diff --git a/model_zoo/official/cv/alexnet/README.md b/model_zoo/official/cv/alexnet/README.md index 84ec3c1d26..7fda3108c2 100644 --- a/model_zoo/official/cv/alexnet/README.md +++ b/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]() +Dataset used: [CIFAR-10]() - 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]() # [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).