampling_2d( size = c( 2, 2 ), interpolation = "bilinear" )
x <- x %>% layer_conv_2d_transpose( gf * 2, c( 3, 3 ), padding = "same", name = "upsample_conv_3" )
x <- x %>% layer_lambda( f = function( x ) {
tensorflow::tf$pad( x, list( c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ) ),
'REFLECT' )
} )
x <- x %>% layer_activation_relu()
x <- x %>% layer_batch_normalization( momentum = 0.8 )
x <- x %>% layer_upsampling_2d( size = c( 2, 2 ), interpolation = "bilinear" )
x <- x %>% layer_conv_2d_transpose( gf, c( 3, 3 ), padding = "same", name = "upsample_conv_4" )
x <- x %>% layer_lambda( f = function( x ) {
tensorflow::tf$pad( x, list( c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ), c( 0L, 0L ) ),
'REFLECT' )
} )
x <- x %>% layer_activation_relu()
x <- x %>% layer_batch_normalization( momentum = 0.8 )
x <- x %>% layer_conv_2d_transpose( channels, c( 3, 3 ), padding = "same", name = "final_output" )
x <- x %>% layer_activation( 'tanh' )
decodedOutput <- x
reversedMaskImage <- list( decodedOutput, inputMask ) %>% layer_multiply()
outputImg <- list( maskedImage, reversedMaskImage ) %>% layer_add()
concatOutputImg <- list( outputImg, inputMask ) %>% layer_concatenate()
model <- keras_model( inputs = list( inputImg, inputMask ), outputs = concatOutputImg )
return( model )
}