From 2fb88e8c46eef9e84faa82c3b62e2e92414d6785 Mon Sep 17 00:00:00 2001 From: wukesong Date: Sat, 29 Aug 2020 16:38:08 +0800 Subject: [PATCH] modify readme --- model_zoo/official/cv/alexnet/README.md | 233 +++++++++++++----- model_zoo/official/cv/alexnet/eval.py | 4 +- model_zoo/official/cv/alexnet/train.py | 4 +- model_zoo/official/cv/lenet/README.md | 248 +++++++++++++++----- model_zoo/official/cv/lenet/eval.py | 4 +- model_zoo/official/cv/lenet/train.py | 4 +- model_zoo/official/cv/mobilenetv2/Readme.md | 2 +- model_zoo/official/cv/resnext50/README.md | 3 +- 8 files changed, 377 insertions(+), 125 deletions(-) diff --git a/model_zoo/official/cv/alexnet/README.md b/model_zoo/official/cv/alexnet/README.md index 1059e22aaeb..84ec3c1d269 100644 --- a/model_zoo/official/cv/alexnet/README.md +++ b/model_zoo/official/cv/alexnet/README.md @@ -1,57 +1,176 @@ -# AlexNet Example - -## Description - -Training AlexNet with dataset in MindSpore. - -This is the simple tutorial for training AlexNet in MindSpore. - -## Requirements - -- Install [MindSpore](https://www.mindspore.cn/install/en). - -- Download the dataset, the directory structure is as follows: - -``` -├─10-batches-bin -│ -└─10-verify-bin -``` - -## Running the example - -```python -# train AlexNet, hyperparameter setting in config.py -python train.py --data_path 10-batches-bin -``` - -You will get the loss value of each step as following: - -```bash -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 -... -``` - -Then, evaluate AlexNet according to network model -```python -# evaluate AlexNet -python eval.py --data_path 10-verify-bin --ckpt_path checkpoint_alexnet-1_1562.ckpt -``` - -## Note -Here are some optional parameters: - -```bash ---device_target {Ascend,GPU} - device where the code will be implemented (default: Ascend) ---data_path DATA_PATH - path where the dataset is saved ---dataset_sink_mode DATASET_SINK_MODE - dataset_sink_mode is False or True -``` - -You can run ```python train.py -h``` or ```python eval.py -h``` to get more information. +# 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) + +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 +- Data format：binary files + - Note：Data will be processed in dataset.py +- Download the dataset, the directory structure is as follows: + +``` +├─cifar-10-batches-bin +│ +└─cifar-10-verify-bin +``` + +# [Environment Requirements](#contents) + +- Hardware（Ascend/GPU） + - 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 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: + +```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) + +``` +├── 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 + +``` +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 +``` + +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. + +## [Evaluation Process](#contents) + +### Evaluation + +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 +``` + +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 +``` + +# [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 | + +# [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). diff --git a/model_zoo/official/cv/alexnet/eval.py b/model_zoo/official/cv/alexnet/eval.py index 6a091aedd89..a057a298aa8 100644 --- a/model_zoo/official/cv/alexnet/eval.py +++ b/model_zoo/official/cv/alexnet/eval.py @@ -18,6 +18,7 @@ eval alexnet according to model file: python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt """ +import ast import argparse from src.config import alexnet_cfg as cfg from src.dataset import create_dataset_cifar10 @@ -36,7 +37,8 @@ if __name__ == "__main__": parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved') parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\ path where the trained ckpt file') - parser.add_argument('--dataset_sink_mode', type=bool, default=True, help='dataset_sink_mode is False or True') + parser.add_argument('--dataset_sink_mode', type=ast.literal_eval, default=True, + help='dataset_sink_mode is False or True') args = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) diff --git a/model_zoo/official/cv/alexnet/train.py b/model_zoo/official/cv/alexnet/train.py index 4512244b922..f7815e3925e 100644 --- a/model_zoo/official/cv/alexnet/train.py +++ b/model_zoo/official/cv/alexnet/train.py @@ -18,6 +18,7 @@ train alexnet and get network model files(.ckpt) : python train.py --data_path /YourDataPath """ +import ast import argparse from src.config import alexnet_cfg as cfg from src.dataset import create_dataset_cifar10 @@ -38,7 +39,8 @@ if __name__ == "__main__": parser.add_argument('--data_path', type=str, default="./", help='path where the dataset is saved') parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\ path where the trained ckpt file') - parser.add_argument('--dataset_sink_mode', type=bool, default=True, help='dataset_sink_mode is False or True') + parser.add_argument('--dataset_sink_mode', type=ast.literal_eval, default=True, + help='dataset_sink_mode is False or True') args = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) diff --git a/model_zoo/official/cv/lenet/README.md b/model_zoo/official/cv/lenet/README.md index 579c9894b2e..2f0b00229ec 100644 --- a/model_zoo/official/cv/lenet/README.md +++ b/model_zoo/official/cv/lenet/README.md @@ -1,62 +1,186 @@ -# LeNet Example - -## Description - -Training LeNet with dataset in MindSpore. - -This is the simple and basic tutorial for constructing a network in MindSpore. - -## Requirements - -- Install [MindSpore](https://www.mindspore.cn/install/en). - -- Download the dataset, the directory structure is as follows: - -``` -└─Data - ├─test - │ t10k-images.idx3-ubyte - │ t10k-labels.idx1-ubyte - │ - └─train - train-images.idx3-ubyte - train-labels.idx1-ubyte -``` - -## Running the example - -```python -# train LeNet, hyperparameter setting in config.py -python train.py --data_path Data -``` - -You will get the loss value of each step as following: - -```bash -epoch: 1 step: 1, loss is 2.3040335 -... -epoch: 1 step: 1739, loss is 0.06952668 -epoch: 1 step: 1740, loss is 0.05038793 -epoch: 1 step: 1741, loss is 0.05018193 -... -``` - -Then, evaluate LeNet according to network model -```python -# evaluate LeNet -python eval.py --data_path Data --ckpt_path checkpoint_lenet-1_1875.ckpt -``` - -## Note -Here are some optional parameters: - -```bash ---device_target {Ascend,GPU,CPU} - device where the code will be implemented (default: Ascend) ---data_path DATA_PATH - path where the dataset is saved ---dataset_sink_mode DATASET_SINK_MODE - dataset_sink_mode is False or True -``` - -You can run ```python train.py -h``` or ```python eval.py -h``` to get more information. +# Contents + +- [LeNet Description](#lenet-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) + + +# [LeNet Description](#contents) + +LeNet was proposed in 1998, a typical convolutional neural network. It was used for digit recognition and got big success. + +[Paper](https://ieeexplore.ieee.org/document/726791): Y.Lecun, L.Bottou, Y.Bengio, P.Haffner. Gradient-Based Learning Applied to Document Recognition. *Proceedings of the IEEE*. 1998. + +# [Model Architecture](#contents) + +LeNet is very simple, which contains 5 layers. The layer composition consists of 2 convolutional layers and 3 fully connected layers. + +# [Dataset](#contents) + +Dataset used: [MNIST]() + +- Dataset size：52.4M，60,000 28*28 in 10 classes + - Train：60,000 images + - Test：10,000 images +- Data format：binary files + - Note：Data will be processed in dataset.py + +- The directory structure is as follows: + +``` +└─Data + ├─test + │ t10k-images.idx3-ubyte + │ t10k-labels.idx1-ubyte + │ + └─train + train-images.idx3-ubyte + train-labels.idx1-ubyte +``` + +# [Environment Requirements](#contents) + +- Hardware（Ascend/GPU/CPU） + - Prepare hardware environment with Ascend, GPU, or CPU 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 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: + +```python +# enter script dir, train LeNet +sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH] +# enter script dir, evaluate LeNet +sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME] +``` + +# [Script Description](#contents) + +## [Script and Sample Code](#contents) + +``` +├── cv + ├── lenet + ├── README.md // descriptions about lenet + ├── requirements.txt // package needed + ├── scripts + │ ├──run_standalone_train_cpu.sh // train in cpu + │ ├──run_standalone_train_gpu.sh // train in gpu + │ ├──run_standalone_train_ascend.sh // train in ascend + │ ├──run_standalone_eval_cpu.sh // evaluate in cpu + │ ├──run_standalone_eval_gpu.sh // evaluate in gpu + │ ├──run_standalone_eval_ascend.sh // evaluate in ascend + ├── src + │ ├──dataset.py // creating dataset + │ ├──lenet.py // lenet 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", "CPU". +--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 + +``` +python train.py --data_path Data --ckpt_path ckpt > log.txt 2>&1 & +or enter script dir, and run the script +sh run_standalone_train_ascend.sh Data ckpt +``` + +After training, the loss value will be achieved as follows: + +``` +# grep "loss is " log.txt +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. + +## [Evaluation Process](#contents) + +### Evaluation + +Before running the command below, please check the checkpoint path used for evaluation. + +``` +python eval.py --data_path Data --ckpt_path ckpt/checkpoint_lenet-1_1875.ckpt > log.txt 2>&1 & +or enter script dir, and run the script +sh run_standalone_eval_ascend.sh Data ckpt/checkpoint_lenet-1_1875.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': 0.9842 +``` + +# [Model Description](#contents) + +## [Performance](#contents) + +### Evaluation Performance + +| Parameters | LeNet | +| -------------------------- | ----------------------------------------------------------- | +| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G | +| uploaded Date | 06/09/2020 (month/day/year) | +| MindSpore Version | 0.5.0-beta | +| Dataset | MNIST | +| Training Parameters | epoch=10, steps=1875, batch_size = 32, lr=0.01 | +| Optimizer | Momentum | +| Loss Function | Softmax Cross Entropy | +| outputs | probability | +| Loss | 0.002 | +| Speed | 1.70 ms/step | +| Total time | 43.1s | | +| Checkpoint for Fine tuning | 482k (.ckpt file) | +| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet | + +# [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). diff --git a/model_zoo/official/cv/lenet/eval.py b/model_zoo/official/cv/lenet/eval.py index bcd5503c399..4f5315f72b6 100644 --- a/model_zoo/official/cv/lenet/eval.py +++ b/model_zoo/official/cv/lenet/eval.py @@ -19,6 +19,7 @@ python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt """ import os +import ast import argparse import mindspore.nn as nn from mindspore import context @@ -37,7 +38,8 @@ if __name__ == "__main__": help='path where the dataset is saved') parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\ path where the trained ckpt file') - parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True') + parser.add_argument('--dataset_sink_mode', type=ast.literal_eval, default=False, + help='dataset_sink_mode is False or True') args = parser.parse_args() diff --git a/model_zoo/official/cv/lenet/train.py b/model_zoo/official/cv/lenet/train.py index 2c45c5b3274..7cd379134aa 100644 --- a/model_zoo/official/cv/lenet/train.py +++ b/model_zoo/official/cv/lenet/train.py @@ -19,6 +19,7 @@ python train.py --data_path /YourDataPath """ import os +import ast import argparse from src.config import mnist_cfg as cfg from src.dataset import create_dataset @@ -38,7 +39,8 @@ if __name__ == "__main__": help='path where the dataset is saved') parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\ path where the trained ckpt file') - parser.add_argument('--dataset_sink_mode', type=bool, default=True, help='dataset_sink_mode is False or True') + parser.add_argument('--dataset_sink_mode', type=ast.literal_eval, default=True, + help='dataset_sink_mode is False or True') args = parser.parse_args() diff --git a/model_zoo/official/cv/mobilenetv2/Readme.md b/model_zoo/official/cv/mobilenetv2/Readme.md index f7021170fdf..430d5b75865 100644 --- a/model_zoo/official/cv/mobilenetv2/Readme.md +++ b/model_zoo/official/cv/mobilenetv2/Readme.md @@ -175,7 +175,7 @@ result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625. | Parameters | | | | | -------------------------- | ----------------------------- | ------------------------- | -------------------- | | Model Version | V1 | | | -| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 | +| Resource | Ascend 910 | NV SMX2 V100-32G | Ascend 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 | diff --git a/model_zoo/official/cv/resnext50/README.md b/model_zoo/official/cv/resnext50/README.md index 75631126aa7..da163c202fe 100644 --- a/model_zoo/official/cv/resnext50/README.md +++ b/model_zoo/official/cv/resnext50/README.md @@ -48,6 +48,7 @@ Dataset used: [imagenet](http://www.image-net.org/) ## [Mixed Precision](#contents) 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) @@ -228,7 +229,7 @@ acc=93.88%(TOP5) | Parameters | | | | | -------------------------- | ----------------------------- | ------------------------- | -------------------- | -| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 | +| Resource | Ascend 910 | NV SMX2 V100-32G | Ascend 310 | | uploaded Date | 06/30/2020 | 07/23/2020 | 07/23/2020 | | MindSpore Version | 0.5.0 | 0.6.0 | 0.6.0 | | Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W |