Upload COCO.py

COCO.py

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