forked from mindspore-Ecosystem/mindspore
188 lines
8.9 KiB
Markdown
188 lines
8.9 KiB
Markdown
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# Mobilenet_V1
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- [Mobilenet_V1](#mobilenet_v1)
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- [MobileNetV1 Description](#mobilenetv1-description)
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- [Model architecture](#model-architecture)
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- [Dataset](#dataset)
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- [[Features]](#features)
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- [[Mixed Precision(Ascend)]](#mixed-precisionascend)
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- [[Environment Requirements]](#environment-requirements)
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- [[Script description]](#script-description)
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- [[Script and sample code]](#script-and-sample-code)
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- [Training process](#training-process)
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- [Usage](#usage)
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- [Launch](#launch)
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- [Result](#result)
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- [Evaluation process](#evaluation-process)
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- [Usage](#usage-1)
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- [Launch](#launch-1)
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- [Result](#result-1)
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- [[Model description]](#model-description)
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- [Performance](#performance)
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- [Training Performance](#training-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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## [MobileNetV1 Description](#contents)
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MobileNetV1 is a efficient network for mobile and embedded vision applications. MobileNetV1 is based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep n.eural networks
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[Paper](https://arxiv.org/abs/1704.04861) Howard A G , Zhu M , Chen B , et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[J]. 2017.
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## [Model architecture](#contents)
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The overall network architecture of MobileNetV1 is show below:
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[Link](https://arxiv.org/abs/1704.04861)
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## [Dataset](#contents)
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Dataset used: [ImageNet2012](http://www.image-net.org/)
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- Dataset size 224*224 colorful images in 1000 classes
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- Train：1,281,167 images
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- Test： 50,000 images
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- Data format：jpeg
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- Note：Data will be processed in dataset.py
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- Download the dataset, the directory structure is as follows:
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```bash
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└─dataset
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├─ilsvrc # train dataset
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└─validation_preprocess # evaluate dataset
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```
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## [Features]
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### [Mixed Precision(Ascend)]
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The [mixed precision](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/enable_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.
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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’.
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## [Environment Requirements]
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- Hardware（Ascend）
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- Prepare hardware environment with Ascend. 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](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below：
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/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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├── MobileNetV1
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├── README.md # descriptions about MobileNetV1
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├── scripts
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│ ├──run_distribute_train.sh # shell script for distribute train
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│ ├──run_standalone_train.sh # shell script for standalone train
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│ ├──run_eval.sh # shell script for evaluation
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├── src
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│ ├──config.py # parameter configuration
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│ ├──dataset.py # creating dataset
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│ ├──lr_generator.py # learning rate config
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│ ├──mobilenet_v1_fpn.py # MobileNetV1 architecture
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│ ├──CrossEntropySmooth.py # loss function
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├── train.py # training script
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├── eval.py # evaluation script
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```
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## [Training process](#contents)
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### Usage
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You can start training using python or shell scripts. The usage of shell scripts as follows:
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- Ascend: sh run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH] (optional)
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For distributed training, a hccl configuration file with JSON format needs to be created in advance.
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Please follow the instructions in the link [hccn_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
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### Launch
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```shell
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# training example
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python:
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Ascend: python train.py --platform Ascend --dataset_path [TRAIN_DATASET_PATH]
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shell:
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Ascend: sh run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
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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 `ckpt_*` by default, and training log will be wrote to `./train_parallel*/log` with the platform Ascend .
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```shell
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epoch: 89 step: 1251, loss is 2.1829057
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Epoch time: 146826.802, per step time: 117.368
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epoch: 90 step: 1251, loss is 2.3499017
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Epoch time: 150950.623, per step time: 120.664
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```
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## [Evaluation process](#contents)
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### Usage
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You can start training using python or shell scripts.If the train method is train or fine tune, should not input the `[CHECKPOINT_PATH]` The usage of shell scripts as follows:
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- Ascend: sh run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
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### Launch
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```shell
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# eval example
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python:
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Ascend: python eval.py --dataset [cifar10|imagenet2012] --dataset_path [VAL_DATASET_PATH] --pretrain_ckpt [CHECKPOINT_PATH]
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shell:
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Ascend: sh run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
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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 `eval/log`.
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```shell
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result: {'top_5_accuracy': 0.9010016025641026, 'top_1_accuracy': 0.7128004807692307} ckpt=./train_parallel0/ckpt_0/mobilenetv1-90_1251.ckpt
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```
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## [Model description]
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### [Performance](#contents)
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#### Training Performance
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| Parameters | MobilenetV1 |
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| -------------------------- | ------------------------------------------------------------------------------------------- |
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| Model Version | V1 |
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| Resource | Ascend 910 * 4, cpu:2.60GHz 192cores, memory:755G |
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| uploaded Date | 11/28/2020 |
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| MindSpore Version | 1.0.0 |
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| Dataset | ImageNet2012 |
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| Training Parameters | src/config.py |
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| Optimizer | Momentum |
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| Loss Function | SoftmaxCrossEntropy |
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| outputs | probability |
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| Loss | 2.3499017 |
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| Accuracy | ACC1[71.28%] |
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| Total time | 225 min |
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| Params (M) | 3.3 M |
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| Checkpoint for Fine tuning | 27.3 M |
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| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/mobilenetv1) |
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## [Description of Random Situation](#contents)
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<!-- In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py. -->
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In train.py, we set the seed which is used by numpy.random, mindspore.common.Initializer, mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.
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## [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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