Update parquet files

pascal_voc.py

@@ -1,411 +0,0 @@
-import os
-import logging
-import datasets
-import xml.etree.ElementTree as ET
-from PIL import Image
-from collections import defaultdict
-
-_CITATION = """
-PASCAL_VOC
-"""
-
-_DESCRIPTION = """
-PASCAL_VOC
-"""
-
-_URLS = {
-    "voc2007": "voc2007.tar.gz",
-    "voc2012": "voc2012.tar.gz",
-}
-
-# fmt: off
-CLASS_INFOS = [
-    #  class name                id      train          color
-    (  'aeroplane'             ,  0 ,        0 , ( 128,   0,   0) ),
-    (  'bicycle'               ,  1 ,        1 , (   0, 128,   0) ),
-    (  'bird'                  ,  2 ,        2 , ( 128, 128,   0) ),
-    (  'boat'                  ,  3 ,        3 , (   0,   0, 128) ),
-    (  'bottle'                ,  4 ,        4 , ( 128,   0, 128) ),
-    (  'bus'                   ,  5 ,        5 , (   0, 128, 128) ),
-    (  'car'                   ,  6 ,        6 , ( 128, 128, 128) ),
-    (  'cat'                   ,  7 ,        7 , (  64,   0,   0) ),
-    (  'chair'                 ,  8 ,        8 , ( 192,   0,   0) ),
-    (  'cow'                   ,  9 ,        9 , (  64, 128,   0) ),
-    (  'diningtable'           , 10 ,       10 , ( 192, 128,   0) ),
-    (  'dog'                   , 11 ,       11 , (  64,   0, 128) ),
-    (  'horse'                 , 12 ,       12 , ( 192,   0, 128) ),
-    (  'motorbike'             , 13 ,       13 , (  64, 128, 128) ),
-    (  'person'                , 14 ,       14 , ( 192, 128, 128) ),
-    (  'pottedplant'           , 15 ,       15 , (   0,  64,   0) ),
-    (  'sheep'                 , 16 ,       16 , ( 128,  64,   0) ),
-    (  'sofa'                  , 17 ,       17 , (   0, 192,   0) ),
-    (  'train'                 , 18 ,       18 , ( 128, 192,   0) ),
-    (  'tvmonitor'             , 19 ,       19 , (   0,  64, 128) ),
-    (  'background'            , 20 ,       20 , (   0,   0,   0) ),
-    (  'borderingregion'       , 255,       21 , ( 224, 224, 192) ),
-]
-
-ACTION_INFOS = [
-    #  class name                id
-    (  'phoning'               ,  0 ),
-    (  'playinginstrument'     ,  1 ),
-    (  'reading'               ,  2 ),
-    (  'ridingbike'            ,  3 ),
-    (  'ridinghorse'           ,  4 ),
-    (  'running'               ,  5 ),
-    (  'takingphoto'           ,  6 ),
-    (  'usingcomputer'         ,  7 ),
-    (  'walking'               ,  8 ),
-    (  'jumping'               ,  9 ),
-    (  'other'                 , 10 ),
-]
-
-LAYOUT_INFOS = [
-    #  class name                id
-    (  'Frontal'               ,  0 ),
-    (  'Left'                  ,  1 ),
-    (  'Rear'                  ,  2 ),
-    (  'Right'                 ,  3 ),
-    (  'Unspecified'           ,  4 ),
-]
-
-# fmt: on
-
-CLASS_NAMES = [CLASS_INFO[0] for CLASS_INFO in CLASS_INFOS]
-ACTION_NAMES = [ACTION_INFO[0] for ACTION_INFO in ACTION_INFOS]
-LAYOUT_NAMES = [LAYOUT_INFO[0] for LAYOUT_INFO in LAYOUT_INFOS]
-
-CLASS_DICT = {CLASS_INFO[0]: CLASS_INFO[2] for CLASS_INFO in CLASS_INFOS}
-ACTION_DICT = {ACTION_INFO[0]: ACTION_INFO[1] for ACTION_INFO in ACTION_INFOS}
-LAYOUT_DICT = {LAYOUT_INFO[0]: LAYOUT_INFO[1] for LAYOUT_INFO in LAYOUT_INFOS}
-
-# datasets.Features
-
-action_features = datasets.Features(
-    {
-        "id": datasets.Value("int32"),
-        "image": datasets.features.Image(),
-        "height": datasets.Value("int32"),
-        "width": datasets.Value("int32"),
-        "classes": datasets.features.Sequence(datasets.Value("int32")),
-        "objects": datasets.features.Sequence(
-            {
-                "bboxes": datasets.Sequence(datasets.Value("float32")),
-                "classes": datasets.features.ClassLabel(names=ACTION_NAMES),
-                "difficult": datasets.Value("int32"),
-            }
-        ),
-    }
-)
-
-layout_features = datasets.Features(
-    {
-        "id": datasets.Value("int32"),
-        "image": datasets.features.Image(),
-        "height": datasets.Value("int32"),
-        "width": datasets.Value("int32"),
-        "classes": datasets.features.Sequence(datasets.Value("int32")),
-        "objects": datasets.features.Sequence(
-            {
-                "bboxes": datasets.Sequence(datasets.Value("float32")),
-                "classes": datasets.features.ClassLabel(names=LAYOUT_NAMES),
-                "difficult": datasets.Value("int32"),
-            }
-        ),
-    }
-)
-
-main_features = datasets.Features(
-    {
-        "id": datasets.Value("int32"),
-        "image": datasets.features.Image(),
-        "height": datasets.Value("int32"),
-        "width": datasets.Value("int32"),
-        "classes": datasets.features.Sequence(datasets.Value("int32")),
-        "objects": datasets.features.Sequence(
-            {
-                "bboxes": datasets.Sequence(datasets.Value("float32")),
-                "classes": datasets.features.ClassLabel(names=CLASS_NAMES),
-                "difficult": datasets.Value("int32"),
-            }
-        ),
-    }
-)
-
-segmentation_features = datasets.Features(
-    {
-        "id": datasets.Value("int32"),
-        "image": datasets.features.Image(),
-        "height": datasets.Value("int32"),
-        "width": datasets.Value("int32"),
-        "classes": datasets.features.Sequence(datasets.Value("int32")),
-        "class_gt_image": datasets.features.Image(),
-        "object_gt_image": datasets.features.Image(),
-    }
-)
-
-_DATASET_FEATURES = {
-    "action": action_features,
-    "layout": layout_features,
-    "main": main_features,
-    "segmentation": segmentation_features,
-}
-
-
-def get_main_classes(data_folder):
-    class_infos = defaultdict(set)
-    class_folder = os.path.join(data_folder, "ImageSets", "Main")
-    for f in os.listdir(class_folder):
-        if not f.endswith(".txt") or len(f.split("_")) != 2:
-            continue
-        lines = open(os.path.join(class_folder, f), "r").read().split("\n")
-        name = f.split("_")[0]
-        for line in lines:
-            spans = line.strip().split(" ")
-            spans = list(filter(lambda x: x.strip() != "", spans))
-            if len(spans) != 2 or int(spans[1]) != 1:
-                continue
-            class_infos[spans[0]].add(name)
-    return class_infos
-
-
-def get_annotation(data_folder):
-    anno_infos = dict()
-    anno_folder = os.path.join(data_folder, "Annotations")
-    for f in os.listdir(anno_folder):
-        if not f.endswith(".xml"):
-            continue
-        anno_file = os.path.join(anno_folder, f)
-        anno_tree = ET.parse(anno_file)
-        objects = []
-        for obj in anno_tree.findall("./object"):
-            info = {
-                "class": obj.findall("./name")[0].text,
-                "bbox": [
-                    int(float(obj.findall("./bndbox/xmin")[0].text)),
-                    int(float(obj.findall("./bndbox/ymin")[0].text)),
-                    int(float(obj.findall("./bndbox/xmax")[0].text)),
-                    int(float(obj.findall("./bndbox/ymax")[0].text)),
-                ],
-            }
-
-            if obj.findall("./pose"):
-                info["pose"] = obj.findall("./pose")[0].text
-            if obj.findall("./truncated"):
-                info["truncated"] = int(obj.findall("./truncated")[0].text)
-            if obj.findall("./difficult"):
-                info["difficult"] = int(obj.findall("./difficult")[0].text)
-            else:
-                info["difficult"] = 0
-            if obj.findall("./occluded"):
-                info["occluded"] = int(obj.findall("./occluded")[0].text)
-
-            if obj.findall("./actions"):
-                info["action"] = [
-                    action.tag
-                    for action in obj.findall("./actions/")
-                    if int(action.text) == 1
-                ][0]
-
-            objects.append(info)
-        anno_info = {
-            "image": anno_tree.findall("./filename")[0].text,
-            "height": int(anno_tree.findall("./size/height")[0].text),
-            "width": int(anno_tree.findall("./size/width")[0].text),
-            "segmented": int(anno_tree.findall("./segmented")[0].text),
-            "objects": objects,
-        }
-        stem, suffix = os.path.splitext(f)
-        anno_infos[stem] = anno_info
-
-    return anno_infos
-
-
-class PASCALConfig(datasets.BuilderConfig):
-    def __init__(self, data_name, task_name, **kwargs):
-        """
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(**kwargs)
-        assert data_name in ["voc2007", "voc2012"] and task_name in [
-            "action",
-            "layout",
-            "main",
-            "segmentation",
-        ]
-        assert not (data_name == "voc2007" and task_name == "action")
-        self.data_name = data_name
-        self.task_name = task_name
-
-
-class PASCALDataset(datasets.GeneratorBasedBuilder):
-
-    BUILDER_CONFIGS = [
-        PASCALConfig(
-            name="voc2007_layout",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2007 layout dataset",
-            data_name="voc2007",
-            task_name="layout",
-        ),
-        PASCALConfig(
-            name="voc2007_main",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2007 main dataset",
-            data_name="voc2007",
-            task_name="main",
-        ),
-        PASCALConfig(
-            name="voc2007_segmentation",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2007 segmentation dataset",
-            data_name="voc2007",
-            task_name="segmentation",
-        ),
-        PASCALConfig(
-            name="voc2012_action",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2012 action dataset",
-            data_name="voc2012",
-            task_name="action",
-        ),
-        PASCALConfig(
-            name="voc2012_layout",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2012 layout dataset",
-            data_name="voc2012",
-            task_name="layout",
-        ),
-        PASCALConfig(
-            name="voc2012_main",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2012 main dataset",
-            data_name="voc2012",
-            task_name="main",
-        ),
-        PASCALConfig(
-            name="voc2012_segmentation",
-            version=datasets.Version("1.0.0", ""),
-            description="voc2012 segmentation dataset",
-            data_name="voc2012",
-            task_name="segmentation",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=_DATASET_FEATURES[self.config.task_name],
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage="https://fuliucansheng.github.io/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS[self.config.data_name])
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": downloaded_files, "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": downloaded_files, "split": "val"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": downloaded_files, "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """This function returns the examples in the raw (text) form."""
-        logging.info("generating examples from = %s, split = %s", filepath, split)
-        data_folder = os.path.join(filepath, os.listdir(filepath)[0])
-        anno_infos = get_annotation(data_folder)
-        class_infos = get_main_classes(data_folder)
-        data_file = os.path.join(
-            data_folder, "ImageSets", self.config.task_name.capitalize(), f"{split}.txt"
-        )
-        with open(data_file, encoding="utf-8") as f:
-            for id_, line in enumerate(f):
-                line = line.strip()
-                if line.count(" ") > 0:
-                    line = line.split(" ")[0]
-                anno_info = anno_infos.get(line)
-                if anno_info is None:
-                    continue
-
-                image = os.path.join(data_folder, "JPEGImages", anno_info["image"])
-                if not os.path.exists(image):
-                    continue
-
-                classes = (
-                    [CLASS_DICT[c] for c in class_infos.get(line.strip())]
-                    if line.strip() in class_infos
-                    else []
-                )
-
-                example = {
-                    "id": id_,
-                    "image": Image.open(os.path.abspath(image)),
-                    "height": anno_info["height"],
-                    "width": anno_info["width"],
-                    "classes": classes,
-                }
-
-                objects_info = anno_info["objects"]
-
-                if self.config.task_name == "action":
-                    example["objects"] = [
-                        {
-                            "bboxes": object_info["bbox"],
-                            "classes": object_info["action"],
-                            "difficult": object_info["difficult"],
-                        }
-                        for object_info in objects_info
-                        if "action" in object_info
-                    ]
-                    if len(example["objects"]) == 0 and split != "test":
-                        continue
-
-                if self.config.task_name == "layout":
-                    example["objects"] = [
-                        {"bboxes": object_info["bbox"], "classes": object_info["pose"], "difficult": object_info["difficult"],}
-                        for object_info in objects_info
-                        if "pose" in object_info
-                    ]
-                    if len(example["objects"]) == 0 and split != "test":
-                        continue
-
-                if self.config.task_name == "main":
-                    example["objects"] = [
-                        {"bboxes": object_info["bbox"], "classes": object_info["class"], "difficult": object_info["difficult"],}
-                        for object_info in objects_info
-                        if "class" in object_info
-                    ]
-                    if len(example["objects"]) == 0 and split != "test":
-                        continue
-
-                if self.config.task_name == "segmentation":
-                    example["class_gt_image"] = Image.open(os.path.abspath(
-                        os.path.join(
-                            data_folder,
-                            "SegmentationClass",
-                            anno_info["image"].replace(".jpg", ".png"),
-                        )
-                    ))
-                    example["object_gt_image"] = Image.open(os.path.abspath(
-                        os.path.join(
-                            data_folder,
-                            "SegmentationObject",
-                            anno_info["image"].replace(".jpg", ".png"),
-                        )
-                    ))
-
-                yield id_, example

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