Upload convert_dataset.py

convert_dataset.py

@@ -0,0 +1,73 @@
+from pathlib import Path
+
+import pandas as pd
+from datasets import Dataset, DatasetDict, Image
+
+
+DATA_ROOT = Path("FBHM")
+
+
+def create_split(split_name):
+    csv_path = DATA_ROOT / f"{split_name}.csv"
+
+    print(f"Loading: {csv_path}")
+
+    df = pd.read_csv(csv_path)
+
+    # Current value:
+    # F1/memes/0151.jpg
+    #
+    # Convert it to:
+    # /full/local/path/.../FBHM/F1/memes/0151.jpg
+    df["img"] = df["img"].apply(
+        lambda x: str((DATA_ROOT / x).resolve())
+    )
+
+    # Check whether images actually exist
+    missing = [
+        p for p in df["img"]
+        if not Path(p).exists()
+    ]
+
+    if missing:
+        print(f"Missing images in {split_name}: {len(missing)}")
+        print(missing[:10])
+        raise FileNotFoundError("Some images could not be found.")
+
+    dataset = Dataset.from_pandas(
+        df,
+        preserve_index=False
+    )
+
+    # VERY IMPORTANT
+    dataset = dataset.cast_column(
+        "img",
+        Image()
+    )
+
+    return dataset
+
+
+train_dataset = create_split("train")
+test_dataset = create_split("test")
+
+
+dataset = DatasetDict({
+    "train": train_dataset,
+    "test": test_dataset,
+})
+
+
+print(dataset)
+
+print("\nFeatures:")
+print(dataset["train"].features)
+
+print("\nTesting first image:")
+print(dataset["train"][0]["img"])
+
+
+dataset.push_to_hub(
+    "nrizwan/FBHM",
+    max_shard_size="300MB"
+)