Yolo_v3_bbox_iou

Yolo_v3_bbox_iou.py

iou = bbox_iou(pred_xywh [:,:,:,:, np.newaxis,:], bboxes[:, np.newaxis, np.newaxis, np.newaxis,:,:]) 

# Find the value of IoU with the real largest prediction box
max_iou = tf.expand_dims(tf.reduce_max(iou, axis = -1 ), axis = -1 ) 

# If the largest IoU is less than the threshold, it is considered that the prediction box contains no objects, then the background box
respond_bgd = (1.0-respond_bbox) * tf.cast(max_iou<IOU_LOSS_THRESH, tf.float32) 
conf_focal = tf.pow(respond_bbox-pred_conf, 2)

# Calculate the loss of confidence
# we hope that if the grid contains objects, then the network output prediction box has a confidence of 1 and 0 when there is no object.
Conf_loss = conf_focal * ( 
     respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits (labels = respond_bbox, logits = conv_raw_conf) 
            +
     respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits (labels = respond_bbox, logits = conv_raw_conf) 
    )