mindspore2022/example/mobilenetv2

MobileNetV2 Description

MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state of the art for mobile visual recognition including classification, object detection and semantic segmentation.

MobileNetV2 builds upon the ideas from MobileNetV1, using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1) linear bottlenecks between the layers, and 2) shortcut connections between the bottlenecks1.

Paper Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

Dataset

Dataset used: imagenet2012

• Dataset size: ~125G
  • Train: 120G, 1281167 images: 1000 directories
  • Test: 5G, 50000 images: images should be classified into 1000 directories firstly, just like train images
• Data format: RGB images.
  • Note: Data will be processed in src/dataset.py

Environment Requirements

• Hardware（Ascend/GPU）
  • Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the application form to ascend@huawei.com. Once approved, you can get the resources.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore tutorials
  • MindSpore API

Script description

Script and sample code

├── MobileNetV2        
  ├── Readme.md                      
  ├── scripts 
  │   ├──run_train.sh                  
  │   ├──run_eval.sh                    
  ├── src                              
  │   ├──config.py                     
  │   ├──dataset.py
  │   ├──luanch.py       
  │   ├──lr_generator.py                                 
  │   ├──mobilenetV2.py
  ├── train.py
  ├── eval.py

Training process

Usage

• Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]
• GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]

Launch

# training example
  Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
  GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/

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.

epoch: [  0/200], step:[  624/  625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
epoch: [  1/200], step:[  624/  625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
epoch time: 138331.250, per step time: 221.330, avg loss: 3.917

Eval process

Usage

• Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
• GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer example
    Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
    GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt

checkpoint can be produced in training process.

Result

Inference result will be stored in the example path, you can find result like the followings in val.log.

result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt

Model description

Performance

Training Performance

Parameters	MobilenetV2
Model Version		large
Resource	Ascend 910, cpu:2.60GHz 56cores, memory:314G	NV SMX2 V100-32G
uploaded Date	05/06/2020	05/06/2020
MindSpore Version	0.3.0	0.3.0
Dataset	ImageNet	ImageNet
Training Parameters	src/config.py	src/config.py
Optimizer	Momentum	Momentum
Loss Function	SoftmaxCrossEntropy	SoftmaxCrossEntropy
outputs
Loss		1.913
Accuracy		ACC1[77.09%] ACC5[92.57%]
Total time
Params (M)
Checkpoint for Fine tuning
Model for inference

Inference Performance

Parameters
Model Version	V1
Resource	Huawei 910	NV SMX2 V100-32G	Huawei 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
batch_size		130(8P)
outputs
Accuracy		ACC1[72.07%] ACC5[90.90%]
Speed
Total time
Model for inference

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