Upload 3 files

01-download_melanoma.R

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+library(here)
+
+training_jpeg_images_url = "https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_JPEG.zip"
+training_metadata_url = "https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_GroundTruth.csv"
+training_metadata_v2_url = "https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_GroundTruth_v2.csv"
+training_duplicate_image_list_url = "https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Training_Duplicates.csv"
+
+test_jpeg_images_url = "https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Test_JPEG.zip"
+test_metadata_url = "https://isic-challenge-data.s3.amazonaws.com/2020/ISIC_2020_Test_Metadata.csv"
+
+
+urls = c(
+  training_jpeg_images_url,
+  training_metadata_url, training_metadata_v2_url, training_duplicate_image_list_url,
+  test_jpeg_images_url,
+  test_metadata_url
+)
+
+cache_dir = here("cache")
+download_melanoma_file = function(url) {
+  op = options(timeout = 36000) 
+  on.exit(options(op))
+  
+  download.file(url, here(cache_dir, basename(url)))
+}
+
+mlr3misc::walk(urls, download_melanoma_file)
+
+unzip(here(cache_dir, basename(training_jpeg_images_url)))
+unzip(here(cache_dir, basename(test_jpeg_images_url)))

02-resize_melanoma.py

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+import torch
+import os
+from tqdm import tqdm
+import torchvision
+
+PATH_TO_MLR3TORCH = "."
+cache_dir = "cache"
+
+path_to_melanoma_train = os.path.join(PATH_TO_MLR3TORCH, cache_dir, "train")
+path_to_melanoma_test = os.path.join(PATH_TO_MLR3TORCH, cache_dir, "ISIC_2020_Test_Input")
+
+path_to_output_train = os.path.join(PATH_TO_MLR3TORCH, cache_dir, "hf_dataset", "train")
+path_to_output_test = os.path.join(PATH_TO_MLR3TORCH, cache_dir, "hf_dataset", "ISIC_2020_Test_Input")
+
+os.makedirs(path_to_output_train)
+os.makedirs(path_to_output_test)
+
+tx = torchvision.transforms.Resize((128, 128))
+
+for f in tqdm(os.listdir(path_to_melanoma_train)):
+    img = torchvision.io.read_image(os.path.join(path_to_melanoma_train, f))
+    small_img = tx(img.float() / 255)
+    torchvision.utils.save_image(small_img, os.path.join(path_to_output_train, f))
+
+for f in tqdm(os.listdir(path_to_melanoma_test)):
+    if f.endswith(".jpg"):
+        img = torchvision.io.read_image(os.path.join(path_to_melanoma_test, f))
+        small_img = tx(img.float() / 255)
+        torchvision.utils.save_image(small_img, os.path.join(path_to_output_test, f))

03-process_melanoma.R

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+library(data.table)
+library(tidytable)
+library(purrr)
+
+library(here)
+
+library(fs)
+
+# this script changes the data into the format expected by Hugging Face
+# It expects that you have downloaded and extracted the original data by running the download_melanoma.R script
+# and that you have already resized it with PyTorch
+
+cache_dir = here("cache")
+
+duplicates = fread(here(cache_dir, "ISIC_2020_Training_Duplicates.csv"))
+
+metadata_file_paths = c(
+  here(cache_dir, "ISIC_2020_Training_GroundTruth.csv"),
+  here(cache_dir, "ISIC_2020_Training_GroundTruth_v2.csv"),
+  here(cache_dir, "ISIC_2020_Test_Metadata.csv")
+)
+metadata_dt_list = map(metadata_file_paths, fread)
+metadata_dt_list[[3]] = rename(metadata_dt_list[[3]], image_name = image)
+
+# deduplicate the metadata
+dedup = function(metadata_dt, duplicate_file_names) {
+  metadata_dt[!(image_name %in% duplicate_file_names), ]
+}
+
+training_metadata = dedup(metadata_dt_list[[1]], duplicates$image_name_2)
+training_metadata_v2 = dedup(metadata_dt_list[[2]], duplicates$image_name_2)
+test_metadata = metadata_dt_list[[3]]
+
+hf_dataset_dir = here(cache_dir, "hf_dataset")
+hf_train_dir = here(hf_dataset_dir, "train")
+hf_test_dir = here(hf_dataset_dir, "ISIC_2020_Test_Input")
+
+train_dirnames_for_each_img = paste0("train", (training_metadata_v2[, .I] %% 4) + 1)
+test_dirnames_for_each_img = paste0("ISIC_2020_Test_Input", (test_metadata[, .I] %% 2) + 1) 
+
+# add a column that Hugging Face wants
+add_hf_file_name_col = function(metadata_dt, image_relative_dirnames) {
+  metadata_dt[, file_name := paste0(file.path(image_relative_dirnames, metadata_dt$image_name), ".jpg")]
+}
+
+# image_relative_paths = c("train", "train", "ISIC_2020_Test_Input")
+
+add_hf_file_name_col(training_metadata, train_dirnames_for_each_img)
+add_hf_file_name_col(training_metadata_v2, train_dirnames_for_each_img)
+add_hf_file_name_col(metadata_dt_list[[3]], test_dirnames_for_each_img)
+
+# delete the duplicated images
+list.files(hf_train_dir) |> length()
+file.remove(here(hf_train_dir, paste0(duplicates$image_name_2, ".jpg")))
+list.files(hf_train_dir) |> length()
+
+old_names = function(metadata_dt, dir) {
+  paste0(file.path(dir, metadata_dt$image_name), ".jpg")
+}
+
+create_if_necessary = function(dirname) {
+  if (!dir.exists(dirname)) {
+    dir.create(dirname)
+  }
+}
+
+walk(here(hf_dataset_dir, unique(train_dirnames_for_each_img)), create_if_necessary)
+walk(here(hf_dataset_dir, unique(test_dirnames_for_each_img)), create_if_necessary)
+
+# file_move(old_names(training_metadata), here(hf_dataset_dir, train_dirnames_for_each_img, paste0(training_metadata$image_name, ".jpg")))
+file_move(old_names(training_metadata_v2, hf_train_dir), here(hf_dataset_dir, train_dirnames_for_each_img, paste0(training_metadata_v2$image_name, ".jpg")))
+file_move(old_names(test_metadata, hf_test_dir), here(hf_dataset_dir, test_dirnames_for_each_img, paste0(test_metadata$image_name, ".jpg")))
+
+test_metadata = rename(test_metadata, image = image_name)
+
+fwrite(training_metadata, here(hf_dataset_dir, "ISIC_2020_Training_GroundTruth.csv"))
+fwrite(training_metadata_v2, here(hf_dataset_dir, "ISIC_2020_Training_GroundTruth_v2.csv"))
+fwrite(test_metadata, here(hf_dataset_dir, "ISIC_2020_Test_Metadata.csv"))
+
+# test1 = list.files(here(hf_dataset_dir, "ISIC_2020_Test_Input1"))
+# test2 = list.files(here(hf_dataset_dir, "ISIC_2020_Test_Input2"))
+# setdiff(test1, test2)
+
+# test_metadata |> filter(image_name == "ISIC_9999302") |> pull(file_name)