put everything in one file

odor.py

@@ -21,8 +21,14 @@ import os
 import pandas as pd
 
 import datasets
-import multiprocessing
-from download_utils import download_all
+import time
+
+import requests
+import pandas as pd
+from tqdm import tqdm
+from multiprocessing.pool import ThreadPool
+from multiprocessing import cpu_count
+from requests.exceptions import MissingSchema, Timeout, ConnectionError
 
 
 _CITATION = """\
@@ -74,11 +80,43 @@ class ODOR(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
+        def _download_one(entry, overwrite=False):
+            fn, uri, target_pth, retries = entry
+            fn = fn.replace("/", "_")
+            path = f'{target_pth}/{fn}'
+            if os.path.exists(path) and not overwrite:
+                return fn
+
+            for i in range(retries):
+                try:
+                    r = requests.get(uri, stream=True, timeout=50)
+                except (MissingSchema, Timeout, ConnectionError, InvalidSchema):
+                    time.sleep(i)
+                    continue
+
+                if r.status_code == 200:
+                    with open(path, 'wb') as f:
+                        for chunk in r:
+                            f.write(chunk)
+                    return fn
+                else:
+                    time.sleep(i)
+                    continue
+
+            return fn
+        def _download_all(metadata_pth, target_pth, retries=3):
+            df = pd.read_csv(metadata_pth)
+            entries = [[*x, target_pth, retries] for x in df[['File Name', 'Image Credits']].values]
+            n_processes = max(1, cpu_count() - 1)
+            with ThreadPool(n_processes) as p:
+                results = list(tqdm(p.imap(_download_one, entries), total=len(entries)))
+            return results
+
         imgs_dir = f'{self.cache_dir}/images' 
         csv_pth = 'meta/meta.csv'
         if not os.path.isdir(imgs_dir):
             os.makedirs(imgs_dir)
-        img_pths = download_all(csv_pth, imgs_dir)
+        img_pths = _download_all(csv_pth, imgs_dir)
 
         return [
             datasets.SplitGenerator(
@@ -137,12 +175,14 @@ class ODOR(datasets.GeneratorBasedBuilder):
                     }
                     idx += 1
 
-# if __name__ == '__main__':
-#     ds_builder = ODOR()
-#     n_processes = min(1, multiprocessing.cpu_count()-1)
+if __name__ == '__main__':
+    # ds_builder = ODOR()
+    # n_processes = min(1, multiprocessing.cpu_count()-1)
+
+    # ds_builder.download_and_prepare()
 
-#     ds_builder.download_and_prepare()
+    # ds = ds_builder.as_dataset()
 
-#     ds = ds_builder.as_dataset()
+    ds = datasets.load_dataset('mathiaszinnen/odor')
 
-#     print('ay')
+    print('ay')