!9367 remove redundant parameters of resnext50 and inceptionv3

From: @zhouyaqiang0
Reviewed-by: 
Signed-off-by:

model_zoo/official/cv/inceptionv3/README.md

@@ -90,8 +90,6 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 ```python
 Major parameters in train.py and config.py are:
 'random_seed'                # fix random seed
-'rank'                       # local rank of distributed
-'group_size'                 # world size of distributed
 'work_nums'                  # number of workers to read the data
 'decay_method'               # learning rate scheduler mode
 "loss_scale"                 # loss scale

model_zoo/official/cv/inceptionv3/src/config.py

@@ -20,8 +20,6 @@ from easydict import EasyDict as edict
 
 config_gpu = edict({
     'random_seed': 1,
-    'rank': 0,
-    'group_size': 1,
     'work_nums': 8,
     'decay_method': 'cosine',
     "loss_scale": 1,
@@ -47,8 +45,6 @@ config_gpu = edict({
 
 config_ascend = edict({
     'random_seed': 1,
-    'rank': 0,
-    'group_size': 1,
     'work_nums': 8,
     'decay_method': 'cosine',
     "loss_scale": 1024,

model_zoo/official/cv/resnext50/README.md

@@ -4,9 +4,9 @@
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
 - [Features](#features)
-    - [Mixed Precision](#mixed-precision)
+- [Mixed Precision](#mixed-precision)
 - [Environment Requirements](#environment-requirements)
-- [Quick Start](#quick-start)    
+- [Quick Start](#quick-start)
 - [Script Description](#script-description)
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
@@ -32,35 +32,33 @@ The overall network architecture of ResNeXt is show below:
 
 [Link](https://arxiv.org/abs/1611.05431)
 
-
 # [Dataset](#contents)
 
 Dataset used: [imagenet](http://www.image-net.org/)
 
 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
-  - Train: 120G, 1.2W images
-  - Test: 5G, 50000 images
-- Data format: RGB images.
-  - Note: Data will be processed in src/dataset.py 
-
+- Train: 120G, 1.2W images
+- Test: 5G, 50000 images
+- Data format: RGB images
+- Note: Data will be processed in src/dataset.py
 
 # [Features](#contents)
 
 ## [Mixed Precision](#contents)
 
 The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
- 
+
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-  - Prepare hardware environment with Ascend or GPU 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. 
+- Prepare hardware environment with Ascend or GPU 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.
 - Framework
-  - [MindSpore](https://www.mindspore.cn/install/en)
+- [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
-  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
+- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
+- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
 
 # [Script description](#contents)
 
@@ -68,9 +66,9 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 
 ```python
 .
-└─resnext50      
+└─resnext50
   ├─README.md
-  ├─scripts      
+  ├─scripts
     ├─run_standalone_train.sh         # launch standalone training for ascend(1p)
     ├─run_distribute_train.sh         # launch distributed training for ascend(8p)
     ├─run_standalone_train_for_gpu.sh # launch standalone training for gpu(1p)
@@ -100,14 +98,14 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
   ├──train.py                         # train net
   ├──export.py                        # export mindir script
   ├──mindspore_hub_conf.py            #  mindspore hub interface
-  
+
 ```
 
 ## [Script Parameters](#contents)
 
 Parameters for both training and evaluating can be set in config.py.
 
-```       
+```config
 "image_height": '224,224'                 # image size
 "num_classes": 1000,                      # dataset class number
 "per_batch_size": 128,                    # batch size of input tensor
@@ -118,7 +116,6 @@ Parameters for both training and evaluating can be set in config.py.
 "eta_min": 0,                             # eta_min in cosine_annealing scheduler
 "T_max": 150,                             # T-max in cosine_annealing scheduler
 "max_epoch": 150,                         # max epoch num to train the model
-"backbone": 'resnext50',                  # backbone metwork
 "warmup_epochs" : 1,                      # warmup epoch
 "weight_decay": 0.0001,                   # weight decay
 "momentum": 0.9,                          # momentum
@@ -135,18 +132,18 @@ Parameters for both training and evaluating can be set in config.py.
 
 ## [Training Process](#contents)
 
-#### Usage
+### Usage
 
 You can start training by python script:
 
-```
+```script
 python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0
 ```
 
 or shell stript:
 
-```
-Ascend: 
+```script
+Ascend:
     # distribute training example(8p)
     sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
     # standalone training
@@ -180,16 +177,17 @@ You can find checkpoint file together with result in log.
 
 You can start training by python script:
 
-```
+```script
 python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt
 ```
 
 or shell stript:
 
-```
+```script
 # Evaluation
 sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH PLATFORM
 ```
+
 PLATFORM is Ascend or GPU, default is Ascend.
 
 #### Launch
@@ -202,8 +200,8 @@ sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckp
 #### Result
 
 Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
- 
-```
+
+```log
 acc=78.16%(TOP1)
 acc=93.88%(TOP5)
 ```
@@ -212,7 +210,7 @@ acc=93.88%(TOP5)
 
 Change the export mode and export file in `src/config.py`, and run `export.py`.
 
-```
+```script
 python export.py --platform PLATFORM --pretrained CKPT_PATH
 ```
 
@@ -242,17 +240,16 @@ python export.py --platform PLATFORM --pretrained CKPT_PATH
 | -------------------------- | ----------------------------- | ------------------------- | -------------------- |
 | Resource                   | Ascend 910                    | NV SMX2 V100-32G          | Ascend 310           |
 | uploaded Date              | 06/30/2020                    | 07/23/2020                | 07/23/2020           |
-| MindSpore Version          | 0.5.0                         | 0.6.0                     | 0.6.0                | 
+| MindSpore Version          | 0.5.0                         | 0.6.0                     | 0.6.0                |
 | Dataset                    | ImageNet, 1.2W                | ImageNet, 1.2W            | ImageNet, 1.2W       |
 | batch_size                 | 1                             | 1                         | 1                    |
 | outputs                    | probability                   | probability               | probability          |
 | Accuracy                   | acc=78.16%(TOP1)              | acc=78.05%(TOP1)          |                      |
 
-
 # [Description of Random Situation](#contents)
 
 In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
 
 # [ModelZoo Homepage](#contents)
- 
-Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). 
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/official/cv/resnext50/eval.py

@@ -74,7 +74,6 @@ def parse_args(cloud_args=None):
     args = merge_args(args, cloud_args)
     args.image_size = config.image_size
     args.num_classes = config.num_classes
-    args.backbone = config.backbone
     args.rank = config.rank
     args.group_size = config.group_size
 
@@ -201,9 +200,7 @@ def test(cloud_args=None):
                                             max_epoch=1, rank=args.rank, group_size=args.group_size,
                                             mode='eval')
         eval_dataloader = de_dataset.create_tuple_iterator(output_numpy=True, num_epochs=1)
-        network = get_network(args.backbone, num_classes=args.num_classes, platform=args.platform)
-        if network is None:
-            raise NotImplementedError('not implement {}'.format(args.backbone))
+        network = get_network(num_classes=args.num_classes, platform=args.platform)
 
         load_pretrain_model(model, network, args)
 

model_zoo/official/cv/resnext50/export.py

@@ -33,7 +33,6 @@ def parse_args():
     args, _ = parser.parse_known_args()
     args.image_size = config.image_size
     args.num_classes = config.num_classes
-    args.backbone = config.backbone
 
     args.image_size = list(map(int, config.image_size.split(',')))
     args.image_height = args.image_size[0]
@@ -46,7 +45,7 @@ if __name__ == '__main__':
     args_export = parse_args()
     context.set_context(mode=context.GRAPH_MODE, device_target=args_export.platform)
 
-    net = get_network(args_export.backbone, num_classes=args_export.num_classes, platform=args_export.platform)
+    net = get_network(num_classes=args_export.num_classes, platform=args_export.platform)
 
     param_dict = load_checkpoint(args_export.pretrained)
     load_param_into_net(net, param_dict)

model_zoo/official/cv/resnext50/src/config.py

@@ -26,7 +26,6 @@ config = ed({
     "eta_min": 0,
     "T_max": 150,
     "max_epoch": 150,
-    "backbone": 'resnext50',
     "warmup_epochs": 1,
 
     "weight_decay": 0.0001,

model_zoo/official/cv/resnext50/src/image_classification.py

@@ -94,7 +94,5 @@ class Resnet(ImageClassificationNetwork):
 
 
 
-def get_network(backbone_name, **kwargs):
-    if backbone_name in ['resnext50']:
-        return Resnet(backbone_name, **kwargs)
-    return None
+def get_network(**kwargs):
+    return Resnet('resnext50', **kwargs)

model_zoo/official/cv/resnext50/train.py

@@ -131,7 +131,6 @@ def parse_args(cloud_args=None):
     args.eta_min = config.eta_min
     args.T_max = config.T_max
     args.max_epoch = config.max_epoch
-    args.backbone = config.backbone
     args.warmup_epochs = config.warmup_epochs
     args.weight_decay = config.weight_decay
     args.momentum = config.momentum
@@ -213,9 +212,7 @@ def train(cloud_args=None):
     # network
     args.logger.important_info('start create network')
     # get network and init
-    network = get_network(args.backbone, num_classes=args.num_classes, platform=args.platform)
-    if network is None:
-        raise NotImplementedError('not implement {}'.format(args.backbone))
+    network = get_network(num_classes=args.num_classes, platform=args.platform)
 
     load_pretrain_model(args.pretrained, network, args)