mindspore/model_zoo/mobilenetv2_quant

MobileNetV2 Quantization Aware Training

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.

Training MobileNetV2 with ImageNet dataset in MindSpore with quantization aware training.

This is the simple and basic tutorial for constructing a network in MindSpore with quantization aware.

In this readme tutorial, you will:

1. Train a MindSpore fusion MobileNetV2 model for ImageNet from scratch using nn.Conv2dBnAct and nn.DenseBnAct.
2. Fine tune the fusion model by applying the quantization aware training auto network converter API convert_quant_network, after the network convergence then export a quantization aware model checkpoint file.

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 use: ImageNet

• Dataset size: about 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)
  • Prepare hardware environment with Ascend 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_quant        
  ├── Readme.md                      
  ├── scripts 
  │   ├──run_train.sh                  
  │   ├──run_infer.sh
  │   ├──run_train_quant.sh                  
  │   ├──run_infer_quant.sh
  ├── src                              
  │   ├──config.py                     
  │   ├──dataset.py
  │   ├──luanch.py       
  │   ├──lr_generator.py                                 
  │   ├──mobilenetV2.py
  ├── train.py
  ├── eval.py

Training process

Train MobileNetV2 model

Train a MindSpore fusion MobileNetV2 model for ImageNet, like:

• 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]

You can just run this command instead.

>>> sh run_train.sh Ascend 4 192.168.0.1 0,1,2,3 ~/imagenet/train/ ~/mobilenet.ckpt

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

Evaluate MobileNetV2 model

Evaluate a MindSpore fusion MobileNetV2 model for ImageNet, like:

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

You can just run this command instead.

>>> sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt

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

Fine-tune for quantization aware training

Fine tune the fusion model by applying the quantization aware training auto network converter API convert_quant_network, after the network convergence then export a quantization aware model checkpoint file.

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

You can just run this command instead.

>>> sh run_train_quant.sh Ascend 4 192.168.0.1 0,1,2,3 ~/imagenet/train/ ~/mobilenet.ckpt

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: [  0/60], 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: [  1/60], 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

Evaluate quantization aware training model

Evaluate a MindSpore fusion MobileNetV2 model for ImageNet by applying the quantization aware training, like:

• sh run_infer_quant.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]

You can just run this command instead.

>>> sh run_infer_quant.sh Ascend ~/imagenet/val/ ~/train/mobilenet-60_625.ckpt

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-60_625.ckpt

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