[MOT] Fix deepsort detectors config (PaddlePaddle#5344)

* fix deepsort detectors cfgs, add ppyoloe * modify yolov3 40e * add ppyoloe mot17half modelzoo * add resnet embedding reid * remove fasterfpn picodet, fix nms for higher MOTA * add mot17 yml * fix _base_ cfgs * fix doc
an1018 · Mar 14, 2022 · 8ffa45a · 8ffa45a
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diff --git a/configs/mot/deepsort/README_cn.md b/configs/mot/deepsort/README_cn.md
@@ -39,11 +39,14 @@
 | :--------         | :-----     | :----:      |:------:  | :----: |:-----: |:----:|:----:   |
 | MIX               | JDE YOLOv3 | PCB Pyramid |  -       |  66.9  |  62.7  |   -    |[配置文件](./deepsort_jde_yolov3_pcb_pyramid.yml) |
 | MIX               | JDE YOLOv3 | PPLCNet     |  -       |  66.3  |  62.1  |   -    |[配置文件](./deepsort_jde_yolov3_pplcnet.yml) |
-| pedestrian(未开放) | YOLOv3     | PPLCNet     |  45.4    |  45.8  |  54.3  |   -    |[配置文件](./deepsort_yolov3_pplcnet.yml) |
-| MOT-17 half train | PPYOLOv2   | PPLCNet     |  46.8    |  48.7  |  54.5  |   -    |[配置文件](./deepsort_ppyolov2_pplcnet.yml) |
+| MOT-17 half train | YOLOv3     | PPLCNet     |  42.7    |  50.2  |  52.4  |   -    |[配置文件](./deepsort_yolov3_pplcnet.yml) |
+| MOT-17 half train | PPYOLOv2   | PPLCNet     |  46.8    |  51.8  |  55.8  |   -    |[配置文件](./deepsort_ppyolov2_pplcnet.yml) |
+| MOT-17 half train | PPYOLOe    | PPLCNet     |  52.9    |  56.7  |  60.5  |   -    |[配置文件](./deepsort_ppyoloe_pplcnet.yml) |
+| MOT-17 half train | PPYOLOe    | ResNet-50   |  52.9    |  56.7  |  64.6  |   -    |[配置文件](./deepsort_ppyoloe_resnet.yml) |
 
 **注意:**
-DeepSORT不需要训练MOT数据集，只用于评估，现在支持两种评估的方式。
+模型权重下载链接在配置文件中的```det_weights```和```reid_weights```，运行验证的命令即可自动下载。
+DeepSORT是分离检测器和ReID模型的，其中检测器单独训练MOT数据集，而组装成DeepSORT后只用于评估，现在支持两种评估的方式。
 - **方式1**：加载检测结果文件和ReID模型，在使用DeepSORT模型评估之前，应该首先通过一个检测模型得到检测结果，然后像这样准备好结果文件:
 ```
 det_results_dir
@@ -75,6 +78,15 @@ wget https://dataset.bj.bcebos.com/mot/det_results_dir.zip
 
 ### 1. 评估
 
+#### 1.1 评估检测效果
+```bash
+CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/mot/deepsort/detector/ppyoloe_crn_l_36e_640x640_mot17half.yml
+```
+
+**注意:**
+ - 评估检测使用的是```tools/eval.py```, 评估跟踪使用的是```tools/eval_mot.py```。
+
+#### 1.2 评估跟踪效果
 **方式1**：加载检测结果文件和ReID模型，得到跟踪结果
 ```bash
 CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/reid/deepsort_pcb_pyramid_r101.yml --det_results_dir {your detection results}
@@ -89,6 +101,8 @@ CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort
 CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_jde_yolov3_pplcnet.yml
 # 或者
 CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_ppyolov2_pplcnet.yml --scaled=True
+# 或者
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_ppyoloe_resnet.yml --scaled=True
 ```
 **注意:**
  - JDE YOLOv3行人检测模型是和JDE和FairMOT使用同样的MOT数据集训练的，因此MOTA较高。而其他通用检测模型如PPYOLOv2只使用了MOT17 half数据集训练。

diff --git a/configs/mot/deepsort/_base_/mot17.yml b/configs/mot/deepsort/_base_/mot17.yml
@@ -0,0 +1,33 @@
+metric: COCO
+num_classes: 1
+
+# Detection Dataset for training
+TrainDataset:
+  !COCODataSet
+    dataset_dir: dataset/mot/MOT17
+    anno_path: annotations/train_half.json
+    image_dir: images/train
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    dataset_dir: dataset/mot/MOT17
+    anno_path: annotations/val_half.json
+    image_dir: images/train
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val_half.json
+
+
+# MOTDataset for MOT evaluation and inference
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: MOT17/images/half
+    keep_ori_im: True # set as True in DeepSORT and ByteTrack
+
+TestMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    keep_ori_im: True # set True if save visualization images or video
diff --git a/configs/mot/deepsort/deepsort_faster_rcnn_fpn_pplcnet.yml b/configs/mot/deepsort/deepsort_faster_rcnn_fpn_pplcnet.yml
diff --git a/configs/mot/deepsort/deepsort_jde_yolov3_pcb_pyramid.yml b/configs/mot/deepsort/deepsort_jde_yolov3_pcb_pyramid.yml
@@ -1,7 +1,6 @@
 _BASE_: [
   'detector/jde_yolov3_darknet53_30e_1088x608_mix.yml',
-  '../../datasets/mot.yml',
-  '../../runtime.yml',
+  '_base_/mot17.yml',
   '_base_/deepsort_reader_1088x608.yml',
 ]
 metric: MOT
@@ -30,6 +29,7 @@ DeepSORT:
 # reid and tracker configuration
 # see 'configs/mot/deepsort/reid/deepsort_pcb_pyramid_r101.yml'
 PCBPyramid:
+  model_name: "ResNet101"
   num_conv_out_channels: 128
   num_classes: 751
 

diff --git a/configs/mot/deepsort/deepsort_jde_yolov3_pplcnet.yml b/configs/mot/deepsort/deepsort_jde_yolov3_pplcnet.yml
@@ -1,7 +1,6 @@
 _BASE_: [
   'detector/jde_yolov3_darknet53_30e_1088x608_mix.yml',
-  '../../datasets/mot.yml',
-  '../../runtime.yml',
+  '_base_/mot17.yml',
   '_base_/deepsort_reader_1088x608.yml',
 ]
 metric: MOT

diff --git a/configs/mot/deepsort/deepsort_picodet_pplcnet.yml b/configs/mot/deepsort/deepsort_picodet_pplcnet.yml
diff --git a/configs/mot/deepsort/deepsort_ppyoloe_pplcnet.yml b/configs/mot/deepsort/deepsort_ppyoloe_pplcnet.yml
@@ -0,0 +1,110 @@
+_BASE_: [
+  'detector/ppyoloe_crn_l_36e_640x640_mot17half.yml',
+  '_base_/mot17.yml',
+  '_base_/deepsort_reader_1088x608.yml',
+]
+metric: MOT
+num_classes: 1
+
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: MOT17/images/half
+    keep_ori_im: True # set as True in DeepSORT
+
+det_weights: https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyoloe_crn_l_36e_640x640_mot17half.pdparams
+reid_weights: https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams
+
+# reader
+EvalMOTReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_size: 1
+
+TestMOTReader:
+  inputs_def:
+    image_shape: [3, 640, 640]
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
+    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
+    - Permute: {}
+  batch_size: 1
+
+
+# DeepSORT configuration
+architecture: DeepSORT
+pretrain_weights: None
+
+DeepSORT:
+  detector: YOLOv3 # PPYOLOe version
+  reid: PPLCNetEmbedding
+  tracker: DeepSORTTracker
+
+
+# reid and tracker configuration
+# see 'configs/mot/deepsort/reid/deepsort_pplcnet.yml'
+PPLCNetEmbedding:
+  input_ch: 1280
+  output_ch: 512
+
+DeepSORTTracker:
+  input_size: [64, 192]
+  min_box_area: 0
+  vertical_ratio: -1
+  budget: 100
+  max_age: 70
+  n_init: 3
+  metric_type: cosine
+  matching_threshold: 0.2
+  max_iou_distance: 0.9
+  motion: KalmanFilter
+
+
+# detector configuration: PPYOLOe version
+# see 'configs/mot/deepsort/detector/ppyoloe_crn_l_300e_640x640_mot17half.yml'
+YOLOv3:
+  backbone: CSPResNet
+  neck: CustomCSPPAN
+  yolo_head: PPYOLOEHead
+  post_process: ~
+
+CSPResNet:
+  layers: [3, 6, 6, 3]
+  channels: [64, 128, 256, 512, 1024]
+  return_idx: [1, 2, 3]
+  use_large_stem: True
+
+CustomCSPPAN:
+  out_channels: [768, 384, 192]
+  stage_num: 1
+  block_num: 3
+  act: 'swish'
+  spp: true
+
+# Tracking requires higher quality boxes, so NMS score_threshold will be higher
+PPYOLOEHead:
+  fpn_strides: [32, 16, 8]
+  grid_cell_scale: 5.0
+  grid_cell_offset: 0.5
+  static_assigner_epoch: -1 # 100
+  use_varifocal_loss: True
+  eval_input_size: [640, 640]
+  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.4 # 0.01 in original detector
+    nms_threshold: 0.6