The current part of the recipe will provide a step-by-step guide to prepare the dataset for the Inception-BN pretrained model.

1. Load the dependent packages:

# Load packages 
require(imager) 
source("download_cifar_data.R") 
The download_cifar_data consists of function to download and read CIFAR10 dataset. 

2. Read the downloaded CIFAR-10 dataset:

# Read Dataset and labels  
DATA_PATH<-paste(SOURCE_PATH, "/Chapter 4/data/cifar-10-batches-bin/", sep="") 
labels <- read.table(paste(DATA_PATH, "batches.meta.txt", sep="")) 
cifar_train <- read.cifar.data(filenames = c("data_batch_1.bin","data_batch_2.bin","data_batch_3.bin","data_batch_4.bin")) 

3. Filter the dataset for aeroplane and automobile. This is an optional step and is done to reduce complexity later:

# Filter data for Aeroplane and Automobile with label 1 and 2, respectively 
Classes = c(1, 2)  
images.rgb.train <- cifar_train$images.rgb 
images.lab.train <- cifar_train$images.lab 
ix<-images.lab.train%in%Classes 
images.rgb.train<-images.rgb.train[ix] 
images.lab.train<-images.lab.train[ix] 
rm(cifar_train)   

4. Transform to image. This step is required as the CIFAR-10 dataset is a 32 x 32 x 3 image, which is flattened to a 1024 x 3 format:

# Function to transform to image 
transform.Image <- function(index, images.rgb) { 
  # Convert each color layer into a matrix,  
  # combine into an rgb object, and display as a plot 
  img <- images.rgb[[index]] 
  img.r.mat <- as.cimg(matrix(img$r, ncol=32, byrow = FALSE)) 
  img.g.mat <- as.cimg(matrix(img$g, ncol=32, byrow = FALSE)) 
  img.b.mat <- as.cimg(matrix(img$b, ncol=32, byrow = FALSE)) 
 
  # Bind the three channels into one image 
  img.col.mat <- imappend(list(img.r.mat,img.g.mat,img.b.mat),"c")  
  return(img.col.mat) 
} 

5. The next step involve padding images with zeros:

  # Function to pad image 
  image.padding <- function(x) { 
  img_width <- max(dim(x)[1:2]) 
  img_height <- min(dim(x)[1:2]) 
  pad.img <- pad(x, nPix = img_width - img_height, 
                 axes = ifelse(dim(x)[1] < dim(x)[2], "x", "y")) 
  return(pad.img) 
}

6. Save the image to a specified folder:

# Save train images 
MAX_IMAGE<-length(images.rgb.train) 
 
# Write Aeroplane images to aero folder 
sapply(1:MAX_IMAGE, FUN=function(x, images.rgb.train, images.lab.train){ 
  if(images.lab.train[[x]]==1){ 
    img<-transform.Image(x, images.rgb.train)   
    pad_img <- image.padding(img) 
    res_img <- resize(pad_img, size_x = 224, size_y = 224) 
    imager::save.image(res_img, paste("train/aero/aero", x, ".jpeg", sep=""))     
  } 
}, images.rgb.train=images.rgb.train, images.lab.train=images.lab.train) 
 
 
# Write Automobile images to auto folder 
sapply(1:MAX_IMAGE, FUN=function(x, images.rgb.train, images.lab.train){ 
  if(images.lab.train[[x]]==2){ 
    img<-transform.Image(x, images.rgb.train)   
    pad_img <- image.padding(img) 
    res_img <- resize(pad_img, size_x = 224, size_y = 224) 
    imager::save.image(res_img, paste("train/auto/auto", x, ".jpeg", sep=""))     
  } 
}, images.rgb.train=images.rgb.train, images.lab.train=images.lab.train) 

The preceding script saves the aeroplane images into the aero folder and the automobile images in the auto folder.

7. Convert to the recording format .rec supported by MXNet. This conversion requires im2rec.py MXnet module from Python as conversion is not supported in R. However, it can be called from R once MXNet is installed in Python using the system command. The splitting of the dataset into train and test can be obtained using the following file:

System("python ~/mxnet/tools/im2rec.py --list True --recursive True --train-ratio 0.90 cifar_224/pks.lst cifar_224/trainf/")

The preceding script will generate two list files: pks.lst_train.lst and pks.lst_train.lst. The splitting of train and validation is controlled by the -train-ratio parameter in the preceding script. The number of classes is based on the number of folders in the trainf directory. In this scenario, two classes are picked: automotive and aeroplane.

8. Convert the *.rec file for training and validation dataset:

# Creating .rec file from training sample list 
System("python ~/mxnet/tools/im2rec.py --num-thread=4 --pass-through=1 /home/prakash/deep learning/cifar_224/pks.lst_train.lst /home/prakash/deep learning/cifar_224/trainf/")   
 
# Creating .rec file from validation sample list 
System("python ~/mxnet/tools/im2rec.py --num-thread=4 --pass-through=1 /home/prakash/deep learning/cifar_224/pks.lst_val.lst /home/prakash/deep learning/cifar_224/trainf/") 

The preceding script will create the pks.lst_train.rec and pks.lst_val.rec files to be used in the next recipe to train the model using a pretrained model.