[cherry-pick] Fix run_benchmark (#7815)

* fix run_benchmark for small model accurate speed

* fix run_benchmark for other det models

deploy/python/infer.py

@@ -181,7 +181,7 @@ def filter_box(self, result, threshold):
  filter_res = {'boxes': boxes, 'boxes_num': filter_num}
  return filter_res
 
- def predict(self, repeats=1):
+ def predict(self, repeats=1, run_benchmark=False):
  '''
  Args:
  repeats (int): repeats number for prediction
@@ -193,6 +193,15 @@ def predict(self, repeats=1):
  '''
  # model prediction
  np_boxes_num, np_boxes, np_masks = np.array([0]), None, None
+
+ if run_benchmark:
+ for i in range(repeats):
+ self.predictor.run()
+ paddle.device.cuda.synchronize()
+ result = dict(
+ boxes=np_boxes, masks=np_masks, boxes_num=np_boxes_num)
+ return result
+
  for i in range(repeats):
  self.predictor.run()
  output_names = self.predictor.get_output_names()
@@ -272,9 +281,9 @@ def predict_image_slice(self,
  self.det_times.preprocess_time_s.end()
 
  # model prediction
- result = self.predict(repeats=50) # warmup
+ result = self.predict(repeats=50, run_benchmark=True) # warmup
  self.det_times.inference_time_s.start()
- result = self.predict(repeats=repeats)
+ result = self.predict(repeats=repeats, run_benchmark=True)
  self.det_times.inference_time_s.end(repeats=repeats)
 
  # postprocess
@@ -370,9 +379,9 @@ def predict_image(self,
  self.det_times.preprocess_time_s.end()
 
  # model prediction
- result = self.predict(repeats=50) # warmup
+ result = self.predict(repeats=50, run_benchmark=True) # warmup
  self.det_times.inference_time_s.start()
- result = self.predict(repeats=repeats)
+ result = self.predict(repeats=repeats, run_benchmark=True)
  self.det_times.inference_time_s.end(repeats=repeats)
 
  # postprocess
@@ -568,7 +577,7 @@ def __init__(
  output_dir=output_dir,
  threshold=threshold, )
 
- def predict(self, repeats=1):
+ def predict(self, repeats=1, run_benchmark=False):
  '''
  Args:
  repeats (int): repeat number for prediction
@@ -577,7 +586,20 @@ def predict(self, repeats=1):
  'cate_label': label of segm, shape:[N]
  'cate_score': confidence score of segm, shape:[N]
  '''
- np_label, np_score, np_segms = None, None, None
+ np_segms, np_label, np_score, np_boxes_num = None, None, None, np.array(
+ [0])
+
+ if run_benchmark:
+ for i in range(repeats):
+ self.predictor.run()
+ paddle.device.cuda.synchronize()
+ result = dict(
+ segm=np_segms,
+ label=np_label,
+ score=np_score,
+ boxes_num=np_boxes_num)
+ return result
+
  for i in range(repeats):
  self.predictor.run()
  output_names = self.predictor.get_output_names()
@@ -659,7 +681,7 @@ def postprocess(self, inputs, result):
  result = dict(boxes=np_boxes, boxes_num=np_boxes_num)
  return result
 
- def predict(self, repeats=1):
+ def predict(self, repeats=1, run_benchmark=False):
  '''
  Args:
  repeats (int): repeat number for prediction
@@ -668,6 +690,14 @@ def predict(self, repeats=1):
  matix element:[class, score, x_min, y_min, x_max, y_max]
  '''
  np_score_list, np_boxes_list = [], []
+
+ if run_benchmark:
+ for i in range(repeats):
+ self.predictor.run()
+ paddle.device.cuda.synchronize()
+ result = dict(boxes=np_score_list, boxes_num=np_boxes_list)
+ return result
+
  for i in range(repeats):
  self.predictor.run()
  np_score_list.clear()