def _generate_model(self):
'''to generate the bounding boxes'''
weights_path = os.path.expanduser(self.weights_path)
assert weights_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
#YOLOv3 model has 9 anchors and 3 feature layers but
#Tiny YOLOv3 model has 6 anchors and 2 feature layers,
#so we can calculate feature layers number to get model type
num_feature_layers = num_anchors // 3
if num_anchors == 5:
# YOLOv2 use 5 anchors
inference_model = get_yolo2_inference_model(
self.model_type,
self.anchors,
num_classes,
weights_path=weights_path,
input_shape=self.model_image_size + (3, ),
confidence=0.1)
else:
inference_model = get_yolo3_inference_model(
self.model_type,
self.anchors,
num_classes,
weights_path=weights_path,
input_shape=self.model_image_size + (3, ),
confidence=0.1)
inference_model.summary()
return inference_model
def _generate_model(self):
'''to generate the bounding boxes'''
weights_path = os.path.expanduser(self.weights_path)
assert weights_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
#YOLOv3 model has 9 anchors and 3 feature layers but
#Tiny YOLOv3 model has 6 anchors and 2 feature layers,
#so we can calculate feature layers number to get model type
num_feature_layers = num_anchors // 3
if self.model_type.startswith(
'scaled_yolo4_') or self.model_type.startswith('yolo5_'):
# Scaled-YOLOv4 & YOLOv5 entrance, enable "elim_grid_sense" by default
inference_model = get_yolo5_inference_model(
self.model_type,
self.anchors,
num_classes,
weights_path=weights_path,
input_shape=self.model_image_size + (3, ),
confidence=self.score,
iou_threshold=self.iou,
elim_grid_sense=True)
elif self.model_type.startswith('yolo3_') or self.model_type.startswith('yolo4_') or \
self.model_type.startswith('tiny_yolo3_') or self.model_type.startswith('tiny_yolo4_'):
# YOLOv3 & v4 entrance
inference_model = get_yolo3_inference_model(
self.model_type,
self.anchors,
num_classes,
weights_path=weights_path,
input_shape=self.model_image_size + (3, ),
confidence=self.score,
iou_threshold=self.iou,
elim_grid_sense=self.elim_grid_sense)
elif self.model_type.startswith(
'yolo2_') or self.model_type.startswith('tiny_yolo2_'):
# YOLOv2 entrance
inference_model = get_yolo2_inference_model(
self.model_type,
self.anchors,
num_classes,
weights_path=weights_path,
input_shape=self.model_image_size + (3, ),
confidence=self.score,
iou_threshold=self.iou,
elim_grid_sense=self.elim_grid_sense)
else:
raise ValueError('Unsupported model type')
inference_model.summary()
return inference_model
