add squeezenet cpu scripts

model_zoo/official/cv/squeezenet/README.md

@@ -62,7 +62,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 
 # [Environment Requirements](#contents)
 
-- Hardware（Ascend）
+- Hardware（Ascend/CPU）
     - Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. Squeezenet training on GPU performs badly now, and it is still in research. See [squeezenet in research](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/squeezenet) to get up-to-date details.
 - Framework
     - [MindSpore](https://www.mindspore.cn/install/en)
@@ -74,7 +74,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 
 After installing MindSpore via the official website, you can start training and evaluation as follows:
 
-- runing on Ascend
+- running on Ascend
 
   ```bash
   # distributed training
@@ -87,6 +87,16 @@ After installing MindSpore via the official website, you can start training and
   Usage: sh scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
   ```
 
+- running on CPU
+
+  ```bash
+  # standalone training
+  Usage: bash scripts/run_train_cpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
+
+  # run evaluation example
+  Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [CHECKPOINT_PATH]
+  ```
+
 # [Script Description](#contents)
 
 ## [Script and Sample Code](#contents)

model_zoo/official/cv/squeezenet/eval.py

@@ -54,7 +54,8 @@ if __name__ == '__main__':
     target = args_opt.device_target
 
     # init context
-    device_id = int(os.getenv('DEVICE_ID'))
+    device_id = os.getenv('DEVICE_ID')
+    device_id = int(device_id) if device_id else 0
     context.set_context(mode=context.GRAPH_MODE,
                         device_target=target,
                         device_id=device_id)

model_zoo/official/cv/squeezenet/scripts/run_eval_cpu.sh

@@ -0,0 +1,70 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 4 ]
+then 
+    echo "Usage: bash scripts/run_eval.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
+then 
+    echo "error: the selected net is neither squeezenet nor squeezenet_residual"
+exit 1
+fi
+
+if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
+then 
+    echo "error: the selected dataset is neither cifar10 nor imagenet"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $3)
+PATH2=$(get_real_path $4)
+
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi 
+
+if [ ! -f $PATH2 ]
+then 
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi 
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ./eval.py ./eval
+cp -r ./src ./eval
+cd ./eval || exit
+env > env.log
+echo "start evaluation for device CPU"
+python eval.py --net=$1 --dataset=$2 --device_target=CPU --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

model_zoo/official/cv/squeezenet/scripts/run_train_cpu.sh

@@ -0,0 +1,81 @@
+#!/bin/bash
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+if [ $# != 3 ] && [ $# != 4 ]
+then 
+    echo "Usage: bash scripts/run_train_cpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
+exit 1
+fi
+
+if [ $1 != "squeezenet" ] && [ $1 != "squeezenet_residual" ]
+then 
+    echo "error: the selected net is neither squeezenet nor squeezenet_residual"
+exit 1
+fi
+
+if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
+then 
+    echo "error: the selected dataset is neither cifar10 nor imagenet"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $3)
+
+if [ $# == 4 ]
+then
+    PATH2=$(get_real_path $4)
+fi
+
+if [ ! -d $PATH1 ]
+then 
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ $# == 4 ] && [ ! -f $PATH2 ]
+then
+    echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ./train.py ./train
+cp -r ./src ./train
+cd ./train || exit
+echo "start training for device CPU"
+env > env.log
+if [ $# == 3 ]
+then
+    python train.py --net=$1 --dataset=$2 --device_target=CPU --dataset_path=$PATH1 &> log &
+fi
+
+if [ $# == 4 ]
+then
+    python train.py --net=$1 --dataset=$2 --device_target=CPU --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
+fi
+cd ..

model_zoo/official/cv/squeezenet/src/dataset.py

@@ -42,6 +42,8 @@ def create_dataset_cifar(dataset_path,
     """
     if target == "Ascend":
         device_num, rank_id = _get_rank_info()
+    elif target == "CPU":
+        device_num = 1
     else:
         init()
         rank_id = get_rank()

model_zoo/official/cv/squeezenet/train.py

@@ -144,9 +144,10 @@ if __name__ == '__main__':
                       amp_level="O2",
                       keep_batchnorm_fp32=False)
     else:
-        # GPU target
-        print("Squeezenet training on GPU performs badly now, and it is still in research..."
-              "See model_zoo/research/cv/squeezenet to get up-to-date details.")
+        if target == "GPU":
+            # GPU target
+            print("Squeezenet training on GPU performs badly now, and it is still in research..."
+                  "See model_zoo/research/cv/squeezenet to get up-to-date details.")
         opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
                        lr,
                        config.momentum,