alexnet readme

model_zoo/official/cv/alexnet/README.md

@@ -4,16 +4,16 @@
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
 - [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)
     - [Training Process](#training-process)
-        - [Training](#training)  
+        - [Training](#training)
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
 - [Model Description](#model-description)
-    - [Performance](#performance)  
+    - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
 - [ModelZoo Homepage](#modelzoo-homepage)
 
@@ -26,15 +26,15 @@ AlexNet was proposed in 2012, one of the most influential neural networks. It go
 
 # [Model Architecture](#contents)
 
-AlexNet composition consists of 5 convolutional layers and 3 fully connected layers. Multiple convolutional kernels can extract interesting features in images and get more accurate classification.  
+AlexNet composition consists of 5 convolutional layers and 3 fully connected layers. Multiple convolutional kernels can extract interesting features in images and get more accurate classification.
 
 # [Dataset](#contents)
 
-Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>) 
+Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
 
 - Dataset size：175M，60,000 32*32 colorful images in 10 classes
-  - Train：146M，50,000 images  
-  - Test：29.3M，10,000 images 
+  - Train：146M，50,000 images
+  - Test：29.3M，10,000 images
 - Data format：binary files
   - Note：Data will be processed in dataset.py
 - Download the dataset, the directory structure is as follows:
@@ -48,20 +48,20 @@ Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
 # [Environment Requirements](#contents)
 
 - Hardware（Ascend/GPU）
-  - Prepare hardware environment with Ascend or GPU processor. 
+  - Prepare hardware environment with Ascend or GPU processor.
 - Framework
   - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
 - For more information, please check the resources below：
-  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) 
+  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
   - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
 
 # [Quick Start](#contents)
 
-After installing MindSpore via the official website, you can start training and evaluation as follows: 
+After installing MindSpore via the official website, you can start training and evaluation as follows:
 
 ```python
 # enter script dir, train AlexNet
-sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]  
+sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]
 # enter script dir, evaluate AlexNet
 sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
 ```
@@ -72,20 +72,20 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
 
 ```
 ├── cv
-    ├── alexnet        
+    ├── alexnet
         ├── README.md                    // descriptions about alexnet
         ├── requirements.txt             // package needed
-        ├── scripts 
-        │   ├──run_standalone_train_gpu.sh             // train in gpu 
-        │   ├──run_standalone_train_ascend.sh          // train in ascend 
-        │   ├──run_standalone_eval_gpu.sh             //  evaluate in gpu 
-        │   ├──run_standalone_eval_ascend.sh          //  evaluate in ascend 
-        ├── src 
+        ├── scripts
+        │   ├──run_standalone_train_gpu.sh             // train in gpu
+        │   ├──run_standalone_train_ascend.sh          // train in ascend
+        │   ├──run_standalone_eval_gpu.sh             //  evaluate in gpu
+        │   ├──run_standalone_eval_ascend.sh          //  evaluate in ascend
+        ├── src
         │   ├──dataset.py             // creating dataset
         │   ├──alexnet.py              // alexnet architecture
-        │   ├──config.py            // parameter configuration 
-        ├── train.py               // training script 
-        ├── eval.py               //  evaluation script  
+        │   ├──config.py            // parameter configuration
+        ├── train.py               // training script
+        ├── eval.py               //  evaluation script
 ```
 
 ## [Script Parameters](#contents)
@@ -93,39 +93,61 @@ sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
 ```python
 Major parameters in train.py and config.py as follows:
 
---data_path: The absolute full path to the train and evaluation datasets. 
---epoch_size: Total training epochs. 
---batch_size: Training batch size.  
+--data_path: The absolute full path to the train and evaluation datasets.
+--epoch_size: Total training epochs.
+--batch_size: Training batch size.
 --image_height: Image height used as input to the model.
---image_width: Image width used as input the model. 
---device_target: Device where the code will be implemented. Optional values are "Ascend", "GPU". 
+--image_width: Image width used as input the model.
+--device_target: Device where the code will be implemented. Optional values are "Ascend", "GPU".
 --checkpoint_path: The absolute full path to the checkpoint file saved after training.
---data_path: Path where the dataset is saved    
+--data_path: Path where the dataset is saved
 ```
 
 ## [Training Process](#contents)
 
-### Training 
+### Training
 
-```
-python train.py --data_path cifar-10-batches-bin --ckpt_path ckpt > log.txt 2>&1 &  
-# or enter script dir, and run the script
-sh run_standalone_train_ascend.sh cifar-10-batches-bin ckpt  
-```
+- running on Ascend
 
-After training, the loss value will be achieved as follows:
+  ```
+  python train.py --data_path cifar-10-batches-bin --ckpt_path ckpt > log.txt 2>&1 &
+  # or enter script dir, and run the script
+  sh run_standalone_train_ascend.sh cifar-10-batches-bin ckpt
+  ```
 
-```
-# grep "loss is " train.log
-epoch: 1 step: 1, loss is 2.2791853
-...
-epoch: 1 step: 1536, loss is 1.9366643
-epoch: 1 step: 1537, loss is 1.6983616
-epoch: 1 step: 1538, loss is 1.0221305
-...
-```
+  After training, the loss value will be achieved as follows:
+
+  ```
+  # grep "loss is " train.log
+  epoch: 1 step: 1, loss is 2.2791853
+  ...
+  epoch: 1 step: 1536, loss is 1.9366643
+  epoch: 1 step: 1537, loss is 1.6983616
+  epoch: 1 step: 1538, loss is 1.0221305
+  ...
+  ```
+
+  The model checkpoint will be saved in the current directory.
+
+- running on GPU
+
+  ```
+  python train.py --device_target "GPU" --data_path cifar-10-batches-bin --ckpt_path ckpt > log.txt 2>&1 &
+  # or enter script dir, and run the script
+  sh run_standalone_train_for_gpu.sh cifar-10-batches-bin ckpt
+  ```
+
+  After training, the loss value will be achieved as follows:
+
+  ```
+  # grep "loss is " train.log
+  epoch: 1 step: 1, loss is 2.3125906
+  ...
+  epoch: 30 step: 1560, loss is 0.6687547
+  epoch: 30 step: 1561, loss is 0.20055409
+  epoch: 30 step: 1561, loss is 0.103845775
+  ```
 
-The model checkpoint will be saved in the current directory. 
 
 ## [Evaluation Process](#contents)
 
@@ -133,44 +155,61 @@ The model checkpoint will be saved in the current directory.
 
 Before running the command below, please check the checkpoint path used for evaluation.
 
-```
-python eval.py --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > log.txt 2>&1 &  
-or enter script dir, and run the script
-sh run_standalone_eval_ascend.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt
-```
+- running on Ascend
 
-You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
+  ```
+  python eval.py --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > log.txt 2>&1 &
+  or enter script dir, and run the script
+  sh run_standalone_eval_ascend.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt
+  ```
 
-```
-# grep "Accuracy: " log.txt
-'Accuracy': 0.8832 
-```
+  You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
+
+  ```
+  # grep "Accuracy: " log.txt
+  'Accuracy': 0.8832
+  ```
+
+- running on GPU
+
+  ```
+  python eval.py --device_target "GPU" --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-30_1562.ckpt > log.txt 2>&1 &
+  or enter script dir, and run the script
+  sh run_standalone_eval_for_gpu.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-30_1562.ckpt
+  ```
+
+  You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
+
+  ```
+  # grep "Accuracy: " log.txt
+  'Accuracy': 0.88512
+  ```
 
 # [Model Description](#contents)
 
 ## [Performance](#contents)
 
-### Evaluation Performance 
-
-| Parameters                 | AlexNet                                                   |
-| -------------------------- | ----------------------------------------------------------- |
-| Resource                   | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G             |
-| uploaded Date              | 06/09/2020 (month/day/year)                                 |
-| MindSpore Version          | 0.5.0-beta                                                       |
-| Dataset                    | CIFAR-10                                                    |
-| Training Parameters        | epoch=30, steps=1562, batch_size = 32, lr=0.002              |
-| Optimizer                  | Momentum                                                         |
-| Loss Function              | Softmax Cross Entropy                                       |
-| outputs                    | probability                                                 |
-| Loss                       | 0.0016                                                      |
-| Speed                      | 21 ms/step                          |
-| Total time                 | 17 mins                          |
-| Checkpoint for Fine tuning | 445M (.ckpt file)                                         |
-| Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet |
+### Evaluation Performance
+
+| Parameters                 | Ascend                                                      | GPU                                              |
+| -------------------------- | ------------------------------------------------------------| -------------------------------------------------|
+| Resource                   | Ascend 910; CPU 2.60GHz, 56cores; Memory, 314G              | NV SMX2 V100-32G                                 |
+| uploaded Date              | 06/09/2020 (month/day/year)                                 | 17/09/2020 (month/day/year)                      |
+| MindSpore Version          | 0.5.0-beta                                                  | 0.7.0-beta                                       |
+| Dataset                    | CIFAR-10                                                    | CIFAR-10                                         |
+| Training Parameters        | epoch=30, steps=1562, batch_size = 32, lr=0.002             | epoch=30, steps=1562, batch_size = 32, lr=0.002  |
+| Optimizer                  | Momentum                                                    | Momentum                                         |
+| Loss Function              | Softmax Cross Entropy                                       | Softmax Cross Entropy                            |
+| outputs                    | probability                                                 | probability                                      |
+| Loss                       | 0.0016                                                      | 0.01                                             |
+| Speed                      | 21 ms/step                                                  | 16.8 ms/step                                     |
+| Total time                 | 17 mins                                                     | 14 mins                                          |
+| Checkpoint for Fine tuning | 445M (.ckpt file)                                           | 445M (.ckpt file)                                |
+| Scripts                    | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet |
 
 # [Description of Random Situation](#contents)
 
 In dataset.py, we set the seed inside ```create_dataset``` function.
 
-# [ModelZoo Homepage](#contents)  
- Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  
+# [ModelZoo Homepage](#contents)
+ Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).