[MOT] fix mot readme and cfgs (#4440)

* remove hardnet85 cfgs, fix readme of mot vehicle pedestrian

* remove hardnet cfg, test=document_fix

configs/mot/README.md

@@ -144,17 +144,14 @@ If you use a stronger detection model, you can get better results. Each txt is t
 | backbone       | input shape |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  download | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_30e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_30e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### Results on MOT-17 Test Set
 | backbone       | input shape |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  download  | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_30e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_30e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 **Notes:**
  FairMOT enhance DLA-34 used 8 GPUs for training and mini-batch size as 16 on each GPU，and trained for 60 epoches. The crowdhuman dataset is added to the train-set during training.
- FairMOT enhance HarDNet-85 used 8 GPUs for training and mini-batch size as 10 on each GPU，and trained for 30 epoches. The crowdhuman dataset is added to the train-set during training.
 
 
 ### FairMOT light model

configs/mot/README_cn.md

@@ -143,17 +143,14 @@ wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  下载链接 | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### 在MOT-17 Test Set上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  下载链接  | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 **注意:**
  FairMOT enhance DLA-34使用8个GPU进行训练，每个GPU上batch size为16，训练60个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
- FairMOT enhance HarDNet-85 使用8个GPU进行训练，每个GPU上batch size为10，训练30个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
 
 
 ### FairMOT轻量级模型

configs/mot/fairmot/README.md

@@ -42,17 +42,14 @@ English | [简体中文](README_cn.md)
 | backbone       | input shape |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  download | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_60e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### Results on MOT-17 Test Set
 | backbone       | input shape |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  download  | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [config](./fairmot_enhance_dla34_60e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [config](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 **Notes:**
  FairMOT enhance DLA-34 used 8 GPUs for training and mini-batch size as 16 on each GPU，and trained for 60 epoches. The crowdhuman dataset is added to the train-set during training.
- FairMOT enhance HarDNet-85 used 8 GPUs for training and mini-batch size as 10 on each GPU，and trained for 30 epoches. The crowdhuman dataset is added to the train-set during training.
 
 
 ### FairMOT light model

configs/mot/fairmot/README_cn.md

@@ -41,17 +41,14 @@
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |  IDS  |   FP  |   FN   |   FPS   |  下载链接 | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.9  |  74.7  |  1021   |  11425  |  31475 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  75.0  |  70.0  |  1050   |  11837  |  32774 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 ### 在MOT-17 Test Set上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  下载链接  | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
 | DLA-34         | 1088x608 |  75.3  |  74.2  |  3270  |  29112  | 106749 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_dla34_60e_1088x608.pdparams) | [配置文件](./fairmot_enhance_dla34_60e_1088x608.yml) |
-| HarDNet-85     | 1088x608 |  74.7  |  70.7  |  3210  |  29790  | 109914 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_enhance_hardnet85_30e_1088x608.pdparams) | [配置文件](./fairmot_enhance_hardnet85_30e_1088x608.yml) |
 
 **注意:**
  FairMOT enhance DLA-34使用8个GPU进行训练，每个GPU上batch size为16，训练60个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
- FairMOT enhance HarDNet-85 使用8个GPU进行训练，每个GPU上batch size为10，训练30个epoch，并且训练集中加入了crowdhuman数据集一起参与训练。
 
 
 ### FairMOT轻量级模型

configs/mot/fairmot/fairmot_enhance_hardnet85_30e_1088x608.yml

-_BASE_: [
-  '../../datasets/mot.yml',
-  '../../runtime.yml',
-  '_base_/optimizer_30e.yml',
-  '_base_/fairmot_hardnet85.yml',
-  '_base_/fairmot_reader_1088x608.yml',
-]
-norm_type: sync_bn
-use_ema: true
-ema_decay: 0.9998
-
-worker_num: 4
-TrainReader:
-  inputs_def:
-    image_shape: [3, 608, 1088]
-  sample_transforms:
-    - Decode: {}
-    - RGBReverse: {}
-    - AugmentHSV: {}
-    - LetterBoxResize: {target_size: [608, 1088]}
-    - MOTRandomAffine: {reject_outside: False}
-    - RandomFlip: {}
-    - BboxXYXY2XYWH: {}
-    - NormalizeBox: {}
-    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1]}
-    - RGBReverse: {}
-    - Permute: {}
-  batch_transforms:
-    - Gt2FairMOTTarget: {}
-  batch_size: 10
-  shuffle: True
-  drop_last: True
-  use_shared_memory: True
-
-epoch: 30
-LearningRate:
-  base_lr: 0.0001
-  schedulers:
-  - !PiecewiseDecay
-    gamma: 0.1
-    milestones: [20,]
-    use_warmup: False
-
-OptimizerBuilder:
-  optimizer:
-    type: Adam
-  regularizer: NULL
-
-weights: output/fairmot_enhance_hardnet85_30e_1088x608/model_final

configs/mot/pedestrian/README_cn.md

@@ -20,7 +20,7 @@
 |  VisDrone     | 1088x608 |  49.2 |   63.1 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml) |
 
 **注意:**
- FairMOT均使用DLA-34为骨干网络，2个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
+ FairMOT均使用DLA-34为骨干网络，4个GPU进行训练，每个GPU上batch size为6，训练30个epoch。
 
 
 ## 数据集准备和处理
@@ -48,8 +48,10 @@ tools/visdrone/visdrone2mot.py: 生成visdrone_pedestrian据集
 │   ├── visdrone2mot.py
 │   ├── visdrone_pedestrian
 │   │   ├── images
+│   │   │   ├── train
 │   │   │   ├── val
 │   │   ├── labels_with_ids
+│   │   │   ├── train
 │   │   │   ├── val
 # 执行
 python visdrone2mot.py --transMot=True --data_name=visdrone_pedestrian --phase=val
@@ -61,36 +63,36 @@ python visdrone2mot.py --transMot=True --data_name=visdrone_pedestrian --phase=t
 ### 1. 训练
 使用2个GPU通过如下命令一键式启动训练
 ```bash
-python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_pathtrack/ --gpus 0,1 tools/train.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml
+python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_visdrone_pedestrian/ --gpus 0,1 tools/train.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml
 ```
 
 ### 2. 评估
 使用单张GPU通过如下命令一键式启动评估
 ```bash
 # 使用PaddleDetection发布的权重
-CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_pathtrack.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams
 
 # 使用训练保存的checkpoint
-CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml -o weights=output/fairmot_dla34_30e_1088x608_pathtrack/model_final.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=output/fairmot_dla34_30e_1088x608_visdrone_pedestrian/model_final.pdparams
 ```
 
 ### 3. 预测
 使用单个GPU通过如下命令预测一个视频，并保存为视频
 ```bash
 # 预测一个视频
-CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_pathtrack.pdparams --video_file={your video name}.mp4  --save_videos
+CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams --video_file={your video name}.mp4  --save_videos
 ```
 **注意:**
  请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
 
 ### 4. 导出预测模型
 ```bash
-CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_pathtrack.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_pathtrack.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/pedestrian/fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams
 ```
 
 ### 5. 用导出的模型基于Python去预测
 ```bash
-python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_pathtrack --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_visdrone_pedestrian --video_file={your video name}.mp4 --device=GPU --save_mot_txts
 ```
 **注意:**
  跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。

configs/mot/vehicle/README_cn.md

@@ -40,16 +40,16 @@ tools/bdd100kmot/gen_labels_MOT.py：生成单类别的labels_with_ids文件
 tools/visdrone/visdrone2mot.py：生成visdrone_vehicle
 ```
 
-### 2、bdd100k_vehicle数据集处理
+### 2、bdd100kmot_vehicle数据集处理
 ```
 # 复制tools/bdd100kmot里的代码到数据集目录下
 # 生成bdd100kmot_vehicle MOT格式的数据，抽取类别classes=2,3,4,9,10 (car, truck, bus, trailer, other vehicle)
 <<--生成前目录-->>
-├── bdd100k_path
+├── bdd100k
 │   ├── images
 │   ├── labels
 <<--生成后目录-->>
-├── bdd100k_path
+├── bdd100k
 │   ├── images
 │   ├── labels
 │   ├── bdd100kmot_vehicle
@@ -77,10 +77,12 @@ sh gen_bdd100kmot_vehicle.sh
 │   ├── annotations
 │   ├── sequences
 │   ├── visdrone2mot.py
-│   ├── visdrone-vehicle
+│   ├── visdrone_vehicle
 │   │   ├── images
+│   │   │   ├── train
 │   │   │   ├── val
 │   │   ├── labels_with_ids
+│   │   │   ├── train
 │   │   │   ├── val
 # 执行
 python visdrone2mot.py --transMot=True --data_name=visdrone_vehicle --phase=val
@@ -92,36 +94,36 @@ python visdrone2mot.py --transMot=True --data_name=visdrone_vehicle --phase=trai
 ### 1. 训练
 使用2个GPU通过如下命令一键式启动训练
 ```bash
-python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_bdd100k_vehicle/ --gpus 0,1 tools/train.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100k_vehicle.yml
+python -m paddle.distributed.launch --log_dir=./fairmot_dla34_30e_1088x608_bdd100kmot_vehicle/ --gpus 0,1 tools/train.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.yml
 ```
 
 ### 2. 评估
 使用单张GPU通过如下命令一键式启动评估
 ```bash
 # 使用PaddleDetection发布的权重
-CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100k_vehicle.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bdd100k_vehicle.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.pdparams
 
 # 使用训练保存的checkpoint
-CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100k_vehicle.yml -o weights=output/fairmot_dla34_30e_1088x608_bdd100k_vehicle/model_final.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.yml -o weights=output/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle/model_final.pdparams
 ```
 
 ### 3. 预测
 使用单个GPU通过如下命令预测一个视频，并保存为视频
 ```bash
 # 预测一个视频
-CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100k_vehicle.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bdd100k_vehicle.pdparams --video_file={your video name}.mp4  --save_videos
+CUDA_VISIBLE_DEVICES=0 python tools/infer_mot.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.pdparams --video_file={your video name}.mp4  --save_videos
 ```
 **注意:**
  请先确保已经安装了[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
 
 ### 4. 导出预测模型
 ```bash
-CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100k_vehicle.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bdd100k_vehicle.pdparams
+CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/vehicle/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.yml -o weights=https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.pdparams
 ```
 
 ### 5. 用导出的模型基于Python去预测
 ```bash
-python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_bdd100k_vehicle --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+python deploy/python/mot_jde_infer.py --model_dir=output_inference/fairmot_dla34_30e_1088x608_bdd100kmot_vehicle --video_file={your video name}.mp4 --device=GPU --save_mot_txts
 ```
 **注意:**
  跟踪模型是对视频进行预测，不支持单张图的预测，默认保存跟踪结果可视化后的视频，可添加`--save_mot_txts`表示保存跟踪结果的txt文件，或`--save_images`表示保存跟踪结果可视化图片。

configs/mot/vehicle/tools/bdd100kmot/bdd100k2mot.py

@@ -19,6 +19,8 @@ import cv2
 import random
 import numpy as np
 import argparse
+import tqdm
+import json
 
 
 def mkdir_if_missing(d):
@@ -62,7 +64,7 @@ def bdd2mot_tracking(img_dir, label_dir, save_img_dir, save_label_dir):
                 os.system('cp {} {}'.format(source_img, target_img))
 
 
-def transBBOx(bbox):
+def transBbox(bbox):
     # bbox --> cx cy w h
     bbox = list(map(lambda x: float(x), bbox))
     bbox[0] = (bbox[0] - bbox[2] / 2) * 1280
@@ -106,10 +108,9 @@ def genSingleImageMot(inputPath, classes=[]):
             mot_line = []
             mot_line.append(id_line[-1])
             mot_line.append(str(id_idx))
-            id_line_temp = transBBOx(id_line[2:6])
+            id_line_temp = transBbox(id_line[2:6])
             mot_line.extend(id_line_temp)
-            mot_line.append('1')
-            # mot_line.append(id_line[0]) # origin class 
+            mot_line.append('1') # origin class: id_line[0]
             mot_line.append('1')  # permanent class  => 1
             mot_line.append('1')
             mot_gt.append(mot_line)
@@ -211,7 +212,7 @@ def VisualGt(dataPath, phase='train'):
     img_list_path = sorted(glob.glob(osp.join(seqPath, 'img1', '*.jpg')))
     imgIndex = random.randint(0, len(img_list_path))
     img_Path = img_list_path[imgIndex]
-    #
+
     frame_value = img_Path.split('/')[-1].replace('.jpg', '')
     frame_value = frame_value.split('-')[-1]
     frame_value = int(frame_value)
@@ -290,18 +291,17 @@ def copyImg(fromRootPath, toRootPath, phase):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='BDD100K to MOT format')
-    parser.add_argument("--data_path", default='/path/to/bdd100k')
+    parser.add_argument("--data_path", default='bdd100k')
     parser.add_argument("--phase", default='train')
     parser.add_argument("--classes", default='2,3,4,9,10')
 
-    parser.add_argument("--img_dir", default="/path/to/bdd/image/")
-    parser.add_argument("--label_dir", default="/path/to/bdd/label/")
-    parser.add_argument("--save_path", default="/save/path")
+    parser.add_argument("--img_dir", default="bdd100k/images/track/")
+    parser.add_argument("--label_dir", default="bdd100k/labels/box_track_20/")
+    parser.add_argument("--save_path", default="bdd100kmot_vehicle")
     parser.add_argument("--height", default=720)
     parser.add_argument("--width", default=1280)
     args = parser.parse_args()
 
-    ### for bdd tracking dataset
     attr_dict = dict()
     attr_dict["categories"] = [{
         "supercategory": "none",
@@ -350,7 +350,7 @@ if __name__ == "__main__":
     }]
     attr_id_dict = {i['name']: i['id'] for i in attr_dict['categories']}
 
-    # create BDD training set tracking in MOT format
+    # create bdd100kmot_vehicle training set in MOT format
     print('Loading and converting training set...')
     train_img_dir = os.path.join(args.img_dir, 'train')
     train_label_dir = os.path.join(args.label_dir, 'train')
@@ -361,7 +361,7 @@ if __name__ == "__main__":
     bdd2mot_tracking(train_img_dir, train_label_dir, save_img_dir,
                      save_label_dir)
 
-    # create BDD validation set tracking in MOT format
+    # create bdd100kmot_vehicle validation set in MOT format
     print('Loading and converting validation set...')
     val_img_dir = os.path.join(args.img_dir, 'val')
     val_label_dir = os.path.join(args.label_dir, 'val')

configs/mot/vehicle/tools/bdd100kmot/gen_bdd100kmot_vehicle.sh

-data_path=bdd100k_path
-img_dir=${data_path}/images
-label_dir=${data_path}/labels
+data_path=bdd100k
+img_dir=${data_path}/images/track
+label_dir=${data_path}/labels/box_track_20
 save_path=${data_path}/bdd100kmot_vehicle
 
 phasetrain=train

configs/mot/vehicle/tools/bdd100kmot/gen_labels_MOT.py

@@ -26,7 +26,7 @@ def mkdirs(d):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='BDD100K to MOT format')
     parser.add_argument(
-        "--mot_data", default='/paddle/dataset/bdd100kmot/bdd100k_small')
+        "--mot_data", default='./bdd100k')
     parser.add_argument("--phase", default='train')
     args = parser.parse_args()