[MOT] add deepsort (#2976)

* add deepsort configs comments

* fix doc, add readme_cn

* fix doc

configs/mot/deepsort/README.md

+English | [简体中文](README_cn.md)
+
+# DeepSORT (Simple Online and Realtime Tracking with a Deep Association Metric)
+
+## Table of Contents
+- [Introduction](#Introduction)
+- [Model Zoo](#Model_Zoo)
+- [Getting Start](#Getting_Start)
+
+## Introduction
+[DeepSORT](https://arxiv.org/abs/1812.00442) is basicly the same with SORT but added a CNN model to extract features in image of human part bounded by a detector. We use JDE as detection model to generate boxes, and select `PCBPyramid` as the ReID model. We also support loading the boxes from saved detection result files.
+
+## Model Zoo
+
+### DeepSORT on MOT-16 training set
+
+| backbone  | input shape  | MOTA   | IDF1   |  IDS  |   FP  |   FN  |   FPS  | Detector | ReID | config |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: |:---: | :---: | :---: |
+| DarkNet53 | 1088x608 |  72.2  |  60.3  | 998  |  8055  | 21631 |  3.28 |[JDE](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams)| [ReID](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+
+**Notes:**
+ DeepSORT does not need to train, only used for evaluation. Before DeepSORT evaluation, you should get detection results by a detection model first, here we use JDE, and then prepare them like this:
+```
+det_results_dir
+   |——————MOT16-02.txt
+   |——————MOT16-04.txt
+   |——————MOT16-05.txt
+   |——————MOT16-09.txt
+   |——————MOT16-10.txt
+   |——————MOT16-11.txt
+   |——————MOT16-13.txt
+```
+
+## Getting Start
+
+### 1. Evaluate a detector to get detection results
+
+```bash
+# use weights released in PaddleDetection model zoo
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams --output ./det_results_dir
+
+# use saved checkpoint after training
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=output/jde_darknet53_30e_1088x608/model_final --output ./det_results_dir
+```
+
+### 2. Tracking
+
+```bash
+# track the objects by loading detected result files
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir ./det_results_dir/mot_results
+```
+
+## Citations
+```
+@inproceedings{Wojke2017simple,
+  title={Simple Online and Realtime Tracking with a Deep Association Metric},
+  author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
+  booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
+  year={2017},
+  pages={3645--3649},
+  organization={IEEE},
+  doi={10.1109/ICIP.2017.8296962}
+}
+
+@inproceedings{Wojke2018deep,
+  title={Deep Cosine Metric Learning for Person Re-identification},
+  author={Wojke, Nicolai and Bewley, Alex},
+  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
+  year={2018},
+  pages={748--756},
+  organization={IEEE},
+  doi={10.1109/WACV.2018.00087}
+}
+```

configs/mot/deepsort/README_cn.md

+简体中文 | [English](README.md)
+
+# DeepSORT (Simple Online and Realtime Tracking with a Deep Association Metric)
+
+## 内容
+- [简介](#简介)
+- [模型库与基线](#模型库与基线)
+- [快速开始](#快速开始)
+
+## 简介
+[DeepSORT](https://arxiv.org/abs/1812.00442) 与SORT基本类似，但增加了一个CNN模型用于在检测器限定的人体部分图像中提取特征。我们使用JDE作为检测模型来生成检测框，并选择`PCBPyramid`作为ReID模型。我们还支持加载保存的检测结果文件来进行预测跟踪。
+
+## 模型库与基线
+
+### DeepSORT on MOT-16 training set
+
+|  骨干网络  | 输入尺寸 | MOTA |  IDF1  |  IDS | FP  |   FN  |   FPS  | 检测模型 | ReID模型 | 配置文件 |
+| :---------| :------- | :----: | :----: | :--: | :----: | :---: | :---: |:---: | :---: | :---: |
+| DarkNet53 | 1088x608 |  72.2  |  60.3  | 998  |  8055  | 21631 |  3.28 |[JDE](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams)| [ReID](https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams)|[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml) |
+
+**Notes:**
+  DeepSORT此处不需要训练，只用于评估。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，这里我们使用JDE，然后像这样准备好结果文件:
+```
+det_results_dir
+   |——————MOT16-02.txt
+   |——————MOT16-04.txt
+   |——————MOT16-05.txt
+   |——————MOT16-09.txt
+   |——————MOT16-10.txt
+   |——————MOT16-11.txt
+   |——————MOT16-13.txt
+```
+
+## 快速开始
+
+### 1. 验证检测模型得到检测结果文件
+
+```bash
+# 使用PaddleDetection发布的权重
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams --output ./det_results_dir
+
+# 使用训练保存的checkpoint
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/jde/jde_darknet53_30e_1088x608_track.yml -o metric=MOT weights=output/jde_darknet53_30e_1088x608/model_final --output ./det_results_dir
+```
+
+### 2. 跟踪预测
+
+```bash
+# 加载检测结果文件得到跟踪结果
+CUDA_VISIBLE_DEVICES=0 python tools/eval_mot.py -c configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml --det_results_dir ./det_results_dir/mot_results
+```
+
+## 引用
+```
+@inproceedings{Wojke2017simple,
+  title={Simple Online and Realtime Tracking with a Deep Association Metric},
+  author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
+  booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
+  year={2017},
+  pages={3645--3649},
+  organization={IEEE},
+  doi={10.1109/ICIP.2017.8296962}
+}
+
+@inproceedings{Wojke2018deep,
+  title={Deep Cosine Metric Learning for Person Re-identification},
+  author={Wojke, Nicolai and Bewley, Alex},
+  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
+  year={2018},
+  pages={748--756},
+  organization={IEEE},
+  doi={10.1109/WACV.2018.00087}
+}
+```

configs/mot/deepsort/_base_/deepsort_reader_1088x608.yml

+TestReader:
+  inputs_def:
+    image_shape: [3, 608, 1088]
+  sample_transforms:
+    - Decode: {}
+    - LetterBoxResize: {target_size: [608, 1088]}
+    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
+    - Permute: {}
+  batch_size: 1
+
+
+EvalMOTReader:
+  sample_transforms:
+    - Decode: {}
+    - LetterBoxResize: {target_size: [608, 1088]}
+    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
+    - Permute: {}
+  batch_size: 1
+
+
+TestMOTReader:
+  inputs_def:
+    image_shape: [3, 608, 1088]
+  sample_transforms:
+    - Decode: {}
+    - LetterBoxResize: {target_size: [608, 1088]}
+    - NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
+    - Permute: {}
+  batch_size: 1

configs/mot/deepsort/_base_/deepsort_yolov3_darknet53_pcb_pyramid_r101.yml

+architecture: DeepSORT
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/DarkNet53_pretrained.pdparams
+
+DeepSORT:
+  detector: YOLOv3
+  reid: PCBPyramid
+  tracker: DeepSORTTracker
+
+YOLOv3:
+  backbone: DarkNet
+  neck: YOLOv3FPN
+  yolo_head: YOLOv3Head
+  post_process: BBoxPostProcess
+
+DarkNet:
+  depth: 53
+  return_idx: [2, 3, 4]
+
+# use default config
+# YOLOv3FPN:
+
+YOLOv3Head:
+  anchors: [[10, 13], [16, 30], [33, 23],
+            [30, 61], [62, 45], [59, 119],
+            [116, 90], [156, 198], [373, 326]]
+  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+  loss: YOLOv3Loss
+
+YOLOv3Loss:
+  ignore_thresh: 0.7
+  downsample: [32, 16, 8]
+  label_smooth: false
+
+BBoxPostProcess:
+  decode:
+    name: YOLOBox
+    conf_thresh: 0.2
+    downsample_ratio: 32
+    clip_bbox: true
+  nms:
+    name: MultiClassNMS
+    keep_top_k: 100
+    score_threshold: 0.01
+    nms_threshold: 0.45
+    nms_top_k: 1000
+
+PCBPyramid:
+  num_conv_out_channels: 128
+  num_classes: 751
+
+DeepSORTTracker:
+  budget: 100
+  max_age: 70
+  n_init: 3
+  metric_type: 'cosine'
+  matching_threshold: 0.2
+  max_iou_distance: 0.9
+  motion: 'KalmanFilter'

configs/mot/deepsort/deepsort_pcb_pyramid_r101.yml

+_BASE_: [
+  '../../datasets/mot.yml',
+  '../../runtime.yml',
+  '_base_/deepsort_yolov3_darknet53_pcb_pyramid_r101.yml',
+  '_base_/deepsort_reader_1088x608.yml',
+]
+
+metric: MOT
+
+EvalMOTDataset:
+  !MOTImageFolder
+    task: MOT16_train
+    dataset_dir: dataset/mot
+    data_root: MOT16/images/train
+    keep_ori_im: True # set True if used in DeepSORT
+
+det_weights: None
+reid_weights: https://paddledet.bj.bcebos.com/models/mot/deepsort_pcb_pyramid_r101.pdparams
+
+DeepSORT:
+  detector: None
+  reid: PCBPyramid
+  tracker: DeepSORTTracker

ppdet/modeling/architectures/deepsort.py

@@ -38,7 +38,7 @@ class DeepSORT(BaseArch):
 
     def __init__(self,
                  detector='YOLOv3',
-                 reid='PCBPlusDropoutPyramid',
+                 reid='PCBPyramid',
                  tracker='DeepSORTTracker'):
         super(DeepSORT, self).__init__()
         self.detector = detector

ppdet/modeling/reid/pyramidal_embedding.py

@@ -24,21 +24,34 @@ from paddle import ParamAttr
 from .resnet import *
 from ppdet.core.workspace import register
 
-__all__ = ['PCBPlusDropoutPyramid']
+__all__ = ['PCBPyramid']
 
 
 @register
-class PCBPlusDropoutPyramid(nn.Layer):
-    def __init__(
-            self,
-            input_ch=2048,
-            num_stripes=6,  # number of sub-parts
-            used_levels=(1, 1, 1, 1, 1, 1),
-            num_classes=751,
-            last_conv_stride=1,
-            last_conv_dilation=1,
-            num_conv_out_channels=128):
-        super(PCBPlusDropoutPyramid, self).__init__()
+class PCBPyramid(nn.Layer):
+    """
+    PCB (Part-based Convolutional Baseline), see https://arxiv.org/abs/1711.09349,
+    Pyramidal Person Re-IDentification, see https://arxiv.org/abs/1810.12193
+
+    Args:
+        input_ch (int): Number of channels of the input feature.
+        num_stripes (int): Number of sub-parts.
+        used_levels (tuple): Whether the level is used, 1 means used.
+        num_classes (int): Number of classes for identities.
+        last_conv_stride (int): Stride of the last conv.
+        last_conv_dilation (int): Dilation of the last conv.
+        num_conv_out_channels (int): Number of channels of conv feature.
+    """
+
+    def __init__(self,
+                 input_ch=2048,
+                 num_stripes=6,
+                 used_levels=(1, 1, 1, 1, 1, 1),
+                 num_classes=751,
+                 last_conv_stride=1,
+                 last_conv_dilation=1,
+                 num_conv_out_channels=128):
+        super(PCBPyramid, self).__init__()
         self.num_stripes = num_stripes
         self.used_levels = used_levels
         self.num_classes = num_classes