报错信息: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: ADD, DIV, EXPAND_DIMS, FLOOR_DIV, FULLY_CONNECTED, GATHER, MAXIMUM, MUL, PACK, REDUCE_MAX, RESHAPE, SOFTMAX, SQRT, SQUEEZE, STRIDED_SLICE, SUB, SUM. Here is a list of operators for which you will need custom implementations: BroadcastTo, Size.
in
10 )
11
---> 12 preprocessor.process(per_pose_class_limit=None
in process(self, per_pose_class_limit, detection_threshold)
111
112 # Draw the prediction result on top of the image for debugging later
--> 113 output_overlay = draw_prediction_on_image(
114 image.numpy().astype(np.uint8), person,
115 close_figure=True, keep_input_size=True)
in draw_prediction_on_image(image, person, crop_region, close_figure, keep_input_size)
18 """
19 # Draw the detection result on top of the image.
---> 20 image_np = utils.visualize(image, [person])
21
22 # Plot the image with detection results.
AttributeError: module 'utils' has no attribute 'visualize'
我报错了,找不到这个visualize
想要自己训练一些动作,编译大佬的文件报错
报错位置: converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.DEFAULT] tflite_model = converter.convert()
报错信息: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: ADD, DIV, EXPAND_DIMS, FLOOR_DIV, FULLY_CONNECTED, GATHER, MAXIMUM, MUL, PACK, REDUCE_MAX, RESHAPE, SOFTMAX, SQRT, SQUEEZE, STRIDED_SLICE, SUB, SUM. Here is a list of operators for which you will need custom implementations: BroadcastTo, Size.
需要增加 这句: converter.allow_custom_ops=True
得到最终:16:23converter = tf.lite.TFLiteConverter.from_keras_model(model) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.allow_custom_ops=True tflite_model = converter.convert()
这样编译通过了 但是得到的tflite的文件运行到AS上面跑不通,想知道up主为什么不需要加就能编译通过