使用 opencv dnn readNetFromModelOptimizer 时出错（预期：'inputShapeLimitation.size() == blobShape.size()'）

Question

我正在尝试使用迁移学习来训练对象检测模型，以便与英特尔神经计算棒 2 (NCS2) 一起使用

到目前为止的步数。

在 google COLAB 上使用 tensorflow 1.15 在我的自定义数据集上使用迁移学习训练 faster_rcnn_inception_v2_coco_2018_01_28 模型。
已验证保存的 tensorflow 模型可与 opencv-python 一起使用 tensorflow.saved_model.load
冻结模型并使用如下所示的 openvino 模型优化器命令创建 IR .bin 和 .xml 以与 opencv-python dnn 函数一起使用。

python mo_tf.py --input_model frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config pipeline.config  --transformations_config extensions/front/tf/faster_rcnn_support_api_v1.15.json --reverse_input_channels --data_type FP16 --input_shape [1,600,600,3] --input image_tensor --output=detection_scores,detection_boxes,num_detections

输出如下

Model Optimizer arguments:
Common parameters:
- Path to the Input Model:  frozen_inference_graph.pb
- Path for generated IR:    /.
- IR output name:   frozen_inference_graph
- Log level:    ERROR
- Batch:    Not specified, inherited from the model
- Input layers:     image_tensor
- Output layers:    detection_scores,detection_boxes,num_detections
- Input shapes:     [1,600,600,3]
- Mean values:  Not specified
- Scale values:     Not specified
- Scale factor:     Not specified
- Precision of IR:  FP16
- Enable fusing:    True
- Enable grouped convolutions fusing:   True
- Move mean values to preprocess section:   False
- Reverse input channels:   True

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:  pipeline.config
- Use the config file:  None

Model Optimizer version:    
[ WARNING ] Model Optimizer removes pre-processing block of the model which resizes image
keeping aspect ratio. The Inference Engine does not support dynamic image size so the
Intermediate Representation file is generated with the input image size of a fixed size.
The Preprocessor block has been removed. Only nodes performing mean value subtraction and
scaling (if applicable) are kept.
The graph output nodes "num_detections", "detection_boxes", "detection_classes",
"detection_scores" have been replaced with a single layer of type "Detection Output".
Refer to IR catalogue in the documentation for information about this layer.

[ WARNING ]  Network has 2 inputs overall, but only 1 of them are suitable for input
channels reversing.
Suitable for input channel reversing inputs are 4-dimensional with 3 channels
All inputs: {'image_tensor': [1, 3, 600, 600], 'image_info': [1, 3]}
Suitable inputs {'image_tensor': [1, 3, 600, 600]}

[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: /./frozen_inference_graph.xml
[ SUCCESS ] BIN file: /./frozen_inference_graph.bin
[ SUCCESS ] Total execution time: 26.84 seconds. 
[ SUCCESS ] Memory consumed: 617 MB.

用opencv加载转换后的模型-pythondnn 使用 openvino ubuntu_dev docker 图片 openvino/ubuntu18_dev:latest 我运行一个包含以下内容的 python 脚本。

net = cv2.dnn.readNetFromModelOptimizer('frozen_inference_graph.xml',
        'frozen_inference_graph.bin') 
blob = cv2.dnn.blobFromImage(image_from_file)
net.setInput(blob)

报告以下错误

Traceback (most recent call last):
  File "xxxxxxxxxxxxxx-dnn.py", line 49, in <module>
  
net.setInput(blob)
cv2.error: OpenCV(4.4.0-openvino) ../opencv/modules/dnn/src/dnn.cpp:4017: error:
    (-2:Unspecified error) in function 'void   cv::dnn::dnn4_v20200609::Net::setInput(cv::InputArray, const String&, double, const Scalar&)'
    (expected: 'inputShapeLimitation.size() == blobShape.size()'), where 'inputShapeLimitation.size()' is 2 must be equal to 'blobShape.size()' is 4

任何人都可以阐明如何解决此错误吗？

Answer 1

我建议您尝试将您的模型加载到 Openvino 的样本中，如下所示：https://docs.openvinotoolkit.org/2018_R5/_samples_object_detection_demo_README.html

似乎使用了与 blob 大小相关的不兼容大小。您的 python 脚本可能与动态整形无关。

这可能对您有用：https://www.youtube.com/watch?v=Ga8j0lgi-OQ

使用 opencv dnn readNetFromModelOptimizer 时出错（预期：'inputShapeLimitation.size() == blobShape.size()'）

Error (expected: 'inputShapeLimitation.size() == blobShape.size()') when using opencv dnn readNetFromModelOptimizer

python

tensorflow

opencv

object-detection

openvino