183 lines
8.0 KiB
JSON
183 lines
8.0 KiB
JSON
{
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/* Model parameters */
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"model": {
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"model_name": "model_name", // Model name
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"model": "<MODEL_PATH>", // Path to model (.xml format)
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"weights": "<PATH_TO_WEIGHTS>" // Path to weights (.bin format)
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},
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/* Parameters of the engine used for model inference */
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// Post-Training Optimization Tool supports engine based on accuracy checker and custom engine.
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// For custom engine you should specify your own set of parameters.
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// The engine based on accuracy checker uses accuracy checker parameters.
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// You can specify the parameters via accuracy checker config file or directly in engine section.
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// More information about accuracy checker parameters can be found here:
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// https://github.com/opencv/open_model_zoo/tree/master/tools/accuracy_checker
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"engine": {
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/* Accuracy checker mode (default) */
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"stat_requests_number": 8, // Number of requests during statistcs collection
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"eval_requests_number": 8, // Number of requests during evaluation
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"config": "<CONFIG_PATH>",
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/* OR */
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"name": "model_name",
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"launchers": [
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{
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"framework": "dlsdk",
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"device": "CPU",
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"adapter": "classification"
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}
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],
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"datasets": [
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{
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"name": "dataset_name",
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"data_source": "<DATASET_PATH>",
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"annotation": "<ANNOTATION_PATH>",
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"preprocessing": [
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{
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"type": "resize",
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"interpolation": "BILINEAR",
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"aspect_ratio_scale": "greater",
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"size": 224
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}
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],
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"metrics": [
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{
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"name": "accuracy@top1",
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"type": "accuracy",
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"top_k": 1
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}
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]
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}
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],
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/* OR */
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/* Simplified mode */
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"type": "simplified", // OR default value "type": "accuracy_checker" for non simplified mode
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"data_source": "PATH_TO_SOURCE" // You can specify path to directory with images. Also you can
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// specify template for file names to filter images to load.
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// Templates are unix style (This option valid only in simplified mode)
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},
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/* Optimization hyperparameters */
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"compression": {
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"target_device": "ANY", // Target device, the specificity of which will be taken
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// into account during optimization
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"model_type": "None", // An optional parameter, needed for additional patterns in the model,
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// default value is None (supported only transformer now)
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"dump_intermediate_model": false, // Save intermediate models for DefaultAlgorithm
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"inplace_statistics": true, // An optional parameter, change method collect statistics,
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// reduces the amount of memory consumed,
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// but increases the calibration time
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// the default value is true
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"algorithms": [
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{
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"name": "DefaultQuantization", // Optimization algorithm name
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"params": {
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"ignored": {
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// List of nodes that are excluded from optimization
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"scope": [
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"<NODE_NAME>"
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],
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// List of types that are excluded from optimization
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"operations": [
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{
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"type": "<NODE_TYPE>",
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// Includes excluding by attributes
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"attributes": {
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"<NAME>": "<VALUE>" // Lists of values is not included
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}
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},
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{
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"type": "<NODE_TYPE>" // Excluding only by type
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}
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]
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},
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"preset": "mixed", // A preset is a collection of optimization algorithm parameters
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// that will specify to the algorithm to improve which metric
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// the algorithm needs to concentrate. Each optimization algorithm
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// supports [performance, mixed, accuracy] presets
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"stat_subset_size": 100, // Size of subset to calculate activations statistics that can be used
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// for quantization parameters calculation
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"shuffle_data": false, // Shuffle data before selecting the subset to calculate activation
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// statistics. An optional parameter, the default value is false
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"seed": 0, // Seed for data shuffle. An optional parameter, the default value is 0
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/* Manually specification quantization parametrs */
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/* Quantization parameters for weights */
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"weights": {
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"bits": 8, // Number of quantization bits
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"mode": "symmetric", // Quantization mode
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"granularity": "perchannel", // Granularity: a scale for each output channel
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"level_low": -127, // Low quantization level
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"level_high": 127, // High quantization level
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/* Parameters specify how to calculate the minimum and maximum of quantization range */
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"range_estimator": {
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"max": {
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"type": "quantile",
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"outlier_prob": 0.0001
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}
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}
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},
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/* Quantization parameters for activations */
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"activations": {
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"bits": 8, // Number of quantization bits
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"mode": "symmetric", // Quantization mode
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"granularity": "pertensor", // Granularity: one scale for output tensor
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/* Parameters specify how to calculate the minimum and maximum of quantization range */
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"range_estimator": {
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"preset": "quantile",
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/* OR */
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/* Minimum of quantization range */
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"min": {
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"clipping_value": 0, // Threshold for min statistic value clipping (lower bound)
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"aggregator": "mean", // Batch aggregation type [mean, max, min, median,
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// mean_no_outliers, median_no_outliers, hl_estimator]
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"type": "quantile", // Estimator type [min, max, abs_max, quantile, abs_quantile]
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"outlier_prob": 0.0001 // Outlier probability: estimator consider samples which
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},
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/* Maximum of quantization range */
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"max": {
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"clipping_value": 6, // Threshold for max statistic value clipping (upper bound)
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"aggregator": "mean", // Batch aggregation type [mean, max, min, median,
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// mean_no_outliers, median_no_outliers, hl_estimator]
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"type": "quantile", // Estimator type [min, max, abs_max, quantile, abs_quantile]
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"outlier_prob": 0.0001 // Outlier probability: estimator consider samples which
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}
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}
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}
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}
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}
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]
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}
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}
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