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| scripts | 5 years ago | |
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| Readme.md | 5 years ago | |
| eval.py | 5 years ago | |
| export.py | 5 years ago | |
| train.py | 5 years ago | |
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 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.
This is the quantitative network of MobileNetV2.
The overall network architecture of MobileNetV2 is show below:
Dataset used: imagenet
The mixed precision 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’.
├── mobileNetv2_quant
├── Readme.md # descriptions about MobileNetV2-Quant
├── scripts
│ ├──run_train.sh # shell script for train on Ascend
│ ├──run_infer.sh # shell script for evaluation on Ascend
├── src
│ ├──config.py # parameter configuration
│ ├──dataset.py # creating dataset
│ ├──launch.py # start python script
│ ├──lr_generator.py # learning rate config
│ ├──mobilenetV2.py # MobileNetV2 architecture
│ ├──utils.py # supply the monitor module
├── train.py # training script
├── eval.py # evaluation script
├── export.py # export checkpoint files into air/onnx
You can start training using python or shell scripts. The usage of shell scripts as follows:
# training example
>>> bash run_train.sh Ascend ~/hccl_4p_0123_x.x.x.x.json ~/imagenet/train/ ~/mobilenet.ckpt
>>> bash run_train.sh GPU 1,2 ~/imagenet/train/ ~/mobilenet.ckpt
Training result will be stored in the example path. Checkpoints trained by Ascend will be stored at ./train/device$i/checkpoint by default, and training log will be redirected to ./train/device$i/train.log. Checkpoints trained by GPU will be stored in ./train/checkpointckpt_$i by default, and training log will be redirected to ./train/train.log.
train.log is as follows:
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
You can start training using python or shell scripts. The usage of shell scripts as follows:
# infer example
shell:
Ascend: sh run_infer_quant.sh Ascend ~/imagenet/val/ ~/train/mobilenet-60_1601.ckpt
checkpoint can be produced in training process.
Inference result will be stored in the example path, you can find result like the followings in ./val/infer.log.
result: {'acc': 0.71976314102564111}
| Parameters | MobilenetV2 |
|---|---|
| Model Version | V2 |
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G |
| uploaded Date | 06/06/2020 |
| MindSpore Version | 0.3.0 |
| Dataset | ImageNet |
| Training Parameters | src/config.py |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropy |
| outputs | ckpt file |
| Loss | 1.913 |
| Accuracy | |
| Total time | 16h |
| Params (M) | batch_size=192, epoch=60 |
| Checkpoint for Fine tuning | |
| Model for inference |
| Parameters | |
|---|---|
| Model Version | V2 |
| Resource | Ascend 910 |
| uploaded Date | 06/06/2020 |
| MindSpore Version | 0.3.0 |
| Dataset | ImageNet, 1.2W |
| batch_size | 130(8P) |
| outputs | probability |
| Accuracy | ACC1[71.78%] ACC5[90.90%] |
| Speed | 200ms/step |
| Total time | 5min |
| Model for inference |
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
Please check the official homepage.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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