def yolov3_head_416_inference(num_classes, yolo_max_boxes):
input_shapes = [(52, 52, 128), (26, 26, 256), (13, 13, 512)]
return yolov3_head(
input_shapes,
anchors=YOLOV4_ANCHORS,
num_classes=num_classes,
training=False,
yolo_max_boxes=yolo_max_boxes,
yolo_iou_threshold=0.5,
yolo_score_threshold=0.8,
)
def YOLOv4(
input_shape,
num_classes,
anchors,
training=False,
yolo_max_boxes=50,
yolo_iou_threshold=0.5,
yolo_score_threshold=0.5,
):
"""
YOLOv4 Model
Args:
input_shape (Tuple[int]): Input shape of the image
anchors (List[numpy.array[int, 2]]): List of 3 numpy arrays containing the anchor sizes used for each stage.
The first and second columns of the numpy arrays contain respectively the width and the height of the
anchors.
num_classes (int): Number of classes.
training (boolean): If False, will output boxes computed through YOLO regression and NMS, and YOLO features
otherwise. Set it True for training, and False for inferences.
yolo_max_boxes (int): Maximum number of boxes predicted on each image (across all anchors/stages)
yolo_iou_threshold (float between 0. and 1.): IOU threshold defining whether close boxes will be merged
during non max regression.
yolo_score_threshold (float between 0. and 1.): Boxes with score lower than this threshold will be filtered
out during non max regression.
"""
backbone = csp_darknet53(input_shape)
neck = yolov4_neck(input_shapes=backbone.output_shape)
normalized_anchors = compute_normalized_anchors(anchors, input_shape)
head = yolov3_head(
input_shapes=neck.output_shape,
anchors=normalized_anchors,
num_classes=num_classes,
training=training,
yolo_max_boxes=yolo_max_boxes,
yolo_iou_threshold=yolo_iou_threshold,
yolo_score_threshold=yolo_score_threshold,
)
inputs = tf.keras.Input(shape=input_shape)
lower_features = backbone(inputs)
medium_features = neck(lower_features)
upper_features = head(medium_features)
return tf.keras.Model(inputs=inputs, outputs=upper_features, name="YOLOv4")
def YOLOv4(
input_shape,
num_classes,
anchors,
training=False,
yolo_max_boxes=50,
yolo_iou_threshold=0.5,
yolo_score_threshold=0.5,
weights="darknet",
):
"""
YOLOv4 Model
Args:
input_shape (Tuple[int]): Input shape of the image (H,W,C) . The Height and Width must be multiple of 32
anchors (List[numpy.array[int, 2]]): List of 3 numpy arrays containing the anchor sizes used for each stage.
The first and second columns of the numpy arrays contain respectively the width and the height of the
anchors.
num_classes (int): Number of classes.
training (boolean): If False, will output boxes computed through YOLO regression and NMS, and YOLO features
otherwise. Set it True for training, and False for inferences.
yolo_max_boxes (int): Maximum number of boxes predicted on each image (across all anchors/stages)
yolo_iou_threshold (float between 0. and 1.): IOU threshold defining whether close boxes will be merged
during non max regression.
yolo_score_threshold (float between 0. and 1.): Boxes with score lower than this threshold will be filtered
out during non max regression.
weights (str): one of `None` (random initialization),
'darknet' (pre-training on COCO),
or the path to the weights file to be loaded.
Returns:
tf.keras.Model: YoloV4 model
Raises:
AttributeError: The ``Raises`` section is a list of all exceptions
that are relevant to the interface.
ValueError: If height and width in the input_shape is not a multiple of 32
"""
if (input_shape[0] % 32 != 0) | (input_shape[1] % 32 != 0):
raise ValueError(
f"Provided height and width in input_shape {input_shape} is not a multiple of 32"
)
backbone = csp_darknet53(input_shape)
neck = yolov4_neck(input_shapes=backbone.output_shape)
normalized_anchors = compute_normalized_anchors(anchors, input_shape)
head = yolov3_head(
input_shapes=neck.output_shape,
anchors=normalized_anchors,
num_classes=num_classes,
training=training,
yolo_max_boxes=yolo_max_boxes,
yolo_iou_threshold=yolo_iou_threshold,
yolo_score_threshold=yolo_score_threshold,
)
inputs = tf.keras.Input(shape=input_shape)
lower_features = backbone(inputs)
medium_features = neck(lower_features)
upper_features = head(medium_features)
yolov4 = tf.keras.Model(inputs=inputs,
outputs=upper_features,
name="YOLOv4")
#weights_path = get_weights_by_keyword_or_path(weights, model=yolov4)
#if weights_path is not None:
#yolov4.load_weights(str(weights_path), by_name=True, skip_mismatch=True)
return yolov4