Installation Instructions
- Ensure a successful installation OpenVINO, it is the best version later 2017R3. As used herein version 2018R4
- Be sure to install tensorflow, this article tensorflow to compile the source code of tensorflow-gpu-1.10 (the latest version of the compiler tensorflow error)
- ubuntu16.0.4 far OpenVINO does not support ubuntu18.0.4
Download ssdv2 version of the archive target detection training
This article addresses the model download ssd download link , download the rest of the model has not been tested in a subsequent test Google Model Zone model is feasible.
Generating intermediate xml file
It should be noted that, Intel is the Official Guide to ./mo_tf.py here should be wrong (after mo_tf.py to add execute permissions might be, did not try, in addition to direct python3 mo_tf.py --input_model ssd_inception_v2_coco_2018_01_28 / frozen_inference_graph. pb will complain, plus input_shape error will only use json + pipeline.config currently not being given)
python3 mo_tf.py --input_model ssd_inception_v2_coco_2018_01_28/frozen_inference_graph.pb --output=detection_boxes,detection_scores,num_detections --tensorflow_use_custom_operations_config /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/ssd_inception_v2_coco_2018_01_28/pipeline.config
Output is as follows:
Model Optimizer arguments:
Common parameters:
- Path to the Input Model: /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/ssd_inception_v2_coco_2018_01_28/frozen_inference_graph.pb
- Path for generated IR: /home/amax/intel/computer_vision_sdk_2018.4.420/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: detection_boxes,detection_scores,num_detections
- 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
- Offload unsupported operations: 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: /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/ssd_inception_v2_coco_2018_01_28/pipeline.config
- Operations to offload: None
- Patterns to offload: None
- Use the config file: /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json
Model Optimizer version: 1.4.292.6ef7232d
/home/amax/anaconda3/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept.
[ SUCCESS ] Generated IR model.
[ SUCCESS ] XML file: /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/./frozen_inference_graph.xml
[ SUCCESS ] BIN file: /home/amax/intel/computer_vision_sdk_2018.4.420/deployment_tools/model_optimizer/./frozen_inference_graph.bin
[ SUCCESS ] Total execution time: 20.20 seconds.
The generated files in the current directory:
- frozen_inference_graph.xml
- frozen_inference_graph.bin
- frozen_inference_graph.mapping
generated downloaded files can be downloaded here:
Using an intermediate file reasoning
Run the script as follows:
Note: The input image may be output in jpeg format but bmp format.
ssd_bin=/home/amax/inference_engine_samples_build/intel64/Release/object_detection_sample_ssd
network=/home/amax/intel/computer_vision_sdk/deployment_tools/ssd_detect/frozen_inference_graph.xml
${ssd_bin} -i example.bmp -m ${network} -d CPU
Output:
[ INFO ] InferenceEngine:
API version ............ 1.4
Build .................. 17328
Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] example.bmp
[ INFO ] Loading plugin
API version ............ 1.4
Build .................. lnx_20181004
Description ....... MKLDNNPlugin
[ INFO ] Loading network files:
/home/amax/intel/computer_vision_sdk/deployment_tools/ssd_detect/frozen_inference_graph.xml
/home/amax/intel/computer_vision_sdk/deployment_tools/ssd_detect/frozen_inference_graph.bin
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the plugin
[ WARNING ] Image is resized from (640, 747) to (300, 300)
[ INFO ] Batch size is 1
[ INFO ] Start inference (1 iterations)
[ INFO ] Processing output blobs
[0,1] element, prob = 0.912171 (28.3366,4.0617)-(640,743.86) batch id : 0 WILL BE PRINTED!
[ INFO ] Image out_0.bmp created!
total inference time: 28.7588
Average running time of one iteration: 28.7588 ms
Throughput: 34.772 FPS
[ INFO ] Execution successful
Original:
output
Testing by model zone
