855d29dba61a53553e8fc89237773368fd7e9cdf,research/object_detection/dataset_tools/context_rcnn/generate_embedding_data.py,GenerateEmbeddingDataFn,_run_inference_and_generate_embedding,#GenerateEmbeddingDataFn#Any#,123

Before Change

    example.features.feature["image/unix_time"].float_list.value.extend(
        [unix_time])

    (detection_features, detection_boxes, num_detections,
     detection_scores) = self._session.run(
         [
             self._embedding_node, self._box_node, self._num_detections[0],
             self._scores_node
         ],
         feed_dict={self._input: [tfrecord_entry]})

    num_detections = int(num_detections)
    embed_all = []
    score_all = []

After Change

        [unix_time])

    detections = self._detect_fn.signatures["serving_default"](
        (tf.expand_dims(tf.convert_to_tensor(tfrecord_entry), 0)))
    detection_features = detections["detection_features"]
    detection_boxes = detections["detection_boxes"]
    num_detections = detections["num_detections"]
    detection_scores = detections["detection_scores"]

    num_detections = int(num_detections)
    embed_all = []

Instances