accuracy_based_auto_calibration

import amct_caffe as amct 
from amct_caffe.common.auto_calibration import AutoCalibrationEvaluatorBase
from amct_caffe.common.auto_calibration import BinarySearchStrategy
from amct_caffe.common.auto_calibration import CosineSimilaritySensitivity
class AutoCalibrationEvaluator(AutoCalibrationEvaluatorBase):
    def __init__(self):
        """
            evaluate_batch_num is the needed batch num for evaluating
            the model. Larger evaluate_batch_num is recommended, because
            the evaluation metric of input model can be more precise
            with larger eval dataset.
        """
        super().__init__()
 
    def calibration(self, model_file, weights_file):
        """"
        Function:
            do the calibration with model
        Parameter:
            model_file: the prototxt model define file of caffe model
            weights_file: the binary caffemodel file of caffe model
        """
        run_caffe_model(args, model_file, weights_file, CALIBRATION_BATCH_NUM)
 
    def evaluate(self, model_file, weights_file):
        """"
        Function:
            evaluate the model with batch_num of data, return the eval
            metric of the input model, such as top1 for classification
            model, mAP for detection model and so on.
        Parameter:
            model_file: the prototxt model define file of caffe model
            weights_file: the binary caffemodel file of caffe model
        """
        return do_benchmark_test(args, model_file, weights_file, args.iterations)
 
    def metric_eval(self, original_metric, new_metric):
        """
        Function:
            whether the metric of new fake quant model can satisfy the
            requirement
        Parameter:
            original_metric: the metric of non quantized model
            new_metric: the metric of new quantized model
        """
        # the loss of top1 acc need to be less than 0.2%
        loss = original_metric - new_metric
        if loss * 100 < 0.2:
            return True, loss
        return False, loss
 
    # step 1: create the quant config file
    config_json_file = './config.json'
    skip_layers = []
    batch_num = CALIBRATION_BATCH_NUM
    activation_offset = True
    amct.create_quant_config(config_json_file, model_file, weights_file,
                        skip_layers, batch_num, activation_offset)
 
    scale_offset_record_file = os.path.join(TMP, 'scale_offset_record.txt')
    result_path = os.path.join(RESULT, 'MobileNetV2')
    evaluator = AutoCalibrationEvaluator()
 
    # step 2: start the accuracy_based_auto_calibration process
    amct.accuracy_based_auto_calibration(
        args.model_file,
        args.weights_file,
        evaluator,
        config_json_file,
        scale_offset_record_file,
        result_path)