mindspore/model_zoo/official/cv/alexnet/README.md

# Contents

- [AlexNet Description](#alexnet-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Environment Requirements](#environment-requirements)
- [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)
    - [Evaluation Process](#evaluation-process)
        - [Evaluation](#evaluation)
- [Model Description](#model-description)
    - [Performance](#performance)
        - [Evaluation Performance](#evaluation-performance)
- [ModelZoo Homepage](#modelzoo-homepage)

## [AlexNet Description](#contents)

AlexNet was proposed in 2012, one of the most influential neural networks. It got big success in ImageNet Dataset recognition than other models.

[Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf): Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep ConvolutionalNeural Networks. *Advances In Neural Information Processing Systems*. 2012.

## [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.

## [Dataset](#contents)

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

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
- Data format：binary files
    - Note：Data will be processed in dataset.py
- Download the dataset, the directory structure is as follows:

```bash
├─cifar-10-batches-bin
│
└─cifar-10-verify-bin
```

## [Environment Requirements](#contents)

- Hardware（Ascend/GPU）
    - Prepare hardware environment with Ascend or GPU processor.
- Framework
    - [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)

## [Quick Start](#contents)

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]
# enter script dir, evaluate AlexNet
sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
```

## [Script Description](#contents)

### [Script and Sample Code](#contents)

```bash
├── cv
    ├── 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
        │   ├──dataset.py             // creating dataset
        │   ├──alexnet.py              // alexnet architecture
        │   ├──config.py            // parameter configuration
        ├── train.py               // training script
        ├── eval.py               //  evaluation script
```

### [Script Parameters](#contents)

```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.
--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".
--checkpoint_path: The absolute full path to the checkpoint file saved after training.
--data_path: Path where the dataset is saved
```

### [Training Process](#contents)

#### Training

- running on Ascend

  ```bash
  python train.py --data_path cifar-10-batches-bin --ckpt_path ckpt > log 2>&1 &
  # or enter script dir, and run the script
  sh run_standalone_train_ascend.sh cifar-10-batches-bin ckpt
  ```

  After training, the loss value will be achieved as follows:

  ```bash
  # grep "loss is " 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

  ```bash
  python train.py --device_target "GPU" --data_path cifar-10-batches-bin --ckpt_path ckpt > log 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:

  ```bash
  # grep "loss is " 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
  ```

### [Evaluation Process](#contents)

#### Evaluation

Before running the command below, please check the checkpoint path used for evaluation.

- running on Ascend

  ```bash
  python eval.py --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > eval_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
  ```

  You can view the results through the file "eval_log". The accuracy of the test dataset will be as follows:

  ```bash
  # grep "Accuracy: " eval_log
  'Accuracy': 0.8832
  ```

- running on GPU

  ```bash
  python eval.py --device_target "GPU" --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-30_1562.ckpt > eval_log 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 "eval_log". The accuracy of the test dataset will be as follows:

  ```bash
  # grep "Accuracy: " eval_log
  'Accuracy': 0.88512
  ```

## [Model Description](#contents)

### [Performance](#contents)

#### Evaluation Performance

| Parameters                 | Ascend                                                      | GPU                                              |
| -------------------------- | ------------------------------------------------------------| -------------------------------------------------|
| Resource                   | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G              | NV SMX2 V100-32G                                 |
| uploaded Date              | 06/09/2020 (month/day/year)                                 | 17/09/2020 (month/day/year)                      |
| MindSpore Version          | 1.0.0                                                  | 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                       | 0.08                                                      | 0.01                                             |
| Speed                      | 7.3 ms/step                                                  | 16.8 ms/step                                     |
| Total time                 | 6 mins                                                     | 14 mins                                          |
| Checkpoint for Fine tuning | 445M (.ckpt file)                                           | 445M (.ckpt file)                                |
| Scripts                    | [AlexNet Script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet) | [AlexNet Script](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).