Here is a list of operators for which you will need custom implementations: FusedBatchNormV3

[luckynote]

TF2.0 使用convert_to_tflite.py 转换0.75的pretrain模型遇到如下问题，环境ubuntu 16.04，tf多个版本都报该问题，设置target_ops无法解决，设置allow_custom_ops需要自己预定义，但不知道如何定义，求助
Traceback (most recent call last):
File "/home/luckynote/anaconda3/bin/toco_from_protos", line 11, in
sys.exit(main())
File "/home/luckynote/anaconda3/lib/python3.6/site-packages/tensorflow/lite/toco/python/toco_from_protos.py", line 59, in main
app.run(main=execute, argv=[sys.argv[0]] + unparsed)
File "/home/luckynote/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/luckynote/anaconda3/lib/python3.6/site-packages/tensorflow/lite/toco/python/toco_from_protos.py", line 33, in execute
output_str = tensorflow_wrap_toco.TocoConvert(model_str, toco_str, input_str)
Exception: We are continually in the process of adding support to TensorFlow Lite for more ops. It would be helpful if you could inform us of how this conversion went by opening a github issue at https://github.com/tensorflow/tensorflow/issues/new?template=40-tflite-op-request.md
and pasting the following:

Some of the operators in the model are not supported by the standard TensorFlow Lite runtime. If those are native TensorFlow operators, you might be able to use the extended runtime by passing --enable_select_tf_ops, or by setting target_ops=TFLITE_BUILTINS,SELECT_TF_OPS when calling tf.lite.TFLiteConverter(). Otherwise, if you have a custom implementation for them you can disable this error with --allow_custom_ops, or by setting allow_custom_ops=True when calling tf.lite.TFLiteConverter(). Here is a list of builtin operators you are using: CONCATENATION, CONV_2D, DEPTHWISE_CONV_2D, FULLY_CONNECTED, LEAKY_RELU, MEAN, MUL, PACK, RELU, RESHAPE, SHAPE, SPLIT, STRIDED_SLICE, SUB, TRANSPOSE. Here is a list of operators for which you will need custom implementations: FusedBatchNormV3.