# Contents - [MobileNetV2 Description](#mobilenetv2-description) - [Model Architecture](#model-architecture) - [Dataset](#dataset) - [Features](#features) - [Mixed Precision](#mixed-precision) - [Environment Requirements](#environment-requirements) - [Script Description](#script-description) - [Script and Sample Code](#script-and-sample-code) - [Training Process](#training-process) - [Evaluation Process](#evaluation-process) - [Evaluation](#evaluation) - [Model Description](#model-description) - [Performance](#performance) - [Training Performance](#evaluation-performance) - [Inference Performance](#evaluation-performance) - [Description of Random Situation](#description-of-random-situation) - [ModelZoo Homepage](#modelzoo-homepage) # [MobileNetV2 Description](#contents) 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](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. # [Model architecture](#contents) The overall network architecture of MobileNetV2 is show below: [Link](https://arxiv.org/pdf/1905.02244) # [Dataset](#contents) Dataset used: [imagenet](http://www.image-net.org/) - Dataset size: ~125G, 1.2W colorful images in 1000 classes - Train: 120G, 1.2W images - Test: 5G, 50000 images - Data format: RGB images. - Note: Data will be processed in src/dataset.py # [Features](#contents) ## [Mixed Precision(Ascend)](#contents) The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) 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’. # [Environment Requirements](#contents) - Hardware（Ascend/GPU/CPU） - Prepare hardware environment with Ascend、GPU or CPU 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. - Framework - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) # [Script description](#contents) ## [Script and sample code](#contents) ```python ├── MobileNetV2 ├── Readme.md # descriptions about MobileNetV2 ├── scripts │ ├──run_train.sh # shell script for train, fine_tune or incremental learn with CPU, GPU or Ascend │ ├──run_eval.sh # shell script for evaluation with CPU, GPU or Ascend ├── src │ ├──args.py # parse args │ ├──config.py # parameter configuration │ ├──dataset.py # creating dataset │ ├──launch.py # start python script │ ├──lr_generator.py # learning rate config │ ├──mobilenetV2.py # MobileNetV2 architecture │ ├──models.py # contain define_net and Loss, Monitor │ ├──utils.py # utils to load ckpt_file for fine tune or incremental learn ├── train.py # training script ├── eval.py # evaluation script ``` ## [Training process](#contents) ### Usage You can start training using python or shell scripts. The usage of shell scripts as follows: - Ascend: sh run_train.sh Ascend [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH] - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH] - CPU: sh run_trian.sh CPU [DATASET_PATH] [TRAIN_METHOD] [CKPT_PATH] ### Launch ``` # training example python: Ascend: python train.py --dataset_path ~/imagenet/train/ --platform Ascend --train_method train GPU: python train.py --dataset_path ~/imagenet/train/ --platform GPU --train_method train CPU: python train.py --dataset_path ~/imagenet/train/ --platform CPU --train_method train shell: Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ train GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ train CPU: sh run_train.sh CPU ~/imagenet/train/ train # fine tune example python: Ascend: python train.py --dataset_path ~/imagenet/train/ --platform Ascend --train_method fine_tune ./pretrain_checkpoint/mobilenetv2_199.ckpt GPU: python train.py --dataset_path ~/imagenet/train/ --platform GPU --train_method fine_tune ./pretrain_checkpoint/mobilenetv2_199.ckpt CPU: python train.py --dataset_path ~/imagenet/train/ --platform CPU --train_method fine_tune ./pretrain_checkpoint/mobilenetv2_199.ckpt shell: Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ fine_tune ./pretrain_checkpoint/mobilenetv2_199.ckpt GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ fine_tune ./pretrain_checkpoint/mobilenetv2_199.ckpt CPU: sh run_train.sh CPU ~/imagenet/train/ fine_tune ./pretrain_checkpoint/mobilenetv2_199.ckpt # incremental learn example python: Ascend: python train.py --dataset_path ~/imagenet/train/ --platform Ascend --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt GPU: python train.py --dataset_path ~/imagenet/train/ --platform GPU --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt CPU: python train.py --dataset_path ~/imagenet/train/ --platform CPU --train_method incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt shell: Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt CPU: sh run_train.sh CPU ~/imagenet/train/ incremental_learn ./pretrain_checkpoint/mobilenetv2_199.ckpt ``` ### 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](#contents) ### Usage You can start training using python or shell scripts. The usage of shell scripts as follows: - Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH] [HEAD_CKPT_PATH] - GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH] [HEAD_CKPT_PATH] - CPU: sh run_infer.sh CPU [DATASET_PATH] [BACKBONE_CKPT_PATH] [HEAD_CKPT_PATH] ### Launch ``` # infer example python: Ascend: python eval.py --dataset_path ~/imagenet/val/ --pretrain_ckpt ~/train/mobilenet-200_625.ckpt --platform Ascend --head_ckpt ./checkpoint/mobilenetv2_199.ckpt GPU: python eval.py --dataset_path ~/imagenet/val/ --pretrain_ckpt ~/train/mobilenet-200_625.ckpt --platform GPU --head_ckpt ./checkpoint/mobilenetv2_199.ckpt CPU: python eval.py --dataset_path ~/imagenet/val/ --pretrain_ckpt ~/train/mobilenet-200_625.ckpt --platform CPU --head_ckpt ./checkpoint/mobilenetv2_199.ckpt shell: Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt ./checkpoint/mobilenetv2_199.ckpt GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt ./checkpoint/mobilenetv2_199.ckpt CPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt ./checkpoint/mobilenetv2_199.ckpt ``` > checkpoint can be produced in training process. ### Result Inference result will be stored in the example path, you can find result like the followings in `val.log`. ``` result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt ``` # [Model description](#contents) ## [Performance](#contents) ### Training Performance | Parameters | MobilenetV2 | | | -------------------------- | ---------------------------------------------------------- | ------------------------- | | Model Version | | large | | Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G | | uploaded Date | 05/06/2020 | 05/06/2020 | | MindSpore Version | 0.3.0 | 0.3.0 | | Dataset | ImageNet | ImageNet | | Training Parameters | src/config.py | src/config.py | | Optimizer | Momentum | Momentum | | Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy | | outputs | | | | Loss | | 1.913 | | Accuracy | | ACC1[77.09%] ACC5[92.57%] | | Total time | | | | Params (M) | | | | Checkpoint for Fine tuning | | | | Model for inference | | | #### Inference Performance | Parameters | | | | | -------------------------- | ----------------------------- | ------------------------- | -------------------- | | Model Version | V1 | | | | Resource | Ascend 910 | NV SMX2 V100-32G | Ascend 310 | | uploaded Date | 05/06/2020 | 05/22/2020 | | | MindSpore Version | 0.2.0 | 0.2.0 | 0.2.0 | | Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W | | batch_size | | 130(8P) | | | outputs | | | | | Accuracy | | ACC1[72.07%] ACC5[90.90%] | | | Speed | | | | | Total time | | | | | Model for inference | | | | # [Description of Random Situation](#contents) In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py. # [ModelZoo Homepage](#contents) Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).