add loading file

gan_hkr.py

@@ -0,0 +1,61 @@
+import os
+import pandas as pd
+import zipfile
+
+import datasets
+from datasets import load_dataset
+
+
+class GanHKR(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "path": datasets.Value("string"),
+                    "name": datasets.Value("string"),
+                    "text": datasets.Value("string")
+                }
+            )
+        )
+
+    def _split_generators(self, dl_manager):
+        _URLS = {f"img{i:02d}": f"img/img-{i:02d}.zip" for i in range(10)}
+        _URLS["labels"] = "gt.txt"
+        
+        data_paths = dl_manager.download(_URLS)
+        archive_paths = [data_paths[key] for key in data_paths if key.startswith("img")]
+        img_path = os.path.join(os.path.split(data_paths["labels"])[0], "img")
+        os.makedirs(img_path, exist_ok=True)
+
+        for archive_path in archive_paths:
+            with zipfile.ZipFile(archive_path) as archive:
+                archive.extractall(img_path)
+            os.remove(archive_path)
+
+        image_paths = [os.path.join(img_path, img_name) for img_name in os.listdir(img_path)]
+        return [datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "image_paths": image_paths,
+                        "labels_path": data_paths["labels"]
+                    })]
+
+    def _generate_examples(self, image_paths, labels_path):
+        df = pd.read_csv(labels_path, sep=",", names=["path", "text"])
+        df["path"] = df.path.str[4:]
+        df.set_index("path", inplace=True)
+
+        for image_path in image_paths:
+            image_name = os.path.basename(image_path)
+ 
+            if image_name in df.index:
+
+                example = {
+                    "image": image_path,
+                    "path": image_path,
+                    "name": image_name,
+                    "text": df["text"][image_name]
+                }
+                yield image_name, example