forked from mindspore-Ecosystem/mindspore
add mobilenetC2 quant
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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.
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[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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[Paper](https://arxiv.org/pdf/1801.04381) 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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# Model architecture
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The overall network architecture of MobileNetV2 is show below:
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[Link](https://arxiv.org/pdf/1905.02244)
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[Link](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html)
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# Dataset
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# MobileNetV2 Description
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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.
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[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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# Model architecture
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The overall network architecture of MobileNetV2 is show below:
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[Link](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html)
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# Dataset
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Dataset used: imagenet
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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# Features
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# Environment Requirements
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- Hardware（Ascend)
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- Prepare hardware environment with Ascend 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.
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- Framework
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- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
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# Script description
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## Script and sample code
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```python
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├── MobileNetV2
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├── Readme.md
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├── scripts
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│ ├──run_train.sh
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│ ├──run_eval.sh
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├── src
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│ ├──config.py
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│ ├──dataset.py
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│ ├──luanch.py
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│ ├──lr_generator.py
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│ ├──mobilenetV2.py
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├── train.py
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├── eval.py
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```
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## Training process
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### Usage
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- 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]
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### Launch
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```
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# training example
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Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
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```
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### Result
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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.
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```
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epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100]
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epoch time: 140522.500, per step time: 224.836, avg loss: 5.258
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epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200]
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epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
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```
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## Eval process
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### Usage
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- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
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### Launch
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```
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# infer example
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Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
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```
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> checkpoint can be produced in training process.
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### Result
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Inference result will be stored in the example path, you can find result like the followings in `val.log`.
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```
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result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
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```
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# Model description
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## Performance
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### Training Performance
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<table>
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<thead>
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<tr>
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<th>Parameters</th>
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<th>MobilenetV2</th>
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<th>MobilenetV2 Quant</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Resource</td>
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<td>Ascend 910 <br />
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cpu:2.60GHz 56cores <br />
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memory:314G</td>
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<td>Ascend 910 <br />
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cpu:2.60GHz 56cores <br />
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memory:314G</td>
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</tr>
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<tr>
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<td>uploaded Date</td>
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<td>05/06/2020</td>
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<td>06/12/2020</td>
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</tr>
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<tr>
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<td>MindSpore Version</td>
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<td>0.3.0</td>
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<td>0.3.0</td>
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</tr>
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<tr>
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<td>Dataset</td>
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<td>ImageNet</td>
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<td>ImageNet</td>
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</tr>
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<tr>
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<td>Training Parameters</td>
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<td>src/config.py</td>
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<td>src/config.py</td>
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</tr>
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<tr>
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<td>Optimizer</td>
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<td>Momentum</td>
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<td>Momentum</td>
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</tr>
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<tr>
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<td>Loss Function</td>
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<td>CrossEntropyWithLabelSmooth</td>
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<td>CrossEntropyWithLabelSmooth</td>
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</tr>
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<tr>
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<td>Loss</td>
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<td>200 epoch:1.913</td>
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<td>50 epoch:1.912</td>
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</tr>
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<tr>
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<td>Train Accuracy</td>
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<td>ACC1[77.09%] ACC5[92.57%]</td>
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<td>ACC1[77.09%] ACC5[92.57%]</td>
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</tr>
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<tr>
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<td>Eval Accuracy</td>
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<td>ACC1[77.09%] ACC5[92.57%]</td>
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<td>ACC1[77.09%] ACC5[92.57%]</td>
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</tr>
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<tr>
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<td>Total time</td>
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<td>48h</td>
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<td>12h</td>
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</tr>
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<tr>
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<td>Checkpoint</td>
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<td>/</td>
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<td>mobilenetv2.ckpt</td>
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</tr>
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</tbody>
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</table>
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#### Inference Performance
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<table>
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<thead>
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<tr>
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<th>Parameters</th>
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<th>Ascend 910</th>
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<th>Ascend 310</th>
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<th>Nvidia V100</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>uploaded Date</td>
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<td>06/12/2020</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>MindSpore Version</td>
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<td>0.3.0</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>Dataset</td>
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<td>ImageNet, 1.2W</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>batch_size</td>
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<td></td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>outputs</td>
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<td></td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>Accuracy</td>
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<td></td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>Speed</td>
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<td></td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>Total time</td>
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<td></td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>Model for inference</td>
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<td></td>
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<td></td>
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<td></td>
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</tr>
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</tbody>
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</table>
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# ModelZoo Homepage
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[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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eval.
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"""
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import os
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import argparse
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from mindspore import context
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from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import dtype as mstype
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from mindspore.model_zoo.mobilenetV2 import mobilenet_v2
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from src.dataset import create_dataset
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from src.config import config_ascend, config_gpu
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--platform', type=str, default=None, help='run platform')
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args_opt = parser.parse_args()
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if __name__ == '__main__':
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config_platform = None
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net = None
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if args_opt.platform == "Ascend":
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config_platform = config_ascend
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
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device_id=device_id, save_graphs=False)
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net = mobilenet_v2(num_classes=config_platform.num_classes, platform="Ascend")
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elif args_opt.platform == "GPU":
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config_platform = config_gpu
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context.set_context(mode=context.GRAPH_MODE,
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device_target="GPU", save_graphs=False)
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net = mobilenet_v2(num_classes=config_platform.num_classes, platform="GPU")
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else:
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raise ValueError("Unsupport platform.")
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loss = nn.SoftmaxCrossEntropyWithLogits(
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is_grad=False, sparse=True, reduction='mean')
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if args_opt.platform == "Ascend":
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net.to_float(mstype.float16)
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for _, cell in net.cells_and_names():
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if isinstance(cell, nn.Dense):
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cell.to_float(mstype.float32)
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_platform,
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platform=args_opt.platform,
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batch_size=config_platform.batch_size)
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step_size = dataset.get_dataset_size()
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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model = Model(net, loss_fn=loss, metrics={'acc'})
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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#!/usr/bin/env bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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if [ $# != 3 ]
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then
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echo "Ascend: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH] \
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GPU: sh run_infer.sh [PLATFORM] [DATASET_PATH] [CHECKPOINT_PATH]"
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exit 1
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fi
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# check dataset path
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if [ ! -d $2 ]
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then
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echo "error: DATASET_PATH=$2 is not a directory"
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exit 1
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fi
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# check checkpoint file
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if [ ! -f $3 ]
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then
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echo "error: CHECKPOINT_PATH=$3 is not a file"
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exit 1
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fi
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# set environment
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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export DEVICE_ID=0
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export RANK_ID=0
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export RANK_SIZE=1
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if [ -d "eval" ];
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then
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rm -rf ../eval
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fi
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mkdir ../eval
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cd ../eval || exit
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# luanch
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python ${BASEPATH}/../eval.py \
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--platform=$1 \
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--dataset_path=$2 \
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--checkpoint_path=$3 \
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&> ../infer.log & # dataset val folder path
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#!/usr/bin/env bash
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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run_ascend()
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{
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if [ $2 -lt 1 ] && [ $2 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$2 is not in (1-8)"
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exit 1
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fi
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if [ ! -d $5 ]
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then
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echo "error: DATASET_PATH=$5 is not a directory"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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if [ -d "train" ];
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then
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rm -rf ../train
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fi
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mkdir ../train
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cd ../train || exit
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python ${BASEPATH}/../src/launch.py \
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--nproc_per_node=$2 \
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--visible_devices=$4 \
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--server_id=$3 \
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--training_script=${BASEPATH}/../train.py \
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--dataset_path=$5 \
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--pre_trained=$6 \
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--platform=$1 &> ../train.log & # dataset train folder
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}
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run_gpu()
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{
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if [ $2 -lt 1 ] && [ $2 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$2 is not in (1-8)"
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exit 1
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fi
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if [ ! -d $4 ]
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then
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echo "error: DATASET_PATH=$4 is not a directory"
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exit 1
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fi
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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if [ -d "train" ];
|
||||
then
|
||||
rm -rf ../train
|
||||
fi
|
||||
mkdir ../train
|
||||
cd ../train || exit
|
||||
|
||||
export CUDA_VISIBLE_DEVICES="$3"
|
||||
mpirun -n $2 --allow-run-as-root \
|
||||
python ${BASEPATH}/../train.py \
|
||||
--dataset_path=$4 \
|
||||
--platform=$1 \
|
||||
--pre_trained=$5 \
|
||||
&> ../train.log & # dataset train folder
|
||||
}
|
||||
|
||||
if [ $# -gt 6 ] || [ $# -lt 4 ]
|
||||
then
|
||||
echo "Usage:\n \
|
||||
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]\n \
|
||||
GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
|
||||
"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ $1 = "Ascend" ] ; then
|
||||
run_ascend "$@"
|
||||
elif [ $1 = "GPU" ] ; then
|
||||
run_gpu "$@"
|
||||
else
|
||||
echo "not support platform"
|
||||
fi;
|
||||
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
network config setting, will be used in train.py and eval.py
|
||||
"""
|
||||
from easydict import EasyDict as ed
|
||||
|
||||
config_ascend = ed({
|
||||
"num_classes": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"batch_size": 256,
|
||||
"epoch_size": 200,
|
||||
"warmup_epochs": 4,
|
||||
"lr": 0.4,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 4e-5,
|
||||
"label_smooth": 0.1,
|
||||
"loss_scale": 1024,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 200,
|
||||
"save_checkpoint_path": "./checkpoint",
|
||||
})
|
||||
|
||||
config_gpu = ed({
|
||||
"num_classes": 1000,
|
||||
"image_height": 224,
|
||||
"image_width": 224,
|
||||
"batch_size": 64,
|
||||
"epoch_size": 200,
|
||||
"warmup_epochs": 4,
|
||||
"lr": 0.5,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 4e-5,
|
||||
"label_smooth": 0.1,
|
||||
"loss_scale": 1024,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 1,
|
||||
"keep_checkpoint_max": 200,
|
||||
"save_checkpoint_path": "./checkpoint",
|
||||
})
|
||||
|
|
@ -0,0 +1,89 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""
|
||||
create train or eval dataset.
|
||||
"""
|
||||
import os
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.dataset.engine as de
|
||||
import mindspore.dataset.transforms.vision.c_transforms as C
|
||||
import mindspore.dataset.transforms.c_transforms as C2
|
||||
|
||||
def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
|
||||
"""
|
||||
create a train or eval dataset
|
||||
|
||||
Args:
|
||||
dataset_path(string): the path of dataset.
|
||||
do_train(bool): whether dataset is used for train or eval.
|
||||
repeat_num(int): the repeat times of dataset. Default: 1.
|
||||
batch_size(int): the batch size of dataset. Default: 32.
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if platform == "Ascend":
|
||||
rank_size = int(os.getenv("RANK_SIZE"))
|
||||
rank_id = int(os.getenv("RANK_ID"))
|
||||
if rank_size == 1:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=rank_size, shard_id=rank_id)
|
||||
elif platform == "GPU":
|
||||
if do_train:
|
||||
from mindspore.communication.management import get_rank, get_group_size
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=get_group_size(), shard_id=get_rank())
|
||||
else:
|
||||
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
resize_height = config.image_height
|
||||
resize_width = config.image_width
|
||||
buffer_size = 1000
|
||||
|
||||
# define map operations
|
||||
decode_op = C.Decode()
|
||||
resize_crop_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
|
||||
horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
|
||||
|
||||
resize_op = C.Resize((256, 256))
|
||||
center_crop = C.CenterCrop(resize_width)
|
||||
rescale_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
|
||||
normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
|
||||
change_swap_op = C.HWC2CHW()
|
||||
|
||||
if do_train:
|
||||
trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
|
||||
else:
|
||||
trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
|
||||
|
||||
type_cast_op = C2.TypeCast(mstype.int32)
|
||||
|
||||
ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
|
||||
ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
|
||||
|
||||
# apply shuffle operations
|
||||
ds = ds.shuffle(buffer_size=buffer_size)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
|
|
@ -0,0 +1,163 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""launch train script"""
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import subprocess
|
||||
import shutil
|
||||
from argparse import ArgumentParser
|
||||
|
||||
def parse_args():
|
||||
"""
|
||||
parse args .
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
args.
|
||||
|
||||
Examples:
|
||||
>>> parse_args()
|
||||
"""
|
||||
parser = ArgumentParser(description="mindspore distributed training launch "
|
||||
"helper utilty that will spawn up "
|
||||
"multiple distributed processes")
|
||||
parser.add_argument("--nproc_per_node", type=int, default=1,
|
||||
help="The number of processes to launch on each node, "
|
||||
"for D training, this is recommended to be set "
|
||||
"to the number of D in your system so that "
|
||||
"each process can be bound to a single D.")
|
||||
parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7",
|
||||
help="will use the visible devices sequentially")
|
||||
parser.add_argument("--server_id", type=str, default="",
|
||||
help="server ip")
|
||||
parser.add_argument("--training_script", type=str,
|
||||
help="The full path to the single D training "
|
||||
"program/script to be launched in parallel, "
|
||||
"followed by all the arguments for the "
|
||||
"training script")
|
||||
# rest from the training program
|
||||
args, unknown = parser.parse_known_args()
|
||||
args.training_script_args = unknown
|
||||
return args
|
||||
|
||||
|
||||
def main():
|
||||
print("start", __file__)
|
||||
args = parse_args()
|
||||
print(args)
|
||||
visible_devices = args.visible_devices.split(',')
|
||||
assert os.path.isfile(args.training_script)
|
||||
assert len(visible_devices) >= args.nproc_per_node
|
||||
print('visible_devices:{}'.format(visible_devices))
|
||||
if not args.server_id:
|
||||
print('pleaser input server ip!!!')
|
||||
exit(0)
|
||||
print('server_id:{}'.format(args.server_id))
|
||||
|
||||
# construct hccn_table
|
||||
hccn_configs = open('/etc/hccn.conf', 'r').readlines()
|
||||
device_ips = {}
|
||||
for hccn_item in hccn_configs:
|
||||
hccn_item = hccn_item.strip()
|
||||
if hccn_item.startswith('address_'):
|
||||
device_id, device_ip = hccn_item.split('=')
|
||||
device_id = device_id.split('_')[1]
|
||||
device_ips[device_id] = device_ip
|
||||
print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
|
||||
hccn_table = {}
|
||||
hccn_table['board_id'] = '0x0000'
|
||||
hccn_table['chip_info'] = '910'
|
||||
hccn_table['deploy_mode'] = 'lab'
|
||||
hccn_table['group_count'] = '1'
|
||||
hccn_table['group_list'] = []
|
||||
instance_list = []
|
||||
usable_dev = ''
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
instance = {}
|
||||
instance['devices'] = []
|
||||
device_id = visible_devices[instance_id]
|
||||
device_ip = device_ips[device_id]
|
||||
usable_dev += str(device_id)
|
||||
instance['devices'].append({
|
||||
'device_id': device_id,
|
||||
'device_ip': device_ip,
|
||||
})
|
||||
instance['rank_id'] = str(instance_id)
|
||||
instance['server_id'] = args.server_id
|
||||
instance_list.append(instance)
|
||||
hccn_table['group_list'].append({
|
||||
'device_num': str(args.nproc_per_node),
|
||||
'server_num': '1',
|
||||
'group_name': '',
|
||||
'instance_count': str(args.nproc_per_node),
|
||||
'instance_list': instance_list,
|
||||
})
|
||||
hccn_table['para_plane_nic_location'] = 'device'
|
||||
hccn_table['para_plane_nic_name'] = []
|
||||
for instance_id in range(args.nproc_per_node):
|
||||
eth_id = visible_devices[instance_id]
|
||||
hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
|
||||
hccn_table['para_plane_nic_num'] = str(args.nproc_per_node)
|
||||
hccn_table['status'] = 'completed'
|
||||
|
||||
# save hccn_table to file
|
||||
table_path = os.getcwd()
|
||||
if not os.path.exists(table_path):
|
||||
os.mkdir(table_path)
|
||||
table_fn = os.path.join(table_path,
|
||||
'rank_table_{}p_{}_{}.json'.format(args.nproc_per_node, usable_dev, args.server_id))
|
||||
with open(table_fn, 'w') as table_fp:
|
||||
json.dump(hccn_table, table_fp, indent=4)
|
||||
sys.stdout.flush()
|
||||
|
||||
# spawn the processes
|
||||
processes = []
|
||||
cmds = []
|
||||
log_files = []
|
||||
env = os.environ.copy()
|
||||
env['RANK_SIZE'] = str(args.nproc_per_node)
|
||||
cur_path = os.getcwd()
|
||||
for rank_id in range(0, args.nproc_per_node):
|
||||
os.chdir(cur_path)
|
||||
device_id = visible_devices[rank_id]
|
||||
device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
|
||||
env['RANK_ID'] = str(rank_id)
|
||||
env['DEVICE_ID'] = str(device_id)
|
||||
if args.nproc_per_node > 1:
|
||||
env['MINDSPORE_HCCL_CONFIG_PATH'] = table_fn
|
||||
env['RANK_TABLE_FILE'] = table_fn
|
||||
if os.path.exists(device_dir):
|
||||
shutil.rmtree(device_dir)
|
||||
os.mkdir(device_dir)
|
||||
os.chdir(device_dir)
|
||||
cmd = [sys.executable, '-u']
|
||||
cmd.append(args.training_script)
|
||||
cmd.extend(args.training_script_args)
|
||||
log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
|
||||
process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
|
||||
processes.append(process)
|
||||
cmds.append(cmd)
|
||||
log_files.append(log_file)
|
||||
for process, cmd, log_file in zip(processes, cmds, log_files):
|
||||
process.wait()
|
||||
if process.returncode != 0:
|
||||
raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
|
||||
log_file.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""learning rate generator"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
|
||||
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
||||
"""
|
||||
generate learning rate array
|
||||
|
||||
Args:
|
||||
global_step(int): total steps of the training
|
||||
lr_init(float): init learning rate
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
total_epochs(int): total epoch of training
|
||||
steps_per_epoch(int): steps of one epoch
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array
|
||||
"""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
||||
else:
|
||||
lr = lr_end + \
|
||||
(lr_max - lr_end) * \
|
||||
(1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
|
||||
if lr < 0.0:
|
||||
lr = 0.0
|
||||
lr_each_step.append(lr)
|
||||
|
||||
current_step = global_step
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
learning_rate = lr_each_step[current_step:]
|
||||
|
||||
return learning_rate
|
||||
|
|
@ -0,0 +1,291 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MobileNetV2 model define"""
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops.operations import TensorAdd
|
||||
from mindspore import Parameter, Tensor
|
||||
from mindspore.common.initializer import initializer
|
||||
|
||||
__all__ = ['mobilenet_v2']
|
||||
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
# Make sure that round down does not go down by more than 10%.
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
Global avg pooling definition.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> GlobalAvgPooling()
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(keep_dims=False)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
return x
|
||||
|
||||
|
||||
class DepthwiseConv(nn.Cell):
|
||||
"""
|
||||
Depthwise Convolution warpper definition.
|
||||
|
||||
Args:
|
||||
in_planes (int): Input channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size.
|
||||
pad_mode (str): pad mode in (pad, same, valid)
|
||||
channel_multiplier (int): Output channel multiplier
|
||||
has_bias (bool): has bias or not
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
|
||||
"""
|
||||
|
||||
def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
|
||||
super(DepthwiseConv, self).__init__()
|
||||
self.has_bias = has_bias
|
||||
self.in_channels = in_planes
|
||||
self.channel_multiplier = channel_multiplier
|
||||
self.out_channels = in_planes * channel_multiplier
|
||||
self.kernel_size = (kernel_size, kernel_size)
|
||||
self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
|
||||
kernel_size=self.kernel_size,
|
||||
stride=stride, pad_mode=pad_mode, pad=pad)
|
||||
self.bias_add = P.BiasAdd()
|
||||
weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
|
||||
self.weight = Parameter(initializer('ones', weight_shape), name='weight')
|
||||
|
||||
if has_bias:
|
||||
bias_shape = [channel_multiplier * in_planes]
|
||||
self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
|
||||
else:
|
||||
self.bias = None
|
||||
|
||||
def construct(self, x):
|
||||
output = self.depthwise_conv(x, self.weight)
|
||||
if self.has_bias:
|
||||
output = self.bias_add(output, self.bias)
|
||||
return output
|
||||
|
||||
|
||||
class ConvBNReLU(nn.Cell):
|
||||
"""
|
||||
Convolution/Depthwise fused with Batchnorm and ReLU block definition.
|
||||
|
||||
Args:
|
||||
in_planes (int): Input channel.
|
||||
out_planes (int): Output channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
|
||||
"""
|
||||
|
||||
def __init__(self, platform, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
|
||||
super(ConvBNReLU, self).__init__()
|
||||
padding = (kernel_size - 1) // 2
|
||||
if groups == 1:
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad', padding=padding)
|
||||
else:
|
||||
if platform == "Ascend":
|
||||
conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
|
||||
elif platform == "GPU":
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride,
|
||||
group=in_planes, pad_mode='pad', padding=padding)
|
||||
|
||||
layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
|
||||
self.features = nn.SequentialCell(layers)
|
||||
|
||||
def construct(self, x):
|
||||
output = self.features(x)
|
||||
return output
|
||||
|
||||
|
||||
class InvertedResidual(nn.Cell):
|
||||
"""
|
||||
Mobilenetv2 residual block definition.
|
||||
|
||||
Args:
|
||||
inp (int): Input channel.
|
||||
oup (int): Output channel.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
expand_ratio (int): expand ration of input channel
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ResidualBlock(3, 256, 1, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, platform, inp, oup, stride, expand_ratio):
|
||||
super(InvertedResidual, self).__init__()
|
||||
assert stride in [1, 2]
|
||||
|
||||
hidden_dim = int(round(inp * expand_ratio))
|
||||
self.use_res_connect = stride == 1 and inp == oup
|
||||
|
||||
layers = []
|
||||
if expand_ratio != 1:
|
||||
layers.append(ConvBNReLU(platform, inp, hidden_dim, kernel_size=1))
|
||||
layers.extend([
|
||||
# dw
|
||||
ConvBNReLU(platform, hidden_dim, hidden_dim,
|
||||
stride=stride, groups=hidden_dim),
|
||||
# pw-linear
|
||||
nn.Conv2d(hidden_dim, oup, kernel_size=1,
|
||||
stride=1, has_bias=False),
|
||||
nn.BatchNorm2d(oup),
|
||||
])
|
||||
self.conv = nn.SequentialCell(layers)
|
||||
self.add = TensorAdd()
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
x = self.conv(x)
|
||||
if self.use_res_connect:
|
||||
return self.add(identity, x)
|
||||
return x
|
||||
|
||||
|
||||
class MobileNetV2(nn.Cell):
|
||||
"""
|
||||
MobileNetV2 architecture.
|
||||
|
||||
Args:
|
||||
class_num (Cell): number of classes.
|
||||
width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
|
||||
has_dropout (bool): Is dropout used. Default is false
|
||||
inverted_residual_setting (list): Inverted residual settings. Default is None
|
||||
round_nearest (list): Channel round to . Default is 8
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> MobileNetV2(num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, platform, num_classes=1000, width_mult=1.,
|
||||
has_dropout=False, inverted_residual_setting=None, round_nearest=8):
|
||||
super(MobileNetV2, self).__init__()
|
||||
block = InvertedResidual
|
||||
input_channel = 32
|
||||
last_channel = 1280
|
||||
# setting of inverted residual blocks
|
||||
self.cfgs = inverted_residual_setting
|
||||
if inverted_residual_setting is None:
|
||||
self.cfgs = [
|
||||
# t, c, n, s
|
||||
[1, 16, 1, 1],
|
||||
[6, 24, 2, 2],
|
||||
[6, 32, 3, 2],
|
||||
[6, 64, 4, 2],
|
||||
[6, 96, 3, 1],
|
||||
[6, 160, 3, 2],
|
||||
[6, 320, 1, 1],
|
||||
]
|
||||
|
||||
# building first layer
|
||||
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
|
||||
self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
|
||||
features = [ConvBNReLU(platform, 3, input_channel, stride=2)]
|
||||
# building inverted residual blocks
|
||||
for t, c, n, s in self.cfgs:
|
||||
output_channel = _make_divisible(c * width_mult, round_nearest)
|
||||
for i in range(n):
|
||||
stride = s if i == 0 else 1
|
||||
features.append(block(platform, input_channel, output_channel, stride, expand_ratio=t))
|
||||
input_channel = output_channel
|
||||
# building last several layers
|
||||
features.append(ConvBNReLU(platform, input_channel, self.out_channels, kernel_size=1))
|
||||
# make it nn.CellList
|
||||
self.features = nn.SequentialCell(features)
|
||||
# mobilenet head
|
||||
head = ([GlobalAvgPooling(), nn.Dense(self.out_channels, num_classes, has_bias=True)] if not has_dropout else
|
||||
[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(self.out_channels, num_classes, has_bias=True)])
|
||||
self.head = nn.SequentialCell(head)
|
||||
|
||||
self._initialize_weights()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
def _initialize_weights(self):
|
||||
"""
|
||||
Initialize weights.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> _initialize_weights()
|
||||
"""
|
||||
for _, m in self.cells_and_names():
|
||||
if isinstance(m, (nn.Conv2d, DepthwiseConv)):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
|
||||
m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.gamma.set_parameter_data(
|
||||
Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
|
||||
m.beta.set_parameter_data(
|
||||
Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.Dense):
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(
|
||||
0, 0.01, m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
|
||||
|
||||
def mobilenet_v2(**kwargs):
|
||||
"""
|
||||
Constructs a MobileNet V2 model
|
||||
"""
|
||||
return MobileNetV2(**kwargs)
|
||||
|
|
@ -0,0 +1,296 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""MobileNetV2 Quant model define"""
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops.operations import TensorAdd
|
||||
from mindspore import Parameter, Tensor
|
||||
from mindspore.common.initializer import initializer
|
||||
|
||||
__all__ = ['mobilenet_v2_quant']
|
||||
|
||||
_ema_decay = 0.999
|
||||
_symmetric = False
|
||||
|
||||
def _make_divisible(v, divisor, min_value=None):
|
||||
if min_value is None:
|
||||
min_value = divisor
|
||||
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||||
# Make sure that round down does not go down by more than 10%.
|
||||
if new_v < 0.9 * v:
|
||||
new_v += divisor
|
||||
return new_v
|
||||
|
||||
|
||||
class GlobalAvgPooling(nn.Cell):
|
||||
"""
|
||||
Global avg pooling definition.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> GlobalAvgPooling()
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(GlobalAvgPooling, self).__init__()
|
||||
self.mean = P.ReduceMean(keep_dims=False)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.mean(x, (2, 3))
|
||||
return x
|
||||
|
||||
|
||||
class DepthwiseConv(nn.Cell):
|
||||
"""
|
||||
Depthwise Convolution warpper definition.
|
||||
|
||||
Args:
|
||||
in_planes (int): Input channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size.
|
||||
pad_mode (str): pad mode in (pad, same, valid)
|
||||
channel_multiplier (int): Output channel multiplier
|
||||
has_bias (bool): has bias or not
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
|
||||
"""
|
||||
|
||||
def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
|
||||
super(DepthwiseConv, self).__init__()
|
||||
self.has_bias = has_bias
|
||||
self.in_channels = in_planes
|
||||
self.channel_multiplier = channel_multiplier
|
||||
self.out_channels = in_planes * channel_multiplier
|
||||
self.kernel_size = (kernel_size, kernel_size)
|
||||
self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
|
||||
kernel_size=self.kernel_size,
|
||||
stride=stride, pad_mode=pad_mode, pad=pad)
|
||||
self.bias_add = P.BiasAdd()
|
||||
weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
|
||||
self.weight = Parameter(initializer('ones', weight_shape), name='weight')
|
||||
|
||||
if has_bias:
|
||||
bias_shape = [channel_multiplier * in_planes]
|
||||
self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
|
||||
else:
|
||||
self.bias = None
|
||||
|
||||
def construct(self, x):
|
||||
output = self.depthwise_conv(x, self.weight)
|
||||
if self.has_bias:
|
||||
output = self.bias_add(output, self.bias)
|
||||
return output
|
||||
|
||||
|
||||
class ConvBNReLU(nn.Cell):
|
||||
"""
|
||||
Convolution/Depthwise fused with Batchnorm and ReLU block definition.
|
||||
|
||||
Args:
|
||||
in_planes (int): Input channel.
|
||||
out_planes (int): Output channel.
|
||||
kernel_size (int): Input kernel size.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
|
||||
"""
|
||||
|
||||
def __init__(self, platform, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
|
||||
super(ConvBNReLU, self).__init__()
|
||||
padding = (kernel_size - 1) // 2
|
||||
if groups == 1:
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad', padding=padding)
|
||||
else:
|
||||
if platform == "Ascend":
|
||||
conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
|
||||
elif platform == "GPU":
|
||||
conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride,
|
||||
group=in_planes, pad_mode='pad', padding=padding)
|
||||
|
||||
layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
|
||||
self.features = nn.SequentialCell(layers)
|
||||
self.fake = nn.FakeQuantWithMinMax(in_planes, ema=True, ema_decay=_ema_decay, symmetric=_symmetric)
|
||||
|
||||
def construct(self, x):
|
||||
output = self.features(x)
|
||||
output = self.fake(output)
|
||||
return output
|
||||
|
||||
|
||||
class InvertedResidual(nn.Cell):
|
||||
"""
|
||||
Mobilenetv2 residual block definition.
|
||||
|
||||
Args:
|
||||
inp (int): Input channel.
|
||||
oup (int): Output channel.
|
||||
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||
expand_ratio (int): expand ration of input channel
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> ResidualBlock(3, 256, 1, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, platform, inp, oup, stride, expand_ratio):
|
||||
super(InvertedResidual, self).__init__()
|
||||
assert stride in [1, 2]
|
||||
|
||||
hidden_dim = int(round(inp * expand_ratio))
|
||||
self.use_res_connect = stride == 1 and inp == oup
|
||||
|
||||
layers = []
|
||||
if expand_ratio != 1:
|
||||
layers.append(ConvBNReLU(platform, inp, hidden_dim, kernel_size=1))
|
||||
layers.extend([
|
||||
# dw
|
||||
ConvBNReLU(platform, hidden_dim, hidden_dim,
|
||||
stride=stride, groups=hidden_dim),
|
||||
# pw-linear
|
||||
nn.Conv2dBatchNormQuant(hidden_dim, oup, kernel_size=1, stride=1, pad_mode='pad', padding=0, group=1),
|
||||
nn.FakeQuantWithMinMax(oup, ema=True, ema_decay=_ema_decay, symmetric=_symmetric)
|
||||
])
|
||||
self.conv = nn.SequentialCell(layers)
|
||||
self.add = TensorAdd()
|
||||
self.add_fake = nn.FakeQuantWithMinMax(oup, ema=True, ema_decay=_ema_decay, symmetric=_symmetric)
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
x = self.conv(x)
|
||||
if self.use_res_connect:
|
||||
x = self.add(identity, x)
|
||||
x = self.add_fake(x)
|
||||
return x
|
||||
|
||||
|
||||
class MobileNetV2Quant(nn.Cell):
|
||||
"""
|
||||
MobileNetV2Quant architecture.
|
||||
|
||||
Args:
|
||||
class_num (Cell): number of classes.
|
||||
width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
|
||||
has_dropout (bool): Is dropout used. Default is false
|
||||
inverted_residual_setting (list): Inverted residual settings. Default is None
|
||||
round_nearest (list): Channel round to . Default is 8
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
>>> MobileNetV2Quant(num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, platform, num_classes=1000, width_mult=1.,
|
||||
has_dropout=False, inverted_residual_setting=None, round_nearest=8):
|
||||
super(MobileNetV2Quant, self).__init__()
|
||||
block = InvertedResidual
|
||||
input_channel = 32
|
||||
last_channel = 1280
|
||||
# setting of inverted residual blocks
|
||||
self.cfgs = inverted_residual_setting
|
||||
if inverted_residual_setting is None:
|
||||
self.cfgs = [
|
||||
# t, c, n, s
|
||||
[1, 16, 1, 1],
|
||||
[6, 24, 2, 2],
|
||||
[6, 32, 3, 2],
|
||||
[6, 64, 4, 2],
|
||||
[6, 96, 3, 1],
|
||||
[6, 160, 3, 2],
|
||||
[6, 320, 1, 1],
|
||||
]
|
||||
|
||||
# building first layer
|
||||
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
|
||||
self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
|
||||
features = [ConvBNReLU(platform, 3, input_channel, stride=2)]
|
||||
# building inverted residual blocks
|
||||
for t, c, n, s in self.cfgs:
|
||||
output_channel = _make_divisible(c * width_mult, round_nearest)
|
||||
for i in range(n):
|
||||
stride = s if i == 0 else 1
|
||||
features.append(block(platform, input_channel, output_channel, stride, expand_ratio=t))
|
||||
input_channel = output_channel
|
||||
# building last several layers
|
||||
features.append(ConvBNReLU(platform, input_channel, self.out_channels, kernel_size=1))
|
||||
# make it nn.CellList
|
||||
self.features = nn.SequentialCell(features)
|
||||
# mobilenet head
|
||||
head = ([GlobalAvgPooling(), nn.Dense(self.out_channels, num_classes, has_bias=True)] if not has_dropout else
|
||||
[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(self.out_channels, num_classes, has_bias=True)])
|
||||
self.head = nn.SequentialCell(head)
|
||||
|
||||
self._initialize_weights()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.features(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
def _initialize_weights(self):
|
||||
"""
|
||||
Initialize weights.
|
||||
|
||||
Args:
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> _initialize_weights()
|
||||
"""
|
||||
for _, m in self.cells_and_names():
|
||||
if isinstance(m, (nn.Conv2d, DepthwiseConv)):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
|
||||
m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.BatchNorm2d):
|
||||
m.gamma.set_parameter_data(
|
||||
Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
|
||||
m.beta.set_parameter_data(
|
||||
Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
|
||||
elif isinstance(m, nn.Dense):
|
||||
m.weight.set_parameter_data(Tensor(np.random.normal(
|
||||
0, 0.01, m.weight.data.shape()).astype("float32")))
|
||||
if m.bias is not None:
|
||||
m.bias.set_parameter_data(
|
||||
Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
|
||||
|
||||
|
||||
def mobilenet_v2_quant(**kwargs):
|
||||
"""
|
||||
Constructs a MobileNet V2 model
|
||||
"""
|
||||
return MobileNetV2Quant(**kwargs)
|
||||
|
|
@ -0,0 +1,274 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""train_imagenet."""
|
||||
import os
|
||||
import time
|
||||
import argparse
|
||||
import random
|
||||
import numpy as np
|
||||
from mindspore import context
|
||||
from mindspore import Tensor
|
||||
from mindspore import nn
|
||||
from mindspore.parallel._auto_parallel_context import auto_parallel_context
|
||||
from mindspore.nn.optim.momentum import Momentum
|
||||
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.train.model import Model, ParallelMode
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
|
||||
from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
||||
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||
from mindspore.communication.management import init, get_group_size
|
||||
import mindspore.dataset.engine as de
|
||||
from src.dataset import create_dataset
|
||||
from src.lr_generator import get_lr
|
||||
from src.config import config_gpu, config_ascend
|
||||
from src.mobilenetV2 import mobilenet_v2
|
||||
from src.mobilenetV2_quant import mobilenet_v2_quant
|
||||
|
||||
random.seed(1)
|
||||
np.random.seed(1)
|
||||
de.config.set_seed(1)
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
|
||||
parser.add_argument('--platform', type=str, default=None, help='run platform')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
if args_opt.platform == "Ascend":
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
rank_id = int(os.getenv('RANK_ID'))
|
||||
rank_size = int(os.getenv('RANK_SIZE'))
|
||||
run_distribute = rank_size > 1
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="Ascend",
|
||||
device_id=device_id, save_graphs=False)
|
||||
elif args_opt.platform == "GPU":
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target="GPU", save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
|
||||
|
||||
class CrossEntropyWithLabelSmooth(_Loss):
|
||||
"""
|
||||
CrossEntropyWith LabelSmooth.
|
||||
|
||||
Args:
|
||||
smooth_factor (float): smooth factor, default=0.
|
||||
num_classes (int): num classes
|
||||
|
||||
Returns:
|
||||
None.
|
||||
|
||||
Examples:
|
||||
>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
|
||||
"""
|
||||
|
||||
def __init__(self, smooth_factor=0., num_classes=1000):
|
||||
super(CrossEntropyWithLabelSmooth, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor /
|
||||
(num_classes - 1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
self.cast = P.Cast()
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
|
||||
self.on_value, self.off_value)
|
||||
out_loss = self.ce(logit, one_hot_label)
|
||||
out_loss = self.mean(out_loss, 0)
|
||||
return out_loss
|
||||
|
||||
|
||||
class Monitor(Callback):
|
||||
"""
|
||||
Monitor loss and time.
|
||||
|
||||
Args:
|
||||
lr_init (numpy array): train lr
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
|
||||
"""
|
||||
|
||||
def __init__(self, lr_init=None):
|
||||
super(Monitor, self).__init__()
|
||||
self.lr_init = lr_init
|
||||
self.lr_init_len = len(lr_init)
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
self.losses = []
|
||||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
|
||||
epoch_mseconds = (time.time() - self.epoch_time) * 1000
|
||||
per_step_mseconds = epoch_mseconds / cb_params.batch_num
|
||||
print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
|
||||
per_step_mseconds,
|
||||
np.mean(self.losses)))
|
||||
|
||||
def step_begin(self, run_context):
|
||||
self.step_time = time.time()
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
step_mseconds = (time.time() - self.step_time) * 1000
|
||||
step_loss = cb_params.net_outputs
|
||||
|
||||
if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
|
||||
step_loss = step_loss[0]
|
||||
if isinstance(step_loss, Tensor):
|
||||
step_loss = np.mean(step_loss.asnumpy())
|
||||
|
||||
self.losses.append(step_loss)
|
||||
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
|
||||
|
||||
print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.5f}]".format(
|
||||
cb_params.cur_epoch_num -
|
||||
1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
|
||||
np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if args_opt.platform == "GPU":
|
||||
# train on gpu
|
||||
print("train args: ", args_opt, "\ncfg: ", config_gpu)
|
||||
|
||||
init('nccl')
|
||||
context.set_auto_parallel_context(parallel_mode="data_parallel",
|
||||
mirror_mean=True,
|
||||
device_num=get_group_size())
|
||||
|
||||
# define net
|
||||
net = mobilenet_v2(num_classes=config_gpu.num_classes, platform="GPU")
|
||||
# define loss
|
||||
if config_gpu.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
# define dataset
|
||||
epoch_size = config_gpu.epoch_size
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_gpu,
|
||||
platform=args_opt.platform,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config_gpu.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
# resume
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
# define optimizer
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_gpu.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_gpu.lr,
|
||||
warmup_epochs=config_gpu.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
|
||||
config_gpu.weight_decay, config_gpu.loss_scale)
|
||||
# define model
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config_gpu.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_gpu.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(
|
||||
prefix="mobilenet", directory=config_gpu.save_checkpoint_path, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
# begine train
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
elif args_opt.platform == "Ascend":
|
||||
# train on ascend
|
||||
print("train args: ", args_opt, "\ncfg: ", config_ascend,
|
||||
"\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
|
||||
|
||||
if run_distribute:
|
||||
context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
parameter_broadcast=True, mirror_mean=True)
|
||||
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
|
||||
init()
|
||||
|
||||
epoch_size = config_ascend.epoch_size
|
||||
net = mobilenet_v2(num_classes=config_ascend.num_classes, platform="Ascend")
|
||||
net = mobilenet_v2_quant(num_classes=config_ascend.num_classes, platform="Ascend")
|
||||
net.to_float(mstype.float16)
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.to_float(mstype.float32)
|
||||
if config_ascend.label_smooth > 0:
|
||||
loss = CrossEntropyWithLabelSmooth(
|
||||
smooth_factor=config_ascend.label_smooth, num_classes=config_ascend.num_classes)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(
|
||||
is_grad=False, sparse=True, reduction='mean')
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path,
|
||||
do_train=True,
|
||||
config=config_ascend,
|
||||
platform=args_opt.platform,
|
||||
repeat_num=epoch_size,
|
||||
batch_size=config_ascend.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
|
||||
loss_scale = FixedLossScaleManager(
|
||||
config_ascend.loss_scale, drop_overflow_update=False)
|
||||
lr = Tensor(get_lr(global_step=0,
|
||||
lr_init=0,
|
||||
lr_end=0,
|
||||
lr_max=config_ascend.lr,
|
||||
warmup_epochs=config_ascend.warmup_epochs,
|
||||
total_epochs=epoch_size,
|
||||
steps_per_epoch=step_size))
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_ascend.momentum,
|
||||
config_ascend.weight_decay, config_ascend.loss_scale)
|
||||
|
||||
model = Model(net, loss_fn=loss, optimizer=opt,
|
||||
loss_scale_manager=loss_scale)
|
||||
|
||||
cb = None
|
||||
if rank_id == 0:
|
||||
cb = [Monitor(lr_init=lr.asnumpy())]
|
||||
if config_ascend.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config_ascend.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config_ascend.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(
|
||||
prefix="mobilenet", directory=config_ascend.save_checkpoint_path, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
model.train(epoch_size, dataset, callbacks=cb)
|
||||
else:
|
||||
raise ValueError("Unsupport platform.")
|
||||
Loading…
Reference in New Issue