我正在尝试将张量流模型转换为OpenVINO IR文件。 我已经从以下地址下载了预先训练的模型:
http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz
然后,我解压缩文件以获得名为“ frozen_inference_graph.pb”的.pb文件。 然后,我在OpenVINO文件夹中使用了转换命令
“ IntelSWTools \ openvino_2019.2.275 \ deployment_tools \ model_optimizer \”
如下:
python mo_tf.py --input_model frozen_inference_graph.pb
但是我收到以下错误消息。 我该如何修改才能解决此问题?
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: <my folder>\frozen_inference_graph.pb
- Path for generated IR: <my OpenVINO folder>\IntelSWTools\openvino_2019.2.275\deployment_tools\model_optimizer\.
- IR output name: frozen_inference_graph
- Log level: ERROR
- Batch: Not specified, inherited from the model
- Input layers: Not specified, inherited from the model
- Output layers: Not specified, inherited from the model
- Input shapes: Not specified, inherited from the model
- Mean values: Not specified
- Scale values: Not specified
- Scale factor: Not specified
- Precision of IR: FP32
- Enable fusing: True
- Enable grouped convolutions fusing: True
- Move mean values to preprocess section: False
- Reverse input channels: False
TensorFlow specific parameters:
- Input model in text protobuf format: False
- Path to model dump for TensorBoard: None
- List of shared libraries with TensorFlow custom layers implementation: None
- Update the configuration file with input/output node names: None
- Use configuration file used to generate the model with Object Detection API: None
- Operations to offload: None
- Patterns to offload: None
- Use the config file: None
Model Optimizer version: 2019.2.0-436-gf5827d4
C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\lib\site-packages\tensorflow\python\framework\dtypes.py:458: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
[ ERROR ] Shape [-1 -1 -1 3] is not fully defined for output 0 of "image_tensor". Use --input_shape with positive integers to override model input shapes.
[ ERROR ] Cannot infer shapes or values for node "image_tensor".
[ ERROR ] Not all output shapes were inferred or fully defined for node "image_tensor". For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #40.
[ ERROR ]
[ ERROR ] It can happen due to bug in custom shape infer function <function Parameter.__init__.<locals>.<lambda> at 0x000002032A17D378>.
[ ERROR ] Or because the node inputs have incorrect values/shapes.
[ ERROR ] Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
[ ERROR ] Run Model Optimizer with --log_level=DEBUG for more information.
[ ERROR ] Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.middle.PartialInfer.PartialInfer'>): Stopped shape/value propagation at "image_tensor" node.
For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #38.
Process finished with exit code 1
我尝试了许多其他的张量流模型,但是都存在相同的问题。 我使用了从 1.2.0 到 1.14.0 的不同tensorflow版本,但相同。
关于形状的关键词似乎是主要原因。但是如何添加一些东西来避免这个问题?
Shape [-1 -1 -1 3] is not fully defined for output 0 of "image_tensor". Use --input_shape with positive integers to override model input shapes.
我希望可以正确生成IR文件。
答案 0 :(得分:2)
Openvino模型优化器(mo_tf.py)需要更多参数。请同时通过以下内容。
python mo_tf.py --output_dir <\ PATH> --input_model <\ PATH> \ mask_rcnn_inception_v2_coco_2018_01_28 \ frozen_inference_graph.pb --tensorflow_use_custom_operations_config扩展名\ front \ tf \ mask_rcnn_support.json --tensorflow_object_config_in_pipe_in_pipe_in_pipe_in_pipe_in_pipe_in_pipe_in_pipe_in_pipe_in_pipe_inc .config
可以从https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md下载mask_rcnn_inception_v2_coco模型
有关更多详细信息,请参见:https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html