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openvino/tools/pot/configs/sparsity_default_quantization_template.json
Alexander Kozlov bbeec714aa Moved Post-training Optimization Tool to open-source (#7940)
* Moved POT to opensource

* Added OMZ as a submodule

* Exclude OMZ from ShellCheck
2021-10-15 16:35:35 +03:00

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/* This configuration file is the fastest way to get started with the default
sparsity and default quantization algorithm. It contains only mandatory options
with commonly used values. All other options can be considered as an advanced
mode and requires deep knowledge of the quantization process. An overall description
of all possible parameters can be found in the default_quantization_spec.json */
{
/* Model parameters */
"model": {
"model_name": "model_name", // Model name
"model": "<MODEL_PATH>", // Path to model (.xml format)
"weights": "<PATH_TO_WEIGHTS>" // Path to weights (.bin format)
},
/* Parameters of the engine used for model inference */
"engine": {
"config": "<CONFIG_PATH>" // Path to Accuracy Checker config
},
/* Optimization hyperparameters */
"compression": {
"target_device": "ANY", // Target device, the specificity of which will be taken
// into account during optimization
"algorithms": [
{
"name": "WeightSparsity",
"params": {
"sparsity_level": 0.3,
"stat_subset_size": 300 // Size of subset to calculate activations statistics that can be used
// for quantization parameters calculation
}
},
{
"name": "DefaultQuantization", // Optimization algorithm name
"params": {
// Preset [performance, mixed, accuracy] which control the quantization mode
// (symmetric, mixed (weights symmetric and activations asymmetric) and fully
// asymmetric respectively)
"preset": "performance",
"stat_subset_size": 300 // Size of subset to calculate activations statistics that can be used
// for quantization parameters calculation
}
}
]
}
}