Best way to import the dogscats dataset

New to Julia
, ,
1

I would like to reproduce in julia some pytorch transfer-learning experiments which use Resnet18 on the Kaggle’s Dogs vs. Cats dataset (direct download from fast.ai).
I would like some feedback on how I’m importing the data.

I’m using the package Images to import the images represented as CHW arrays (this should be what the code I’m trying to reproduce does, by calling torchvision.datasets.ImageFolder() with torchvision.transforms.ToTensor() as a parameter).
Resnet18 takes as input 224\times 224 images, so I’m also using Images.PaddedView to crop them.

Here’s my code:

using Images

cd("~/data/dogscats/")

# Storing data as an array of tuples
img_example = load("train/dogs/dog.1933.jpg")
data_elem_example = (img = copy(channelview(img_example)), class = "dog", filename = "dog.1933.jpg")
data_elem_type = typeof(data_elem_example)

train_set = Array{data_elem_type}(undef,0)
valid_set = Array{data_elem_type}(undef,0)

function crop_center(new_size::Number, img::Array{RGB{Normed{UInt8,8}},2})
   radius = new_size/2
   h_size, v_size = size(img)
   h_shift = floor(Int32, radius - h_size/2)
   v_shift = floor(Int32, radius - v_size/2)

   shift_img = (h_shift, v_shift)
   out_dims= (new_size, new_size)
   return copy(PaddedView(0, img, out_dims, shift_img))
end

for s in ["train", "valid"]
   for (root, dirs, files) in walkdir(data_path*"/"*s)
      for file in files
         img_path = joinpath(root, file)
         img_cropped = crop_center(224, load(img_path))
         CHW_img = copy(channelview(img_cropped))
         class = splitpath(root)[end]
         target_set = s == "train" ? train_set : valid_set
         push!(target_set, (img = CHW_img, class = class, filename = file))
      end
   end
end

Thanks in advance for your time and suggestions.