fix

coco_dataset.py

@@ -1,215 +0,0 @@
-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', 'valid', 'test']),
-    ]
-    DEFAULT_CONFIG_NAME = "2017"
-
-    def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-
-        feature_dict = {
-            "image_id": datasets.Value("int64"),
-            "caption_id": datasets.Value("int64"),
-            "caption": datasets.Value("string"),
-            "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"),
-            "test": os.path.join(data_dir, "test2017.zip"),
-            "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
-            "image_info_test": os.path.join(data_dir, "image_info_test2017.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", "captions_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", "captions_val2017.json"),
-                        "image_dir": os.path.join(archive_path["val"], "val2017"),
-                        "split": "valid",
-                    },
-                )
-            elif split == 'test':
-                dataset = datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "json_path": os.path.join(archive_path["image_info_test"], "annotations", "image_info_test2017.json"),
-                        "image_dir": os.path.join(archive_path["test"], "test2017"),
-                        "split": "test",
-                    },
-                )
-            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 = ["image_id", "caption_id", "caption", "height", "width", "file_name", "coco_url", "image_path", "id"]
-        features = list(_features)
-
-        if split in "valid":
-            split = "val"
-
-        with open(json_path, 'r', encoding='UTF-8') as fp:
-            data = json.load(fp)
-
-        # list of dict
-        images = data["images"]
-        entries = images
-
-        # build a dict of image_id -> image info dict
-        d = {image["id"]: image for image in images}
-
-        # list of dict
-        if split in ["train", "val"]:
-            annotations = data["annotations"]
-
-            # build a dict of image_id ->
-            for annotation in annotations:
-                _id = annotation["id"]
-                image_info = d[annotation["image_id"]]
-                annotation.update(image_info)
-                annotation["id"] = _id
-
-            entries = annotations
-
-        for id_, entry in enumerate(entries):
-
-            entry = {k: v for k, v in entry.items() if k in features}
-
-            if split == "test":
-                entry["image_id"] = entry["id"]
-                entry["id"] = -1
-                entry["caption"] = -1
-
-            entry["caption_id"] = entry.pop("id")
-            entry["image_path"] = os.path.join(image_dir, entry["file_name"])
-
-            entry = {k: entry[k] for k in _features if k in entry}
-
-            yield str((entry["image_id"], entry["caption_id"])), entry

dummy_coco_dataset.py

@@ -113,6 +113,15 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                 "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"),
+            "test": os.path.join(data_dir, "test2017.zip"),
+            "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
+            "image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
+        }
+        archive_path = dl_manager.download_and_extract(_DL_URLS)
+
         splits = []
         for split in self.config.splits:
             if split == 'train':
@@ -120,8 +129,8 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(data_dir, f"captions_train{self.config.name}.json"),
-                        "image_dir": os.path.join(data_dir, f'train{self.config.name}'),
+                        "json_path": os.path.join(archive_path["annotations_trainval"], "annotations", "captions_train2017.json"),
+                        "image_dir": os.path.join(archive_path["train"], "train2017"),
                         "split": "train",
                     }
                 )
@@ -130,8 +139,8 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.VALIDATION,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(data_dir, f"captions_val{self.config.name}.json"),
-                        "image_dir": os.path.join(data_dir, f'val{self.config.name}'),
+                        "json_path": os.path.join(archive_path["annotations_trainval"], "annotations", "captions_val2017.json"),
+                        "image_dir": os.path.join(archive_path["val"], "val2017"),
                         "split": "valid",
                     },
                 )
@@ -140,8 +149,8 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TEST,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(data_dir, f'image_info_test{self.config.name}.json'),
-                        "image_dir": os.path.join(data_dir, f'test{self.config.name}'),
+                        "json_path": os.path.join(archive_path["image_info_test"], "annotations", "image_info_test2017.json"),
+                        "image_dir": os.path.join(archive_path["test"], "test2017"),
                         "split": "test",
                     },
                 )