[Feature] Support visdrone dataset

docs/en/common_usage/amp_training.md

@@ -1 +1,13 @@
 # Automatic mixed precision（AMP）training
+
+To enable Automatic Mixing Precision (AMP) training, add `--amp` to the end of the training command, which is as follows:
+
+```shell
+python tools/train.py python ./tools/train.py ${CONFIG} --amp
+```
+
+Specific examples are as follows:
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py --amp
+```

docs/en/common_usage/resume_training.md

@@ -1 +1,9 @@
 # Resume training
+
+Resume training means to continue training from the state saved from one of the previous trainings, where the state includes the model weights, the state of the optimizer and the optimizer parameter adjustment strategy.
+
+The user can add `--resume` at the end of the training command to resume training, and the program will automatically load the latest weight file from `work_dirs` to resume training. If there is an updated checkpoint in `work_dir` (e.g. the training was interrupted during the last training), the training will be resumed from that checkpoint, otherwise (e.g. the last training did not have time to save the checkpoint or a new training task was started) the training will be restarted. Here is an example of resuming training:
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py --resume
+```

docs/zh_cn/common_usage/amp_training.md

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 # 自动混合精度（AMP）训练
+
+如果要开启自动混合精度（AMP）训练，在训练命令最后加上 `--amp` 即可， 命令如下：
+
+```shell
+python tools/train.py python ./tools/train.py ${CONFIG} --amp
+```
+
+具体例子如下：
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py --amp
+```

docs/zh_cn/common_usage/resume_training.md

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 # 恢复训练
+
+恢复训练是指从之前某次训练保存下来的状态开始继续训练，这里的状态包括模型的权重、优化器和优化器参数调整策略的状态。
+
+用户可以在训练命令最后加上 `--resume` 恢复训练，程序会自动从 `work_dirs` 中加载最新的权重文件恢复训练。如果 `work_dir` 中有最新的 checkpoint（例如该训练在上一次训练时被中断），则会从该 checkpoint 恢复训练，否则（例如上一次训练还没来得及保存 checkpoint 或者启动了新的训练任务）会重新开始训练。下面是一个恢复训练的示例:
+
+```shell
+python tools/train.py configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py --resume
+```

tools/dataset_converters/visdrone2coco.py

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+import json
+import os
+
+import cv2
+from tqdm import tqdm
+
+# Recommended dataset download address:
+# https://cdn.opendatalab.com/Visdrone_DET/raw/Visdrone_DET.tar.gz
+
+
+def convert_visdrone_to_coco(ann_file, out_file, image_prefix):
+    id_num = 0
+    img_num = 0
+    categories = [{
+        'id': 0,
+        'name': 'ignored regions'
+    }, {
+        'id': 1,
+        'name': 'pedestrian'
+    }, {
+        'id': 2,
+        'name': 'people'
+    }, {
+        'id': 3,
+        'name': 'bicycle'
+    }, {
+        'id': 4,
+        'name': 'car'
+    }, {
+        'id': 5,
+        'name': 'van'
+    }, {
+        'id': 6,
+        'name': 'truck'
+    }, {
+        'id': 7,
+        'name': 'tricycle'
+    }, {
+        'id': 8,
+        'name': 'awning-tricycle'
+    }, {
+        'id': 9,
+        'name': 'bus'
+    }, {
+        'id': 10,
+        'name': 'motor'
+    }, {
+        'id': 11,
+        'name': 'others'
+    }]
+    images = []
+    annotations = []
+    print('start loading data')
+    set = os.listdir(ann_file)
+    annotations_path = ann_file
+    images_path = image_prefix
+    for i in tqdm(set):
+        f = open(annotations_path + '/' + i)
+        name = i.replace('.txt', '')
+        image = {}
+        height, width = cv2.imread(images_path + '/' + name + '.jpg').shape[:2]
+        file_name = name + '.jpg'
+        image['file_name'] = file_name
+        image['height'] = height
+        image['width'] = width
+        image['id'] = img_num
+        images.append(image)
+        for line in f.readlines():
+            annotation = {}
+            line = line.replace('\n', '')
+            if line.endswith(','):
+                line = line.rstrip(',')
+            line_list = [int(i) for i in line.split(',')]
+            bbox_xywh = [
+                line_list[0], line_list[1], line_list[2], line_list[3]
+            ]
+            annotation['image_id'] = img_num
+            annotation['score'] = line_list[4]
+            annotation['bbox'] = bbox_xywh
+            annotation['category_id'] = int(line_list[5])
+            annotation['id'] = id_num
+            annotation['iscrowd'] = 0
+            annotation['segmentation'] = []
+            annotation['area'] = bbox_xywh[2] * bbox_xywh[3]
+            id_num += 1
+            annotations.append(annotation)
+        img_num += 1
+    dataset_dict = {}
+    dataset_dict['images'] = images
+    dataset_dict['annotations'] = annotations
+    dataset_dict['categories'] = categories
+    json_str = json.dumps(dataset_dict)
+    with open(out_file, 'w') as json_file:
+        json_file.write(json_str)
+
+
+print('json file write done')
+
+if __name__ == '__main__':
+    convert_visdrone_to_coco(
+        '../data/visdrone/VisDrone2019-DET-train/annotations',
+        '../data/visdrone/train_coco.json',
+        '../data/visdrone/VisDrone2019-DET-train/images')
+    convert_visdrone_to_coco(
+        '../data/visdrone/VisDrone2019-DET-val/annotations',
+        '../data/visdrone/val_coco.json',
+        '../data/visdrone/VisDrone2019-DET-val/images')