From 35b7dd465152142132dd05ff9b4b438ae8a918cf Mon Sep 17 00:00:00 2001
From: xiaoyisd <wuyichao4@huawei.com>
Date: Wed, 2 Dec 2020 09:21:34 +0800
Subject: [PATCH] fix mobilenetv2_quant gpu bug

---
 model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh | 4 ++--
 model_zoo/official/cv/mobilenetv2_quant/train.py             | 3 +--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh b/model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh
index 79c929402d..67e82eaacf 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh
+++ b/model_zoo/official/cv/mobilenetv2_quant/scripts/run_train.sh
@@ -133,7 +133,7 @@ run_ascend(){
 }
 
 run_gpu(){
-    if [ $# -gt 3 ] || [ $# -lt 2 ]
+    if [ $# -gt 4 ] || [ $# -lt 3 ]
     then
         echo "Usage:  bash run_train.sh  [GPU] [DEVICE_ID_LIST] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)\n "
         exit 1
@@ -182,7 +182,7 @@ run_gpu(){
     if [ $# == 4 ]
     then
         mpirun --allow-run-as-root -n ${RANK_SIZE} --output-filename log_output --merge-stderr-to-stdout \
-        python train.py --device_traget=$1  --dataset_path=$PATH1 --pre_trained=$PATH2 &> train.log &
+        python train.py --device_target=$1  --dataset_path=$PATH1 --pre_trained=$PATH2 &> train.log &
     fi
 
     cd ..
diff --git a/model_zoo/official/cv/mobilenetv2_quant/train.py b/model_zoo/official/cv/mobilenetv2_quant/train.py
index 20f3007022..9d883cf431 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/train.py
+++ b/model_zoo/official/cv/mobilenetv2_quant/train.py
@@ -166,8 +166,7 @@ def train_on_gpu():
                                           per_channel=[True, False],
                                           symmetric=[True, False],
                                           freeze_bn=1000000,
-                                          quant_delay=step_size * 2,
-                                          use_oneconv_fold=True)
+                                          quant_delay=step_size * 2)
     network = quantizer.quantize(network)
 
     # get learning rate