classification sample asyncで動作を学ぶ
Openvino toolkitをインストールした際にデモ実行するclassification sample asyncで詳しく見てみます
実行環境
CPU: Intel(R) Core(TM) i7-6770HQ CPU @ 2.60GHz
MemTotal: 16318440 kB
OS: Ubuntu 16.04LTS
スクリプトを見る
通常は/opt/intel/openvino/deployment_tools/demo 内に格納されている、demo_squeezenet_download_convert_run.shの動作を確認します
スクリプトは５つのパートに分かれています
# Step 1. Download the Caffe model and the prototxt of the model
ビルド環境をaptでダウンロードします
そのため、sudoコマンドが走りますので権限が必要です
# Step 2. Configure Model Optimizer
caffeをダウンロードします
# Step 3. Convert a model with Model Optimizer
このステップでモデルを変換します
# Step 4. Build samples
ホームディレクトリにinference_engine_samples_buildと言うディレクトリを作成して、実行ファイルを作成します
# Step 5. Run samples
実際に実行する部分です
./classification_sample_async -d "$target" -i "$target_image_path" -m "${ir_dir}/${model_name}.xml" ${sampleoptions}
プログラムの実行
実行部分だけを抜き出したプログラムで実行してみましょう
inference_engine_samples_build/intel64/Release/classification_sample_async -h [ INFO ] InferenceEngine: API version ............ 2.1 Build .................. 37988 Description ....... API [ INFO ] Parsing input parameters classification_sample_async [OPTION] Options: -h Print a usage message. -i "<path>" Required. Path to a folder with images or path to an image files: a .ubyte file for LeNetand a .bmp file for the other networks. -m "<path>" Required. Path to an .xml file with a trained model. -l "<absolute_path>" Required for CPU custom layers.Absolute path to a shared library with the kernels implementation Or -c "<absolute_path>" Required for GPU custom kernels.Absolute path to the .xml file with kernels description -d "<device>" Optional. Specify the target device to infer on (the list of available devices is shown below). Default value is CPU. Sample will look for a suitable plugin for device specified. -nt "<integer>" Optional. Number of top results. Default value is 10. -p_msg Optional. Enables messages from a plugin Available target devices: CPU GPU MYRIAD
ターゲットデバイスはCPU、GPU、MYRIAD(NCS2)が使用可能です
-dオプションによって設定可能です
プログラムの実行
inference_engine_samples_build/intel64/Release/classification_sample_async -d CPU -i /opt/intel/openvino/deployment_tools/demo/car.png -m /home/klf/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml [ INFO ] InferenceEngine: API version ............ 2.1 Build .................. 37988 Description ....... API [ INFO ] Parsing input parameters [ INFO ] Parsing input parameters [ INFO ] Files were added: 1 [ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png [ INFO ] Creating Inference Engine CPU MKLDNNPlugin version ......... 2.1 Build ........... 37988 [ INFO ] Loading network files [ INFO ] Preparing input blobs [ WARNING ] Image is resized from (787, 259) to (227, 227) [ INFO ] Batch size is 1 [ INFO ] Loading model to the device [ INFO ] Create infer request [ INFO ] Start inference (10 asynchronous executions) [ INFO ] Completed 1 async request execution [ INFO ] Completed 2 async request execution [ INFO ] Completed 3 async request execution [ INFO ] Completed 4 async request execution [ INFO ] Completed 5 async request execution [ INFO ] Completed 6 async request execution [ INFO ] Completed 7 async request execution [ INFO ] Completed 8 async request execution [ INFO ] Completed 9 async request execution [ INFO ] Completed 10 async request execution [ INFO ] Processing output blobs Top 10 results: Image /opt/intel/openvino/deployment_tools/demo/car.png classid probability label ------- ----------- ----- 817 0.6853030 sports car, sport car 479 0.1835197 car wheel 511 0.0917197 convertible 436 0.0200694 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 751 0.0069604 racer, race car, racing car 656 0.0044177 minivan 717 0.0024739 pickup, pickup truck 581 0.0017788 grille, radiator grille 468 0.0013083 cab, hack, taxi, taxicab 661 0.0007443 Model T [ INFO ] Execution successful [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool inference_engine_samples_build/intel64/Release/classification_sample_async -d CPU -i /opt/intel/openvino/deployment_tools/demo/car.png -m /home/klf/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml [ INFO ] InferenceEngine: API version ............ 2.1 Build .................. 37988 Description ....... API [ INFO ] Parsing input parameters [ INFO ] Parsing input parameters [ INFO ] Files were added: 1 [ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png [ INFO ] Creating Inference Engine MYRIAD myriadPlugin version ......... 2.1 Build ........... 37988 [ INFO ] Loading network files [ INFO ] Preparing input blobs [ WARNING ] Image is resized from (787, 259) to (227, 227) [ INFO ] Batch size is 1 [ INFO ] Loading model to the device [ INFO ] Create infer request [ INFO ] Start inference (10 asynchronous executions) [ INFO ] Completed 1 async request execution [ INFO ] Completed 2 async request execution [ INFO ] Completed 3 async request execution [ INFO ] Completed 4 async request execution [ INFO ] Completed 5 async request execution [ INFO ] Completed 6 async request execution [ INFO ] Completed 7 async request execution [ INFO ] Completed 8 async request execution [ INFO ] Completed 9 async request execution [ INFO ] Completed 10 async request execution [ INFO ] Processing output blobs Top 10 results: Image /opt/intel/openvino/deployment_tools/demo/car.png classid probability label ------- ----------- ----- 817 0.6708984 sports car, sport car 479 0.1922607 car wheel 511 0.0936890 convertible 436 0.0216064 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 751 0.0075760 racer, race car, racing car 656 0.0049667 minivan 717 0.0027428 pickup, pickup truck 581 0.0019779 grille, radiator grille 468 0.0014219 cab, hack, taxi, taxicab 661 0.0008636 Model T [ INFO ] Execution successful [ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
67%の確率でsports car, sport carと認識されています
ラベルに関しては、
/opt/intel/openvino/deployment_tools/demo/squeezenet1.1.labels
で定義されています
産業用画像処理装置開発、
ゲームコンソール開発、半導体エンジニアなどを経て、
Webエンジニア＆マーケティングをやっています
好きな分野はハードウェアとソフトウェアの境界くらい
