sbjkx
/
mindspore
forked from OSSInnovation/mindspore
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

alexnet readme

This commit is contained in:
wilfChen 2020-09-17 16:49:23 +08:00
parent a60800d7ba
commit c267aaefe9
1 changed files with 111 additions and 72 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

183
model_zoo/official/cv/alexnet/README.md
View File

@ -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
### 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 |
| 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).