• Contents
• MobileNetV3 Description
• Model architecture
• Dataset
• Environment Requirements
• Script description
• • Script and sample code
  • Training process
  • • Usage
    • Launch
    • Result
  • Eval process
  • • Usage
    • Launch
    • Result
  • Inference Process
  • • Export MindIR
    • Infer on Ascend310
    • • Result
    • Infer with ONNX
    • • Result
• Model description
• • Performance
  • • Training Performance
• Description of Random Situation
• ModelZoo Homepage

Contents

• Contents
• MobileNetV3 Description
• Model architecture
• Dataset
• Environment Requirements
• Script description
  • Script and sample code
  • Training process
    • Usage
    • Launch
    • Result
  • Eval process
    • Usage
    • Launch
    • Result
  • Inference Process
    • Export MindIR
    • Infer on Ascend310
      • Result
    • Infer with ONNX
      • Result
• Model description
  • Performance
    • Training Performance
• Description of Random Situation
• ModelZoo Homepage

MobileNetV3 Description

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.

Paper 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.

Model architecture

The overall network architecture of MobileNetV3 is show below:

Link

Dataset

Dataset used: imagenet

• Dataset size: ~125G, 224*224 colorful images in 1000 classes
  • Train: 120G, 1281167 images
  • Test: 5G, 50000 images
• Data format: RGB images.
  • Note: Data will be processed in src/dataset.py

Environment Requirements

• Hardware（GPU/CPU）
  • Prepare hardware environment with GPU/CPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Script description

Script and sample code

├── MobileNetV3
  ├── Readme.md              # descriptions about MobileNetV3
  ├── scripts
  │   ├──run_train.sh        # shell script for train
  │   ├──run_eval.sh         # shell script for evaluation
  │   ├──run_infer_310.sh         # shell script for inference
  │   ├──run_onnx.sh         # shell script for onnx inference
  ├── src
  │   ├──config.py           # parameter configuration
  │   ├──dataset.py          # creating dataset
  │   ├──lr_generator.py     # learning rate config
  │   ├──mobilenetV3.py      # MobileNetV3 architecture
  ├── train.py               # training script
  ├── eval.py                #  evaluation script
  ├── infer_onnx.py          #  onnx inference script
  ├── export.py              # export mindir script
  ├── preprocess.py              # inference data preprocess script
  ├── postprocess.py              # inference result calculation script  
  ├── mindspore_hub_conf.py  #  mindspore hub interface

Training process

Usage

You can start training using python or shell scripts. The usage of shell scripts as follows:

• GPU: bash run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
• CPU: bash run_trian.sh CPU [DATASET_PATH]

Launch

# training example
  python:
      GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
      CPU: python train.py --dataset_path ~/cifar10/train/ --device_targe CPU
  shell:
      GPU: bash run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
      CPU: bash run_train.sh CPU ~/cifar10/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

You can start training using python or shell scripts. The usage of shell scripts as follows:

• GPU: bash run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
• CPU: bash run_infer.sh CPU [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer example
  python:
    GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
    CPU: python eval.py --dataset_path ~/cifar10/val/ --checkpoint_path mobilenet_199.ckpt --device_targe CPU

  shell:
    GPU: bash run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
    CPU: bash run_infer.sh CPU ~/cifar10/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

Inference Process

Export MindIR

python export.py --checkpoint_path [CKPT_PATH] --device_target [DEVICE] --file_name [FILE_NAME] --file_format [FILE_FORMAT]

The ckpt_file parameter is required,
DEVICE should be in ['Ascend', 'GPU', 'CPU']
FILE_FORMAT should be in "MINDIR"

Infer on Ascend310

Before performing inference, the mindir file must be exported by export.py script. We only provide an example of inference using MINDIR model.
Current batch_Size for imagenet2012 dataset can only be set to 1.

# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]

• MINDIR_PATH specifies path of used "MINDIR" model.
• DATA_PATH specifies path of imagenet datasets.
• DEVICE_ID is optional, default value is 0.

Result

Inference result is saved in current path, you can find result like this in acc.log file.

Eval: top1_correct=37051, tot=50000, acc=74.10%

Infer with ONNX

Before Inferring, you need to export the ONNX model.

• Download the [ckpt] of the model from the mindspore official website.
• The command to export the ONNX model is as follows

python export.py --checkpoint_path[ckpt_path] --device_target "GPU" --file_name mobilenetv3.onnx --file_format  "ONNX"

• Infer with Python scripts

python3 infer_onnx.py --onnx_path 'mobilenetv3.onnx' --dataset_path './imagenet/val'

• Infer with shell scripts

bash ./scripts/run_onnx.sh [ONNX_PATH] [DATASET_PATH] [PLATFORM]

Infer results are output in infer_onnx.log

• Note 1: the above scripts need to be run in the mobilenetv3 directory.

• Note 2: the validation data set needs to be preprocessed in the form of folder. For example,

  imagenet
   -- val
    -- n01985128
    -- n02110063
    -- n03041632
    -- ...
  

• Note 3: PLATFORM only supports CPU and GPU, default value is GPU.

Result

The reasoning results are output on the command line, and the results are as follows

ACC_TOP1 = 0.74436
ACC_TOP5 = 0.91762

Model description

Performance

Training Performance

Parameters	MobilenetV3
Model Version	large
Resource	NV SMX2 V100-32G
uploaded Date	07/05/2021
MindSpore Version	1.3.0
Dataset	ImageNet
Training Parameters	src/config.py
Optimizer	Momentum
Loss Function	SoftmaxCrossEntropy
outputs	probability
Loss	1.913
Accuracy	ACC1[77.57%] ACC5[92.51%]
Total time	1433 min
Params (M)	5.48 M
Checkpoint for Fine tuning	44 M
Scripts	Link

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.