add dataset minicoco

.gitattributes

@@ -14,3 +14,4 @@
 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+./minicoco.tar.gz filter=lfs diff=lfs merge=lfs -text

minicoco.py

@@ -0,0 +1,531 @@
+import os
+import json
+import logging
+import datasets
+import xml.etree.ElementTree as ET
+from collections import defaultdict
+
+
+_CITATION = """
+MINICOCO2017
+"""
+
+_DESCRIPTION = """
+MINICOCO2017
+"""
+
+_URLS = {
+    "minicoco2017": "minicoco.tar.gz"
+}
+
+# fmt: off
+CLASS_INFOS = [
+    # name  id  train
+    ('person', 1, 0), 
+    ('bicycle', 2, 1), 
+    ('car', 3, 2), 
+    ('motorcycle', 4, 3), 
+    ('airplane', 5, 4), 
+    ('bus', 6, 5), 
+    ('train', 7, 6), 
+    ('truck', 8, 7), 
+    ('boat', 9, 8), 
+    ('traffic light', 10, 9), 
+    ('fire hydrant', 11, 10), 
+    ('stop sign', 13, 11), 
+    ('parking meter', 14, 12), 
+    ('bench', 15, 13), 
+    ('bird', 16, 14), 
+    ('cat', 17, 15), 
+    ('dog', 18, 16), 
+    ('horse', 19, 17), 
+    ('sheep', 20, 18), 
+    ('cow', 21, 19), 
+    ('elephant', 22, 20), 
+    ('bear', 23, 21), 
+    ('zebra', 24, 22), 
+    ('giraffe', 25, 23), 
+    ('backpack', 27, 24), 
+    ('umbrella', 28, 25), 
+    ('handbag', 31, 26), 
+    ('tie', 32, 27), 
+    ('suitcase', 33, 28), 
+    ('frisbee', 34, 29), 
+    ('skis', 35, 30), 
+    ('snowboard', 36, 31), 
+    ('sports ball', 37, 32), 
+    ('kite', 38, 33), 
+    ('baseball bat', 39, 34), 
+    ('baseball glove', 40, 35), 
+    ('skateboard', 41, 36), 
+    ('surfboard', 42, 37), 
+    ('tennis racket', 43, 38), 
+    ('bottle', 44, 39), 
+    ('wine glass', 46, 40), 
+    ('cup', 47, 41), 
+    ('fork', 48, 42), 
+    ('knife', 49, 43), 
+    ('spoon', 50, 44), 
+    ('bowl', 51, 45), 
+    ('banana', 52, 46), 
+    ('apple', 53, 47), 
+    ('sandwich', 54, 48), 
+    ('orange', 55, 49), 
+    ('broccoli', 56, 50), 
+    ('carrot', 57, 51), 
+    ('hot dog', 58, 52), 
+    ('pizza', 59, 53), 
+    ('donut', 60, 54),
+    ('cake', 61, 55), 
+    ('chair', 62, 56), 
+    ('couch', 63, 57), 
+    ('potted plant', 64, 58), 
+    ('bed', 65, 59), 
+    ('dining table', 67, 60), 
+    ('toilet', 70, 61), 
+    ('tv', 72, 62), 
+    ('laptop', 73, 63), 
+    ('mouse', 74, 64), 
+    ('remote', 75, 65), 
+    ('keyboard', 76, 66), 
+    ('cell phone', 77, 67), 
+    ('microwave', 78, 68), 
+    ('oven', 79, 69), 
+    ('toaster', 80, 70), 
+    ('sink', 81, 71), 
+    ('refrigerator', 82, 72), 
+    ('book', 84, 73), 
+    ('clock', 85, 74), 
+    ('vase', 86, 75), 
+    ('scissors', 87, 76), 
+    ('teddy bear', 88, 77), 
+    ('hair drier', 89, 78), 
+    ('toothbrush', 90, 79)
+]
+
+KEYPOINTS_INFOS=[
+    # name  id  train
+    # ('nose', 1, 0), 
+    # ('left_eye', 2, 1), 
+    # ('right_eye', 3, 2), 
+    # ('left_ear', 4, 3), 
+    # ('right_ear', 5, 4), 
+    # ('left_shoulder', 6, 5), 
+    # ('right_shoulder', 7, 6), 
+    # ('left_elbow', 8, 7), 
+    # ('right_elbow', 9, 8), 
+    # ('left_wrist', 10, 9), 
+    # ('right_wrist', 11, 10), 
+    # ('left_hip', 12, 11), 
+    # ('right_hip', 13, 12), 
+    # ('left_knee', 14, 13), 
+    # ('right_knee', 15, 14), 
+    # ('left_ankle', 16, 15), 
+    # ('right_ankle', 17, 16)
+    ('none', 1, 0),
+    ('nose', 2, 1), 
+    ('left_eye', 3, 2), 
+    ('right_eye', 4, 3), 
+    ('left_ear', 5, 4), 
+    ('right_ear', 6, 5), 
+    ('left_shoulder', 7, 6), 
+    ('right_shoulder', 8, 7), 
+    ('left_elbow', 9, 8), 
+    ('right_elbow', 10, 9), 
+    ('left_wrist', 11, 10), 
+    ('right_wrist', 12, 11), 
+    ('left_hip', 13, 12), 
+    ('right_hip', 14, 13), 
+    ('left_knee', 15, 14), 
+    ('right_knee', 16, 15), 
+    ('left_ankle', 17, 16), 
+    ('right_ankle', 18, 17)
+]
+
+
+# fmt: on
+CLASS_NAMES = [CLASS_INFO[0] for CLASS_INFO in CLASS_INFOS]
+KEYPOINTS_NAMES = [KEYPOINTS_INFO[0] for KEYPOINTS_INFO in KEYPOINTS_INFOS]
+
+CLASS_DICT = {CLASS_INFO[0]: CLASS_INFO[2] for CLASS_INFO in CLASS_INFOS}
+CATEGORY_ID2CLASS_NAMES = {CLASS_INFO[1]: CLASS_INFO[0] for CLASS_INFO in CLASS_INFOS}
+KEYPOINTS_DICT = {KEYPOINTS_INFO[0]: KEYPOINTS_INFO[1] for KEYPOINTS_INFO in KEYPOINTS_INFOS}
+
+
+# datasets.Features
+detection_features = datasets.Features(
+    {
+        "id": datasets.Value("int32"),
+        "image": datasets.Value("string"),
+        "height": datasets.Value("int32"),
+        "width": datasets.Value("int32"),
+        "objects": datasets.features.Sequence(
+            {
+                "bboxes": datasets.Sequence(datasets.Value("float32")),
+                "classes": datasets.features.ClassLabel(names=CLASS_NAMES),
+            }
+        ),
+    }
+)
+
+segmentation_features = datasets.Features(
+    {
+        "id": datasets.Value("int32"),
+        "image": datasets.Value("string"),
+        "height": datasets.Value("int32"),
+        "width": datasets.Value("int32"),
+        "objects": datasets.features.Sequence(
+            {
+                "bboxes": datasets.Sequence(datasets.Value("float32")),
+                "classes": datasets.features.ClassLabel(names=CLASS_NAMES),
+                'segmentation':datasets.Sequence(datasets.Value("float32")),
+                'iscrowd':datasets.Value("int32"),
+            }
+        ),
+    }
+)
+
+captions_features = datasets.Features(
+    {
+        "id": datasets.Value("int32"),
+        "image": datasets.Value("string"),
+        "height": datasets.Value("int32"),
+        "width": datasets.Value("int32"),
+        "captions": datasets.features.Sequence(datasets.Value("string")),
+    }
+)
+
+keypoint_features = datasets.Features( 
+    # 这里可能有点问题，因为模型的keypoint的标注的类别没别没有增加进来，
+    # 有点复杂，后面再finetune，现在基本信息已经正确
+    {
+        "id": datasets.Value("int32"),
+        "image": datasets.Value("string"),
+        "height": datasets.Value("int32"),
+        "width": datasets.Value("int32"),
+        "objects": datasets.features.Sequence(
+            {
+                "bboxes": datasets.Sequence(datasets.Value("float32")),
+                "classes": datasets.features.ClassLabel(names=CLASS_NAMES),
+                'keypoints':datasets.Sequence(datasets.Value("float32")),
+                "num_keypoints":datasets.Value("int32")
+            }
+        ),
+    }
+)
+
+_DATASET_FEATURES = {
+    "detection": detection_features,
+    "segmentation":segmentation_features,
+    "caption": captions_features,
+    "keypoint": keypoint_features
+}
+
+
+def get_captions_annotation(captions_path):
+    with open(captions_path,'r') as f:
+        anno_captions = json.load(f)
+
+    anno_infos = defaultdict(list)
+    images_infos = list()
+
+    for caption_info in anno_captions['annotations']:
+        # caption_info={'image_id': 179765, 'id': 38, 'caption': 'A black Honda motorcycle parked in front of a garage.'}
+        caption = caption_info['caption']
+        image_id = caption_info['image_id']
+
+        anno_infos[image_id].append(caption)
+
+    for image in anno_captions['images']:
+        # image={'license': 4, 'file_name': '000000397133.jpg', 'coco_url': 'http://images.cocodataset.org/val2017/000000397133.jpg', 'height': 427, 'width': 640, 'date_captured': '2013-11-14 17:02:52', 'flickr_url': 'http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg', 'id': 397133}
+        images_infos.append({
+            "image_name":image['file_name'],
+            "height": image["height"],
+            "width":image["width"],
+            "image_id":image['id']
+        })
+    
+    return anno_infos, images_infos
+
+
+def get_instances_annotation(instances_path):
+    with open(instances_path,'r') as f:
+        anno_instances = json.load(f)
+
+    anno_infos = dict()
+    images_infos = list()
+
+    for instance_info in anno_instances['annotations']:
+        # instance_info = {'segmentation': [[510.66, 423.01, 511.72, 420.03, 510.45, 416.0, 510.34, 413.02, 
+        # 510.77, 410.26, 510.77, 407.5, 510.34, 405.16, 511.51, 402.83, 511.41, 400.49, 510.24, 398.16, 
+        # 509.39, 397.31, 504.61, 399.22, 502.17, 399.64, 500.89, 401.66, 500.47, 402.08, 499.09, 401.87, 
+        # 495.79, 401.98, 490.59, 401.77, 488.79, 401.77, 485.39, 398.58, 483.9, 397.31, 481.56, 396.35, 
+        # 478.48, 395.93, 476.68, 396.03, 475.4, 396.77, 473.92, 398.79, 473.28, 399.96, 473.49, 401.87, 
+        # 474.56, 403.47, 473.07, 405.59, 473.39, 407.71, 476.68, 409.41, 479.23, 409.73, 481.56, 410.69, 
+        # 480.4, 411.85, 481.35, 414.93, 479.86, 418.65, 477.32, 420.03, 476.04, 422.58, 479.02, 422.58, 
+        # 480.29, 423.01, 483.79, 419.93, 486.66, 416.21, 490.06, 415.57, 492.18, 416.85, 491.65, 420.24, 
+        # 492.82, 422.9, 493.56, 424.39, 496.43, 424.6, 498.02, 423.01, 498.13, 421.31, 497.07, 420.03, 
+        # 497.07, 415.15, 496.33, 414.51, 501.1, 411.96, 502.06, 411.32, 503.02, 415.04, 503.33, 418.12, 
+        # 501.1, 420.24, 498.98, 421.63, 500.47, 424.39, 505.03, 423.32, 506.2, 421.31, 507.69, 419.5, 
+        # 506.31, 423.32, 510.03, 423.01, 510.45, 423.01]], 'area': 702.1057499999998, 'iscrowd': 0, 
+        # 'image_id': 289343, 'bbox': [473.07, 395.93, 38.65, 28.67], 'category_id': 18, 'id': 1768}
+        bbox = instance_info['bbox']
+        image_id = instance_info['image_id']
+        segmentation = instance_info['segmentation'][0]
+
+        if image_id in anno_infos:
+            anno_infos[image_id].append(
+                {
+                    "segmentation": segmentation,
+                    "bbox": bbox,
+                    'iscrowd':instance_info['iscrowd'],
+                    "classes":CATEGORY_ID2CLASS_NAMES[instance_info['category_id']]
+                }
+            )
+        else:
+            anno_infos[image_id]=[
+                {
+                    "segmentation": segmentation,
+                    "bbox": bbox,
+                    'iscrowd':instance_info['iscrowd'],
+                    "classes":CATEGORY_ID2CLASS_NAMES[instance_info['category_id']]
+                }
+            ]
+                
+
+    for image in anno_instances['images']:
+        # image={'license': 4, 'file_name': '000000397133.jpg', 
+        # 'coco_url': 'http://images.cocodataset.org/val2017/000000397133.jpg', 
+        # 'height': 427, 'width': 640, 'date_captured': '2013-11-14 17:02:52', 
+        # 'flickr_url': 'http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg', 'id': 397133}
+        images_infos.append({
+            "image_name":image['file_name'],
+            "height": image["height"],
+            "width":image["width"],
+            "image_id":image['id']
+        })
+    
+    return anno_infos, images_infos
+
+
+def get_keypoints_annotation(keypoints_path):
+    with open(keypoints_path,'r') as f:
+        anno_keypoints = json.load(f)
+
+    anno_infos = dict()
+    images_infos = list()
+
+    for keypoint_info in anno_keypoints['annotations']:
+        # keypoint_info = {'segmentation': [[63.2, 229.21, 65.73, 208.99, 70.79, 187.92, 78.37, 162.64, 84.27, 146.63, 84.27, 132.3, 75.84, 109.55, 90.17, 97.75, 104.49, 96.91, 114.61, 102.81, 123.88, 123.88, 137.36, 136.52, 153.37, 150.84, 146.63, 169.38, 144.1, 180.34, 142.42, 190.45, 137.36, 209.83, 139.89, 230.9, 128.09, 232.58, 97.75, 235.11, 81.74, 237.64, 87.64, 208.99, 85.96, 186.24, 78.37, 198.88, 75.84, 224.16, 68.26, 239.33, 60.67, 230.9]], 'num_keypoints': 12, 'area': 8096.3096, 'iscrowd': 0, 'keypoints': [100, 135, 2, 102, 127, 2, 94, 131, 2, 112, 121, 2, 91, 132, 2, 137, 148, 2, 81, 158, 2, 150, 179, 1, 76, 193, 2, 0, 0, 0, 70, 234, 2, 136, 242, 1, 104, 246, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'image_id': 275749, 'bbox': [60.67, 96.91, 92.7, 142.42], 'category_id': 1, 'id': 232027}
+        bbox = keypoint_info['bbox']
+        image_id = keypoint_info['image_id']
+
+        if image_id in anno_infos:
+            anno_infos[image_id].append(
+                {
+                    "bbox": bbox,
+                    "classes":CATEGORY_ID2CLASS_NAMES[keypoint_info['category_id']],
+                    'keypoints':keypoint_info['keypoints'],
+                    "num_keypoints":keypoint_info['num_keypoints'],
+                }
+            )
+        else:
+            anno_infos[image_id]=[
+                {
+                    "bbox": bbox,
+                    "classes":CATEGORY_ID2CLASS_NAMES[keypoint_info['category_id']],
+                    'keypoints':keypoint_info['keypoints'],
+                    "num_keypoints":keypoint_info['num_keypoints'],
+                }
+            ]
+                
+
+    for image in anno_keypoints['images']:
+        # image={'license': 4, 'file_name': '000000397133.jpg', 'coco_url': 'http://images.cocodataset.org/val2017/000000397133.jpg', 'height': 427, 'width': 640, 'date_captured': '2013-11-14 17:02:52', 'flickr_url': 'http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg', 'id': 397133}
+        images_infos.append({
+            "image_name":image['file_name'],
+            "height": image["height"],
+            "width":image["width"],
+            "image_id":image['id']
+        })
+    
+    return anno_infos, images_infos
+
+
+class MINICOCOConfig(datasets.BuilderConfig):
+    def __init__(self, data_name, task_name, **kwargs):
+        """
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(**kwargs)
+        assert data_name in ["minicoco2017"] and task_name in [
+            "detection",
+            "segmentation",
+            "caption",
+            "keypoint"
+        ]
+        self.data_name = data_name
+        self.task_name = task_name
+
+
+class PASCALDataset(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        MINICOCOConfig(
+            name="minicoco2017_detection",
+            version=datasets.Version("1.0.0", ""),
+            description="minicoco2017 detection dataset",
+            data_name="minicoco2017",
+            task_name="detection",
+        ),
+        MINICOCOConfig(
+            name="minicoco2017_segmentation",
+            version=datasets.Version("1.0.0", ""),
+            description="minicoco2017 segmentation dataset",
+            data_name="minicoco2017",
+            task_name="segmentation",
+        ),
+        MINICOCOConfig(
+            name="minicoco2017_caption",
+            version=datasets.Version("1.0.0", ""),
+            description="minicoco2017 caption dataset",
+            data_name="minicoco2017",
+            task_name="caption",
+        ),
+        MINICOCOConfig(
+            name="minicoco2017_keypoint",
+            version=datasets.Version("1.0.0", ""),
+            description="minicoco2017 keypoint dataset",
+            data_name="minicoco2017",
+            task_name="keypoint",
+        )
+    ]
+
+    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."""
+
+        # filepath = os.path.join(filepath, os.listdir(filepath)[0]) # mine add
+
+        logging.info("generating examples from = %s, split = %s", filepath, split)
+        task_name = self.config.task_name
+
+        if task_name == "caption":
+            captions_path = os.path.join(filepath, "annotations", "captions_" + split + "2017.json")
+            anno_infos, images_infos = get_captions_annotation(captions_path)
+
+            for id_, image in enumerate(images_infos):
+                image_path = os.path.join(filepath, split + "2017", image["image_name"])
+                if not os.path.exists(image_path):
+                    continue
+                example = {
+                    "id": id_,
+                    "image": os.path.abspath(image_path),
+                    "height": image["height"],
+                    "width": image["width"],
+                    "captions": anno_infos[image['image_id']],
+                }
+                yield id_, example
+
+        elif task_name=="detection":
+            instances_path = os.path.join(filepath, "annotations", "instances_" + split + "2017.json")
+            anno_infos, images_infos = get_instances_annotation(instances_path)
+
+            for id_, image in enumerate(images_infos):
+                image_path = os.path.join(filepath, split + "2017", image["image_name"])
+                if not os.path.exists(image_path):
+                    continue
+                example = {
+                    "id": id_,
+                    "image": os.path.abspath(image_path),
+                    "height": image["height"],
+                    "width": image["width"],
+                    "objects":[
+                        {
+                            "bboxes": object_info["bbox"], 
+                            "classes": object_info["classes"]
+                        }
+                        for object_info in anno_infos[image['image_id']]
+                    ]
+                }
+                yield id_, example
+
+        elif task_name=="segmentation":
+            instances_path = os.path.join(filepath, "annotations", "instances_" + split + "2017.json")
+            anno_infos, images_infos = get_instances_annotation(instances_path)
+
+            for id_, image in enumerate(images_infos):
+                image_path = os.path.join(filepath, split + "2017", image["image_name"])
+                if not os.path.exists(image_path):
+                    continue
+                example = {
+                    "id": id_,
+                    "image": os.path.abspath(image_path),
+                    "height": image["height"],
+                    "width": image["width"],
+                    "objects":[
+                        {
+                            "bboxes": object_info["bbox"], 
+                            "classes": object_info["classes"],
+                            'segmentation':object_info['segmentation'],
+                            'iscrowd':object_info['iscrowd']
+                        }
+                        for object_info in anno_infos[image['image_id']]
+                    ]
+                }
+                yield id_, example  
+
+        elif task_name=="keypoint":
+            keypoints_path = os.path.join(filepath, "annotations", "person_keypoints_" + split + "2017.json")
+            anno_infos, images_infos = get_keypoints_annotation(keypoints_path)
+
+            for id_, image in enumerate(images_infos):
+                image_path = os.path.join(filepath, split + "2017", image["image_name"])
+                if not os.path.exists(image_path):
+                    continue
+                example = {
+                    "id": id_,
+                    "image": os.path.abspath(image_path),
+                    "height": image["height"],
+                    "width": image["width"],
+                    "objects":[
+                        {                    
+                            "bboxes": object_info["bbox"], 
+                            "classes": object_info["classes"],
+                            'keypoints':object_info['keypoints'],
+                            "num_keypoints":object_info["num_keypoints"]
+                        }
+                        for object_info in anno_infos[image['image_id']]
+                    ]
+                }
+                yield id_, example

minicoco.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:347d6613f2564d33029f78fb52eebdaf4036013274b4cd9daa2aa52fb0e59fcf
+size 6792618279