问题
So, I'm using TensorFlow SSD-Mobilnet V1 coco dataset. That I have further trained on my own dataset but when I try to convert it to OpenVino IR to run it on Raspberry PI with Movidius Chip. I get an error
➜ utils sudo python3 summarize_graph.py --input_model ssd.pb
WARNING: Logging before flag parsing goes to stderr.
W0722 17:17:05.565755 4678620608 __init__.py:308] Limited tf.compat.v2.summary API due to missing TensorBoard installation.
W0722 17:17:06.696880 4678620608 deprecation_wrapper.py:119] From ../../mo/front/tf/loader.py:35: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.
W0722 17:17:06.697348 4678620608 deprecation_wrapper.py:119] From ../../mo/front/tf/loader.py:109: The name tf.MetaGraphDef is deprecated. Please use tf.compat.v1.MetaGraphDef instead.
W0722 17:17:06.697680 4678620608 deprecation_wrapper.py:119] From ../../mo/front/tf/loader.py:235: The name tf.NodeDef is deprecated. Please use tf.compat.v1.NodeDef instead.
1 input(s) detected:
Name: image_tensor, type: uint8, shape: (-1,-1,-1,3)
7 output(s) detected:
detection_boxes
detection_scores
detection_multiclass_scores
detection_classes
num_detections
raw_detection_boxes
raw_detection_scores
When I try to convert the ssd.pb(frozen model) to OpenVino IR
➜ model_optimizer sudo python3 mo_tf.py --input_model ssd.pb
Password:
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/ssd.pb
- Path for generated IR: /opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/.
- IR output name: ssd
- 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: 2019.1.1-83-g28dfbfd
WARNING: Logging before flag parsing goes to stderr.
E0722 17:24:22.964164 4474824128 infer.py:158] 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.
E0722 17:24:22.964462 4474824128 infer.py:178] Cannot infer shapes or values for node "image_tensor".
E0722 17:24:22.964554 4474824128 infer.py:179] Not all output shapes were inferred or fully defined for node "image_tensor".
For more information please refer to Model Optimizer FAQ (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #40.
E0722 17:24:22.964632 4474824128 infer.py:180]
E0722 17:24:22.964720 4474824128 infer.py:181] It can happen due to bug in custom shape infer function <function tf_placeholder_ext.<locals>.<lambda> at 0x12ab64bf8>.
E0722 17:24:22.964787 4474824128 infer.py:182] Or because the node inputs have incorrect values/shapes.
E0722 17:24:22.964850 4474824128 infer.py:183] Or because input shapes are incorrect (embedded to the model or passed via --input_shape).
E0722 17:24:22.965915 4474824128 infer.py:192] Run Model Optimizer with --log_level=DEBUG for more information.
E0722 17:24:22.966033 4474824128 main.py:317] 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 (<INSTALL_DIR>/deployment_tools/documentation/docs/MO_FAQ.html), question #38.
How do you think we should fix this?
回答1:
I updated my OpenVINO to OpenVINO toolkit R2 2019 & using the below command I was able to generate IR file
python3 ~/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model frozen_inference_graph.pb --tensorflow_use_custom_operations_config ~/intel/openvino/deployment_tools/model_optimizer/extension/front/tf/ssd_support_api_v1.14.json --tensorflow_object_detection_api_pipeline_config pipeline.config -b 1 --data_type FP16 --reverse_input_channels
回答2:
When you try to convert ssd.pb(your frozen model), you are passing only the input model parameter to mo_tf.py scripts. To convert an object detection model to IR, go to the model optimizer directory, run the mo_tf.py script with the following required parameters:
--input_model :
File with a pre-trained model (binary or text .pb file after freezing)
--tensorflow_use_custom_operations_config : Configuration file that describes rules to convert specific TensorFlow* topologies. For the models downloaded from the TensorFlow* Object Detection API zoo, you can find the configuration files in the /deployment_tools/model_optimizer/extensions/front/tf directory You can use ssd_v2_support.json / ssd_support.json — for frozen SSD topologies from the models zoo. It will be available in the above mentioned directory.
--tensorflow_object_detection_api_pipeline_config : A special configuration file that describes the topology hyper-parameters and structure of the TensorFlow Object Detection API model. For the models downloaded from the TensorFlow* Object Detection API zoo, the configuration file is named pipeline.config. If you plan to train a model yourself, you can find templates for these files in the models repository
--input_shape(optional):
A custom input image shape, we need to pass these values based on the pretrained model you used.
The model takes input image in the format [1 H W C], Where the parameter refers to the batch size, height, width, channel respectively.
Model Optimizer does not accept negative values for batch, height, width and channel number.
So, you need to pass a valid set of 4 positive numbers using --input_shape parameter, if input image dimensions of the model(SSD mobilenet) is known in advance.
If it is not available, you don't need to pass input shape.
An example mo_tf.py command which uses the model SSD-MobileNet-v2-COCO downloaded from model downloader comes up with openvino is shown below.
python mo_tf.py
--input_model "c:\Program Files (x86)\IntelSWTools\openvino_2019.1.087\deployment_tools\tools\model_downloader\object_detection\common\ssd_mobilenet_v2_coco\tf\ssd_mobilenet_v2_coco.frozen.pb"
--tensorflow_use_custom_operations_config "c:\Program Files (x86)\IntelSWTools\openvino_2019.1.087\deployment_tools\model_optimizer\extensions\front\tf\ssd_v2_support.json"
--tensorflow_object_detection_api_pipeline_config "c:\Program Files (x86)\IntelSWTools\openvino_2019.1.087\deployment_tools\tools\model_downloader\object_detection\common\ssd_mobilenet_v2_coco\tf\ssd_mobilenet_v2_coco.config"
--data_type FP16
--log_level DEBUG
For more details, refer to the link https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_Object_Detection_API_Models.html
Hope it helps.
来源：https://stackoverflow.com/questions/57145603/tensorflow-openvino-ssd-mobilnet-coco-custom-dataset-error-input-layer