Created by: zyxcambridge

https://github.com/paddlepaddle/paddle/blob/develop/python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py

314行

def _calculate_scale_factor(self):
    '''
    Calculate the scale factor of quantized variables.
    '''
    # apply channel_wise_abs_max quantization for weights
    for var_name in self._quantized_weight_var_name:
        data = self._sampling_data[var_name]
        scale_factor_per_channel = []
        for i in range(data.shape[0]):
            abs_max_value = np.max(np.abs(data[i]))
            scale_factor_per_channel.append(abs_max_value)
        self._quantized_var_scale_factor[
            var_name] = scale_factor_per_channel

    # apply kl quantization for activation
    if self._is_use_cache_file:
        for var_name in self._quantized_act_var_name:
            sampling_data = []
            filenames = [f for f in os.listdir(self._cache_dir) \
                if re.match(var_name + '_[0-9]+.npy', f)]
            for filename in filenames:
                file_path = os.path.join(self._cache_dir, filename)
                sampling_data.append(np.load(file_path))
                os.remove(file_path)
            sampling_data = np.concatenate(sampling_data)

            if self._algo == "KL":
                self._quantized_var_scale_factor[var_name] = \
                    self._get_kl_scaling_factor(np.abs(sampling_data))
            else:
                self._quantized_var_scale_factor[var_name] = \
                    np.max(np.abs(sampling_data))
    else:
        for var_name in self._quantized_act_var_name:
            self._sampling_data[var_name] = np.concatenate(
                self._sampling_data[var_name])
            if self._algo == "KL":
                self._quantized_var_scale_factor[var_name] = \
                    self._get_kl_scaling_factor(np.abs(self._sampling_data[var_name]))
            else:
                self._quantized_var_scale_factor[var_name] = \
                    np.max(np.abs(self._sampling_data[var_name]))

超过200张图片后，yolov3 离线量化，第三层，featuremap 也就是 sampling_data 总数据量会达到100多个G，直接回卡死电脑、