add new file

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+./voc2007.tar.gz filter=lfs diff=lfs merge=lfs -text
+./voc2012.tar.gz filter=lfs diff=lfs merge=lfs -text

pascal_voc.py

@@ -0,0 +1,411 @@
+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

voc2007.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68a097378cacb0c2a0c3823999ecbb5fb1f7633a5115bf37421ac8c8f23c5fa0
+size 877627496

voc2012.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d7b734550f56aacd52fd39f20cc2087b5282743f44bff9bd365a56412b136dd
+size 3760275485