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import gravdataset |
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import os |
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from gravdataset.features import Features, Sequence, Value |
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from pycocotools.coco import COCO |
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_DESCRIPTION = 'COCO dataset for detection and instance segmentation task.' |
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_URLS = { |
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'COCO2014': { |
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'train_prefix': 'train2014', |
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'train_meta': 'annotations/instances_train2014.json', |
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'val_prefix': 'val2014', |
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'val_meta': 'annotations/instances_val2014.json' |
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}, |
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'COCO2017': { |
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'train_prefix': 'train2017', |
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'train_meta': 'annotations/instances_train2017.json', |
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'val_prefix': 'val2017', |
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'val_meta': 'annotations/instances_val2017.json' |
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}, |
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} |
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_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', |
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant', |
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', |
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'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', |
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'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', |
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', |
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'baseball glove', 'skateboard', 'surfboard', 'tennis racket', |
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'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', |
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'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', |
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'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', |
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', |
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'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', |
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'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', |
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'scissors', 'teddy bear', 'hair drier', 'toothbrush') |
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class Coco(gravdataset.GeneratorBasedBuilder): |
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"""COCO dataset for detection and instance segmentation task.""" |
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VERSION = gravdataset.Version('0.1.0') |
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BUILDER_CONFIGS = [ |
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gravdataset.BuilderConfig( |
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name='COCO2014', |
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version=VERSION, |
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description='COCO2014 dataset for det and segm'), |
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gravdataset.BuilderConfig( |
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name='COCO2017', |
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version=VERSION, |
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description='COCO2017 dataset for det and segm'), |
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] |
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DEFAULT_CONFIG_NAME = 'train' |
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def _info(self): |
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return gravdataset.DatasetInfo( |
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description=_DESCRIPTION, |
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meta_info=dict(classes=_CLASSES), |
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features=Features({ |
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'img_info': { |
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'filename': Value('string'), |
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'height': Value('int32'), |
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'width': Value('int32'), |
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}, |
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'ann_info': { |
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'bboxes': Sequence(Sequence(Value('float64'))), |
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'labels': Sequence(Value('int64')), |
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'masks': Sequence(Sequence(Sequence(Value('float64')))), |
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'bboxes_ignore': Sequence(Sequence(Value('float64'))), |
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'label_ignore': Sequence(Value('int64')), |
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'masks_ignore': Sequence( |
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{ |
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'counts': Sequence(Value('int64')), |
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'size': Sequence(Value('int64')) |
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} |
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), |
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'seg_map': Value('string') |
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} |
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})) |
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def _split_generators(self, dl_manager): |
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train_prefix = _URLS[self.config.name]['train_prefix'] |
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train_meta = _URLS[self.config.name]['train_meta'] |
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val_prefix = _URLS[self.config.name]['val_prefix'] |
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val_meta = _URLS[self.config.name]['val_meta'] |
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train_meta = dl_manager.download(train_meta) |
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val_meta = dl_manager.download(val_meta) |
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return [ |
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gravdataset.SplitGenerator( |
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name='train', |
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gen_kwargs={ |
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'img_prefix': train_prefix, |
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'ann_file': train_meta |
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}), |
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gravdataset.SplitGenerator( |
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name='val', |
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gen_kwargs={ |
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'img_prefix': val_prefix, |
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'ann_file': val_meta |
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}), |
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] |
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def _generate_examples(self, img_prefix, ann_file): |
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"""Parser coco format annotation file.""" |
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coco = COCO(ann_file) |
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cat_ids = coco.getCatIds(_CLASSES) |
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cat2label = {cat_id: i for i, cat_id in enumerate(cat_ids)} |
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img_ids = coco.getImgIds() |
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index = 0 |
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for i in img_ids: |
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sample = dict(img_info=dict()) |
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info = coco.loadImgs([i])[0] |
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sample['img_info']['filename'] = os.path.join( |
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img_prefix, info['file_name']) |
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sample['img_info']['height'] = info['height'] |
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sample['img_info']['width'] = info['width'] |
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ann_ids = coco.getAnnIds([i]) |
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ann_info = coco.loadAnns(ann_ids) |
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gt_bboxes = [] |
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gt_labels = [] |
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gt_bboxes_ignore = [] |
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gt_label_ignore = [] |
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gt_masks_ann = [] |
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gt_masks_ignore = [] |
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for i, ann in enumerate(ann_info): |
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if ann.get('ignore', False): |
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continue |
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x1, y1, w, h = ann['bbox'] |
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inter_w = max(0, min(x1 + w, info['width']) - max(x1, 0)) |
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inter_h = max(0, min(y1 + h, info['height']) - max(y1, 0)) |
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if inter_w * inter_h == 0: |
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continue |
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if ann['area'] <= 0 or w < 1 or h < 1: |
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continue |
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if ann['category_id'] not in cat_ids: |
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continue |
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bbox = [x1, y1, x1 + w, y1 + h] |
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if ann.get('iscrowd', False): |
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gt_bboxes_ignore.append(bbox) |
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gt_label_ignore.append(cat2label[ann['category_id']]) |
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gt_masks_ignore.append(ann.get('segmentation', None)) |
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else: |
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gt_bboxes.append(bbox) |
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gt_labels.append(cat2label[ann['category_id']]) |
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gt_masks_ann.append(ann.get('segmentation', None)) |
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seg_map = sample['img_info']['filename'].rsplit('.', 1)[0] + '.png' |
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sample['ann_info'] = dict( |
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bboxes=gt_bboxes, |
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labels=gt_labels, |
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bboxes_ignore=gt_bboxes_ignore, |
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label_ignore=gt_label_ignore, |
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masks=gt_masks_ann, |
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masks_ignore=gt_masks_ignore, |
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seg_map=seg_map) |
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yield index, sample |
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index += 1 |
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