add script

README.md

@@ -1,3 +1,249 @@
 ---
 license: apache-2.0
+task_categories:
+- object-detection
+tags:
+- COCO
+- Detection
+- '2017'
+pretty_name: COCO detection dataset script
+size_categories:
+- 100K<n<1M
+dataset_info:
+ config_name: '2017'
+ features:
+ - name: id
+ dtype: int64
+ - name: objects
+ struct:
+ - name: bbox_id
+ sequence: int64
+ - name: category_id
+ sequence:
+ class_label:
+ names:
+ '0': N/A
+ '1': person
+ '2': bicycle
+ '3': car
+ '4': motorcycle
+ '5': airplane
+ '6': bus
+ '7': train
+ '8': truck
+ '9': boat
+ '10': traffic light
+ '11': fire hydrant
+ '12': street sign
+ '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
+ '26': hat
+ '27': backpack
+ '28': umbrella
+ '29': shoe
+ '30': eye glasses
+ '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
+ '45': plate
+ '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
+ '66': mirror
+ '67': dining table
+ '68': window
+ '69': desk
+ '70': toilet
+ '71': door
+ '72': tv
+ '73': laptop
+ '74': mouse
+ '75': remote
+ '76': keyboard
+ '77': cell phone
+ '78': microwave
+ '79': oven
+ '80': toaster
+ '81': sink
+ '82': refrigerator
+ '83': blender
+ '84': book
+ '85': clock
+ '86': vase
+ '87': scissors
+ '88': teddy bear
+ '89': hair drier
+ '90': toothbrush
+ - name: bbox
+ sequence:
+ sequence: float64
+ length: 4
+ - name: iscrowd
+ sequence: int64
+ - name: area
+ sequence: float64
+ - name: height
+ dtype: int64
+ - name: width
+ dtype: int64
+ - name: file_name
+ dtype: string
+ - name: coco_url
+ dtype: string
+ - name: image_path
+ dtype: string
+ splits:
+ - name: train
+ num_bytes: 87231216
+ num_examples: 117266
+ - name: validation
+ num_bytes: 3692192
+ num_examples: 4952
+ download_size: 20405354669
+ dataset_size: 90923408
 ---
+## Usage
+For using the COCO dataset (2017), you need to download it manually first:
+```bash
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+```
+
+Then to load the dataset:
+```python
+COCO_DIR = ...(path to the downloaded dataset directory)...
+ds = datasets.load_dataset(
+ "yonigozlan/coco_2017_detection_script",
+ "2017",
+ data_dir=COCO_DIR,
+ trust_remote_code=True,
+)
+```
+
+## Benchmarking
+Here is an example of how to benchmark a 🤗 Transformers object detection model on the validation data of the COCO dataset:
+
+```python
+import datasets
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from torchmetrics.detection.mean_ap import MeanAveragePrecision
+from tqdm import tqdm
+
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+# prepare data
+COCO_DIR = ...(path to the downloaded dataset directory)...
+ds = datasets.load_dataset(
+ "yonigozlan/coco_2017_detection_script",
+ "2017",
+ data_dir=COCO_DIR,
+ trust_remote_code=True,
+)
+val_data = ds["validation"]
+categories = val_data.features["objects"]["category_id"].feature.names
+id2label = {index: x for index, x in enumerate(categories, start=0)}
+label2id = {v: k for k, v in id2label.items()}
+checkpoint = "facebook/detr-resnet-50"
+
+# load model and processor
+model = AutoModelForObjectDetection.from_pretrained(
+ checkpoint, torch_dtype=torch.float16
+).to("cuda")
+id2label_model = model.config.id2label
+processor = AutoImageProcessor.from_pretrained(checkpoint)
+
+
+def collate_fn(batch):
+ data = {}
+ images = [Image.open(x["image_path"]).convert("RGB") for x in batch]
+ data["images"] = images
+ annotations = []
+ for x in batch:
+ boxes = x["objects"]["bbox"]
+ # convert to xyxy format
+ boxes = [[box[0], box[1], box[0] + box[2], box[1] + box[3]] for box in boxes]
+ labels = x["objects"]["category_id"]
+ boxes = torch.tensor(boxes)
+ labels = torch.tensor(labels)
+ annotations.append({"boxes": boxes, "labels": labels})
+ data["original_size"] = [(x["height"], x["width"]) for x in batch]
+ data["annotations"] = annotations
+ return data
+
+
+# prepare dataloader
+dataloader = DataLoader(val_data, batch_size=8, collate_fn=collate_fn)
+
+# prepare metric
+metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True)
+
+# evaluation loop
+for i, batch in tqdm(enumerate(dataloader), total=len(dataloader)):
+ inputs = (
+ processor(batch["images"], return_tensors="pt").to("cuda").to(torch.float16)
+ )
+ with torch.no_grad():
+ outputs = model(**inputs)
+ target_sizes = torch.tensor([x for x in batch["original_size"]]).to("cuda")
+ results = processor.post_process_object_detection(
+ outputs, threshold=0.0, target_sizes=target_sizes
+ )
+
+ # convert predicted label id to dataset label id
+ if len(id2label_model) != len(id2label):
+ for result in results:
+ result["labels"] = torch.tensor(
+ [label2id.get(id2label_model[x.item()], 0) for x in result["labels"]]
+ )
+ # put results back to cpu
+ for result in results:
+ for k, v in result.items():
+ if isinstance(v, torch.Tensor):
+ result[k] = v.to("cpu")
+ metric.update(results, batch["annotations"])
+
+metrics = metric.compute()
+print(metrics)
+```

coco_detection_dataset_script.py

@@ -0,0 +1,325 @@
+import json
+import os
+
+import datasets
+
+
+class COCOBuilderConfig(datasets.BuilderConfig):
+ def __init__(self, name, splits, **kwargs):
+ super().__init__(name, **kwargs)
+ self.splits = splits
+
+
+# Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{DBLP:journals/corr/LinMBHPRDZ14,
+ author = {Tsung{-}Yi Lin and
+ Michael Maire and
+ Serge J. Belongie and
+ Lubomir D. Bourdev and
+ Ross B. Girshick and
+ James Hays and
+ Pietro Perona and
+ Deva Ramanan and
+ Piotr Doll{'{a} }r and
+ C. Lawrence Zitnick},
+ title = {Microsoft {COCO:} Common Objects in Context},
+ journal = {CoRR},
+ volume = {abs/1405.0312},
+ year = {2014},
+ url = {http://arxiv.org/abs/1405.0312},
+ archivePrefix = {arXiv},
+ eprint = {1405.0312},
+ timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+ biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
+ bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+COCO is a large-scale object detection, segmentation, and captioning dataset.
+"""
+
+# Add a link to an official homepage for the dataset here
+_HOMEPAGE = "http://cocodataset.org/#home"
+
+# Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+
+# This script is supposed to work with local (downloaded) COCO dataset.
+_URLs = {}
+
+
+# Name of the dataset usually match the script name with CamelCase instead of snake_case
+class COCODataset(datasets.GeneratorBasedBuilder):
+ """An example dataset script to work with the local (downloaded) COCO dataset"""
+
+ VERSION = datasets.Version("0.0.0")
+
+ BUILDER_CONFIG_CLASS = COCOBuilderConfig
+ BUILDER_CONFIGS = [
+ COCOBuilderConfig(name="2017", splits=["train", "val"]),
+ ]
+ DEFAULT_CONFIG_NAME = "2017"
+
+ def _info(self):
+ # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+ feature_dict = {
+ "id": datasets.Value("int64"),
+ "objects": {
+ "bbox_id": datasets.Sequence(datasets.Value("int64")),
+ "category_id": datasets.Sequence(
+ datasets.ClassLabel(
+ names=[
+ "N/A",
+ "person",
+ "bicycle",
+ "car",
+ "motorcycle",
+ "airplane",
+ "bus",
+ "train",
+ "truck",
+ "boat",
+ "traffic light",
+ "fire hydrant",
+ "street sign",
+ "stop sign",
+ "parking meter",
+ "bench",
+ "bird",
+ "cat",
+ "dog",
+ "horse",
+ "sheep",
+ "cow",
+ "elephant",
+ "bear",
+ "zebra",
+ "giraffe",
+ "hat",
+ "backpack",
+ "umbrella",
+ "shoe",
+ "eye glasses",
+ "handbag",
+ "tie",
+ "suitcase",
+ "frisbee",
+ "skis",
+ "snowboard",
+ "sports ball",
+ "kite",
+ "baseball bat",
+ "baseball glove",
+ "skateboard",
+ "surfboard",
+ "tennis racket",
+ "bottle",
+ "plate",
+ "wine glass",
+ "cup",
+ "fork",
+ "knife",
+ "spoon",
+ "bowl",
+ "banana",
+ "apple",
+ "sandwich",
+ "orange",
+ "broccoli",
+ "carrot",
+ "hot dog",
+ "pizza",
+ "donut",
+ "cake",
+ "chair",
+ "couch",
+ "potted plant",
+ "bed",
+ "mirror",
+ "dining table",
+ "window",
+ "desk",
+ "toilet",
+ "door",
+ "tv",
+ "laptop",
+ "mouse",
+ "remote",
+ "keyboard",
+ "cell phone",
+ "microwave",
+ "oven",
+ "toaster",
+ "sink",
+ "refrigerator",
+ "blender",
+ "book",
+ "clock",
+ "vase",
+ "scissors",
+ "teddy bear",
+ "hair drier",
+ "toothbrush",
+ ]
+ )
+ ),
+ "bbox": datasets.Sequence(
+ datasets.Sequence(datasets.Value("float64"), length=4)
+ ),
+ "iscrowd": datasets.Sequence(datasets.Value("int64")),
+ "area": datasets.Sequence(datasets.Value("float64")),
+ },
+ "height": datasets.Value("int64"),
+ "width": datasets.Value("int64"),
+ "file_name": datasets.Value("string"),
+ "coco_url": datasets.Value("string"),
+ "image_path": datasets.Value("string"),
+ }
+
+ features = datasets.Features(feature_dict)
+
+ return datasets.DatasetInfo(
+ # This is the description that will appear on the datasets page.
+ description=_DESCRIPTION,
+ # This defines the different columns of the dataset and their types
+ features=features, # Here we define them above because they are different between the two configurations
+ # If there's a common (input, target) tuple from the features,
+ # specify them here. They'll be used if as_supervised=True in
+ # builder.as_dataset.
+ supervised_keys=None,
+ # Homepage of the dataset for documentation
+ homepage=_HOMEPAGE,
+ # License for the dataset if available
+ license=_LICENSE,
+ # Citation for the dataset
+ citation=_CITATION,
+ )
+
+ def _split_generators(self, dl_manager):
+ """Returns SplitGenerators."""
+ # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+ data_dir = self.config.data_dir
+ if not data_dir:
+ raise ValueError(
+ "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
+ )
+
+ _DL_URLS = {
+ "train": os.path.join(data_dir, "train2017.zip"),
+ "val": os.path.join(data_dir, "val2017.zip"),
+ "annotations_trainval": os.path.join(
+ data_dir, "annotations_trainval2017.zip"
+ ),
+ }
+ archive_path = dl_manager.download_and_extract(_DL_URLS)
+
+ splits = []
+ for split in self.config.splits:
+ if split == "train":
+ dataset = datasets.SplitGenerator(
+ name=datasets.Split.TRAIN,
+ # These kwargs will be passed to _generate_examples
+ gen_kwargs={
+ "json_path": os.path.join(
+ archive_path["annotations_trainval"],
+ "annotations",
+ "instances_train2017.json",
+ ),
+ "image_dir": os.path.join(archive_path["train"], "train2017"),
+ "split": "train",
+ },
+ )
+ elif split in ["val", "valid", "validation", "dev"]:
+ dataset = datasets.SplitGenerator(
+ name=datasets.Split.VALIDATION,
+ # These kwargs will be passed to _generate_examples
+ gen_kwargs={
+ "json_path": os.path.join(
+ archive_path["annotations_trainval"],
+ "annotations",
+ "instances_val2017.json",
+ ),
+ "image_dir": os.path.join(archive_path["val"], "val2017"),
+ "split": "valid",
+ },
+ )
+ else:
+ continue
+
+ splits.append(dataset)
+
+ return splits
+
+ def _generate_examples(
+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+ self,
+ json_path,
+ image_dir,
+ split,
+ ):
+ """Yields examples as (key, example) tuples."""
+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+ # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+ features = [
+ "id",
+ "objects",
+ "height",
+ "width",
+ "file_name",
+ "coco_url",
+ "image_path",
+ ]
+ object_features = [
+ "bbox_id",
+ "category_id",
+ "bbox",
+ "iscrowd",
+ "area",
+ ]
+
+ with open(json_path, "r", encoding="UTF-8") as fp:
+ data = json.load(fp)
+
+ images = data["images"]
+ images_entry = {image["id"]: image for image in images}
+ for image_id, image_entry in images_entry.items():
+ image_entry["image_path"] = os.path.join(
+ image_dir, image_entry["file_name"]
+ )
+ image_entry["objects"] = []
+
+ objects = data["annotations"]
+ for id_, object_entry in enumerate(objects):
+ image_id = object_entry["image_id"]
+
+ entry = {k: v for k, v in object_entry.items() if k in object_features}
+ entry["bbox_id"] = object_entry["id"]
+ if entry["iscrowd"]:
+ continue
+ images_entry[image_id]["objects"].append(entry)
+
+ for id_, entry in images_entry.items():
+ entry = {k: v for k, v in entry.items() if k in features}
+ # collate objects
+ objects = entry.pop("objects")
+ if not objects:
+ continue
+ entry["objects"] = {
+ object_feature: [obj[object_feature] for obj in objects]
+ for object_feature in object_features
+ }
+
+ yield str(entry["id"]), entry