[Feature]: Support nuimages dataset and provide baseline models (#91)

* [Feature]: support using nuimages for instance segmentation

* Update performance and start benchmark

* Change default path names

* Update performance

* Update mmcv version

* Update model links

* Update model links and fix unit tests

* rephrase

tools/data_converter/nuimage_converter.py

+import argparse
+import base64
+import mmcv
+import numpy as np
+from nuimages import NuImages
+from os import path as osp
+from pycocotools import mask as mask_util
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Data converter arg parser')
+    parser.add_argument(
+        '--data-root',
+        type=str,
+        default='./data/nuimages',
+        help='specify the root path of dataset')
+    parser.add_argument(
+        '--version',
+        type=str,
+        nargs='+',
+        default='v1.0-mini',
+        required=False,
+        help='specify the dataset version')
+    parser.add_argument(
+        '--out-dir',
+        type=str,
+        default='./data/nuimages/annotations/',
+        required=False,
+        help='path to save the exported json')
+    parser.add_argument('--extra-tag', type=str, default='nuimages')
+    args = parser.parse_args()
+    return args
+
+
+def export_nuim_to_coco(nuim, out_dir, extra_tag, version):
+    NameMapping = {
+        'movable_object.barrier': 'barrier',
+        'vehicle.bicycle': 'bicycle',
+        'vehicle.bus.bendy': 'bus',
+        'vehicle.bus.rigid': 'bus',
+        'vehicle.car': 'car',
+        'vehicle.construction': 'construction_vehicle',
+        'vehicle.motorcycle': 'motorcycle',
+        'human.pedestrian.adult': 'pedestrian',
+        'human.pedestrian.child': 'pedestrian',
+        'human.pedestrian.construction_worker': 'pedestrian',
+        'human.pedestrian.police_officer': 'pedestrian',
+        'movable_object.trafficcone': 'traffic_cone',
+        'vehicle.trailer': 'trailer',
+        'vehicle.truck': 'truck',
+    }
+
+    print('Process category information')
+    categories = []
+    cat2id = dict()
+    for cate_info in mmcv.track_iter_progress(nuim.category):
+        if cate_info['name'] in NameMapping:
+            name = NameMapping[cate_info['name']]
+            if name not in cat2id:
+                idx = len(categories)
+                categories.append(dict(id=idx, name=name))
+                cat2id.update({name: idx})
+
+    images = []
+    img2id = dict()
+    print('Process image meta information...')
+    for sample_info in mmcv.track_iter_progress(nuim.sample_data):
+        if sample_info['is_key_frame']:
+            img_idx = len(images)
+            images.append(
+                dict(
+                    id=img_idx,
+                    file_name=sample_info['filename'],
+                    width=sample_info['width'],
+                    height=sample_info['height']))
+            img2id.update({sample_info['token']: img_idx})
+
+    print('Process annotation information...')
+    annotations = []
+    for single_obj in mmcv.track_iter_progress(nuim.object_ann):
+        category_info = nuim.get('category', single_obj['category_token'])
+        if category_info['name'] in NameMapping:
+            cat_name = NameMapping[category_info['name']]
+            cat_id = cat2id[cat_name]
+        else:
+            continue
+
+        image_id = img2id[single_obj['sample_data_token']]
+        x_min, y_min, x_max, y_max = single_obj['bbox']
+
+        mask_anno = dict()
+        if single_obj['mask'] is None:
+            empty_mask = np.zeros((900, 1600, 1), order='F', dtype='uint8')
+            mask_anno = mask_util.encode(empty_mask)[0]
+            mask_anno['counts'] = mask_anno['counts'].decode()
+        else:
+            mask_anno['counts'] = base64.b64decode(
+                single_obj['mask']['counts']).decode()
+            mask_anno['size'] = single_obj['mask']['size']
+
+        data_anno = dict(
+            image_id=image_id,
+            id=len(annotations),
+            category_id=cat_id,
+            bbox=[x_min, y_min, x_max - x_min, y_max - y_min],
+            area=(x_max - x_min) * (y_max - y_min),
+            segmentation=mask_anno,
+            iscrowd=0)
+        annotations.append(data_anno)
+
+    coco_format_json = dict(
+        images=images, annotations=annotations, categories=categories)
+
+    mmcv.mkdir_or_exist(out_dir)
+    out_file = osp.join(out_dir, f'{extra_tag}_{version}.json')
+    print(f'Annotation dumped to {out_file}')
+    mmcv.dump(coco_format_json, out_file)
+
+
+def main():
+    args = parse_args()
+    for version in args.version:
+        nuim = NuImages(
+            dataroot=args.data_root, version=version, verbose=True, lazy=True)
+        export_nuim_to_coco(nuim, args.out_dir, args.extra_tag, version)
+
+
+if __name__ == '__main__':
+    main()