!4792 normalize mobilenetv2 mobilenetv3 readme, delete mobilenetv3 ascend

Merge pull request !4792 from zhaoting/readme

model_zoo/official/cv/mobilenetv2/Readme.md

@@ -1,17 +1,37 @@
-# MobileNetV2 Description
+# Contents
+
+- [MobileNetV2 Description](#mobilenetv2-description)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [Features](#features)
+    - [Mixed Precision](#mixed-precision)
+- [Environment Requirements](#environment-requirements)
+- [Script Description](#script-description)
+    - [Script and Sample Code](#script-and-sample-code)
+    - [Training Process](#training-process)
+    - [Evaluation Process](#evaluation-process)
+        - [Evaluation](#evaluation)
+- [Model Description](#model-description)
+    - [Performance](#performance)  
+        - [Training Performance](#evaluation-performance)
+        - [Inference Performance](#evaluation-performance)
+- [Description of Random Situation](#description-of-random-situation)
+- [ModelZoo Homepage](#modelzoo-homepage)
+
+# [MobileNetV2 Description](#contents)
 
 
 MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
 
 [Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
 
-# Model architecture
+# [Model architecture](#contents)
 
 The overall network architecture of MobileNetV2 is show below:
 
 [Link](https://arxiv.org/pdf/1905.02244)
 
-# Dataset
+# [Dataset](#contents)
 
 Dataset used: [imagenet](http://www.image-net.org/)
 
@@ -22,10 +42,14 @@ Dataset used: [imagenet](http://www.image-net.org/)
 	- Note: Data will be processed in src/dataset.py 
 
 
-# Features
+# [Features](#contents)
 
+## [Mixed Precision(Ascend)](#contents)
 
-# Environment Requirements
+The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/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. 
@@ -36,30 +60,33 @@ Dataset used: [imagenet](http://www.image-net.org/)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
 
 
-# Script description
+# [Script description](#contents)
 
-## Script and sample code
+## [Script and sample code](#contents)
 
 ```python
 ├── MobileNetV2        
-  ├── Readme.md                      
+  ├── Readme.md     # descriptions about MobileNetV2                 
   ├── scripts 
-  │   ├──run_train.sh                  
-  │   ├──run_eval.sh                    
+  │   ├──run_train.sh   # shell script for train               
+  │   ├──run_eval.sh    # shell script for evaluation                
   ├── src                              
-  │   ├──config.py                     
-  │   ├──dataset.py
-  │   ├──luanch.py       
-  │   ├──lr_generator.py                                 
-  │   ├──mobilenetV2.py
-  ├── train.py
-  ├── eval.py
+  │   ├──config.py      # parameter configuration               
+  │   ├──dataset.py     # creating dataset
+  │   ├──launch.py      # start python script
+  │   ├──lr_generator.py     # learning rate config                            
+  │   ├──mobilenetV2.py      # MobileNetV2 architecture
+  ├── train.py      # training script
+  ├── eval.py       #  evaluation script
 ```
 
-## Training process
+## [Training process](#contents)
 
 ### Usage
 
+
+You can start training using python or shell scripts. The usage of shell scripts as follows:
+
 - Ascend: sh run_train.sh Ascend [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [CKPT_PATH]
 - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
 
@@ -67,8 +94,13 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ``` 
 # training example
-  Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ mobilenet_199.ckpt
-  GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
+  python:
+      Ascend: python train.py --dataset_path ~/imagenet/train/ --device_targe Ascend
+      GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
+
+  shell:
+      Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ mobilenet_199.ckpt
+      GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
 ```
 
 ### Result
@@ -82,10 +114,12 @@ epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:
 epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
 ```
 
-## Eval process
+## [Eval process](#contents)
 
 ### Usage
 
+You can start training using python or shell scripts. The usage of shell scripts as follows:
+
 - Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
 - GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
 
@@ -93,8 +127,13 @@ epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
 
 ``` 
 # infer example
-    Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
-    GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
+  python:
+      Ascend: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe Ascend
+      GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
+
+  shell:
+      Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
+      GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
 ```
 
 > checkpoint can be produced in training process. 
@@ -107,9 +146,9 @@ Inference result will be stored in the example path, you can find result like th
 result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
 ```
 
-# Model description
+# [Model description](#contents)
 
-## Performance
+## [Performance](#contents)
 
 ### Training Performance
 
@@ -147,5 +186,11 @@ result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.
 | Total time                 |                               |                           |                      |
 | Model for inference        |                               |                           |                      |
 
-# ModelZoo Homepage  
- [Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)  
+# [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). 
+ 

model_zoo/official/cv/mobilenetv2/eval.py

@@ -39,7 +39,7 @@ if __name__ == '__main__':
     net = None
     if args_opt.device_target == "Ascend":
         config = config_ascend
-        device_id = int(os.getenv('DEVICE_ID'))
+        device_id = int(os.getenv('DEVICE_ID', '0'))
         context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
                             device_id=device_id, save_graphs=False)
         net = mobilenet_v2(num_classes=config.num_classes, device_target="Ascend")

model_zoo/official/cv/mobilenetv2/scripts/run_infer.sh

@@ -47,7 +47,7 @@ fi
 mkdir ../eval
 cd ../eval || exit
 
-# luanch
+# launch
 python ${BASEPATH}/../eval.py \
         --device_target=$1 \
         --dataset_path=$2 \

model_zoo/official/cv/mobilenetv2/src/dataset.py

@@ -35,8 +35,8 @@ def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1,
         dataset
     """
     if device_target == "Ascend":
-        rank_size = int(os.getenv("RANK_SIZE"))
-        rank_id = int(os.getenv("RANK_ID"))
+        rank_size = int(os.getenv("RANK_SIZE", '1'))
+        rank_id = int(os.getenv("RANK_ID", '0'))
         if rank_size == 1:
             ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
         else:

model_zoo/official/cv/mobilenetv2/train.py

@@ -53,11 +53,10 @@ parser.add_argument('--device_target', type=str, default=None, help='run device_
 args_opt = parser.parse_args()
 
 if args_opt.device_target == "Ascend":
-    device_id = int(os.getenv('DEVICE_ID'))
-    rank_id = int(os.getenv('RANK_ID'))
-    rank_size = int(os.getenv('RANK_SIZE'))
+    device_id = int(os.getenv('DEVICE_ID', '0'))
+    rank_id = int(os.getenv('RANK_ID', '0'))
+    rank_size = int(os.getenv('RANK_SIZE', '1'))
     run_distribute = rank_size > 1
-    device_id = int(os.getenv('DEVICE_ID'))
     context.set_context(mode=context.GRAPH_MODE,
                         device_target="Ascend",
                         device_id=device_id, save_graphs=False)

model_zoo/official/cv/mobilenetv3/Readme.md

@@ -1,17 +1,35 @@
-# MobileNetV3 Description
+# Contents
+
+- [MobileNetV3 Description](#mobilenetv3-description)
+- [Model Architecture](#model-architecture)
+- [Dataset](#dataset)
+- [Environment Requirements](#environment-requirements)
+- [Script Description](#script-description)
+    - [Script and Sample Code](#script-and-sample-code)
+    - [Training Process](#training-process)
+    - [Evaluation Process](#evaluation-process)
+        - [Evaluation](#evaluation)
+- [Model Description](#model-description)
+    - [Performance](#performance)  
+        - [Training Performance](#evaluation-performance)
+        - [Inference Performance](#evaluation-performance)
+- [Description of Random Situation](#description-of-random-situation)
+- [ModelZoo Homepage](#modelzoo-homepage)
+
+# [MobileNetV3 Description](#contents)
 
 
 MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
 
 [Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
 
-# Model architecture
+# [Model architecture](#contents)
 
 The overall network architecture of MobileNetV3 is show below:
 
 [Link](https://arxiv.org/pdf/1905.02244)
 
-# Dataset
+# [Dataset](#contents)
 
 Dataset used: [imagenet](http://www.image-net.org/)
 
@@ -22,10 +40,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
 	- Note: Data will be processed in src/dataset.py 
 
 
-# Features
-
-
-# Environment Requirements
+# [Environment Requirements](#contents)
 
 - Hardware（GPU）
   - Prepare hardware environment with GPU processor.
@@ -36,37 +51,42 @@ Dataset used: [imagenet](http://www.image-net.org/)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
 
 
-# Script description
+# [Script description](#contents)
 
-## Script and sample code
+## [Script and sample code](#contents)
 
 ```python
-├── MobilenetV3        
-  ├── Readme.md                      
+├── MobileNetV3        
+  ├── Readme.md     # descriptions about MobileNetV3                 
   ├── scripts 
-  │   ├──run_train.sh                  
-  │   ├──run_eval.sh                    
+  │   ├──run_train.sh   # shell script for train               
+  │   ├──run_eval.sh    # shell script for evaluation                
   ├── src                              
-  │   ├──config.py                     
-  │   ├──dataset.py
-  │   ├──luanch.py       
-  │   ├──lr_generator.py                                 
-  │   ├──mobilenetV2.py
-  ├── train.py
-  ├── eval.py
+  │   ├──config.py      # parameter configuration               
+  │   ├──dataset.py     # creating dataset
+  │   ├──launch.py      # start python script
+  │   ├──lr_generator.py     # learning rate config                            
+  │   ├──mobilenetV3.py      # MobileNetV3 architecture
+  ├── train.py      # training script
+  ├── eval.py       #  evaluation script
 ```
 
-## Training process
+## [Training process](#contents)
 
 ### Usage
 
+You can start training using python or shell scripts. The usage of shell scripts as follows:
+
 - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
 
 ### Launch
 
 ``` 
 # training example
-  GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
+  python:
+      GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
+  shell:
+      GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
 ```
 
 ### Result
@@ -80,16 +100,22 @@ epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:
 epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
 ```
 
-## Eval process
+## [Eval process](#contents)
 
 ### Usage
 
+You can start training using python or shell scripts. The usage of shell scripts as follows:
+
 - GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
 
 ### Launch
 
 ``` 
 # infer example
+  python:
+    GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
+
+  shell:
     GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
 ```
 
@@ -103,46 +129,50 @@ Inference result will be stored in the example path, you can find result like th
 result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
 ```
 
-# Model description
+# [Model description](#contents)
 
-## Performance
+## [Performance](#contents)
 
 ### Training Performance
 
-| Parameters                 | MobilenetV3                                                |                           |
-| -------------------------- | ---------------------------------------------------------- | ------------------------- |
-| Model Version              |                                                            | large                     |
-| Resource                   | Ascend 910, cpu:2.60GHz 56cores, memory:314G               | NV SMX2 V100-32G          |
-| uploaded Date              | 05/06/2020                                                 | 05/06/2020                |
-| MindSpore Version          | 0.3.0                                                      | 0.3.0                     |
-| Dataset                    | ImageNet                                                   | ImageNet                  |
-| Training Parameters        | src/config.py                                              | src/config.py             |
-| Optimizer                  | Momentum                                                   | Momentum                  |
-| Loss Function              | SoftmaxCrossEntropy                                        | SoftmaxCrossEntropy       |
-| outputs                    |                                                            |                           |
-| Loss                       |                                                            | 1.913                     |
-| Accuracy                   |                                                            | ACC1[77.57%] ACC5[92.51%] |
-| Total time                 |                                                            |                           |
-| Params (M)                 |                                                            |                           |
-| Checkpoint for Fine tuning |                                                            |                           |
-| Model for inference        |                                                            |                           |
+| Parameters                 | MobilenetV3               |
+| -------------------------- | ------------------------- |
+| Model Version              | large                     |
+| Resource                   | NV SMX2 V100-32G          |
+| uploaded Date              | 05/06/2020                |
+| MindSpore Version          | 0.3.0                     |
+| Dataset                    | ImageNet                  |
+| Training Parameters        | src/config.py             |
+| Optimizer                  | Momentum                  |
+| Loss Function              | SoftmaxCrossEntropy       |
+| outputs                    |                           |
+| Loss                       | 1.913                     |
+| Accuracy                   | ACC1[77.57%] ACC5[92.51%] |
+| Total time                 |                           |
+| Params (M)                 |                           |
+| Checkpoint for Fine tuning |                           |
+| Model for inference        |                           |
 
 #### Inference Performance
 
-| Parameters                 |                               |                           |                      |
-| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
-| Model Version              | V1                            |                           |                      |
-| Resource                   | Huawei 910                    | NV SMX2 V100-32G          | Huawei 310           |
-| uploaded Date              | 05/06/2020                    | 05/22/2020                |                      |
-| MindSpore Version          | 0.2.0                         | 0.2.0                     | 0.2.0                | 
-| Dataset                    | ImageNet, 1.2W                | ImageNet, 1.2W            | ImageNet, 1.2W       |
-| batch_size                 |                               | 130(8P)                   |                      |
-| outputs                    |                               |                           |                      |
-| Accuracy                   |                               | ACC1[75.43%] ACC5[92.51%] |                      |
-| Speed                      |                               |                           |                      |
-| Total time                 |                               |                           |                      |
-| Model for inference        |                               |                           |                      |
-
-
-# ModelZoo Homepage  
- [Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)  
+| Parameters                 |                      |
+| -------------------------- | -------------------- |
+| Model Version              |                           |
+| Resource                   | NV SMX2 V100-32G          |
+| uploaded Date              | 05/22/2020                |
+| MindSpore Version          | 0.2.0                     |
+| Dataset                    | ImageNet, 1.2W            |
+| batch_size                 | 130(8P)                   |
+| outputs                    |                           |
+| Accuracy                   | ACC1[75.43%] ACC5[92.51%] |
+| Speed                      |                           |
+| Total time                 |                           |
+| Model for inference        |                           |
+
+# [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). 

model_zoo/official/cv/mobilenetv3/eval.py

@@ -15,33 +15,26 @@
 """
 eval.
 """
-import os
 import argparse
 from mindspore import context
 from mindspore import nn
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.common import dtype as mstype
 from src.dataset import create_dataset
-from src.config import config_ascend, config_gpu
+from src.config import config_gpu
 from src.mobilenetV3 import mobilenet_v3_large
 
 
 parser = argparse.ArgumentParser(description='Image classification')
 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=None, help='run device_target')
+parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
 args_opt = parser.parse_args()
 
 
 if __name__ == '__main__':
     config = None
-    if args_opt.device_target == "Ascend":
-        config = config_ascend
-        device_id = int(os.getenv('DEVICE_ID'))
-        context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
-                            device_id=device_id, save_graphs=False)
-    elif args_opt.device_target == "GPU":
+    if args_opt.device_target == "GPU":
         config = config_gpu
         context.set_context(mode=context.GRAPH_MODE,
                             device_target="GPU", save_graphs=False)
@@ -52,12 +45,6 @@ if __name__ == '__main__':
         is_grad=False, sparse=True, reduction='mean')
     net = mobilenet_v3_large(num_classes=config.num_classes)
 
-    if args_opt.device_target == "Ascend":
-        net.to_float(mstype.float16)
-        for _, cell in net.cells_and_names():
-            if isinstance(cell, nn.Dense):
-                cell.to_float(mstype.float32)
-
     dataset = create_dataset(dataset_path=args_opt.dataset_path,
                              do_train=False,
                              config=config,

model_zoo/official/cv/mobilenetv3/train.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """train_imagenet."""
-import os
+
 import time
 import argparse
 import random
@@ -47,20 +47,10 @@ de.config.set_seed(1)
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
 parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
-parser.add_argument('--device_target', type=str, default=None, help='run device_target')
+parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
 args_opt = parser.parse_args()
 
-if args_opt.device_target == "Ascend":
-    device_id = int(os.getenv('DEVICE_ID'))
-    rank_id = int(os.getenv('RANK_ID'))
-    rank_size = int(os.getenv('RANK_SIZE'))
-    run_distribute = rank_size > 1
-    device_id = int(os.getenv('DEVICE_ID'))
-    context.set_context(mode=context.GRAPH_MODE,
-                        device_target="Ascend",
-                        device_id=device_id,
-                        save_graphs=False)
-elif args_opt.device_target == "GPU":
+if args_opt.device_target == "GPU":
     context.set_context(mode=context.GRAPH_MODE,
                         device_target="GPU",
                         save_graphs=False)