refactor dataset

.gitignore

@@ -1,2 +1,4 @@
 .vscode/
 .ipynb_checkpoints/
+*.jpg
+__pycache__

download_utils.py

@@ -0,0 +1,45 @@
+import os
+import time
+
+import requests
+import pandas as pd
+from tqdm import tqdm
+from multiprocessing.pool import ThreadPool
+from multiprocessing import cpu_count
+from pathlib import Path
+from requests.exceptions import MissingSchema, Timeout, ConnectionError, InvalidSchema
+
+
+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

odor.py

@@ -21,6 +21,8 @@ import os
 import pandas as pd
 
 import datasets
+import multiprocessing
+from download_utils import download_all
 
 
 _CITATION = """\
@@ -72,34 +74,32 @@ class ODOR(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        # df_meta = pd.read_csv('meta/meta.csv')
-        # dl_manager.download(df_meta['Image Credits'].values)
-        dl_manager.download_and_extract(_URL)
-        
+        imgs_dir = f'{self.cache_dir}/images' # probably better to use the huggingface cache dir here
+        csv_pth = 'meta/meta.csv'
+        if not os.path.isdir(imgs_dir):
+            os.makedirs(imgs_dir)
+        img_pths = download_all(csv_pth, imgs_dir)
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
                     "annotation_file_path": "annotations/train.json",
-                    "metadata_file_path": "meta/meta_train.csv"
+                    "metadata_file_path": csv_pth,
+                    "img_dir": imgs_dir
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
                     "annotation_file_path": "annotations/test.json",
-                    "metadata_file_path": "meta/meta_test.csv"
+                    "metadata_file_path": csv_pth,
+                    "img_dir": imgs_dir
                 },
             ),
         ]
 
-    def _generate_examples(self, annotation_file_path, metadata_file_path):
-        return None
-        # load metadata 
-        # meta_df = pd.read_csv(metadata_file_path)
-
-        # files = download_images(meta_df) 
-
+    def _generate_examples(self, annotation_file_path, metadata_file_path, img_dir):
 
         def process_annot(annot, category_id_to_category):
             return {
@@ -111,18 +111,18 @@ class ODOR(datasets.GeneratorBasedBuilder):
 
         image_id_to_image = {}
         idx = 0
-        # This loop relies on the ordering of the files in the archive:
-        # Annotation files come first, then the images.
-        for path, f in files:
+
+        with open(annotation_file_path) as f:
+            annotations = json.load(f)
+        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+        image_id_to_annotations = collections.defaultdict(list)
+        for annot in annotations["annotations"]:
+            image_id_to_annotations[annot["image_id"]].append(annot)
+        image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+
+        for path in os.listdir(img_dir):
             file_name = os.path.basename(path)
-            if path == annotation_file_path:
-                annotations = json.load(f)
-                category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
-                image_id_to_annotations = collections.defaultdict(list)
-                for annot in annotations["annotations"]:
-                    image_id_to_annotations[annot["image_id"]].append(annot)
-                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
-            elif file_name in image_id_to_image:
+            if file_name in image_id_to_image:
                 image = image_id_to_image[file_name]
                 objects = [
                     process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
@@ -135,3 +135,13 @@ class ODOR(datasets.GeneratorBasedBuilder):
                     "objects": objects,
                 }
                 idx += 1
+
+if __name__ == '__main__':
+    ds_builder = ODOR()
+    n_processes = min(1, multiprocessing.cpu_count()-1)
+
+    ds_builder.download_and_prepare()
+
+    ds = ds_builder.as_dataset()
+
+    print('ay')