1. remove unused variable in resnet.py

2. add relative path support in resnet50 example
3. optimize allreuce split strategy

example/resnet50_cifar10/run_distribute_train.sh

@@ -20,22 +20,33 @@ then
 exit 1
 fi
 
-if [ ! -f $1 ]
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+PATH2=$(get_real_path $2)
+
+if [ ! -f "$PATH1" ]
 then 
-    echo "error: DMINDSPORE_HCCL_CONFIG_PATH=$1 is not a file"
+    echo "error: MINDSPORE_HCCL_CONFIG_PATH=$PATH1 is not a file"
 exit 1
 fi 
 
-if [ ! -d $2 ]
+if [ ! -d "$PATH2" ]
 then 
-    echo "error: DATASET_PATH=$2 is not a directory"
+    echo "error: DATASET_PATH=$PATH2 is not a directory"
 exit 1
 fi 
 
 ulimit -u unlimited
 export DEVICE_NUM=8
 export RANK_SIZE=8
-export MINDSPORE_HCCL_CONFIG_PATH=$1
+export MINDSPORE_HCCL_CONFIG_PATH=$PATH1
 
 for((i=0; i<${DEVICE_NUM}; i++))
 do
@@ -48,6 +59,6 @@ do
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
-    python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$2 &> log &
+    python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
     cd ..
 done

example/resnet50_cifar10/run_standalone_train.sh

@@ -20,9 +20,19 @@ then
 exit 1
 fi
 
-if [ ! -d $1 ]
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -d "$PATH1" ]
 then 
-    echo "error: DATASET_PATH=$1 is not a directory"
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
 exit 1
 fi 
 
@@ -41,5 +51,5 @@ cp *.sh ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
-python train.py --do_train=True --dataset_path=$1 &> log &
+python train.py --do_train=True --dataset_path=$PATH1 &> log &
 cd ..

example/resnet50_cifar10/train.py

@@ -57,12 +57,12 @@ if __name__ == '__main__':
     if not args_opt.do_eval and args_opt.run_distribute:
         context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           mirror_mean=True)
-        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
         init()
 
     epoch_size = config.epoch_size
     net = resnet50(class_num=config.class_num)
-    loss = SoftmaxCrossEntropyWithLogits(sparse=True)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 
 
     if args_opt.do_train:

mindspore/model_zoo/resnet.py

@@ -168,7 +168,7 @@ class ResNet(nn.Cell):
         self.conv1 = _conv7x7(3, 64, stride=2)
         self.bn1 = _bn(64)
         self.relu = P.ReLU()
-        self.maxpool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
 
         self.layer1 = self._make_layer(block,
                                        layer_nums[0],
@@ -227,7 +227,7 @@ class ResNet(nn.Cell):
         x = self.conv1(x)
         x = self.bn1(x)
         x = self.relu(x)
-        c1, argmax = self.maxpool(x)
+        c1 = self.maxpool(x)
 
         c2 = self.layer1(c1)
         c3 = self.layer2(c2)