def get_predictions(self,
score_threshold=0.1,
iou_threshold=0.6,
max_boxes=20):
"""Postprocess outputs of the network.
Returns:
boxes: a float tensor with shape [batch_size, N, 4].
scores: a float tensor with shape [batch_size, N].
num_boxes: an int tensor with shape [batch_size], it
represents the number of detections on an image.
where N = max_boxes.
"""
# 良心活：输入的图像有rescale操作，得到的预测结果也有对应的还原操作
with tf.name_scope('postprocessing'):
boxes = batch_decode(self.box_encodings, self.anchors)
# if the images were padded we need to rescale predicted boxes:
boxes = boxes / self.box_scaler
boxes = tf.clip_by_value(boxes, 0.0, 1.0)
# it has shape [batch_size, num_anchors, 4]
scores = tf.nn.softmax(self.class_predictions_with_background,
axis=2)[:, :, 1]
# it has shape [batch_size, num_anchors]
# 执行NMS操作来去除重复的检测框
with tf.device('/cpu:0'), tf.name_scope('nms'):
boxes, scores, num_detections = batch_non_max_suppression(
boxes, scores, score_threshold, iou_threshold, max_boxes)
# 返回的字典包括：检测框、检测框对应的分数、以及检测框的总数目
return {'boxes': boxes, 'scores': scores, 'num_boxes': num_detections}
def get_predictions(self,
score_threshold=0.1,
iou_threshold=0.6,
max_boxes=20):
"""Postprocess outputs of the network.
Returns:
boxes: a float tensor with shape [batch_size, N, 4].
scores: a float tensor with shape [batch_size, N].
num_boxes: an int tensor with shape [batch_size], it
represents the number of detections on an image.
where N = max_boxes.
"""
with tf.name_scope('postprocessing'):
boxes = batch_decode(self.box_encodings, self.anchors)
# it has shape [batch_size, num_anchors, 4]
scores = tf.nn.softmax(self.class_predictions_with_background,
axis=2)[:, :, 1]
# it has shape [batch_size, num_anchors]
with tf.device('/cpu:0'), tf.name_scope('nms'):
boxes, scores, num_detections = batch_non_max_suppression(
boxes, scores, score_threshold, iou_threshold, max_boxes)
return {'boxes': boxes, 'scores': scores, 'num_boxes': num_detections}