Upload get_bigdocs_75m.py

get_bigdocs_75m.py

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+from typing import Optional
+import datasets
+import io
+import PIL
+import PIL.PngImagePlugin
+import os
+import hashlib
+import warnings
+
+
+ASSEMBLED_COLUMNS = (
+    'sample_id', 
+    'dataset_name', 
+    'task_name', 
+    'query',
+    'annotations', 
+    'image', 
+    'query_info', 
+    'annotations_info', 
+    'image_info', 
+    'image_sha256'
+)
+
+
+def _hash_bytes(b: bytes) -> str:
+    m = hashlib.sha256()
+    m.update(b)
+    return m.hexdigest()
+
+
+def get_bigdocs_75m(
+        formal_name: datasets.DatasetDict, 
+        user_local_path: Optional[str], 
+        load_from_cache_file:Optional[bool]=None,
+        num_proc: Optional[int]=None,
+        raise_on_missing: Optional[bool]=None,
+        skip_bad_sha256: Optional[bool]=None,
+        bigdocs_load_dataset_kwargs: Optional[dict]=None
+    ) -> datasets.DatasetDict:
+    """
+    Get a subset of BigDocs-7.5M
+
+    Some parts of BigDocs-7.5M are distributed without their "image" column,
+    and instead have an "img_id" column. The present function substitutes
+    such images back in.
+
+    For the following `formal_name`, the the user is responsible to download
+    the specified dataset and specify its location through `user_local_path`.
+
+        - COCOtext: http://images.cocodataset.org/zips/train2014.zip
+        - pubtables-1m: https://www.microsoft.com/en-us/research/publication/pubtables-1m
+        - TextOCR: https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
+
+    Args:
+        formal_name (`DatasetDict`): The BigDocs-7.5M dataset to augment with local images.
+        user_local_path (`Optional[str]`, defaults to `None`): The local path containing the images to be linked.
+        load_from_cache_file (`Optional[bool], defaults to `None`): Passed to `map`, `filter` and the likes.
+        num_proc (`Optional[int], defaults to `None`): Passed to `map`, `filter` and the likes.
+        raise_on_missing (`Optional[bool]`, defaults to `None`):
+            Determines what to do when there is an error loading an image.
+                - `True`: raise an error.
+                - `None`: print a warning and skip the sample (default).
+                - `False`: silently skip the sample.
+        use_bad_sha256 (`Optional[bool], defaults to `None`):
+            Determines what to do when the sha256 integrity test fails.
+                - `True`: ignore the sha256 integrity test.
+                - `None`: print a warning and skip samples with bad sha256 (default).
+                - `False`: silently skip entries with bad sha256.
+        load_dataset_kwargs (`Optional[dict]`, defaults to `None`): Arguments passed to datasets.load_dataset .
+    """
+    if bigdocs_load_dataset_kwargs is None:
+        bigdocs_load_dataset_kwargs = {}
+    unprocessed = datasets.load_dataset("ServiceNow/BigDocs-7.5M", formal_name, **bigdocs_load_dataset_kwargs)    
+
+    def on_disk_processor(sample):
+        img_path = os.path.join(user_local_path, sample['img_id'])
+        # Load the image
+        try:
+            image = PIL.Image.open(img_path)
+        except Exception as e:
+            if raise_on_missing:
+                raise RuntimeError(f"Error loading image at {img_path}\n{e}")
+            if raise_on_missing is None:
+                warnings.warn(f"Skipping due to error loading image {img_path}", RuntimeWarning)
+            image = None  # Sample will be filtered out
+        if image is not None:
+            # Place into `buffer` using PNG image format
+            buffer = io.BytesIO()
+            image.save(buffer, "png")
+            # Reload the image with guaranteed PNG format
+            image = PIL.Image.open(buffer)
+            # Check sha256
+            if not skip_bad_sha256:
+                sha256 = _hash_bytes(buffer.getvalue())
+                if sha256 != sample["image_sha256"]:
+                    image = None  # Sample will be filtered out
+                    if skip_bad_sha256 is None:
+                        warnings.warn(f"Skipping due to bad sha256 for {img_path}", RuntimeWarning)
+        return {"image": image}
+
+    # Get the correct processor
+    try:
+        processor = {
+            "COCOtext": on_disk_processor,
+            "pubtables-1m": on_disk_processor,
+            "TextOCR": on_disk_processor,
+        }[formal_name]
+    except KeyError:
+        raise ValueError(f"Unknown formal_name: {formal_name}")
+    if processor is on_disk_processor:
+        assert user_local_path is not None, f"user_local_path is mandatory for formal_name={formal_name}"
+
+    if processor is None:
+        processed = unprocessed
+    else:
+        processed = unprocessed.map(
+            processor, 
+            remove_columns="img_id", 
+            load_from_cache_file=load_from_cache_file, 
+            num_proc=num_proc
+        )
+        # Drop missing images.
+        if not raise_on_missing:
+            processed = processed.filter((lambda image: image is not None), input_columns="image", num_proc=num_proc)
+        # Column order
+        processed = processed.select_columns(list(ASSEMBLED_COLUMNS))
+    
+    return processed