From 99bea145a050e12f1b5f8301979713d9a9b04e12 Mon Sep 17 00:00:00 2001
From: Kevin Cheng <kevin.cheng@arm.com>
Date: Mon, 19 Oct 2020 12:35:05 -0700
Subject: Update apply_scale_32()

Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Ida8e3a17d74e5d6379b2244896ddf9e295d0ecc9
---
 reference_model/src/ops/tensor_ops.cc      | 4 ++--
 reference_model/src/ops/type_conversion.cc | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

(limited to 'reference_model/src/ops')

diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc
index a735334..82ce3d2 100644
--- a/reference_model/src/ops/tensor_ops.cc
+++ b/reference_model/src/ops/tensor_ops.cc
@@ -268,9 +268,9 @@ int OpAvgPool2d<Dtype>::eval()
     {
         this->out->getTensor() = sum.binaryExpr(div_map, [](AccEigenType value, int32_t div) -> OutEigenType {
             int32_t multiplier, shift;
-            TosaReference::QuantUtil<AccDtype>::reciprocal_scale(div, multiplier, shift);
+            TosaReference::QuantUtil::reciprocal_scale(div, multiplier, shift);
 
-            return (OutEigenType)TosaReference::QuantUtil<AccDtype>::apply_scale(value, multiplier, shift, false);
+            return (OutEigenType)TosaReference::QuantUtil::apply_scale_32(value, multiplier, shift, false);
         });
         this->out->getTensor() = this->out->getTensor() + (OutEigenType)(this->qinfo->output_zp());
         this->out->getTensor() = this->out->getTensor().cwiseMax((OutEigenType)QMin);
diff --git a/reference_model/src/ops/type_conversion.cc b/reference_model/src/ops/type_conversion.cc
index 61a19f4..a97bc0d 100644
--- a/reference_model/src/ops/type_conversion.cc
+++ b/reference_model/src/ops/type_conversion.cc
@@ -130,7 +130,7 @@ int OpRescale<Rank, InDtype, OutDtype>::eval()
                 curr_channel_slice_prescaled.unaryExpr([input_zp, output_zp, channel_multiplier, channel_shift,
                                                         double_round](InEigenType in_val) -> OutEigenType {
                     InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
-                    int32_t scaled               = TosaReference::QuantUtil<InDtype>::apply_scale(
+                    int32_t scaled               = TosaReference::QuantUtil::apply_scale_32(
                         input_zp_shifted, channel_multiplier, channel_shift, double_round);
                     OutEigenType out_val = (OutEigenType)(scaled + output_zp);
                     out_val              = std::max<OutEigenType>(out_val, QMin);
@@ -151,8 +151,8 @@ int OpRescale<Rank, InDtype, OutDtype>::eval()
         output_2d                 = input_reshaped.unaryExpr(
             [input_zp, output_zp, tensor_multiplier, tensor_shift, double_round](InEigenType in_val) -> OutEigenType {
                 InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
-                int32_t scaled = TosaReference::QuantUtil<InDtype>::apply_scale(input_zp_shifted, tensor_multiplier,
-                                                                                tensor_shift, double_round);
+                int32_t scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, tensor_multiplier,
+                                                                          tensor_shift, double_round);
                 OutEigenType out_val = (OutEigenType)(scaled + output_zp);
                 out_val              = std::max<OutEigenType>(out_val, QMin);
                 out_val              = std::min<OutEigenType>(out_val, QMax);
-- 
cgit v1.2.1