在data_decoders/tf_example_decoder.py if label_map_proto_file: label_map = label_map_util.get_label_map_dict(label_map_proto_file, use_display_name) # We use a default_value of -1, but we expect all labels to be contained # in the label map. table = tf.contrib.lookup.HashTable( initializer=tf.contrib.lookup.KeyValueTensorInitializer( keys=tf.constant(list(label_map.keys())), values=tf.constant(list(label_map.values()), dtype=tf.int64)), default_value=-1) # If the label_map_proto is provided, try to use it in conjunction with # the class text, and fall back to a materialized ID. # TODO(lzc): note that here we are using BackupHandler defined in this # file(which is branching slim_example_decoder.BackupHandler). Need to # switch back to slim_example_decoder.BackupHandler once tf 1.5 becomes # more popular. label_handler = BackupHandler( LookupTensor('image/object/class/text', table, default_value=''), slim_example_decoder.Tensor('image/object/class/label')) else: label_handler = slim_example_decoder.Tensor('image/object/class/label')
harshtable 不明白之处
最新推荐文章于 2021-12-18 14:48:30 发布