From 3d617e4ff6419169d05c317eb9271236b1b0fb71 Mon Sep 17 00:00:00 2001 From: bai-yangfan Date: Fri, 9 Oct 2020 19:29:58 +0800 Subject: [PATCH] weight_export_v2 --- mindspore/train/quant/quant.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/mindspore/train/quant/quant.py b/mindspore/train/quant/quant.py index 78ffc00f94..6089cc7ba4 100644 --- a/mindspore/train/quant/quant.py +++ b/mindspore/train/quant/quant.py @@ -358,7 +358,7 @@ class ExportToQuantInferNetwork: param_dict["std_dev"] = self.std_dev param_dict["symmetric"] = fake_quant_a_out.symmetric - scale_w, zp_w, _, _ = \ + scale_w, zp_w, param_dict["filter_maxq"], param_dict["filter_minq"] = \ quant_utils.scale_zp_max_min_from_fake_quant_cell(cell_core.fake_quant_weight, np_type) scale_a_out, _, param_dict["output_maxq"], param_dict["output_minq"] = \ quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_a_out, np_type) @@ -401,9 +401,6 @@ class ExportToQuantInferNetwork: weight, bias = quant_utils.fold_batchnorm(weight, cell_core) elif isinstance(cell_core, quant.Conv2dBnWithoutFoldQuant): weight, bias = quant_utils.without_fold_batchnorm(weight, cell_core) - if self.is_mindir: - param_dict["filter_maxq"], param_dict["filter_minq"] = cell_core.fake_quant_weight.maxq, \ - cell_core.fake_quant_weight.minq weight_b = weight bias_b = bias # apply the quant