Update inference.py

inference.py

@@ -52,7 +52,7 @@ def do_full_inference(dataloader, image_meter_width, image_meter_height, gp=None
 
     # Load hyperparameters
     if 'model' not in hyperparams: hyperparams['model'] = WEIGHTS
-    if 'conf_thresh' not in hyperparams: hyperparams['conf_tresh'] = CONF_THRES
+    if 'conf_thresh' not in hyperparams: hyperparams['conf_thresh'] = CONF_THRES
     if 'iou_thresh' not in hyperparams: hyperparams['iou_thresh'] = NMS_IOU
     if 'min_hits' not in hyperparams: hyperparams['min_hits'] = MIN_HITS
     if 'max_age' not in hyperparams: hyperparams['max_age'] = MAX_AGE
@@ -87,13 +87,13 @@ def do_full_inference(dataloader, image_meter_width, image_meter_height, gp=None
         return
 
 
-    outputs = do_suppression(inference, conf_thres=hyperparams['conf_tresh'], iou_thres=hyperparams['iou_tresh'], gp=gp)
+    outputs = do_suppression(inference, conf_thres=hyperparams['conf_thresh'], iou_thres=hyperparams['iou_thresh'], gp=gp)
 
     if hyperparams['use_associative_tracking']:
-        
+
         do_confidence_boost(inference, outputs, gp=gp)
 
-        outputs = do_suppression(inference, conf_thres=hyperparams['conf_tresh'], iou_thres=hyperparams['iou_tresh'], gp=gp)
+        outputs = do_suppression(inference, conf_thres=hyperparams['conf_thresh'], iou_thres=hyperparams['iou_thresh'], gp=gp)
 
     all_preds, real_width, real_height = format_predictions(image_shapes, outputs, width, height, gp=gp)
 
@@ -288,13 +288,13 @@ def do_confidence_boost(inference, safe_preds, gp=None, batch_size=BATCH_SIZE, v
             pbar.update(1*batch_size)
           
 
-def boost_frame(safe_frame, base_frame, dt):
+def boost_frame(safe_frame, base_frame, dt, decay=1):
     safe_boxes = safe_frame[:, :4]
     boxes = xywh2xyxy(base_frame[:, :4])  # center_x, center_y, width, height) to (x1, y1, x2, y2)
     ious = box_iou(boxes, safe_boxes)
     score = torch.matmul(ious, safe_frame[:, 4])
     # score = iou(safe_box, base_box) * confidence(safe_box)
-    base_frame[:, 4] *= 1 + (score)*math.exp(-dt*dt)
+    base_frame[:, 4] *= 1 + (score)*math.exp(-decay*dt*dt)
     return base_frame
 
 def do_tracking(all_preds, image_meter_width, image_meter_height, gp=None, max_age=MAX_AGE, iou_thres=IOU_THRES, min_hits=MIN_HITS, min_length=MIN_LENGTH, verbose=True):