From f9bebb1bc11c5f368defc80d6238fe1533c3d6b6 Mon Sep 17 00:00:00 2001
From: lilei <lilei120@huawei.com>
Date: Tue, 8 Jun 2021 10:01:18 +0800
Subject: [PATCH] modify model_zoo squeezenet

---
 model_zoo/research/cv/squeezenet/README.md    |  67 ++--------
 model_zoo/research/cv/squeezenet/eval.py      |  40 +++---
 model_zoo/research/cv/squeezenet/export.py    |  21 ++-
 .../cv/squeezenet/model_utils/config.py       | 124 ------------------
 .../squeezenet/model_utils/device_adapter.py  |  27 ----
 .../squeezenet/model_utils/local_adapter.py   |  36 -----
 .../squeezenet/model_utils/moxing_adapter.py  | 115 ----------------
 .../scripts/run_distribute_train.sh           |  26 +---
 .../scripts/run_distribute_train_gpu.sh       |  28 +---
 .../cv/squeezenet/scripts/run_eval.sh         |  21 +--
 .../cv/squeezenet/scripts/run_eval_gpu.sh     |  21 +--
 .../scripts/run_standalone_train.sh           |  23 +---
 .../scripts/run_standalone_train_gpu.sh       |  24 +---
 .../squeezenet/squeezenet_cifar10_config.yaml |  60 ---------
 .../squeezenet_imagenet_config.yaml           |  62 ---------
 .../squeezenet_residual_cifar10_config.yaml   |  59 ---------
 .../squeezenet_residual_imagenet_config.yaml  |  62 ---------
 .../research/cv/squeezenet/src/config.py      | 102 ++++++++++++++
 model_zoo/research/cv/squeezenet/train.py     |  57 ++++----
 19 files changed, 200 insertions(+), 775 deletions(-)
 delete mode 100644 model_zoo/research/cv/squeezenet/model_utils/config.py
 delete mode 100644 model_zoo/research/cv/squeezenet/model_utils/device_adapter.py
 delete mode 100644 model_zoo/research/cv/squeezenet/model_utils/local_adapter.py
 delete mode 100644 model_zoo/research/cv/squeezenet/model_utils/moxing_adapter.py
 delete mode 100644 model_zoo/research/cv/squeezenet/squeezenet_cifar10_config.yaml
 delete mode 100644 model_zoo/research/cv/squeezenet/squeezenet_imagenet_config.yaml
 delete mode 100644 model_zoo/research/cv/squeezenet/squeezenet_residual_cifar10_config.yaml
 delete mode 100644 model_zoo/research/cv/squeezenet/squeezenet_residual_imagenet_config.yaml
 create mode 100755 model_zoo/research/cv/squeezenet/src/config.py

diff --git a/model_zoo/research/cv/squeezenet/README.md b/model_zoo/research/cv/squeezenet/README.md
index 207e22f6b00..a22defbf589 100644
--- a/model_zoo/research/cv/squeezenet/README.md
+++ b/model_zoo/research/cv/squeezenet/README.md
@@ -100,37 +100,6 @@ After installing MindSpore via the official website, you can start training and
   sh scripts/run_eval_gpu.sh [squeezenet|squeezenet_residual] [cifar10|imagenet] [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
   ```
 
-  If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training and evaluation as follows:
-
-```python
-# run distributed training on modelarts example
-# (1) First, Perform a or b.
-#       a. Set "enable_modelarts=True" on yaml file.
-#          Set other parameters on yaml file you need.
-#       b. Add "enable_modelarts=True" on the website UI interface.
-#          Add other parameters on the website UI interface.
-# (2) Set the Dataset directory in config file.
-# (3) Set the code directory to "/path/squeezenet" on the website UI interface.
-# (4) Set the startup file to "train.py" on the website UI interface.
-# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
-# (6) Create your job.
-
-# run evaluation on modelarts example
-# (1) Copy or upload your trained model to S3 bucket.
-# (2) Perform a or b.
-#       a. Set "enable_modelarts=True" on yaml file.
-#          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
-#          Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
-#       b. Add "enable_modelarts=True" on the website UI interface.
-#          Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
-#          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
-# (3) Set the Dataset directory in config file.
-# (4) Set the code directory to "/path/squeezenet" on the website UI interface.
-# (5) Set the startup file to "eval.py" on the website UI interface.
-# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
-# (7) Create your job.
-```
-
 # [Script Description](#contents)
 
 ## [Script and Sample Code](#contents)
@@ -140,29 +109,21 @@ After installing MindSpore via the official website, you can start training and
 └── squeezenet
   ├── README.md
   ├── scripts
-    ├── run_distribute_train.sh                 # launch ascend distributed training(8 pcs)
-    ├── run_standalone_train.sh                 # launch ascend standalone training(1 pcs)
-    ├── run_distribute_train_gpu.sh             # launch gpu distributed training(8 pcs)
-    ├── run_standalone_train_gpu.sh             # launch gpu standalone training(1 pcs)
-    ├── run_eval.sh                             # launch ascend evaluation
-    └── run_eval_gpu.sh                         # launch gpu evaluation
+    ├── run_distribute_train.sh            # launch ascend distributed training(8 pcs)
+    ├── run_standalone_train.sh            # launch ascend standalone training(1 pcs)
+    ├── run_distribute_train_gpu.sh        # launch gpu distributed training(8 pcs)
+    ├── run_standalone_train_gpu.sh        # launch gpu standalone training(1 pcs)
+    ├── run_eval.sh                        # launch ascend evaluation
+    └── run_eval_gpu.sh                    # launch gpu evaluation
   ├── src
-    ├── dataset.py                              # data preprocessing
-    ├── CrossEntropySmooth.py                   # loss definition for ImageNet dataset
-    ├── lr_generator.py                         # generate learning rate for each step
-    └── squeezenet.py                           # squeezenet architecture, including squeezenet and squeezenet_residual
-  ├── model_utils
-  │   ├── device_adapter.py                    # device adapter
-  │   ├── local_adapter.py                     # local adapter
-  │   ├── moxing_adapter.py                    # moxing adapter
-  │   ├── config.py                            # parameter analysis
-  ├── squeezenet_cifar10_config.yaml            # parameter configuration
-  ├── squeezenet_imagenet_config.yaml           # parameter configuration
-  ├── squeezenet_residual_cifar10_config.yaml   # parameter configuration
-  ├── squeezenet_residual_imagenet_config.yaml  # parameter configuration
-  ├── train.py                                  # train net
-  ├── eval.py                                   # eval net
-  └── export.py                                 # export checkpoint files into geir/onnx
+    ├── config.py                          # parameter configuration
+    ├── dataset.py                         # data preprocessing
+    ├── CrossEntropySmooth.py              # loss definition for ImageNet dataset
+    ├── lr_generator.py                    # generate learning rate for each step
+    └── squeezenet.py                      # squeezenet architecture, including squeezenet and squeezenet_residual
+  ├── train.py                             # train net
+  ├── eval.py                              # eval net
+  └── export.py                            # export checkpoint files into geir/onnx
 ```
 
 ## [Script Parameters](#contents)
diff --git a/model_zoo/research/cv/squeezenet/eval.py b/model_zoo/research/cv/squeezenet/eval.py
index 352afdca88c..a1eda27f3e1 100755
--- a/model_zoo/research/cv/squeezenet/eval.py
+++ b/model_zoo/research/cv/squeezenet/eval.py
@@ -14,34 +14,44 @@
 # ============================================================================
 """eval squeezenet."""
 import os
+import argparse
 from mindspore import context
 from mindspore.common import set_seed
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from model_utils.config import config
-from model_utils.moxing_adapter import moxing_wrapper
 from src.CrossEntropySmooth import CrossEntropySmooth
 
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
+                    help='Model.')
+parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
+args_opt = parser.parse_args()
+
 set_seed(1)
 
-if config.net_name == "squeezenet":
+if args_opt.net == "squeezenet":
     from src.squeezenet import SqueezeNet as squeezenet
-    if config.dataset == "cifar10":
+    if args_opt.dataset == "cifar10":
+        from src.config import config1 as config
         from src.dataset import create_dataset_cifar as create_dataset
     else:
+        from src.config import config2 as config
         from src.dataset import create_dataset_imagenet as create_dataset
 else:
     from src.squeezenet import SqueezeNet_Residual as squeezenet
-    if config.dataset == "cifar10":
+    if args_opt.dataset == "cifar10":
+        from src.config import config3 as config
         from src.dataset import create_dataset_cifar as create_dataset
     else:
+        from src.config import config4 as config
         from src.dataset import create_dataset_imagenet as create_dataset
 
-@moxing_wrapper()
-def eval_net():
-    """eval net """
-    target = config.device_target
+if __name__ == '__main__':
+    target = args_opt.device_target
 
     # init context
     device_id = int(os.getenv('DEVICE_ID'))
@@ -50,21 +60,22 @@ def eval_net():
                         device_id=device_id)
 
     # create dataset
-    dataset = create_dataset(dataset_path=config.data_path,
+    dataset = create_dataset(dataset_path=args_opt.dataset_path,
                              do_train=False,
                              batch_size=config.batch_size,
                              target=target)
+    step_size = dataset.get_dataset_size()
 
     # define net
     net = squeezenet(num_classes=config.class_num)
 
     # load checkpoint
-    param_dict = load_checkpoint(config.checkpoint_file_path)
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
     load_param_into_net(net, param_dict)
     net.set_train(False)
 
     # define loss
-    if config.dataset == "imagenet":
+    if args_opt.dataset == "imagenet":
         if not config.use_label_smooth:
             config.label_smooth_factor = 0.0
         loss = CrossEntropySmooth(sparse=True,
@@ -81,7 +92,4 @@ def eval_net():
 
     # eval model
     res = model.eval(dataset)
-    print("result:", res, "ckpt=", config.checkpoint_file_path)
-
-if __name__ == '__main__':
-    eval_net()
+    print("result:", res, "ckpt=", args_opt.checkpoint_path)
diff --git a/model_zoo/research/cv/squeezenet/export.py b/model_zoo/research/cv/squeezenet/export.py
index d0b04d68466..b3d83d7306c 100755
--- a/model_zoo/research/cv/squeezenet/export.py
+++ b/model_zoo/research/cv/squeezenet/export.py
@@ -17,29 +17,36 @@
 python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
 """
 
+import argparse
 import numpy as np
 from mindspore import Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
-from model_utils.config import config
 
 if __name__ == '__main__':
-    if config.net_name == "squeezenet":
+    parser = argparse.ArgumentParser(description='Image classification')
+    parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
+                        help='Model.')
+    parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
+    parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+    args_opt = parser.parse_args()
+
+    if args_opt.net == "squeezenet":
         from src.squeezenet import SqueezeNet as squeezenet
     else:
         from src.squeezenet import SqueezeNet_Residual as squeezenet
-    if config.dataset == "cifar10":
+    if args_opt.dataset == "cifar10":
         num_classes = 10
     else:
         num_classes = 1000
 
-    onnx_filename = config.net_name + '_' + config.dataset
-    air_filename = config.net_name + '_' + config.dataset
+    onnx_filename = args_opt.net + '_' + args_opt.dataset
+    air_filename = args_opt.net + '_' + args_opt.dataset
 
     net = squeezenet(num_classes=num_classes)
 
-    assert config.checkpoint_file_path is not None, "checkpoint_file_path is None."
+    assert args_opt.checkpoint_path is not None, "checkpoint_path is None."
 
-    param_dict = load_checkpoint(config.checkpoint_file_path)
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
     load_param_into_net(net, param_dict)
 
     input_arr = Tensor(np.zeros([1, 3, 227, 227], np.float32))
diff --git a/model_zoo/research/cv/squeezenet/model_utils/config.py b/model_zoo/research/cv/squeezenet/model_utils/config.py
deleted file mode 100644
index 5d591e2c50e..00000000000
--- a/model_zoo/research/cv/squeezenet/model_utils/config.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# 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.
-# ============================================================================
-
-"""Parse arguments"""
-
-import os
-import ast
-import argparse
-from pprint import pformat
-import yaml
-
-_config_path = "./squeezenet_cifar10_config.yaml"
-
-class Config:
-    """
-    Configuration namespace. Convert dictionary to members.
-    """
-    def __init__(self, cfg_dict):
-        for k, v in cfg_dict.items():
-            if isinstance(v, (list, tuple)):
-                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
-            else:
-                setattr(self, k, Config(v) if isinstance(v, dict) else v)
-
-    def __str__(self):
-        return pformat(self.__dict__)
-
-    def __repr__(self):
-        return self.__str__()
-
-
-def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="squeezenet_cifar10_config.yaml"):
-    """
-    Parse command line arguments to the configuration according to the default yaml.
-
-    Args:
-        parser: Parent parser.
-        cfg: Base configuration.
-        helper: Helper description.
-        cfg_path: Path to the default yaml config.
-    """
-    parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
-                                     parents=[parser])
-    helper = {} if helper is None else helper
-    choices = {} if choices is None else choices
-    for item in cfg:
-        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
-            help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
-            choice = choices[item] if item in choices else None
-            if isinstance(cfg[item], bool):
-                parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
-                                    help=help_description)
-            else:
-                parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
-                                    help=help_description)
-    args = parser.parse_args()
-    return args
-
-
-def parse_yaml(yaml_path):
-    """
-    Parse the yaml config file.
-
-    Args:
-        yaml_path: Path to the yaml config.
-    """
-    with open(yaml_path, 'r') as fin:
-        try:
-            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
-            cfgs = [x for x in cfgs]
-            if len(cfgs) == 1:
-                cfg_helper = {}
-                cfg = cfgs[0]
-            elif len(cfgs) == 2:
-                cfg, cfg_helper = cfgs
-            else:
-                raise ValueError("At most 2 docs (config and help description for help) are supported in config yaml")
-            print(cfg_helper)
-        except:
-            raise ValueError("Failed to parse yaml")
-    return cfg, cfg_helper
-
-
-def merge(args, cfg):
-    """
-    Merge the base config from yaml file and command line arguments.
-
-    Args:
-        args: Command line arguments.
-        cfg: Base configuration.
-    """
-    args_var = vars(args)
-    for item in args_var:
-        cfg[item] = args_var[item]
-    return cfg
-
-
-def get_config():
-    """
-    Get Config according to the yaml file and cli arguments.
-    """
-    parser = argparse.ArgumentParser(description="default name", add_help=False)
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, \
-        "../squeezenet_cifar10_config.yaml"), help="Config file path")
-    path_args, _ = parser.parse_known_args()
-    default, helper = parse_yaml(path_args.config_path)
-    args = parse_cli_to_yaml(parser, default, helper, path_args.config_path)
-    final_config = merge(args, default)
-    return Config(final_config)
-
-config = get_config()
diff --git a/model_zoo/research/cv/squeezenet/model_utils/device_adapter.py b/model_zoo/research/cv/squeezenet/model_utils/device_adapter.py
deleted file mode 100644
index 44811a34245..00000000000
--- a/model_zoo/research/cv/squeezenet/model_utils/device_adapter.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# 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.
-# ============================================================================
-
-"""Device adapter for ModelArts"""
-
-from model_utils.config import config
-
-if config.enable_modelarts:
-    from model_utils.moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
-else:
-    from model_utils.local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
-
-__all__ = [
-    "get_device_id", "get_device_num", "get_rank_id", "get_job_id"
-]
diff --git a/model_zoo/research/cv/squeezenet/model_utils/local_adapter.py b/model_zoo/research/cv/squeezenet/model_utils/local_adapter.py
deleted file mode 100644
index 769fa6dc78e..00000000000
--- a/model_zoo/research/cv/squeezenet/model_utils/local_adapter.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# 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.
-# ============================================================================
-
-"""Local adapter"""
-
-import os
-
-def get_device_id():
-    device_id = os.getenv('DEVICE_ID', '0')
-    return int(device_id)
-
-
-def get_device_num():
-    device_num = os.getenv('RANK_SIZE', '1')
-    return int(device_num)
-
-
-def get_rank_id():
-    global_rank_id = os.getenv('RANK_ID', '0')
-    return int(global_rank_id)
-
-
-def get_job_id():
-    return "Local Job"
diff --git a/model_zoo/research/cv/squeezenet/model_utils/moxing_adapter.py b/model_zoo/research/cv/squeezenet/model_utils/moxing_adapter.py
deleted file mode 100644
index 4638424066a..00000000000
--- a/model_zoo/research/cv/squeezenet/model_utils/moxing_adapter.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# 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.
-# ============================================================================
-
-"""Moxing adapter for ModelArts"""
-
-import os
-import functools
-from mindspore import context
-from model_utils.config import config
-
-_global_sync_count = 0
-
-def get_device_id():
-    device_id = os.getenv('DEVICE_ID', '0')
-    return int(device_id)
-
-
-def get_device_num():
-    device_num = os.getenv('RANK_SIZE', '1')
-    return int(device_num)
-
-
-def get_rank_id():
-    global_rank_id = os.getenv('RANK_ID', '0')
-    return int(global_rank_id)
-
-
-def get_job_id():
-    job_id = os.getenv('JOB_ID')
-    job_id = job_id if job_id != "" else "default"
-    return job_id
-
-def sync_data(from_path, to_path):
-    """
-    Download data from remote obs to local directory if the first url is remote url and the second one is local path
-    Upload data from local directory to remote obs in contrast.
-    """
-    import moxing as mox
-    import time
-    global _global_sync_count
-    sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
-    _global_sync_count += 1
-
-    # Each server contains 8 devices as most.
-    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
-        print("from path: ", from_path)
-        print("to path: ", to_path)
-        mox.file.copy_parallel(from_path, to_path)
-        print("===finish data synchronization===")
-        try:
-            os.mknod(sync_lock)
-        except IOError:
-            pass
-        print("===save flag===")
-
-    while True:
-        if os.path.exists(sync_lock):
-            break
-        time.sleep(1)
-
-    print("Finish sync data from {} to {}.".format(from_path, to_path))
-
-
-def moxing_wrapper(pre_process=None, post_process=None):
-    """
-    Moxing wrapper to download dataset and upload outputs.
-    """
-    def wrapper(run_func):
-        @functools.wraps(run_func)
-        def wrapped_func(*args, **kwargs):
-            # Download data from data_url
-            if config.enable_modelarts:
-                if config.data_url:
-                    sync_data(config.data_url, config.data_path)
-                    print("Dataset downloaded: ", os.listdir(config.data_path))
-                if config.checkpoint_url:
-                    sync_data(config.checkpoint_url, config.load_path)
-                    print("Preload downloaded: ", os.listdir(config.load_path))
-                if config.train_url:
-                    sync_data(config.train_url, config.output_path)
-                    print("Workspace downloaded: ", os.listdir(config.output_path))
-
-                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
-                config.device_num = get_device_num()
-                config.device_id = get_device_id()
-                if not os.path.exists(config.output_path):
-                    os.makedirs(config.output_path)
-
-                if pre_process:
-                    pre_process()
-
-            run_func(*args, **kwargs)
-
-            # Upload data to train_url
-            if config.enable_modelarts:
-                if post_process:
-                    post_process()
-
-                if config.train_url:
-                    print("Start to copy output directory")
-                    sync_data(config.output_path, config.train_url)
-        return wrapped_func
-    return wrapper
diff --git a/model_zoo/research/cv/squeezenet/scripts/run_distribute_train.sh b/model_zoo/research/cv/squeezenet/scripts/run_distribute_train.sh
index a507a1ceec5..ee8c13651f1 100755
--- a/model_zoo/research/cv/squeezenet/scripts/run_distribute_train.sh
+++ b/model_zoo/research/cv/squeezenet/scripts/run_distribute_train.sh
@@ -44,7 +44,7 @@ PATH1=$(get_real_path $3)
 PATH2=$(get_real_path $4)
 
 if [ $# == 5 ]
-then
+then 
     PATH3=$(get_real_path $5)
 fi
 
@@ -74,22 +74,6 @@ export RANK_TABLE_FILE=$PATH1
 export SERVER_ID=0
 rank_start=$((DEVICE_NUM * SERVER_ID))
 
-BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
-CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-
-if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
-else
-     echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
-exit 1
-fi
-
 for((i=0; i<${DEVICE_NUM}; i++))
 do
     export DEVICE_ID=${i}
@@ -98,21 +82,17 @@ do
     mkdir ./train_parallel$i
     cp ./train.py ./train_parallel$i
     cp -r ./src ./train_parallel$i
-    cp -r ./model_utils ./train_parallel$i
-    cp -r ./*.yaml ./train_parallel$i
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
     if [ $# == 4 ]
     then
-        python train.py --net_name=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --data_path=$PATH2 \
-        --config_path=$CONFIG_FILE --output_path './output' &> log &
+        python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
     fi
     
     if [ $# == 5 ]
     then
-        python train.py --net_name=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --data_path=$PATH2 \
-        --pre_trained=$PATH3 --config_path=$CONFIG_FILE --output_path './output' &> log &
+        python train.py --net=$1 --dataset=$2 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
     fi
 
     cd ..
diff --git a/model_zoo/research/cv/squeezenet/scripts/run_distribute_train_gpu.sh b/model_zoo/research/cv/squeezenet/scripts/run_distribute_train_gpu.sh
index 5f617060321..2df3f9719eb 100755
--- a/model_zoo/research/cv/squeezenet/scripts/run_distribute_train_gpu.sh
+++ b/model_zoo/research/cv/squeezenet/scripts/run_distribute_train_gpu.sh
@@ -64,42 +64,22 @@ ulimit -u unlimited
 export DEVICE_NUM=8
 export RANK_SIZE=8
 
-BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
-CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-
-if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
-else
-     echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
-exit 1
-fi
-
 rm -rf ./train_parallel
 mkdir ./train_parallel
 cp ./train.py ./train_parallel
 cp -r ./src ./train_parallel
-cp -r ./model_utils ./train_parallel
-cp -r ./*.yaml ./train_parallel
 cd ./train_parallel || exit
 
 if [ $# == 3 ]
 then
     mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
-    python train.py --net_name=$1 --dataset=$2 --run_distribute=True \
-    --device_num=$DEVICE_NUM --device_target="GPU" --data_path=$PATH1 \
-    --config_path=$CONFIG_FILE --output_path './output' &> log &
+    python train.py --net=$1 --dataset=$2 --run_distribute=True \
+    --device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 &> log &
 fi
     
 if [ $# == 4 ]
 then
     mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
-    python train.py --net_name=$1 --dataset=$2 --run_distribute=True \
-    --device_num=$DEVICE_NUM --device_target="GPU" --data_path=$PATH1 --pre_trained=$PATH2 \
-    --config_path=$CONFIG_FILE --output_path './output' &> log &
+    python train.py --net=$1 --dataset=$2 --run_distribute=True \
+    --device_num=$DEVICE_NUM --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
 fi
diff --git a/model_zoo/research/cv/squeezenet/scripts/run_eval.sh b/model_zoo/research/cv/squeezenet/scripts/run_eval.sh
index c0ff51408b6..8ac34e25ce2 100755
--- a/model_zoo/research/cv/squeezenet/scripts/run_eval.sh
+++ b/model_zoo/research/cv/squeezenet/scripts/run_eval.sh
@@ -62,22 +62,6 @@ export DEVICE_ID=$3
 export RANK_SIZE=$DEVICE_NUM
 export RANK_ID=0
 
-BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
-CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-
-if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
-else
-     echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
-exit 1
-fi
-
 if [ -d "eval" ];
 then
     rm -rf ./eval
@@ -85,11 +69,8 @@ fi
 mkdir ./eval
 cp ./eval.py ./eval
 cp -r ./src ./eval
-cp -r ./model_utils ./eval
-cp -r ./*.yaml ./eval
 cd ./eval || exit
 env > env.log
 echo "start evaluation for device $DEVICE_ID"
-python eval.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --checkpoint_file_path=$PATH2 \
---config_path=$CONFIG_FILE --output_path './output' &> log &
+python eval.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
 cd ..
diff --git a/model_zoo/research/cv/squeezenet/scripts/run_eval_gpu.sh b/model_zoo/research/cv/squeezenet/scripts/run_eval_gpu.sh
index 29a199d8ddb..f5bfaa4ade0 100755
--- a/model_zoo/research/cv/squeezenet/scripts/run_eval_gpu.sh
+++ b/model_zoo/research/cv/squeezenet/scripts/run_eval_gpu.sh
@@ -62,22 +62,6 @@ export DEVICE_ID=$3
 export RANK_SIZE=$DEVICE_NUM
 export RANK_ID=0
 
-BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
-CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-
-if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
-else
-     echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
-exit 1
-fi
-
 if [ -d "eval" ];
 then
     rm -rf ./eval
@@ -85,11 +69,8 @@ fi
 mkdir ./eval
 cp ./eval.py ./eval
 cp -r ./src ./eval
-cp -r ./model_utils ./eval
-cp -r ./*.yaml ./eval
 cd ./eval || exit
 env > env.log
 echo "start evaluation for device $DEVICE_ID"
-python eval.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --checkpoint_file_path=$PATH2 --device_target="GPU" \
---config_path=$CONFIG_FILE --output_path './output' &> log &
+python eval.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 --device_target="GPU" &> log &
 cd ..
diff --git a/model_zoo/research/cv/squeezenet/scripts/run_standalone_train.sh b/model_zoo/research/cv/squeezenet/scripts/run_standalone_train.sh
index fd9d42f6502..cd4c637075f 100755
--- a/model_zoo/research/cv/squeezenet/scripts/run_standalone_train.sh
+++ b/model_zoo/research/cv/squeezenet/scripts/run_standalone_train.sh
@@ -65,22 +65,6 @@ export DEVICE_ID=$3
 export RANK_ID=0
 export RANK_SIZE=1
 
-BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
-CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-
-if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
-else
-     echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
-exit 1
-fi
-
 if [ -d "train" ];
 then
     rm -rf ./train
@@ -88,19 +72,16 @@ fi
 mkdir ./train
 cp ./train.py ./train
 cp -r ./src ./train
-cp -r ./model_utils ./train
-cp -r ./*.yaml ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
 if [ $# == 4 ]
 then
-    python train.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --config_path=$CONFIG_FILE &> log &
+    python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 &> log &
 fi
 
 if [ $# == 5 ]
 then
-    python train.py --net_name=$1 --dataset=$2 --data_path=$PATH1 --pre_trained=$PATH2 --config_path=$CONFIG_FILE \
-    --output_path './output' &> log &
+    python train.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
 fi
 cd ..
diff --git a/model_zoo/research/cv/squeezenet/scripts/run_standalone_train_gpu.sh b/model_zoo/research/cv/squeezenet/scripts/run_standalone_train_gpu.sh
index ea6462066ac..8a80526e048 100755
--- a/model_zoo/research/cv/squeezenet/scripts/run_standalone_train_gpu.sh
+++ b/model_zoo/research/cv/squeezenet/scripts/run_standalone_train_gpu.sh
@@ -65,22 +65,6 @@ export DEVICE_ID=$3
 export RANK_ID=0
 export RANK_SIZE=1
 
-BASE_PATH=$(dirname "$(dirname "$(readlink -f $0)")")
-CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-
-if [ $1 == "squeezenet" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_cifar10_config.yaml"
-elif [ $1 == "squeezenet" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_imagenet_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "cifar10" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_cifar10_config.yaml"
-elif [ $1 == "squeezenet_residual" ] && [ $2 == "imagenet" ]; then
-    CONFIG_FILE="${BASE_PATH}/squeezenet_residual_imagenet_config.yaml"
-else
-     echo "error: the selected dataset is not in supported set{squeezenet, squeezenet_residual, cifar10, imagenet}"
-exit 1
-fi
-
 if [ -d "train" ];
 then
     rm -rf ./train
@@ -88,20 +72,16 @@ fi
 mkdir ./train
 cp ./train.py ./train
 cp -r ./src ./train
-cp -r ./model_utils ./train
-cp -r ./*.yaml ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
 if [ $# == 4 ]
 then
-    python train.py --net_name=$1 --dataset=$2 --device_target="GPU" --data_path=$PATH1 \
-    --config_path=$CONFIG_FILE --output_path './output' &> log &
+    python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 &> log &
 fi
 
 if [ $# == 5 ]
 then
-    python train.py --net_name=$1 --dataset=$2 --device_target="GPU" --data_path=$PATH1 --pre_trained=$PATH2 \
-    --config_path=$CONFIG_FILE --output_path './output' &> log &
+    python train.py --net=$1 --dataset=$2 --device_target="GPU" --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
 fi
 cd ..
diff --git a/model_zoo/research/cv/squeezenet/squeezenet_cifar10_config.yaml b/model_zoo/research/cv/squeezenet/squeezenet_cifar10_config.yaml
deleted file mode 100644
index a3073f44f97..00000000000
--- a/model_zoo/research/cv/squeezenet/squeezenet_cifar10_config.yaml
+++ /dev/null
@@ -1,60 +0,0 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
-enable_modelarts: False
-# Url for modelarts
-data_url: ""
-train_url: ""
-checkpoint_url: ""
-# Path for local
-run_distribute: False
-enable_profiling: False
-data_path: "/cache/data"
-output_path: "/cache/train"
-load_path: "/cache/checkpoint_path/"
-device_num: 1
-device_id: 0
-device_target: 'Ascend'
-checkpoint_path: './checkpoint/'
-checkpoint_file_path: 'suqeezenet_cifar10-120_195.ckpt'
-
-# ==============================================================================
-# Training options
-net_name: ""
-dataset : "cifar10"
-class_num: 10
-batch_size: 32
-loss_scale: 1024
-momentum: 0.9
-weight_decay: 0.0001
-epoch_size: 120
-pretrain_epoch_size: 0
-save_checkpoint: True
-save_checkpoint_epochs: 1
-keep_checkpoint_max: 10
-warmup_epochs: 5
-lr_decay_mode: "poly"
-lr_init: 0
-lr_end: 0
-lr_max: 0.01
-pre_trained: ""
-
-# export
-file_name: "squeezenet"
-file_format: "AIR"
-
----
-# Help description for each configuration
-enable_modelarts: 'Whether training on modelarts, default: False'
-data_url: 'Dataset url for obs'
-train_url: 'Training output url for obs'
-checkpoint_url: 'The location of checkpoint for obs'
-data_path: 'Dataset path for local'
-output_path: 'Training output path for local'
-load_path: 'The location of checkpoint for obs'
-device_target: 'Target device type, available: [Ascend, GPU, CPU]'
-enable_profiling: 'Whether enable profiling while training, default: False'
-num_classes: 'Class for dataset'
-batch_size: "Batch size for training and evaluation"
-epoch_size: "Total training epochs."
-keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
-checkpoint_path: "The location of the checkpoint file."
-checkpoint_file_path: "The location of the checkpoint file."
diff --git a/model_zoo/research/cv/squeezenet/squeezenet_imagenet_config.yaml b/model_zoo/research/cv/squeezenet/squeezenet_imagenet_config.yaml
deleted file mode 100644
index 1ed1559f175..00000000000
--- a/model_zoo/research/cv/squeezenet/squeezenet_imagenet_config.yaml
+++ /dev/null
@@ -1,62 +0,0 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
-enable_modelarts: False
-# Url for modelarts
-data_url: ""
-train_url: ""
-checkpoint_url: ""
-# Path for local
-run_distribute: False
-enable_profiling: False
-data_path: "/cache/data"
-output_path: "/cache/train"
-load_path: "/cache/checkpoint_path/"
-device_num: 1
-device_id: 0
-device_target: 'Ascend'
-checkpoint_path: './checkpoint/'
-checkpoint_file_path: 'suqeezenet_imagenet-200_5004.ckpt'
-
-# ==============================================================================
-# Training options
-net_name: ""
-dataset : "imagenet"
-class_num: 1000
-batch_size: 32
-loss_scale: 1024
-momentum: 0.9
-weight_decay: 0.00007
-epoch_size: 200
-pretrain_epoch_size: 0
-save_checkpoint: True
-save_checkpoint_epochs: 1
-keep_checkpoint_max: 10
-warmup_epochs: 0
-lr_decay_mode: "poly"
-use_label_smooth: True
-label_smooth_factor: 0.1
-lr_init: 0
-lr_end: 0
-lr_max: 0.01
-pre_trained: ""
-
-# export
-file_name: "squeezenet"
-file_format: "AIR"
-
----
-# Help description for each configuration
-enable_modelarts: 'Whether training on modelarts, default: False'
-data_url: 'Dataset url for obs'
-train_url: 'Training output url for obs'
-checkpoint_url: 'The location of checkpoint for obs'
-data_path: 'Dataset path for local'
-output_path: 'Training output path for local'
-load_path: 'The location of checkpoint for obs'
-device_target: 'Target device type, available: [Ascend, GPU, CPU]'
-enable_profiling: 'Whether enable profiling while training, default: False'
-num_classes: 'Class for dataset'
-batch_size: "Batch size for training and evaluation"
-epoch_size: "Total training epochs."
-keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
-checkpoint_path: "The location of the checkpoint file."
-checkpoint_file_path: "The location of the checkpoint file."
diff --git a/model_zoo/research/cv/squeezenet/squeezenet_residual_cifar10_config.yaml b/model_zoo/research/cv/squeezenet/squeezenet_residual_cifar10_config.yaml
deleted file mode 100644
index d0ff7bc342b..00000000000
--- a/model_zoo/research/cv/squeezenet/squeezenet_residual_cifar10_config.yaml
+++ /dev/null
@@ -1,59 +0,0 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
-enable_modelarts: False
-# Url for modelarts
-data_url: ""
-train_url: ""
-checkpoint_url: ""
-# Path for local
-run_distribute: False
-enable_profiling: False
-data_path: "/cache/data"
-output_path: "/cache/train"
-load_path: "/cache/checkpoint_path/"
-device_num: 1
-device_target: 'Ascend'
-checkpoint_path: './checkpoint/'
-checkpoint_file_path: 'suqeezenet_residual_cifar10-150_195.ckpt'
-
-# ==============================================================================
-# Training options
-net_name: ""
-dataset : "cifar10"
-class_num: 10
-batch_size: 32
-loss_scale: 1024
-momentum: 0.9
-weight_decay: 0.0001
-epoch_size: 150
-pretrain_epoch_size: 0
-save_checkpoint: True
-save_checkpoint_epochs: 1
-keep_checkpoint_max: 10
-warmup_epochs: 5
-lr_decay_mode: "linear"
-lr_init: 0
-lr_end: 0
-lr_max: 0.01
-pre_trained: ""
-
-#export
-file_name: "squeezenet"
-file_format: "AIR"
-
----
-# Help description for each configuration
-enable_modelarts: 'Whether training on modelarts, default: False'
-data_url: 'Dataset url for obs'
-train_url: 'Training output url for obs'
-checkpoint_url: 'The location of checkpoint for obs'
-data_path: 'Dataset path for local'
-output_path: 'Training output path for local'
-load_path: 'The location of checkpoint for obs'
-device_target: 'Target device type, available: [Ascend, GPU, CPU]'
-enable_profiling: 'Whether enable profiling while training, default: False'
-num_classes: 'Class for dataset'
-batch_size: "Batch size for training and evaluation"
-epoch_size: "Total training epochs."
-keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
-checkpoint_path: "The location of the checkpoint file."
-checkpoint_file_path: "The location of the checkpoint file."
diff --git a/model_zoo/research/cv/squeezenet/squeezenet_residual_imagenet_config.yaml b/model_zoo/research/cv/squeezenet/squeezenet_residual_imagenet_config.yaml
deleted file mode 100644
index f9f69e7147c..00000000000
--- a/model_zoo/research/cv/squeezenet/squeezenet_residual_imagenet_config.yaml
+++ /dev/null
@@ -1,62 +0,0 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
-enable_modelarts: False
-# Url for modelarts
-data_url: ""
-train_url: ""
-checkpoint_url: ""
-# Path for local
-run_distribute: False
-enable_profiling: False
-data_path: "/cache/data"
-output_path: "/cache/train"
-load_path: "/cache/checkpoint_path/"
-device_num: 1
-device_id: 0
-device_target: 'Ascend'
-checkpoint_path: './checkpoint/'
-checkpoint_file_path: 'suqeezenet_residual_imagenet-300_5004.ckpt'
-
-# ==============================================================================
-# Training options
-net_name: ""
-dataset : "imagenet"
-class_num: 1000
-batch_size: 32
-loss_scale: 1024
-momentum: 0.9
-weight_decay: 0.00007
-epoch_size: 300
-pretrain_epoch_size: 0
-save_checkpoint: True
-save_checkpoint_epochs: 1
-keep_checkpoint_max: 10
-warmup_epochs: 0
-lr_decay_mode: "cosine"
-use_label_smooth: True
-label_smooth_factor: 0.1
-lr_init: 0
-lr_end: 0
-lr_max: 0.01
-pre_trained: ""
-
-#export
-file_name: "squeezenet"
-file_format: "AIR"
-
----
-# Help description for each configuration
-enable_modelarts: 'Whether training on modelarts, default: False'
-data_url: 'Dataset url for obs'
-train_url: 'Training output url for obs'
-checkpoint_url: 'The location of checkpoint for obs'
-data_path: 'Dataset path for local'
-output_path: 'Training output path for local'
-load_path: 'The location of checkpoint for obs'
-device_target: 'Target device type, available: [Ascend, GPU, CPU]'
-enable_profiling: 'Whether enable profiling while training, default: False'
-num_classes: 'Class for dataset'
-batch_size: "Batch size for training and evaluation"
-epoch_size: "Total training epochs."
-keep_checkpoint_max: "keep the last keep_checkpoint_max checkpoint"
-checkpoint_path: "The location of the checkpoint file."
-checkpoint_file_path: "The location of the checkpoint file."
diff --git a/model_zoo/research/cv/squeezenet/src/config.py b/model_zoo/research/cv/squeezenet/src/config.py
new file mode 100755
index 00000000000..40d119c5bde
--- /dev/null
+++ b/model_zoo/research/cv/squeezenet/src/config.py
@@ -0,0 +1,102 @@
+# Copyright 2020 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.
+# ============================================================================
+"""
+network config setting, will be used in train.py and eval.py
+"""
+from easydict import EasyDict as ed
+
+# config for squeezenet, cifar10
+config1 = ed({
+    "class_num": 10,
+    "batch_size": 32,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "epoch_size": 120,
+    "pretrain_epoch_size": 0,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 10,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 5,
+    "lr_decay_mode": "poly",
+    "lr_init": 0,
+    "lr_end": 0,
+    "lr_max": 0.01
+})
+
+# config for squeezenet, imagenet
+config2 = ed({
+    "class_num": 1000,
+    "batch_size": 32,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 7e-5,
+    "epoch_size": 200,
+    "pretrain_epoch_size": 0,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 10,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 0,
+    "lr_decay_mode": "poly",
+    "use_label_smooth": True,
+    "label_smooth_factor": 0.1,
+    "lr_init": 0,
+    "lr_end": 0,
+    "lr_max": 0.01
+})
+
+# config for squeezenet_residual, cifar10
+config3 = ed({
+    "class_num": 10,
+    "batch_size": 32,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 1e-4,
+    "epoch_size": 150,
+    "pretrain_epoch_size": 0,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 10,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 5,
+    "lr_decay_mode": "linear",
+    "lr_init": 0,
+    "lr_end": 0,
+    "lr_max": 0.01
+})
+
+# config for squeezenet_residual, imagenet
+config4 = ed({
+    "class_num": 1000,
+    "batch_size": 32,
+    "loss_scale": 1024,
+    "momentum": 0.9,
+    "weight_decay": 7e-5,
+    "epoch_size": 300,
+    "pretrain_epoch_size": 0,
+    "save_checkpoint": True,
+    "save_checkpoint_epochs": 1,
+    "keep_checkpoint_max": 10,
+    "save_checkpoint_path": "./",
+    "warmup_epochs": 0,
+    "lr_decay_mode": "cosine",
+    "use_label_smooth": True,
+    "label_smooth_factor": 0.1,
+    "lr_init": 0,
+    "lr_end": 0,
+    "lr_max": 0.01
+})
diff --git a/model_zoo/research/cv/squeezenet/train.py b/model_zoo/research/cv/squeezenet/train.py
index 195d5706e9d..d35529faff5 100755
--- a/model_zoo/research/cv/squeezenet/train.py
+++ b/model_zoo/research/cv/squeezenet/train.py
@@ -14,6 +14,7 @@
 # ============================================================================
 """train squeezenet."""
 import os
+import argparse
 from mindspore import context
 from mindspore import Tensor
 from mindspore.nn.optim.momentum import Momentum
@@ -23,45 +24,55 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMoni
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.communication.management import init, get_rank
+from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.common import set_seed
-from model_utils.config import config
-from model_utils.device_adapter import get_device_num
-from model_utils.moxing_adapter import moxing_wrapper
 from src.lr_generator import get_lr
 from src.CrossEntropySmooth import CrossEntropySmooth
 
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--net', type=str, default='squeezenet', choices=['squeezenet', 'squeezenet_residual'],
+                    help='Model.')
+parser.add_argument('--dataset', type=str, default='cifar10', choices=['cifar10', 'imagenet'], help='Dataset.')
+parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
+parser.add_argument('--device_num', type=int, default=1, help='Device num.')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
+parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
+args_opt = parser.parse_args()
+
 set_seed(1)
 
-if config.net_name == "squeezenet":
+if args_opt.net == "squeezenet":
     from src.squeezenet import SqueezeNet as squeezenet
-    if config.dataset == "cifar10":
+    if args_opt.dataset == "cifar10":
+        from src.config import config1 as config
         from src.dataset import create_dataset_cifar as create_dataset
     else:
+        from src.config import config2 as config
         from src.dataset import create_dataset_imagenet as create_dataset
 else:
     from src.squeezenet import SqueezeNet_Residual as squeezenet
-    if config.dataset == "cifar10":
+    if args_opt.dataset == "cifar10":
+        from src.config import config3 as config
         from src.dataset import create_dataset_cifar as create_dataset
     else:
+        from src.config import config4 as config
         from src.dataset import create_dataset_imagenet as create_dataset
 
-@moxing_wrapper()
-def train_net():
-    """train net"""
-    target = config.device_target
-    ckpt_save_dir = config.output_path
+if __name__ == '__main__':
+    target = args_opt.device_target
+    ckpt_save_dir = config.save_checkpoint_path
 
     # init context
     context.set_context(mode=context.GRAPH_MODE,
                         device_target=target)
-    if config.run_distribute:
+    if args_opt.run_distribute:
         if target == "Ascend":
             device_id = int(os.getenv('DEVICE_ID'))
             context.set_context(device_id=device_id,
                                 enable_auto_mixed_precision=True)
             context.set_auto_parallel_context(
-                device_num=config.device_num,
+                device_num=args_opt.device_num,
                 parallel_mode=ParallelMode.DATA_PARALLEL,
                 gradients_mean=True)
             init()
@@ -69,13 +80,14 @@ def train_net():
         else:
             init()
             context.set_auto_parallel_context(
-                device_num=get_device_num(),
+                device_num=get_group_size(),
                 parallel_mode=ParallelMode.DATA_PARALLEL,
                 gradients_mean=True)
-        ckpt_save_dir = ckpt_save_dir + "/ckpt_" + str(get_rank()) + "/"
+        ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(
+            get_rank()) + "/"
 
     # create dataset
-    dataset = create_dataset(dataset_path=config.data_path,
+    dataset = create_dataset(dataset_path=args_opt.dataset_path,
                              do_train=True,
                              repeat_num=1,
                              batch_size=config.batch_size,
@@ -86,8 +98,8 @@ def train_net():
     net = squeezenet(num_classes=config.class_num)
 
     # load checkpoint
-    if config.pre_trained:
-        param_dict = load_checkpoint(config.pre_trained)
+    if args_opt.pre_trained:
+        param_dict = load_checkpoint(args_opt.pre_trained)
         load_param_into_net(net, param_dict)
 
     # init lr
@@ -102,7 +114,7 @@ def train_net():
     lr = Tensor(lr)
 
     # define loss
-    if config.dataset == "imagenet":
+    if args_opt.dataset == "imagenet":
         if not config.use_label_smooth:
             config.label_smooth_factor = 0.0
         loss = CrossEntropySmooth(sparse=True,
@@ -146,7 +158,7 @@ def train_net():
         config_ck = CheckpointConfig(
             save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
             keep_checkpoint_max=config.keep_checkpoint_max)
-        ckpt_cb = ModelCheckpoint(prefix=config.net_name + '_' + config.dataset,
+        ckpt_cb = ModelCheckpoint(prefix=args_opt.net + '_' + args_opt.dataset,
                                   directory=ckpt_save_dir,
                                   config=config_ck)
         cb += [ckpt_cb]
@@ -155,6 +167,3 @@ def train_net():
     model.train(config.epoch_size - config.pretrain_epoch_size,
                 dataset,
                 callbacks=cb)
-
-if __name__ == '__main__':
-    train_net()