A benchmarking tool under development. Current it supports running wasm and webgl backends with profiling for tfjs and ort-web frameworks.
Change the package version for onnxruntime-web in package.json to point to either an official release or a local build.
i.e. if using local build and onnxruntime's base folder is ~/onnxruntime, set onnxruntime-web dependency in package.json to:
"onnxruntime-web": "file:~/onnxruntime/js/web".
Note that all of the .wasm libraries including threaded and simd.threaded files must exist in the dist folder(js/web/dist) of ort-web.
If using official release, simply set it with a version number.
In the root directory, run npm install to install all required packages.
To build the bundle, run npm run build in the root directory.
To start benchmarking, run npm run benchmark. Users need to provide a runtime configuration file that contains all parameters. By default, it looks for run_config.json in the root folder. Some sample configuration files are provided under /sample-configs. To pass a custom config, run npm run benchmark --run_config=path_to_config. In example,
npm run benchmark --run_config=./sample-configs/run_config_softmax_test.json
Profiling data can be generated using npm run profile command. It will create a chrome-trace event file named trace.json in the root folder. This file can be loaded into chrome://tracing for visualization. Note that model files must exist under the data/ directory in the root. The browser doesn't have access to local file system so files need to be uploaded to the local server when benchmark starts up. All content under data/ will be served.
This tool automatically generates synthetic input data referring to a model input shape. If a shape is not a fixed dimension except batch size, a hint by 'config.ortweb.shape' or 'config.tfjs.shape' can be added into configuration as follows,
"ortweb": {
"shape": {
"images:0": [1, 224, 224, 3]
},
Only .onnx models are supported as of now. Set config.ortweb.path to the file path.
Tensorflow.js models can be either a layers model or a graph model with model.json and model.bin -> This is the official way provided by TFJS project to load models. It will use tf.LoadLayeredModel or tf.LoadGraphModel to load the model structure and weights. Set config.tfjs.path to the path of model.json. The bin file which contains the weights should be in the same directory as model.json.