forked from mindspore-Ecosystem/mindspore
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commit
893c2cd772
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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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# 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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"""eval mobilenet_v1."""
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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.common import set_seed
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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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 src.CrossEntropySmooth import CrossEntropySmooth
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from src.mobilenet_v1 import mobilenet_v1 as mobilenet
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
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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('--device_target', type=str, default='Ascend', help='Device target')
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args_opt = parser.parse_args()
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set_seed(1)
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if args_opt.dataset == 'cifar10':
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from src.config import config1 as config
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from src.dataset import create_dataset1 as create_dataset
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else:
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from src.config import config2 as config
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from src.dataset import create_dataset2 as create_dataset
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if __name__ == '__main__':
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target = args_opt.device_target
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# init context
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context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
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if target != "GPU":
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(device_id=device_id)
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# create dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
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target=target)
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step_size = dataset.get_dataset_size()
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# define net
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net = mobilenet(class_num=config.class_num)
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# load checkpoint
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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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# define loss, model
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if args_opt.dataset == "imagenet2012":
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(sparse=True, reduction='mean',
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smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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else:
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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# define model
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model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
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# eval model
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res = model.eval(dataset)
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print("result:", res, "ckpt=", args_opt.checkpoint_path)
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#!/bin/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 ] && [ $# != 4 ]
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then
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echo "Usage: sh run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
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exit 1
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fi
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if [ $1 != "cifar10" ] && [ $1 != "imagenet2012" ]
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then
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echo "error: the selected dataset is neither cifar10 nor imagenet2012"
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exit 1
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fi
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get_real_path(){
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if [ "${1:0:1}" == "/" ]; then
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echo "$1"
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else
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echo "$(realpath -m $PWD/$1)"
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fi
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}
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PATH1=$(get_real_path $2)
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PATH2=$(get_real_path $3)
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if [ $# == 4 ]
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then
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PATH3=$(get_real_path $4)
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fi
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if [ ! -f $PATH1 ]
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then
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echo "error: RANK_TABLE_FILE=$PATH1 is not a file"
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exit 1
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fi
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if [ ! -d $PATH2 ]
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then
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echo "error: DATASET_PATH=$PATH2 is not a directory"
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exit 1
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fi
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if [ $# == 4 ] && [ ! -f $PATH3 ]
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then
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echo "error: PRETRAINED_CKPT_PATH=$PATH3 is not a file"
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exit 1
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fi
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ulimit -u unlimited
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export DEVICE_NUM=8
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export RANK_SIZE=8
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export RANK_TABLE_FILE=$PATH1
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export SERVER_ID=0
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rank_start=$((DEVICE_NUM * SERVER_ID))
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for((i=0; i<${DEVICE_NUM}; i++))
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do
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export DEVICE_ID=${i}
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export RANK_ID=$((rank_start + i))
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rm -rf ./train_parallel$i
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mkdir ./train_parallel$i
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cp ../*.py ./train_parallel$i
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cp *.sh ./train_parallel$i
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cp -r ../src ./train_parallel$i
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cd ./train_parallel$i || exit
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echo "start training for rank $RANK_ID, device $DEVICE_ID"
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env > env.log
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if [ $# == 3 ]
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then
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python train.py --dataset=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 &> log &
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fi
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if [ $# == 4 ]
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then
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python train.py --dataset=$1 --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$PATH2 --pre_trained=$PATH3 &> log &
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fi
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cd ..
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done
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#!/bin/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.
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 3 ]
|
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then
|
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echo "Usage: sh run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
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exit 1
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fi
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if [ $1 != "cifar10" ] && [ $1 != "imagenet2012" ]
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then
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echo "error: the selected dataset is neither cifar10 nor imagenet2012"
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exit 1
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fi
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get_real_path(){
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if [ "${1:0:1}" == "/" ]; then
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echo "$1"
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else
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echo "$(realpath -m $PWD/$1)"
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fi
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}
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PATH1=$(get_real_path $2)
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PATH2=$(get_real_path $3)
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|
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if [ ! -d $PATH1 ]
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then
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echo "error: DATASET_PATH=$PATH1 is not a directory"
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exit 1
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||||
fi
|
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|
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if [ ! -f $PATH2 ]
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then
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echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
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exit 1
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fi
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ulimit -u unlimited
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export DEVICE_NUM=1
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export DEVICE_ID=0
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export RANK_SIZE=$DEVICE_NUM
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export RANK_ID=0
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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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cp ../*.py ./eval
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cp *.sh ./eval
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cp -r ../src ./eval
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cd ./eval || exit
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env > env.log
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echo "start evaluation for device $DEVICE_ID"
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python eval.py --dataset=$1 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
|
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cd ..
|
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|
|
@ -0,0 +1,82 @@
|
|||
#!/bin/bash
|
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# 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.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# != 2 ] && [ $# != 3 ]
|
||||
then
|
||||
echo "Usage: sh run_distribute_train.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ $1 != "cifar10" ] && [ $1 != "imagenet2012" ]
|
||||
then
|
||||
echo "error: the selected dataset is neither cifar10 nor imagenet2012"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
|
||||
PATH1=$(get_real_path $2)
|
||||
|
||||
if [ $# == 3 ]
|
||||
then
|
||||
PATH2=$(get_real_path $3)
|
||||
fi
|
||||
|
||||
if [ ! -d $PATH1 ]
|
||||
then
|
||||
echo "error: DATASET_PATH=$PATH1 is not a directory"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ $# == 3 ] && [ ! -f $PATH2 ]
|
||||
then
|
||||
echo "error: PRETRAINED_CKPT_PATH=$PATH2 is not a file"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
ulimit -u unlimited
|
||||
export DEVICE_NUM=1
|
||||
export DEVICE_ID=0
|
||||
export RANK_ID=0
|
||||
export RANK_SIZE=1
|
||||
|
||||
if [ -d "train" ];
|
||||
then
|
||||
rm -rf ./train
|
||||
fi
|
||||
mkdir ./train
|
||||
cp ../*.py ./train
|
||||
cp *.sh ./train
|
||||
cp -r ../src ./train
|
||||
cd ./train || exit
|
||||
echo "start training for device $DEVICE_ID"
|
||||
env > env.log
|
||||
if [ $# == 2 ]
|
||||
then
|
||||
python train.py --dataset=$1 --dataset_path=$PATH1 &> log &
|
||||
fi
|
||||
|
||||
if [ $# == 3 ]
|
||||
then
|
||||
python train.py --dataset=$1 --dataset_path=$PATH1 --pre_trained=$PATH2 &> log &
|
||||
fi
|
||||
cd ..
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""define loss function for network"""
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.ops import operations as P
|
||||
|
||||
|
||||
class CrossEntropySmooth(_Loss):
|
||||
"""CrossEntropy"""
|
||||
def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
|
||||
super(CrossEntropySmooth, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.sparse = sparse
|
||||
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(reduction=reduction)
|
||||
|
||||
def construct(self, logit, label):
|
||||
if self.sparse:
|
||||
label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
|
||||
loss = self.ce(logit, label)
|
||||
return loss
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
# 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 for mobilenet, cifar10
|
||||
config1 = ed({
|
||||
"class_num": 10,
|
||||
"batch_size": 32,
|
||||
"loss_scale": 1024,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 1e-4,
|
||||
"epoch_size": 90,
|
||||
"pretrain_epoch_size": 0,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 5,
|
||||
"keep_checkpoint_max": 10,
|
||||
"save_checkpoint_path": "./",
|
||||
"warmup_epochs": 5,
|
||||
"lr_decay_mode": "poly",
|
||||
"lr_init": 0.01,
|
||||
"lr_end": 0.00001,
|
||||
"lr_max": 0.1
|
||||
})
|
||||
|
||||
# config for mobilenet, imagenet2012
|
||||
config2 = ed({
|
||||
"class_num": 1001,
|
||||
"batch_size": 256,
|
||||
"loss_scale": 1024,
|
||||
"momentum": 0.9,
|
||||
"weight_decay": 1e-4,
|
||||
"epoch_size": 90,
|
||||
"pretrain_epoch_size": 0,
|
||||
"save_checkpoint": True,
|
||||
"save_checkpoint_epochs": 5,
|
||||
"keep_checkpoint_max": 10,
|
||||
"save_checkpoint_path": "./",
|
||||
"warmup_epochs": 0,
|
||||
"lr_decay_mode": "linear",
|
||||
"use_label_smooth": True,
|
||||
"label_smooth_factor": 0.1,
|
||||
"lr_init": 0,
|
||||
"lr_max": 0.8,
|
||||
"lr_end": 0.0
|
||||
})
|
||||
|
|
@ -0,0 +1,155 @@
|
|||
# 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.vision.c_transforms as C
|
||||
import mindspore.dataset.transforms.c_transforms as C2
|
||||
from mindspore.communication.management import init, get_rank, get_group_size
|
||||
|
||||
|
||||
def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
|
||||
"""
|
||||
create a train or evaluate cifar10 dataset for mobilenet
|
||||
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
|
||||
target(str): the device target. Default: Ascend
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if target == "Ascend":
|
||||
device_num, rank_id = _get_rank_info()
|
||||
else:
|
||||
init()
|
||||
rank_id = get_rank()
|
||||
device_num = get_group_size()
|
||||
|
||||
if device_num == 1:
|
||||
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=device_num, shard_id=rank_id)
|
||||
|
||||
# define map operations
|
||||
trans = []
|
||||
if do_train:
|
||||
trans += [
|
||||
C.RandomCrop((32, 32), (4, 4, 4, 4)),
|
||||
C.RandomHorizontalFlip(prob=0.5)
|
||||
]
|
||||
|
||||
trans += [
|
||||
C.Resize((224, 224)),
|
||||
C.Rescale(1.0 / 255.0, 0.0),
|
||||
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
|
||||
C.HWC2CHW()
|
||||
]
|
||||
|
||||
type_cast_op = C2.TypeCast(mstype.int32)
|
||||
|
||||
ds = ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
|
||||
ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=8)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
|
||||
|
||||
def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
|
||||
"""
|
||||
create a train or eval imagenet2012 dataset for mobilenet
|
||||
|
||||
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
|
||||
target(str): the device target. Default: Ascend
|
||||
|
||||
Returns:
|
||||
dataset
|
||||
"""
|
||||
if target == "Ascend":
|
||||
device_num, rank_id = _get_rank_info()
|
||||
else:
|
||||
init()
|
||||
rank_id = get_rank()
|
||||
device_num = get_group_size()
|
||||
|
||||
if device_num == 1:
|
||||
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
|
||||
else:
|
||||
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
|
||||
num_shards=device_num, shard_id=rank_id)
|
||||
|
||||
image_size = 224
|
||||
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
|
||||
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
|
||||
|
||||
# define map operations
|
||||
if do_train:
|
||||
trans = [
|
||||
C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
|
||||
C.RandomHorizontalFlip(prob=0.5),
|
||||
C.Normalize(mean=mean, std=std),
|
||||
C.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
trans = [
|
||||
C.Decode(),
|
||||
C.Resize(256),
|
||||
C.CenterCrop(image_size),
|
||||
C.Normalize(mean=mean, std=std),
|
||||
C.HWC2CHW()
|
||||
]
|
||||
|
||||
type_cast_op = C2.TypeCast(mstype.int32)
|
||||
|
||||
ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=8)
|
||||
ds = ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
|
||||
|
||||
# apply batch operations
|
||||
ds = ds.batch(batch_size, drop_remainder=True)
|
||||
|
||||
# apply dataset repeat operation
|
||||
ds = ds.repeat(repeat_num)
|
||||
|
||||
return ds
|
||||
|
||||
|
||||
def _get_rank_info():
|
||||
"""
|
||||
get rank size and rank id
|
||||
"""
|
||||
rank_size = int(os.environ.get("RANK_SIZE", 1))
|
||||
|
||||
if rank_size > 1:
|
||||
rank_size = get_group_size()
|
||||
rank_id = get_rank()
|
||||
else:
|
||||
rank_size = 1
|
||||
rank_id = 0
|
||||
|
||||
return rank_size, rank_id
|
||||
|
|
@ -0,0 +1,207 @@
|
|||
# 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 _generate_steps_lr(lr_init, lr_max, total_steps, warmup_steps):
|
||||
"""
|
||||
Applies three steps decay to generate learning rate array.
|
||||
|
||||
Args:
|
||||
lr_init(float): init learning rate.
|
||||
lr_max(float): max learning rate.
|
||||
total_steps(int): all steps in training.
|
||||
warmup_steps(int): all steps in warmup epochs.
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array.
|
||||
"""
|
||||
decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
||||
else:
|
||||
if i < decay_epoch_index[0]:
|
||||
lr = lr_max
|
||||
elif i < decay_epoch_index[1]:
|
||||
lr = lr_max * 0.1
|
||||
elif i < decay_epoch_index[2]:
|
||||
lr = lr_max * 0.01
|
||||
else:
|
||||
lr = lr_max * 0.001
|
||||
lr_each_step.append(lr)
|
||||
return lr_each_step
|
||||
|
||||
|
||||
def _generate_poly_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
|
||||
"""
|
||||
Applies polynomial decay to generate learning rate array.
|
||||
|
||||
Args:
|
||||
lr_init(float): init learning rate.
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate.
|
||||
total_steps(int): all steps in training.
|
||||
warmup_steps(int): all steps in warmup epochs.
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array.
|
||||
"""
|
||||
lr_each_step = []
|
||||
if warmup_steps != 0:
|
||||
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
else:
|
||||
inc_each_step = 0
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = float(lr_init) + inc_each_step * float(i)
|
||||
else:
|
||||
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
|
||||
lr = float(lr_max) * base * base
|
||||
if lr < 0.0:
|
||||
lr = 0.0
|
||||
lr_each_step.append(lr)
|
||||
return lr_each_step
|
||||
|
||||
|
||||
def _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
|
||||
"""
|
||||
Applies cosine decay to generate learning rate array.
|
||||
|
||||
Args:
|
||||
lr_init(float): init learning rate.
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate.
|
||||
total_steps(int): all steps in training.
|
||||
warmup_steps(int): all steps in warmup epochs.
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array.
|
||||
"""
|
||||
decay_steps = total_steps - warmup_steps
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
lr = float(lr_init) + lr_inc * (i + 1)
|
||||
else:
|
||||
linear_decay = (total_steps - i) / decay_steps
|
||||
cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
|
||||
decayed = linear_decay * cosine_decay + 0.00001
|
||||
lr = lr_max * decayed
|
||||
lr_each_step.append(lr)
|
||||
return lr_each_step
|
||||
|
||||
|
||||
def _generate_liner_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps):
|
||||
"""
|
||||
Applies liner decay to generate learning rate array.
|
||||
|
||||
Args:
|
||||
lr_init(float): init learning rate.
|
||||
lr_end(float): end learning rate
|
||||
lr_max(float): max learning rate.
|
||||
total_steps(int): all steps in training.
|
||||
warmup_steps(int): all steps in warmup epochs.
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array.
|
||||
"""
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
||||
else:
|
||||
lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
|
||||
lr_each_step.append(lr)
|
||||
return lr_each_step
|
||||
|
||||
|
||||
|
||||
def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
|
||||
"""
|
||||
generate learning rate array
|
||||
|
||||
Args:
|
||||
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
|
||||
lr_decay_mode(string): learning rate decay mode, including steps, poly, cosine or liner(default)
|
||||
|
||||
Returns:
|
||||
np.array, learning rate array
|
||||
"""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
|
||||
if lr_decay_mode == 'steps':
|
||||
lr_each_step = _generate_steps_lr(lr_init, lr_max, total_steps, warmup_steps)
|
||||
elif lr_decay_mode == 'poly':
|
||||
lr_each_step = _generate_poly_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
|
||||
elif lr_decay_mode == 'cosine':
|
||||
lr_each_step = _generate_cosine_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
|
||||
else:
|
||||
lr_each_step = _generate_liner_lr(lr_init, lr_end, lr_max, total_steps, warmup_steps)
|
||||
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
return lr_each_step
|
||||
|
||||
|
||||
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
|
||||
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
|
||||
lr = float(init_lr) + lr_inc * current_step
|
||||
return lr
|
||||
|
||||
|
||||
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch=120, global_step=0):
|
||||
"""
|
||||
generate learning rate array with cosine
|
||||
|
||||
Args:
|
||||
lr(float): base learning rate
|
||||
steps_per_epoch(int): steps size of one epoch
|
||||
warmup_epochs(int): number of warmup epochs
|
||||
max_epoch(int): total epochs of training
|
||||
global_step(int): the current start index of lr array
|
||||
Returns:
|
||||
np.array, learning rate array
|
||||
"""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
decay_steps = total_steps - warmup_steps
|
||||
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
linear_decay = (total_steps - i) / decay_steps
|
||||
cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
|
||||
decayed = linear_decay * cosine_decay + 0.00001
|
||||
lr = base_lr * decayed
|
||||
lr_each_step.append(lr)
|
||||
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
learning_rate = lr_each_step[global_step:]
|
||||
return learning_rate
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
import mindspore.nn as nn
|
||||
from mindspore.ops import operations as P
|
||||
|
||||
def conv_bn_relu(in_channel, out_channel, kernel_size, stride, depthwise, activation='relu6'):
|
||||
output = []
|
||||
output.append(nn.Conv2d(in_channel, out_channel, kernel_size, stride, pad_mode="same",
|
||||
group=1 if not depthwise else in_channel))
|
||||
output.append(nn.BatchNorm2d(out_channel))
|
||||
if activation:
|
||||
output.append(nn.get_activation(activation))
|
||||
return nn.SequentialCell(output)
|
||||
|
||||
|
||||
class MobileNetV1(nn.Cell):
|
||||
"""
|
||||
MobileNet V1 backbone
|
||||
"""
|
||||
def __init__(self, class_num=1001, features_only=False):
|
||||
super(MobileNetV1, self).__init__()
|
||||
self.features_only = features_only
|
||||
cnn = [
|
||||
conv_bn_relu(3, 32, 3, 2, False), # Conv0
|
||||
|
||||
conv_bn_relu(32, 32, 3, 1, True), # Conv1_depthwise
|
||||
conv_bn_relu(32, 64, 1, 1, False), # Conv1_pointwise
|
||||
conv_bn_relu(64, 64, 3, 2, True), # Conv2_depthwise
|
||||
conv_bn_relu(64, 128, 1, 1, False), # Conv2_pointwise
|
||||
|
||||
conv_bn_relu(128, 128, 3, 1, True), # Conv3_depthwise
|
||||
conv_bn_relu(128, 128, 1, 1, False), # Conv3_pointwise
|
||||
conv_bn_relu(128, 128, 3, 2, True), # Conv4_depthwise
|
||||
conv_bn_relu(128, 256, 1, 1, False), # Conv4_pointwise
|
||||
|
||||
conv_bn_relu(256, 256, 3, 1, True), # Conv5_depthwise
|
||||
conv_bn_relu(256, 256, 1, 1, False), # Conv5_pointwise
|
||||
conv_bn_relu(256, 256, 3, 2, True), # Conv6_depthwise
|
||||
conv_bn_relu(256, 512, 1, 1, False), # Conv6_pointwise
|
||||
|
||||
conv_bn_relu(512, 512, 3, 1, True), # Conv7_depthwise
|
||||
conv_bn_relu(512, 512, 1, 1, False), # Conv7_pointwise
|
||||
conv_bn_relu(512, 512, 3, 1, True), # Conv8_depthwise
|
||||
conv_bn_relu(512, 512, 1, 1, False), # Conv8_pointwise
|
||||
conv_bn_relu(512, 512, 3, 1, True), # Conv9_depthwise
|
||||
conv_bn_relu(512, 512, 1, 1, False), # Conv9_pointwise
|
||||
conv_bn_relu(512, 512, 3, 1, True), # Conv10_depthwise
|
||||
conv_bn_relu(512, 512, 1, 1, False), # Conv10_pointwise
|
||||
conv_bn_relu(512, 512, 3, 1, True), # Conv11_depthwise
|
||||
conv_bn_relu(512, 512, 1, 1, False), # Conv11_pointwise
|
||||
|
||||
conv_bn_relu(512, 512, 3, 2, True), # Conv12_depthwise
|
||||
conv_bn_relu(512, 1024, 1, 1, False), # Conv12_pointwise
|
||||
conv_bn_relu(1024, 1024, 3, 1, True), # Conv13_depthwise
|
||||
conv_bn_relu(1024, 1024, 1, 1, False), # Conv13_pointwise
|
||||
]
|
||||
|
||||
if self.features_only:
|
||||
self.network = nn.CellList(cnn)
|
||||
else:
|
||||
self.network = nn.SequentialCell(cnn)
|
||||
self.fc = nn.Dense(1024, class_num)
|
||||
|
||||
def construct(self, x):
|
||||
output = x
|
||||
if self.features_only:
|
||||
features = ()
|
||||
for block in self.network:
|
||||
output = block(output)
|
||||
features = features + (output,)
|
||||
return features
|
||||
output = self.network(x)
|
||||
output = P.ReduceMean()(output, (2, 3))
|
||||
output = self.fc(output)
|
||||
return output
|
||||
|
||||
|
||||
def mobilenet_v1(class_num=1001):
|
||||
return MobileNetV1(class_num)
|
||||
|
|
@ -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.
|
||||
# ============================================================================
|
||||
"""train mobilenet_v1."""
|
||||
import os
|
||||
import argparse
|
||||
import ast
|
||||
from mindspore import context
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.optim.momentum import Momentum
|
||||
from mindspore.train.model import Model
|
||||
from mindspore.context import ParallelMode
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
||||
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
||||
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_rank, get_group_size
|
||||
from mindspore.common import set_seed
|
||||
import mindspore.nn as nn
|
||||
import mindspore.common.initializer as weight_init
|
||||
from src.lr_generator import get_lr
|
||||
from src.CrossEntropySmooth import CrossEntropySmooth
|
||||
from src.mobilenet_v1 import mobilenet_v1 as mobilenet
|
||||
|
||||
parser = argparse.ArgumentParser(description='Image classification')
|
||||
parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
|
||||
parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
|
||||
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
|
||||
|
||||
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
|
||||
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
|
||||
parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
set_seed(1)
|
||||
|
||||
if args_opt.dataset == 'cifar10':
|
||||
from src.config import config1 as config
|
||||
from src.dataset import create_dataset1 as create_dataset
|
||||
else:
|
||||
from src.config import config2 as config
|
||||
from src.dataset import create_dataset2 as create_dataset
|
||||
|
||||
if __name__ == '__main__':
|
||||
target = args_opt.device_target
|
||||
ckpt_save_dir = config.save_checkpoint_path
|
||||
|
||||
# init context
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
|
||||
if args_opt.parameter_server:
|
||||
context.set_ps_context(enable_ps=True)
|
||||
if args_opt.run_distribute:
|
||||
if target == "Ascend":
|
||||
device_id = int(os.getenv('DEVICE_ID'))
|
||||
context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
|
||||
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
gradients_mean=True)
|
||||
init()
|
||||
# GPU target
|
||||
else:
|
||||
init()
|
||||
context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
gradients_mean=True)
|
||||
ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
|
||||
|
||||
# create dataset
|
||||
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
|
||||
batch_size=config.batch_size, target=target)
|
||||
step_size = dataset.get_dataset_size()
|
||||
|
||||
# define net
|
||||
net = mobilenet(class_num=config.class_num)
|
||||
if args_opt.parameter_server:
|
||||
net.set_param_ps()
|
||||
|
||||
# init weight
|
||||
if args_opt.pre_trained:
|
||||
param_dict = load_checkpoint(args_opt.pre_trained)
|
||||
load_param_into_net(net, param_dict)
|
||||
else:
|
||||
for _, cell in net.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(),
|
||||
cell.weight.shape,
|
||||
cell.weight.dtype))
|
||||
if isinstance(cell, nn.Dense):
|
||||
cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(),
|
||||
cell.weight.shape,
|
||||
cell.weight.dtype))
|
||||
|
||||
# init lr
|
||||
lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
|
||||
warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
|
||||
lr_decay_mode=config.lr_decay_mode)
|
||||
lr = Tensor(lr)
|
||||
|
||||
# define opt
|
||||
decayed_params = []
|
||||
no_decayed_params = []
|
||||
for param in net.trainable_params():
|
||||
if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
|
||||
decayed_params.append(param)
|
||||
else:
|
||||
no_decayed_params.append(param)
|
||||
|
||||
group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
|
||||
{'params': no_decayed_params},
|
||||
{'order_params': net.trainable_params()}]
|
||||
opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
|
||||
# define loss, model
|
||||
if target == "Ascend":
|
||||
if args_opt.dataset == "imagenet2012":
|
||||
if not config.use_label_smooth:
|
||||
config.label_smooth_factor = 0.0
|
||||
loss = CrossEntropySmooth(sparse=True, reduction="mean",
|
||||
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
||||
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
|
||||
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
|
||||
amp_level="O2", keep_batchnorm_fp32=False)
|
||||
else:
|
||||
# GPU target
|
||||
if args_opt.dataset == "imagenet2012":
|
||||
if not config.use_label_smooth:
|
||||
config.label_smooth_factor = 0.0
|
||||
loss = CrossEntropySmooth(sparse=True, reduction="mean",
|
||||
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
|
||||
else:
|
||||
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
||||
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay,
|
||||
config.loss_scale)
|
||||
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
|
||||
# Mixed precision
|
||||
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
|
||||
amp_level="O2", keep_batchnorm_fp32=False)
|
||||
|
||||
# define callbacks
|
||||
time_cb = TimeMonitor(data_size=step_size)
|
||||
loss_cb = LossMonitor()
|
||||
cb = [time_cb, loss_cb]
|
||||
if config.save_checkpoint:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
|
||||
keep_checkpoint_max=config.keep_checkpoint_max)
|
||||
ckpt_cb = ModelCheckpoint(prefix="mobilenetv1", directory=ckpt_save_dir, config=config_ck)
|
||||
cb += [ckpt_cb]
|
||||
|
||||
# train model
|
||||
model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb,
|
||||
sink_size=dataset.get_dataset_size(), dataset_sink_mode=(not args_opt.parameter_server))
|
||||
|
|
@ -1,49 +1,59 @@
|
|||
# Contents
|
||||
|
||||
- [SSD Description](#ssd-description)
|
||||
- [Model Architecture](#model-architecture)
|
||||
- [Dataset](#dataset)
|
||||
- [Environment Requirements](#environment-requirements)
|
||||
- [Quick Start](#quick-start)
|
||||
- [Script Description](#script-description)
|
||||
- [Script and Sample Code](#script-and-sample-code)
|
||||
- [Script Parameters](#script-parameters)
|
||||
- [Training Process](#training-process)
|
||||
- [Training](#training)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Evaluation](#evaluation)
|
||||
- [Export MindIR](#export-mindir)
|
||||
- [Model Description](#model-description)
|
||||
- [Performance](#performance)
|
||||
- [Evaluation Performance](#evaluation-performance)
|
||||
- [Inference Performance](#evaluation-performance)
|
||||
- [Description of Random Situation](#description-of-random-situation)
|
||||
- [ModelZoo Homepage](#modelzoo-homepage)
|
||||
- [Contents](#contents)
|
||||
- [SSD Description](#ssd-description)
|
||||
- [Model Architecture](#model-architecture)
|
||||
- [Dataset](#dataset)
|
||||
- [Environment Requirements](#environment-requirements)
|
||||
- [Quick Start](#quick-start)
|
||||
- [Prepare the model](#prepare-the-model)
|
||||
- [Run the scripts](#run-the-scripts)
|
||||
- [Script Description](#script-description)
|
||||
- [Script and Sample Code](#script-and-sample-code)
|
||||
- [Script Parameters](#script-parameters)
|
||||
- [Training Process](#training-process)
|
||||
- [Training on Ascend](#training-on-ascend)
|
||||
- [Training on GPU](#training-on-gpu)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Evaluation on Ascend](#evaluation-on-ascend)
|
||||
- [Evaluation on GPU](#evaluation-on-gpu)
|
||||
- [Export MindIR](#export-mindir)
|
||||
- [Model Description](#model-description)
|
||||
- [Performance](#performance)
|
||||
- [Evaluation Performance](#evaluation-performance)
|
||||
- [Inference Performance](#inference-performance)
|
||||
- [Description of Random Situation](#description-of-random-situation)
|
||||
- [ModelZoo Homepage](#modelzoo-homepage)
|
||||
|
||||
## [SSD Description](#contents)
|
||||
|
||||
# [SSD Description](#contents)
|
||||
|
||||
SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape.Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
|
||||
|
||||
[Paper](https://arxiv.org/abs/1512.02325): Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg.European Conference on Computer Vision (ECCV), 2016 (In press).
|
||||
|
||||
# [Model Architecture](#contents)
|
||||
## [Model Architecture](#contents)
|
||||
|
||||
The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.
|
||||
|
||||
# [Dataset](#contents)
|
||||
We present two different base architecture.
|
||||
|
||||
- **ssd300**, reference from the paper. Using mobilenetv2 as backbone and the same bbox predictor as the paper pressent.
|
||||
- ***ssd-mobilenet-v1-fpn**, using mobilenet-v1 and FPN as feature extractor with weight-shared box predcitors.
|
||||
|
||||
## [Dataset](#contents)
|
||||
|
||||
Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.
|
||||
|
||||
Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
||||
Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
||||
|
||||
- Dataset size：19G
|
||||
- Train：18G，118000 images
|
||||
- Val：1G，5000 images
|
||||
- Annotations：241M，instances，captions，person_keypoints etc
|
||||
- Train：18G，118000 images
|
||||
- Val：1G，5000 images
|
||||
- Annotations：241M，instances，captions，person_keypoints etc
|
||||
- Data format：image and json files
|
||||
- Note：Data will be processed in dataset.py
|
||||
- Note：Data will be processed in dataset.py
|
||||
|
||||
# [Environment Requirements](#contents)
|
||||
## [Environment Requirements](#contents)
|
||||
|
||||
- Install [MindSpore](https://www.mindspore.cn/install/en).
|
||||
|
||||
|
|
@ -52,19 +62,19 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
|||
- We use COCO2017 as training dataset in this example by default, and you can also use your own datasets.
|
||||
First, install Cython ,pycocotool and opencv to process data and to get evaluation result.
|
||||
|
||||
```
|
||||
```shell
|
||||
pip install Cython
|
||||
|
||||
pip install pycocotools
|
||||
|
||||
pip install opencv-python
|
||||
|
||||
```
|
||||
|
||||
1. If coco dataset is used. **Select dataset to coco when run script.**
|
||||
|
||||
Change the `coco_root` and other settings you need in `src/config.py`. The directory structure is as follows:
|
||||
|
||||
```
|
||||
|
||||
```shell
|
||||
.
|
||||
└─coco_dataset
|
||||
├─annotations
|
||||
|
|
@ -72,12 +82,12 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
|||
└─instance_val2017.json
|
||||
├─val2017
|
||||
└─train2017
|
||||
|
||||
```
|
||||
|
||||
2. If VOC dataset is used. **Select dataset to voc when run script.**
|
||||
Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config.py`. `voc_json` is the path of json file with coco format for evalution, `voc_root` is the path of VOC dataset, the directory structure is as follows:
|
||||
```
|
||||
|
||||
```shell
|
||||
.
|
||||
└─voc_dataset
|
||||
└─train
|
||||
|
|
@ -92,33 +102,42 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
|
|||
...
|
||||
├─xxxx.jpg
|
||||
└─xxxx.xml
|
||||
|
||||
```
|
||||
|
||||
3. If your own dataset is used. **Select dataset to other when run script.**
|
||||
Organize the dataset infomation into a TXT file, each row in the file is as follows:
|
||||
|
||||
```
|
||||
```shell
|
||||
train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
|
||||
|
||||
```
|
||||
|
||||
Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are setting in `src/config.py`.
|
||||
Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the `image_dir`(dataset directory) and the relative path in `anno_path`(the TXT file path), `image_dir` and `anno_path` are setting in `src/config.py`.
|
||||
|
||||
# [Quick Start](#contents)
|
||||
## [Quick Start](#contents)
|
||||
|
||||
### Prepare the model
|
||||
|
||||
1. Chose the model by chaning the `using_model` in `src/confgi.py`. The optional models are: `ssd300`, `ssd_mobilenet_v1_fpn`.
|
||||
2. Change the datset config in the corresponding config. `src/config_ssd300.py` or `src/config_ssd_mobilenet_v1_fpn.py`.
|
||||
3. If you are running with `ssd_mobilenet_v1_fpn`, you need a pretrained model for `mobilenet_v1`. Set the checkpoint path to `feature_extractor_base_param` in `src/config_ssd_mobilenet_v1_fpn.py`. For more detail about training mobilnet_v1, please refer to the mobilenetv1 model.
|
||||
|
||||
### Run the scripts
|
||||
|
||||
After installing MindSpore via the official website, you can start training and evaluation as follows:
|
||||
|
||||
After installing MindSpore via the official website, you can start training and evaluation as follows:
|
||||
- runing on Ascend
|
||||
|
||||
```
|
||||
```shell
|
||||
# distributed training on Ascend
|
||||
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]
|
||||
|
||||
# run eval on Ascend
|
||||
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||
```
|
||||
|
||||
- runing on GPU
|
||||
```
|
||||
|
||||
```shell
|
||||
# distributed training on GPU
|
||||
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET]
|
||||
|
||||
|
|
@ -130,7 +149,7 @@ sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
|||
|
||||
**CPU is usually used for fine-tuning, which needs pre_trained checkpoint.**
|
||||
|
||||
```
|
||||
```shell
|
||||
# training on CPU
|
||||
python train.py --run_platform=CPU --lr=[LR] --dataset=[DATASET] --epoch_size=[EPOCH_SIZE] --batch_size=[BATCH_SIZE] --pre_trained=[PRETRAINED_CKPT] --filter_weight=True --save_checkpoint_epochs=1
|
||||
|
||||
|
|
@ -138,14 +157,14 @@ python train.py --run_platform=CPU --lr=[LR] --dataset=[DATASET] --epoch_size=[E
|
|||
python eval.py --run_platform=CPU --dataset=[DATASET] --checkpoint_path=[PRETRAINED_CKPT]
|
||||
```
|
||||
|
||||
# [Script Description](#contents)
|
||||
## [Script Description](#contents)
|
||||
|
||||
## [Script and Sample Code](#contents)
|
||||
### [Script and Sample Code](#contents)
|
||||
|
||||
```shell
|
||||
.
|
||||
└─ cv
|
||||
└─ ssd
|
||||
└─ ssd
|
||||
├─ README.md # descriptions about SSD
|
||||
├─ scripts
|
||||
├─ run_distribute_train.sh # shell script for distributed on ascend
|
||||
|
|
@ -167,9 +186,9 @@ python eval.py --run_platform=CPU --dataset=[DATASET] --checkpoint_path=[PRETRAI
|
|||
└─ mindspore_hub_conf.py # mindspore hub interface
|
||||
```
|
||||
|
||||
## [Script Parameters](#contents)
|
||||
### [Script Parameters](#contents)
|
||||
|
||||
```
|
||||
```shell
|
||||
Major parameters in train.py and config.py as follows:
|
||||
|
||||
"device_num": 1 # Use device nums
|
||||
|
|
@ -195,19 +214,20 @@ python eval.py --run_platform=CPU --dataset=[DATASET] --checkpoint_path=[PRETRAI
|
|||
|
||||
```
|
||||
|
||||
|
||||
## [Training Process](#contents)
|
||||
### [Training Process](#contents)
|
||||
|
||||
To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc dataset) or `image_dir` and `anno_path`(own dataset). **Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.**
|
||||
|
||||
### Training on Ascend
|
||||
#### Training on Ascend
|
||||
|
||||
- Distribute mode
|
||||
|
||||
```
|
||||
```shell
|
||||
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
||||
```
|
||||
|
||||
We need five or seven parameters for this scripts.
|
||||
|
||||
- `DEVICE_NUM`: the device number for distributed train.
|
||||
- `EPOCH_NUM`: epoch num for distributed train.
|
||||
- `LR`: learning rate init value for distributed train.
|
||||
|
|
@ -216,9 +236,9 @@ We need five or seven parameters for this scripts.
|
|||
- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
|
||||
- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
|
||||
|
||||
Training result will be stored in the current path, whose folder name begins with "LOG". Under this, you can find checkpoint file together with result like the followings in log
|
||||
Training result will be stored in the current path, whose folder name begins with "LOG". Under this, you can find checkpoint file together with result like the followings in log
|
||||
|
||||
```
|
||||
```shell
|
||||
epoch: 1 step: 458, loss is 3.1681802
|
||||
epoch time: 228752.4654865265, per step time: 499.4595316299705
|
||||
epoch: 2 step: 458, loss is 2.8847265
|
||||
|
|
@ -235,14 +255,16 @@ epoch: 500 step: 458, loss is 0.5548882
|
|||
epoch time: 39064.8467540741, per step time: 85.29442522723602
|
||||
```
|
||||
|
||||
### Training on GPU
|
||||
#### Training on GPU
|
||||
|
||||
- Distribute mode
|
||||
|
||||
```
|
||||
```shell
|
||||
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
|
||||
```
|
||||
|
||||
We need five or seven parameters for this scripts.
|
||||
|
||||
- `DEVICE_NUM`: the device number for distributed train.
|
||||
- `EPOCH_NUM`: epoch num for distributed train.
|
||||
- `LR`: learning rate init value for distributed train.
|
||||
|
|
@ -250,9 +272,9 @@ We need five or seven parameters for this scripts.
|
|||
- `PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
|
||||
- `PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
|
||||
|
||||
Training result will be stored in the current path, whose folder name is "LOG". Under this, you can find checkpoint files together with result like the followings in log
|
||||
Training result will be stored in the current path, whose folder name is "LOG". Under this, you can find checkpoint files together with result like the followings in log
|
||||
|
||||
```
|
||||
```shell
|
||||
epoch: 1 step: 1, loss is 420.11783
|
||||
epoch: 1 step: 2, loss is 434.11032
|
||||
epoch: 1 step: 3, loss is 476.802
|
||||
|
|
@ -263,14 +285,16 @@ epoch time: 150753.701, per step time: 329.157
|
|||
|
||||
```
|
||||
|
||||
## [Evaluation Process](#contents)
|
||||
### [Evaluation Process](#contents)
|
||||
|
||||
### Evaluation on Ascend
|
||||
#### Evaluation on Ascend
|
||||
|
||||
```shell
|
||||
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||
```
|
||||
sh run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||
```
|
||||
|
||||
We need two parameters for this scripts.
|
||||
|
||||
- `DATASET`：the dataset mode of evaluation dataset.
|
||||
- `CHECKPOINT_PATH`: the absolute path for checkpoint file.
|
||||
- `DEVICE_ID`: the device id for eval.
|
||||
|
|
@ -279,7 +303,7 @@ We need two parameters for this scripts.
|
|||
|
||||
Inference result will be stored in the example path, whose folder name begins with "eval". Under this, you can find result like the followings in log.
|
||||
|
||||
```
|
||||
```shell
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.238
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.400
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.240
|
||||
|
|
@ -298,12 +322,14 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697
|
|||
mAP: 0.23808886505483504
|
||||
```
|
||||
|
||||
### Evaluation on GPU
|
||||
#### Evaluation on GPU
|
||||
|
||||
```
|
||||
```shell
|
||||
sh run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID]
|
||||
```
|
||||
|
||||
We need two parameters for this scripts.
|
||||
|
||||
- `DATASET`：the dataset mode of evaluation dataset.
|
||||
- `CHECKPOINT_PATH`: the absolute path for checkpoint file.
|
||||
- `DEVICE_ID`: the device id for eval.
|
||||
|
|
@ -312,7 +338,7 @@ We need two parameters for this scripts.
|
|||
|
||||
Inference result will be stored in the example path, whose folder name begins with "eval". Under this, you can find result like the followings in log.
|
||||
|
||||
```
|
||||
```shell
|
||||
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.224
|
||||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.375
|
||||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.228
|
||||
|
|
@ -331,17 +357,19 @@ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686
|
|||
mAP: 0.2244936111705981
|
||||
```
|
||||
|
||||
## [Export MindIR](#contents)
|
||||
### [Export MindIR](#contents)
|
||||
|
||||
```
|
||||
```shell
|
||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||
```
|
||||
|
||||
The ckpt_file parameter is required.
|
||||
|
||||
# [Model Description](#contents)
|
||||
## [Performance](#contents)
|
||||
## [Model Description](#contents)
|
||||
|
||||
### Evaluation Performance
|
||||
### [Performance](#contents)
|
||||
|
||||
#### Evaluation Performance
|
||||
|
||||
| Parameters | Ascend | GPU |
|
||||
| -------------------------- | -------------------------------------------------------------| -------------------------------------------------------------|
|
||||
|
|
@ -356,10 +384,9 @@ The ckpt_file parameter is required.
|
|||
| Speed | 8pcs: 90ms/step | 8pcs: 121ms/step |
|
||||
| Total time | 8pcs: 4.81hours | 8pcs: 12.31hours |
|
||||
| Parameters (M) | 34 | 34 |
|
||||
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd |
|
||||
| Scripts | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd> | <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd> |
|
||||
|
||||
|
||||
### Inference Performance
|
||||
#### Inference Performance
|
||||
|
||||
| Parameters | Ascend | GPU |
|
||||
| ------------------- | ----------------------------| ----------------------------|
|
||||
|
|
@ -373,10 +400,10 @@ The ckpt_file parameter is required.
|
|||
| Accuracy | IoU=0.50: 23.8% | IoU=0.50: 22.4% |
|
||||
| Model for inference | 34M(.ckpt file) | 34M(.ckpt file) |
|
||||
|
||||
# [Description of Random Situation](#contents)
|
||||
## [Description of Random Situation](#contents)
|
||||
|
||||
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
|
||||
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
|
||||
|
||||
## [ModelZoo Homepage](#contents)
|
||||
|
||||
# [ModelZoo Homepage](#contents)
|
||||
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
|
||||
|
|
|
|||
Loading…
Reference in New Issue