-[Script and Sample Code](#script-and-sample-code)
-[Script and Sample Code](#script-and-sample-code)
-[Training Process](#training-process)
-[Evaluation Process](#evaluation-process)
-[Evaluation](#evaluation)
-[Evaluation](#evaluation)
-[Model Description](#model-description)
-[Performance](#performance)
-[Training Performance](#evaluation-performance)
-[Inference Performance](#evaluation-performance)
-[Performance](#performance)
-[Training Performance](#evaluation-performance)
-[Inference Performance](#evaluation-performance)
-[Description of Random Situation](#description-of-random-situation)
-[ModelZoo Homepage](#modelzoo-homepage)
# [MobileNetV2 Description](#contents)
MobileNetV2 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.
[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 MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
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@@ -36,78 +35,103 @@ The overall network architecture of MobileNetV2 is show below:
- Dataset size: ~125G, 1.2W colorful images in 1000 classes
- Train: 120G, 1.2W images
- Test: 5G, 50000 images
- Train: 120G, 1.2W images
- Test: 5G, 50000 images
- Data format: RGB images.
- Note: Data will be processed in src/dataset.py
- Note: Data will be processed in src/dataset.py
# [Features](#contents)
## [Mixed Precision(Ascend)](#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.
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)
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Hardware（Ascend/GPU/CPU）
- Prepare hardware environment with Ascend、GPU or CPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
CPU: python train.py --dataset_path ~/imagenet/train/ --platform CPU --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt
shell:
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt
CPU: sh run_train.sh CPU ~/imagenet/train/ incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt
```
### Result
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.
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.
