bai-yangfan
5 years ago
1 changed files with 2 additions and 9 deletions
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+ 2
- 9
mindspore/train/quant/quant.py
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| @@ -342,14 +342,6 @@ class ExportToQuantInferNetwork: | |||||
| network = self._convert_quant2deploy(network) | network = self._convert_quant2deploy(network) | ||||
| return network | return network | ||||
| def statistic_weight(self, weight): | |||||
| out_nums = np.shape(weight)[0] | |||||
| sta_metric = np.zeros((out_nums, 2), dtype=np.float32) | |||||
| for num in range(out_nums): | |||||
| sta_metric[num, 0] = np.min(weight[num]) | |||||
| sta_metric[num, 1] = np.max(weight[num]) | |||||
| return np.mean(sta_metric[:, 1]).tolist(), np.mean(sta_metric[:, 0]).tolist() | |||||
| def _get_quant_block(self, cell_core, activation, fake_quant_a_out): | def _get_quant_block(self, cell_core, activation, fake_quant_a_out): | ||||
| """convet network's quant subcell to deploy subcell""" | """convet network's quant subcell to deploy subcell""" | ||||
| # Calculate the scale and zero point | # Calculate the scale and zero point | ||||
| @@ -410,7 +402,8 @@ class ExportToQuantInferNetwork: | |||||
| elif isinstance(cell_core, quant.Conv2dBnWithoutFoldQuant): | elif isinstance(cell_core, quant.Conv2dBnWithoutFoldQuant): | ||||
| weight, bias = quant_utils.without_fold_batchnorm(weight, cell_core) | weight, bias = quant_utils.without_fold_batchnorm(weight, cell_core) | ||||
| if self.is_mindir: | if self.is_mindir: | ||||
| param_dict["filter_maxq"], param_dict["filter_minq"] = self.statistic_weight(weight) | |||||
| param_dict["filter_maxq"], param_dict["filter_minq"] = cell_core.fake_quant_weight.maxq, \ | |||||
| cell_core.fake_quant_weight.minq | |||||
| weight_b = weight | weight_b = weight | ||||
| bias_b = bias | bias_b = bias | ||||
| # apply the quant | # apply the quant | ||||