!12028 Add MobileNetV3 CPU scripts
From: @wuxuejian Reviewed-by: @c_34,@liangchenghui,@oacjiewen,@liangchenghui Signed-off-by: @liangchenghui
This commit is contained in:
Gitee
commit
07e9c79de8
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@ -50,8 +50,8 @@ MobileNetV3总体网络架构如下：
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# 环境要求
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- 硬件：GPU
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- 准备GPU处理器搭建硬件环境。
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- 硬件：GPU/CPU
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- 准备GPU/CPU处理器搭建硬件环境。
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- 框架
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- [MindSpore](https://www.mindspore.cn/install)
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- 如需查看详情，请参见如下资源：
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@ -86,6 +86,7 @@ MobileNetV3总体网络架构如下：
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使用python或shell脚本开始训练。shell脚本的使用方法如下：
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- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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- CPU: sh run_trian.sh CPU [DATASET_PATH]
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### 启动
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@ -93,8 +94,10 @@ MobileNetV3总体网络架构如下：
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# 训练示例
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python:
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GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
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CPU: python train.py --dataset_path ~/cifar10/train/ --device_targe CPU
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shell:
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GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
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CPU: sh run_train.sh CPU ~/cifar10/train/
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```
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### 结果
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@ -115,6 +118,7 @@ epoch time:138331.250, per step time:221.330, avg loss:3.917
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使用python或shell脚本开始训练。shell脚本的使用方法如下：
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- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
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- CPU: sh run_infer.sh CPU [DATASET_PATH] [CHECKPOINT_PATH]
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### 启动
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@ -122,9 +126,11 @@ epoch time:138331.250, per step time:221.330, avg loss:3.917
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# 推理示例
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python:
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GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
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CPU: python eval.py --dataset_path ~/cifar10/val/ --checkpoint_path mobilenet_199.ckpt --device_targe CPU
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shell:
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GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
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CPU: sh run_infer.sh CPU ~/cifar10/val/ ~/train/mobilenet-200_625.ckpt
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```
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> 训练过程中可以生成检查点。
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@ -19,7 +19,6 @@
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# [MobileNetV3 Description](#contents)
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MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
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[Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
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@ -35,22 +34,20 @@ The overall network architecture of MobileNetV3 is show below:
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Dataset used: [imagenet](http://www.image-net.org/)
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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- Note: Data will be processed in src/dataset.py
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# [Environment Requirements](#contents)
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- Hardware（GPU）
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- Prepare hardware environment with GPU processor.
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- Hardware（GPU/CPU）
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- Prepare hardware environment with GPU/CPU processor.
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- Framework
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- [MindSpore](https://www.mindspore.cn/install/en)
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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below：
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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# [Script description](#contents)
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@ -80,22 +77,25 @@ Dataset used: [imagenet](http://www.image-net.org/)
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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- CPU: sh run_trian.sh CPU [DATASET_PATH]
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### Launch
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```
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```shell
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# training example
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python:
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GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
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CPU: python train.py --dataset_path ~/cifar10/train/ --device_targe CPU
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shell:
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GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
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CPU: sh run_train.sh CPU ~/cifar10/train/
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```
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### Result
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Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings.
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```
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```bash
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epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
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epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
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epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
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@ -109,16 +109,19 @@ epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
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- CPU: sh run_infer.sh CPU [DATASET_PATH] [CHECKPOINT_PATH]
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### Launch
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```
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```shell
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# infer example
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python:
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GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
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CPU: python eval.py --dataset_path ~/cifar10/val/ --checkpoint_path mobilenet_199.ckpt --device_targe CPU
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shell:
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GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
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CPU: sh run_infer.sh CPU ~/cifar10/val/ ~/train/mobilenet-200_625.ckpt
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```
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> checkpoint can be produced in training process.
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@ -127,7 +130,7 @@ You can start training using python or shell scripts. The usage of shell scripts
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Inference result will be stored in the example path, you can find result like the followings in `val.log`.
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```
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```bash
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result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
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```
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@ -135,7 +138,7 @@ result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.
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Change the export mode and export file in `src/config.py`, and run `export.py`.
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```
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```python
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python export.py --device_target [PLATFORM] --checkpoint_path [CKPT_PATH]
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```
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@ -21,7 +21,9 @@ from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.dataset import create_dataset
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from src.dataset import create_dataset_cifar
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from src.config import config_gpu
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from src.config import config_cpu
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from src.mobilenetV3 import mobilenet_v3_large
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@ -38,17 +40,24 @@ if __name__ == '__main__':
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config = config_gpu
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context.set_context(mode=context.GRAPH_MODE,
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device_target="GPU", save_graphs=False)
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config,
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device_target=args_opt.device_target,
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batch_size=config.batch_size)
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elif args_opt.device_target == "CPU":
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config = config_cpu
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context.set_context(mode=context.GRAPH_MODE,
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device_target="CPU", save_graphs=False)
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dataset = create_dataset_cifar(dataset_path=args_opt.dataset_path,
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do_train=False,
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batch_size=config.batch_size)
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else:
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raise ValueError("Unsupported device_target.")
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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net = mobilenet_v3_large(num_classes=config.num_classes, activation="Softmax")
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config,
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device_target=args_opt.device_target,
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batch_size=config.batch_size)
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step_size = dataset.get_dataset_size()
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if args_opt.checkpoint_path:
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@ -19,6 +19,7 @@ import argparse
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import numpy as np
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from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
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from src.config import config_gpu
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from src.config import config_cpu
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from src.mobilenetV3 import mobilenet_v3_large
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@ -32,6 +33,9 @@ if __name__ == '__main__':
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if args_opt.device_target == "GPU":
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cfg = config_gpu
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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elif args_opt.device_target == "CPU":
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cfg = config_cpu
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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else:
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raise ValueError("Unsupported device_target.")
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@ -16,6 +16,7 @@
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if [ $# != 3 ]
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then
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echo "GPU: sh run_infer.sh [DEVICE_TARGET] [DATASET_PATH] [CHECKPOINT_PATH]"
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echo "CPU: sh run_infer.sh [DEVICE_TARGET] [DATASET_PATH] [CHECKPOINT_PATH]"
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exit 1
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fi
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@ -16,6 +16,14 @@
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run_gpu()
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{
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if [ $# -gt 5 ] || [ $# -lt 4 ]
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then
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echo "Usage:\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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CPU: sh run_train.sh CPU [DATASET_PATH]\n \
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"
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exit 1
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fi
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if [ $2 -lt 1 ] && [ $2 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$2 is not in (1-8)"
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@ -45,16 +53,42 @@ run_gpu()
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&> ../train.log & # dataset train folder
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}
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if [ $# -gt 5 ] || [ $# -lt 4 ]
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then
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echo "Usage:\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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"
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exit 1
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fi
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run_cpu()
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{
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if [ $# -gt 3 ] || [ $# -lt 2 ]
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then
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echo "Usage:\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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CPU: sh run_train.sh CPU [DATASET_PATH]\n \
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"
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exit 1
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fi
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if [ ! -d $2 ]
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then
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echo "error: DATASET_PATH=$2 is not a directory"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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if [ -d "../train" ];
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then
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rm -rf ../train
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fi
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mkdir ../train
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cd ../train || exit
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python ${BASEPATH}/../train.py \
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--dataset_path=$2 \
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--device_target=$1 \
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&> ../train.log & # dataset train folder
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}
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if [ $1 = "GPU" ] ; then
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run_gpu "$@"
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elif [ $1 = "CPU" ] ; then
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run_cpu "$@"
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else
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echo "Unsupported device_target"
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fi;
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@ -36,3 +36,23 @@ config_gpu = ed({
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"export_format": "MINDIR",
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"export_file": "mobilenetv3"
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})
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config_cpu = ed({
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"num_classes": 10,
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"image_height": 224,
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"image_width": 224,
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"batch_size": 32,
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"epoch_size": 120,
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"warmup_epochs": 5,
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"lr": 0.1,
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"momentum": 0.9,
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"weight_decay": 1e-4,
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"label_smooth": 0.1,
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"loss_scale": 1024,
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 500,
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"save_checkpoint_path": "./checkpoint",
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"export_format": "MINDIR",
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"export_file": "mobilenetv3"
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})
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@ -83,3 +83,60 @@ def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1,
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data_set = data_set.repeat(repeat_num)
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return data_set
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def create_dataset_cifar(dataset_path,
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do_train,
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repeat_num=1,
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batch_size=32,
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target="CPU"):
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"""
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create a train or evaluate cifar10 dataset
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Args:
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dataset_path(string): the path of dataset.
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do_train(bool): whether dataset is used for train or eval.
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repeat_num(int): the repeat times of dataset. Default: 1
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batch_size(int): the batch size of dataset. Default: 32
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target(str): the device target. Default: Ascend
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Returns:
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dataset
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"""
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data_set = ds.Cifar10Dataset(dataset_path,
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num_parallel_workers=8,
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shuffle=True)
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# define map operations
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if do_train:
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trans = [
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C.RandomCrop((32, 32), (4, 4, 4, 4)),
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C.RandomHorizontalFlip(prob=0.5),
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C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
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C.Resize((224, 224)),
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C.Rescale(1.0 / 255.0, 0.0),
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C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
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C.CutOut(112),
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C.HWC2CHW()
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]
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else:
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trans = [
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C.Resize((224, 224)),
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C.Rescale(1.0 / 255.0, 0.0),
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C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
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C.HWC2CHW()
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]
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type_cast_op = C2.TypeCast(mstype.int32)
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data_set = data_set.map(operations=type_cast_op,
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input_columns="label",
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num_parallel_workers=8)
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data_set = data_set.map(operations=trans,
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input_columns="image",
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num_parallel_workers=8)
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# apply batch operations
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data_set = data_set.batch(batch_size, drop_remainder=True)
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# apply dataset repeat operation
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data_set = data_set.repeat(repeat_num)
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return data_set
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@ -37,8 +37,10 @@ from mindspore.common import set_seed
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from mindspore.communication.management import init, get_group_size, get_rank
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from src.dataset import create_dataset
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from src.dataset import create_dataset_cifar
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from src.lr_generator import get_lr
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from src.config import config_gpu
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from src.config import config_cpu
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from src.mobilenetV3 import mobilenet_v3_large
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set_seed(1)
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@ -59,6 +61,10 @@ if args_opt.device_target == "GPU":
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context.set_auto_parallel_context(device_num=get_group_size(),
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parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True)
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elif args_opt.device_target == "CPU":
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context.set_context(mode=context.GRAPH_MODE,
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device_target="CPU",
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save_graphs=False)
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else:
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raise ValueError("Unsupported device_target.")
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@ -151,58 +157,71 @@ class Monitor(Callback):
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if __name__ == '__main__':
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config_ = None
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if args_opt.device_target == "GPU":
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# train on gpu
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print("train args: ", args_opt)
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print("cfg: ", config_gpu)
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config_ = config_gpu
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elif args_opt.device_target == "CPU":
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config_ = config_cpu
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else:
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raise ValueError("Unsupported device_target.")
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# train on device
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print("train args: ", args_opt)
|
||||
print("cfg: ", config_)
|
||||
|
||||
# define net
|
||||
net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
|
||||
# define loss
|
||||
if config_gpu.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
# define dataset
|
||||
epoch_size = config_gpu.epoch_size
|
||||
# define net
|
||||
net = mobilenet_v3_large(num_classes=config_.num_classes)
|
||||
# define loss
|
||||
if config_.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_.label_smooth, num_classes=config_.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
# define dataset
|
||||
epoch_size = config_.epoch_size
|
||||
if args_opt.device_target == "GPU":
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_gpu,
|
||||
config=config_,
|
||||
device_target=args_opt.device_target,
|
||||
repeat_num=1,
|
||||
batch_size=config_gpu.batch_size,
|
||||
run_distribute=args_opt.run_distribute)
|
||||
step_size = dataset.get_dataset_size()
|
||||
# resume
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
# define optimizer
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_gpu.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_gpu.lr,
|
||||
warmup_epochs=config_gpu.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
|
||||
config_gpu.weight_decay, config_gpu.loss_scale)
|
||||
# define model
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
batch_size=config_.batch_size,
|
||||
run_distribute=False)
|
||||
elif args_opt.device_target == "CPU":
|
||||
dataset = create_dataset_cifar(args_opt.dataset_path,
|
||||
do_train=True,
|
||||
batch_size=config_.batch_size)
|
||||
else:
|
||||
raise ValueError("Unsupported device_target.")
|
||||
step_size = dataset.get_dataset_size()
|
||||
# resume
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
# define optimizer
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_.lr,
|
||||
warmup_epochs=config_.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_.momentum,
|
||||
config_.weight_decay, config_.loss_scale)
|
||||
# define model
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if args_opt.run_distribute:
|
||||
ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
|
||||
else:
|
||||
ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + "/"
|
||||
if config_gpu.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_gpu.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", directory=ckpt_save_dir, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
# begine train
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if args_opt.run_distribute:
|
||||
ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
|
||||
else:
|
||||
ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + "/"
|
||||
if config_.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", directory=ckpt_save_dir, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
# begine train
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
|
|
|
|||
Loading…
Reference in New Issue