由于操作错误，TensorFlow 从 .pb 转换为 .tflite 失败

Question

大家好，这是我的第一个问题post。如果我做错了什么或者您需要更多信息，请告诉我，我会尽力而为。

我尝试为 TensorFlow lite 创建对象检测。为此，我训练了一个名为 ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8 的模型，从这里下载：https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md

为此我遵循了一些教程。这一切都很好。完成后，我通过

导出了我的 newst ckpt

python exporter_main_v2.py 
--pipeline_config_path=training/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.config
--trained_checkpoint_dir=/training 
--output_directory=/training/model

结果我收到了 training/model/save_model 我的 saved_model.pd。现在我的计划是将此 saved_model.pd 转换为 .tflite 文件。

这就是我被困的地方。我试着用

转换它

tflite_convert 
--output_file=tflite/ 
--saved_model_dir=model/saved_model 
--graph_def_file=model/saved_model/saved_model.pb 
--input_arrays=input --output_arrays=TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3 
--input_shape=1,320,320,3 
--allow_custom_ops

并与

toco 
--saved_model_dir=model/saved_model 
--output_file=tflite/detect.tflite

但是无论我做什么，我总是收到一条很长的错误消息

tflite_convert --output_file=training/tflite/ --saved_model_dir=training/model/saved_model --graph_def_file=training/model/saved_model/saved_model.pb --input_arrays=input --output_arrays=TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3 --
input_shape=1,320,320,3 --allow_custom_ops
2022-05-11 15:01:16.421059: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operati
ons:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-05-11 15:01:16.905178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7445 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:02:00.
0, compute capability: 8.6
2022-05-11 15:01:25.665750: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:357] Ignored output_format.
2022-05-11 15:01:25.665982: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored drop_control_dependency.
2022-05-11 15:01:25.666905: I tensorflow/cc/saved_model/reader.cc:43] Reading SavedModel from: training/model/saved_model

    raise converter_error
tensorflow.lite.python.convert_phase.ConverterError: <unknown>:0: error: loc(callsite(callsite(fused["ConcatV2:", "Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ChangeCoordinateFrame/Scale/concat@__inferenc
e_call_func_11394"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper_13768"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"])): 'tf.ConcatV2' op is neither a custom op nor a f
lex op
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
<unknown>:0: note: loc(callsite(callsite(fused["ConcatV2:", "Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ChangeCoordinateFrame/Scale/concat@__inference_call_func_11394"] at fused["StatefulPartitionedCall:
", "StatefulPartitionedCall@__inference_signature_wrapper_13768"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"])): Error code: ERROR_NEEDS_FLEX_OPS
<unknown>:0: error: loc(callsite(fused["StridedSlice:", "map/while/strided_slice@map_while_body_7735"] at callsite(callsite(fused["StatelessWhile:", "map/while@__inference_call_func_11394"] at fused["StatefulPartitionedCall:", "Statefu
lPartitionedCall@__inference_signature_wrapper_13768"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]))): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc(callsite(callsite(fused["StatelessWhile:", "map/while@__inference_call_func_11394"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper_13768"]) at fused["StatefulPartition
edCall:", "StatefulPartitionedCall"])): called from
<unknown>:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from
<unknown>:0: note: loc(callsite(fused["StridedSlice:", "map/while/strided_slice@map_while_body_7735"] at callsite(callsite(fused["StatelessWhile:", "map/while@__inference_call_func_11394"] at fused["StatefulPartitionedCall:", "Stateful
PartitionedCall@__inference_signature_wrapper_13768"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]))): Error code: ERROR_NEEDS_FLEX_OPS
<unknown>:0: error: failed while converting: 'main':
Some ops are not supported by the native TFLite runtime, you can enable TF kernels fallback using TF Select. See instructions: https://www.tensorflow.org/lite/guide/ops_select
TF Select ops: ConcatV2, StridedSlice
Details:
        tf.ConcatV2(tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<i32>) -> (tensor<4xf32>) : {device = ""}
        tf.StridedSlice(tensor<?x?x3xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) -> (tensor<1x?x?x3xf32>) : {begin_mask = 14 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 14 : i64, new_axis_mask = 1 : i64, shrink_axis_
mask = 0 : i64}

我尝试使用的那些脚本来自这里：https://github.com/tensorflow/models/tree/master/research/object_detection

由于我是 TensorFlow 的初学者，所以我不太了解这里的问题。据我所知，这个问题与 TensorFlow Lite 不支持 TensorFlow 2 支持的某些功能的操作有关。

Answer 1

我自己找到了解决方案。

我只是使用了错误的脚本将检查点转换为冻结图 (.pd)

我需要使用 export_tflite_graph_tf2.py 脚本而不是 exporter_main_v2.py。在此之后我可以简单地使用 tflite_convert 脚本导出到 .tflite

由于操作错误，TensorFlow 从 .pb 转换为 .tflite 失败

TensorFlow convert from .pb to .tflite failes due to ops error

python

tensorflow

tensorflow2.0

tensorflow-lite