init

coco.py

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+# Copyright 2022 Lance Developers
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""COCO: Microsoft COCO Dataset.
+
+https://cocodataset.org/#home
+"""
+
+import os
+from typing import List
+
+import datasets
+import lance
+import pyarrow as pa
+import pyarrow.compute as pc
+
+_CLASS_MAP = {
+    1: "person",
+    2: "bicycle",
+    3: "car",
+    4: "motorcycle",
+    5: "airplane",
+    6: "bus",
+    7: "train",
+    8: "truck",
+    9: "boat",
+    10: "traffic light",
+    11: "fire hydrant",
+    13: "stop sign",
+    14: "parking meter",
+    15: "bench",
+    16: "bird",
+    17: "cat",
+    18: "dog",
+    19: "horse",
+    20: "sheep",
+    21: "cow",
+    22: "elephant",
+    23: "bear",
+    24: "zebra",
+    25: "giraffe",
+    27: "backpack",
+    28: "umbrella",
+    31: "handbag",
+    32: "tie",
+    33: "suitcase",
+    34: "frisbee",
+    35: "skis",
+    36: "snowboard",
+    37: "sports ball",
+    38: "kite",
+    39: "baseball bat",
+    40: "baseball glove",
+    41: "skateboard",
+    42: "surfboard",
+    43: "tennis racket",
+    44: "bottle",
+    46: "wine glass",
+    47: "cup",
+    48: "fork",
+    49: "knife",
+    50: "spoon",
+    51: "bowl",
+    52: "banana",
+    53: "apple",
+    54: "sandwich",
+    55: "orange",
+    56: "broccoli",
+    57: "carrot",
+    58: "hot dog",
+    59: "pizza",
+    60: "donut",
+    61: "cake",
+    62: "chair",
+    63: "couch",
+    64: "potted plant",
+    65: "bed",
+    67: "dining table",
+    70: "toilet",
+    72: "tv",
+    73: "laptop",
+    74: "mouse",
+    75: "remote",
+    76: "keyboard",
+    77: "cell phone",
+    78: "microwave",
+    79: "oven",
+    80: "toaster",
+    81: "sink",
+    82: "refrigerator",
+    84: "book",
+    85: "clock",
+    86: "vase",
+    87: "scissors",
+    88: "teddy bear",
+    89: "hair drier",
+    90: "toothbrush",
+}
+_DATASET_URI = (
+    "https://eto-public.s3.us-west-2.amazonaws.com/datasets/coco/coco.lance.tar.gz"
+)
+
+
+class Coco(datasets.ArrowBasedBuilder):
+    """COCO: Microsoft common object in context dataset"""
+
+    def _info(self):
+        class_names = []
+        for i in range(0, max(_CLASS_MAP.keys()) + 1):
+            class_names.append(_CLASS_MAP.get(i, f"N/A-{i}"))
+        return datasets.DatasetInfo(
+            description="COCO: Microsoft object detection dataset",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "split": datasets.Value("string"),
+                    "annotations": datasets.Sequence(
+                        {
+                            "bbox": datasets.Sequence(
+                                datasets.Value("float32"), length=4
+                            ),
+                            "category_id": datasets.ClassLabel(names=class_names),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/eto-ai/lance/tree/main/python/benchmarks/coco",
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        extracted_dir = dl_manager.download_and_extract(_DATASET_URI)
+        base_uri = os.path.join(extracted_dir, "coco.lance")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split": "train", "base_uri": base_uri},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"split": "val", "base_uri": base_uri},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split": "test", "base_uri": base_uri},
+            ),
+        ]
+
+    def _generate_tables(self, split, base_uri):
+        idx = 0
+        dataset = lance.dataset(base_uri)
+        scanner = dataset.scanner(
+            filter=pc.field("split") == split,
+        )
+        for batch in scanner.to_batches():  # type: pa.RecordBatch
+            cols = []
+            names = []
+
+            annotations = batch.column("annotations")
+            if len(annotations) == 0:
+                continue
+            cols.append(annotations)
+            names.append("annotations")
+
+            # Decode split because Huggingface does not support dictionary yet.
+            split_arr = batch.column("split").dictionary_decode()
+            cols.append(split_arr)
+            names.append("split")
+
+            bytes_arr = batch.column("image").storage
+            arr = pa.StructArray.from_arrays([bytes_arr], ["bytes"])
+            cols.append(arr)
+            names.append("image")
+
+            yield idx, pa.Table.from_arrays(cols, names)
+            idx += 1