[MOT] fix deploy tracker diff (#3636)

* fix tracker trt, add tracker to infer_cfg yml

* fix doc and deploy tracker

configs/mot/deepsort/README.md

@@ -95,7 +95,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/deep
 ### 4. Using exported model for python inference
 
 ```bash
-python deploy/python/mot_reid_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+python deploy/python/mot_sde_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --video_file={your video name}.mp4 --device=GPU --save_mot_txts
 ```
 **Notes:**
 The tracking model is used to predict the video, and does not support the prediction of a single image. The visualization video of the tracking results is saved by default. You can add `--save_mot_txts` to save the txt result file, or `--save_images` to save the visualization images.

configs/mot/deepsort/README_cn.md

@@ -98,7 +98,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/mot/deepsort/deep
 ### 4. 用导出的模型基于Python去预测
 
 ```bash
-python deploy/python/mot_reid_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --video_file={your video name}.mp4 --device=GPU --save_mot_txts
+python deploy/python/mot_sde_infer.py --model_dir=output_inference/jde_yolov3_darknet53_30e_1088x608/ --reid_model_dir=output_inference/deepsort_yolov3_pcb_pyramid_r101/ --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。

deploy/python/infer.py

@@ -345,6 +345,9 @@ class PredictConfig():
         self.use_dynamic_shape = yml_conf['use_dynamic_shape']
         if 'mask' in yml_conf:
             self.mask = yml_conf['mask']
+        self.tracker = None
+        if 'tracker' in yml_conf:
+            self.tracker = yml_conf['tracker']
         self.print_config()
 
     def check_model(self, yml_conf):

deploy/python/mot_jde_infer.py

@@ -79,7 +79,12 @@ class JDE_Detector(Detector):
             cpu_threads=cpu_threads,
             enable_mkldnn=enable_mkldnn)
         assert batch_size == 1, "The JDE Detector only supports batch size=1 now"
-        self.tracker = JDETracker()
+        assert pred_config.tracker, "Tracking model should have tracker"
+        tp = pred_config.tracker
+        conf_thres = tp['conf_thres'] if 'conf_thres' in tp else 0.
+        tracked_thresh = tp['tracked_thresh'] if 'tracked_thresh' in tp else 0.7
+        metric_type = tp['metric_type'] if 'metric_type' in tp else 'euclidean'
+        self.tracker = JDETracker(conf_thres=conf_thres, tracked_thresh=tracked_thresh, metric_type=metric_type)
 
     def postprocess(self, pred_dets, pred_embs, threshold):
         online_targets = self.tracker.update(pred_dets, pred_embs)

deploy/python/mot_sde_infer.py

@@ -214,7 +214,7 @@ class SDE_ReID(object):
             enable_mkldnn=enable_mkldnn)
         self.det_times = Timer()
         self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
-
+        assert pred_config.tracker, "Tracking model should have tracker"
         self.tracker = DeepSORTTracker()
 
     def preprocess(self, crops):
@@ -246,7 +246,6 @@ class SDE_ReID(object):
         inputs = self.preprocess(crops)
         self.det_times.preprocess_time_s.end()
 
-        pred_dets, pred_embs = None, None
         input_names = self.predictor.get_input_names()
         for i in range(len(input_names)):
             input_tensor = self.predictor.get_input_handle(input_names[i])

ppdet/engine/export_utils.py

@@ -82,6 +82,11 @@ def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
 
     return preprocess_list, label_list
 
+def _parse_tracker(tracker_cfg):
+    tracker_params = {}
+    for k, v in tracker_cfg.items():
+        tracker_params.update({k: v})
+    return tracker_params
 
 def _dump_infer_config(config, path, image_shape, model):
     arch_state = False
@@ -96,6 +101,13 @@ def _dump_infer_config(config, path, image_shape, model):
     })
     infer_arch = config['architecture']
 
+    if infer_arch in MOT_ARCH:
+        if infer_arch == 'DeepSORT':
+            tracker_cfg = config['DeepSORTTracker']
+        else:
+            tracker_cfg = config['JDETracker']
+        infer_cfg['tracker'] = _parse_tracker(tracker_cfg)
+
     for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
         if arch in infer_arch:
             infer_cfg['arch'] = arch