Create sample.py

sample.py

@@ -0,0 +1,77 @@
+import csv
+
+import datasets
+from datasets import DatasetDict
+
+LABELS = {"aerial", "interior", "exterior", "upshot", "skyline", "night"}
+
+_DATA_URL = {
+    "train": [f"data/train_images.tar.gz" for i in range(5)],
+    "validation": ["data/validation_images.tar.gz"],
+    "test": ["data/test_images.tar.gz"],
+}
+
+
+class Sample(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="A sample dataset to illustrate how to use HF APIs",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=list(LABELS)),
+                }
+            ),
+            homepage="github.com/SOM-Enterprise/hf-dataset-sample-representation",
+            citation="None",
+            task_templates=[
+                datasets.ImageClassification(image_column="image", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        archives = dl_manager.download(_DATA_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
+                    "split": "train",
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["validation"]],
+                    "split": "validation",
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "archives": [dl_manager.iter_archive(archive) for archive in archives["test"]],
+                    "split": "test",
+                }
+            )
+        ]
+
+    def _generate_examples(self, archives, split):
+        labels_dict = {}
+        with open('metadata.csv', newline='') as csvfile:
+            reader = csv.DictReader(csvfile)
+            for row in reader:
+                labels_dict[row['id']] = set(row['label'].split('|'))
+
+        idx = 0
+        for archive in archives:
+            for path, file in archive:
+                if path.endswith(".jpeg"):
+                    if split != "test":
+                        labels = labels_dict.get(path.split('/')[-1])
+                        label = labels if labels else ['']
+                    else:
+                        label = -1
+                    ex = {"image": {"path": path, "bytes": file.read()}, "label": label}
+                    yield idx, ex
+                    idx += 1