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# Contents
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- [ResNeXt152 Description](#resnext152-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](#mixed-precision)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Model Export](#model-export)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#evaluation-performance)
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- [Inference Performance](#evaluation-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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# [ResNeXt152 Description](#contents)
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ResNeXt is a simple, highly modularized network architecture for image classification. It designs results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set in ResNeXt. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.
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[Paper](https://arxiv.org/abs/1611.05431): Xie S, Girshick R, Dollár, Piotr, et al. Aggregated Residual Transformations for Deep Neural Networks. 2016.
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# [Model architecture](#contents)
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The overall network architecture of ResNeXt is show below:
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[Link](https://arxiv.org/abs/1611.05431)
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# [Dataset](#contents)
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Dataset used: [imagenet](http://www.image-net.org/)
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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- Test: 5G, 50000 images
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- Data format: RGB images
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- Note: Data will be processed in src/dataset.py
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# [Features](#contents)
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## [Mixed Precision](#contents)
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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](#contents)
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- Hardware（Ascend）
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- Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Framework
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- [MindSpore](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](#contents)
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## [Script and sample code](#contents)
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```python
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.
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└─resnext152
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├─README.md
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├─scripts
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├─run_standalone_train.sh # launch standalone training for ascend(1p)
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├─run_distribute_train.sh # launch distributed training for ascend(8p)
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└─run_eval.sh # launch evaluating
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├─src
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├─backbone
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├─_init_.py # initialize
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├─resnet.py # resnext152 backbone
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├─utils
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├─_init_.py # initialize
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├─cunstom_op.py # network operation
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├─logging.py # print log
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├─optimizers_init_.py # get parameters
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├─sampler.py # distributed sampler
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├─var_init_.py # calculate gain value
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├─_init_.py # initialize
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├─config.py # parameter configuration
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├─crossentropy.py # CrossEntropy loss function
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├─dataset.py # data preprocessing
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├─eval_callback.py # Inference during training
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├─head.py # common head
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├─image_classification.py # get resnet
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├─metric.py # Inference
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├─linear_warmup.py # linear warmup learning rate
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├─warmup_cosine_annealing.py # learning rate each step
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├─warmup_step_lr.py # warmup step learning rate
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├─eval.py # eval net
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├──train.py # train net
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├──export.py # export mindir script
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├──mindspore_hub_conf.py # mindspore hub interface
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```
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## [Script Parameters](#contents)
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Parameters for both training and evaluating can be set in config.py.
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```config
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"image_height": '224,224' # image size
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"num_classes": 1000, # dataset class number
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"per_batch_size": 128, # batch size of input tensor
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"lr": 0.05, # base learning rate
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"lr_scheduler": 'cosine_annealing', # learning rate mode
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"lr_epochs": '30,60,90,120', # epoch of lr changing
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"lr_gamma": 0.1, # decrease lr by a factor of exponential lr_scheduler
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"eta_min": 0, # eta_min in cosine_annealing scheduler
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"T_max": 150, # T-max in cosine_annealing scheduler
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"max_epoch": 150, # max epoch num to train the model
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"warmup_epochs" : 1, # warmup epoch
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"weight_decay": 0.0001, # weight decay
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"momentum": 0.9, # momentum
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"is_dynamic_loss_scale": 0, # dynamic loss scale
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"loss_scale": 1024, # loss scale
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"label_smooth": 1, # label_smooth
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"label_smooth_factor": 0.1, # label_smooth_factor
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"ckpt_interval": 2000, # ckpt_interval
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"ckpt_path": 'outputs/', # checkpoint save location
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"is_save_on_master": 1,
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"rank": 0, # local rank of distributed
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"group_size": 1 # world size of distributed
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```
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## [Training Process](#contents)
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### Usage
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You can start training by python script:
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```script
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python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0
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```
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or shell script:
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```script
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Ascend:
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# distribute training example(8p)
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sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
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# standalone training
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sh run_standalone_train.sh DEVICE_ID DATA_PATH
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```
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#### Launch
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```bash
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# distributed training example(8p) for Ascend
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sh scripts/run_distribute_train.sh RANK_TABLE_FILE /dataset/train
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# standalone training example for Ascend
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sh scripts/run_standalone_train.sh 0 /dataset/train
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```
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You can find checkpoint file together with result in log.
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## [Evaluation Process](#contents)
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### Usage
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You can start training by python script:
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```script
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python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt
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```
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or shell script:
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```script
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# Evaluation
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sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH PLATFORM
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```
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PLATFORM is Ascend, default is Ascend.
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#### Launch
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```bash
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# Evaluation with checkpoint
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sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext152_100.ckpt Ascend
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#Directly use the script to run
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python eval.py --data_dir /opt/npu/pvc/dataset/storage/imagenet/val/ --platform Ascend --pretrained /root/test/resnext152_64x4d/outputs_demo/best_acc_4.ckpt
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```
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#### Result
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Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
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```log
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acc=80.08%(TOP1)
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acc=94.71%(TOP5)
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```
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## [Model Export](#contents)
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```shell
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python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT]
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```
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`EXPORT_FORMAT` should be in ["AIR", "ONNX", "MINDIR"]
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# [Model description](#contents)
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## [Performance](#contents)
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### Training Performance
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| Parameters | ResNeXt152 | |
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| -------------------------- | --------------------------------------------- | ---- |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | |
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| uploaded Date | 06/30/2021 | |
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| MindSpore Version | 1.2 | |
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| Dataset | ImageNet | |
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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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| Loss | 1.28923 | |
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| Accuracy | 80.08%(TOP1) | |
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| Total time | 7.8 h 8ps | |
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| Checkpoint for Fine tuning | 192 M(.ckpt file) | |
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#### Inference Performance
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| Parameters | | | |
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| ----------------- | ---- | ---- | ---------------- |
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| Resource | | | Ascend 910 |
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| uploaded Date | | | 06/20/2021 |
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| MindSpore Version | | | 1.2 |
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| Dataset | | | ImageNet, 1.2W |
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| batch_size | | | 1 |
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| outputs | | | probability |
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| Accuracy | | | acc=80.08%(TOP1) |
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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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# [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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# 目录
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- [目录](#目录)
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- [ResNeXt152说明](#resnext152说明)
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- [模型架构](#模型架构)
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- [数据集](#数据集)
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- [特性](#特性)
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- [混合精度](#混合精度)
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- [环境要求](#环境要求)
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- [脚本说明](#脚本说明)
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- [脚本及样例代码](#脚本及样例代码)
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- [脚本参数](#脚本参数)
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- [训练过程](#训练过程)
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- [用法](#用法)
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- [样例](#样例)
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- [评估过程](#评估过程)
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- [用法](#用法-1)
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- [样例](#样例-1)
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- [结果](#结果)
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- [模型导出](#模型导出)
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- [模型描述](#模型描述)
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- [性能](#性能)
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- [训练性能](#训练性能)
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- [推理性能](#推理性能)
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- [随机情况说明](#随机情况说明)
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- [ModelZoo主页](#modelzoo主页)
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# ResNeXt152说明
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ResNeXt是一个简单、高度模块化的图像分类网络架构。ResNeXt的设计为统一的、多分支的架构，该架构仅需设置几个超参数。此策略提供了一个新维度，我们将其称为“基数”（转换集的大小），它是深度和宽度维度之外的一个重要因素。
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[论文](https://arxiv.org/abs/1611.05431)： Xie S, Girshick R, Dollár, Piotr, et al. Aggregated Residual Transformations for Deep Neural Networks. 2016.
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# 模型架构
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ResNeXt整体网络架构如下：
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[链接](https://arxiv.org/abs/1611.05431)
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# 数据集
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使用的数据集：[ImageNet](http://www.image-net.org/)
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- 数据集大小：约125G, 共1000个类，包含1.2万张彩色图像
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- 训练集：120G，1.2万张图像
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- 测试集：5G，5万张图像
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- 数据格式：RGB图像。
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- 注：数据在src/dataset.py中处理。
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# 特性
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## 混合精度
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采用[混合精度](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html)的训练方法使用支持单精度和半精度数据来提高深度学习神经网络的训练速度，同时保持单精度训练所能达到的网络精度。混合精度训练提高计算速度、减少内存使用的同时，支持在特定硬件上训练更大的模型或实现更大批次的训练。
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以FP16算子为例，如果输入数据类型为FP32，MindSpore后台会自动降低精度来处理数据。用户可打开INFO日志，搜索“reduce precision”查看精度降低的算子。
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# 环境要求
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- 硬件（Ascend）
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- 准备Ascend处理器搭建硬件环境。如需试用昇腾处理器，请发送[申请表](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)至ascend@huawei.com，审核通过即可获得资源。
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- 框架
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- [MindSpore](https://www.mindspore.cn/install)
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- 如需查看详情，请参见如下资源：
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- [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
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# 脚本说明
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## 脚本及样例代码
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```path
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.
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└─resnext152
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├─README.md
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├─scripts
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├─run_standalone_train.sh # 启动Ascend单机训练（单卡）
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├─run_distribute_train.sh # 启动Ascend分布式训练（8卡）
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└─run_eval.sh # 启动评估
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├─src
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├─backbone
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├─_init_.py # 初始化
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├─resnet.py # ResNeXt152骨干
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├─utils
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├─_init_.py # 初始化
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├─cunstom_op.py # 网络操作
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├─logging.py # 打印日志
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|
├─optimizers_init_.py # 获取参数
|
||||||
|
├─sampler.py # 分布式采样器
|
||||||
|
├─var_init_.py # 计算增益值
|
||||||
|
├─_init_.py # 初始化
|
||||||
|
├─config.py # 参数配置
|
||||||
|
├─crossentropy.py # 交叉熵损失函数
|
||||||
|
├─dataset.py # 数据预处理
|
||||||
|
├─eval_callback.py # 训练时推理
|
||||||
|
├─head.py # 常见头
|
||||||
|
├─image_classification.py # 获取ResNet
|
||||||
|
├─metric.py # 推理
|
||||||
|
├─linear_warmup.py # 线性热身学习率
|
||||||
|
├─warmup_cosine_annealing.py # 每次迭代的学习率
|
||||||
|
├─warmup_step_lr.py # 热身迭代学习率
|
||||||
|
├─eval.py # 评估网络
|
||||||
|
├──train.py # 训练网络
|
||||||
|
├──mindspore_hub_conf.py # MindSpore Hub接口
|
||||||
|
```
|
||||||
|
|
||||||
|
## 脚本参数
|
||||||
|
|
||||||
|
在config.py中可以同时配置训练和评估参数。
|
||||||
|
|
||||||
|
```python
|
||||||
|
"image_height": '224,224' # 图像大小
|
||||||
|
"num_classes": 1000, # 数据集类数
|
||||||
|
"per_batch_size": 128, # 输入张量的批次大小
|
||||||
|
"lr": 0.05, # 基础学习率
|
||||||
|
"lr_scheduler": 'cosine_annealing', # 学习率模式
|
||||||
|
"lr_epochs": '30,60,90,120', # LR变化轮次
|
||||||
|
"lr_gamma": 0.1, # 减少LR的exponential lr_scheduler因子
|
||||||
|
"eta_min": 0, # cosine_annealing调度器中的eta_min
|
||||||
|
"T_max": 150, # cosine_annealing调度器中的T-max
|
||||||
|
"max_epoch": 150, # 训练模型的最大轮次数量
|
||||||
|
"backbone": 'resnext152', # 骨干网络
|
||||||
|
"warmup_epochs" : 1, # 热身轮次
|
||||||
|
"weight_decay": 0.0001, # 权重衰减
|
||||||
|
"momentum": 0.9, # 动量
|
||||||
|
"is_dynamic_loss_scale": 0, # 动态损失放大
|
||||||
|
"loss_scale": 1024, # 损失放大
|
||||||
|
"label_smooth": 1, # 标签平滑
|
||||||
|
"label_smooth_factor": 0.1, # 标签平滑因子
|
||||||
|
"ckpt_interval": 2000, # 检查点间隔
|
||||||
|
"ckpt_path": 'outputs/', # 检查点保存位置
|
||||||
|
"is_save_on_master": 1,
|
||||||
|
"rank": 0, # 分布式本地进程序号
|
||||||
|
"group_size": 1 # 分布式进程总数
|
||||||
|
```
|
||||||
|
|
||||||
|
## 训练过程
|
||||||
|
|
||||||
|
### 用法
|
||||||
|
|
||||||
|
您可以通过python脚本开始训练：
|
||||||
|
|
||||||
|
```shell
|
||||||
|
python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0
|
||||||
|
```
|
||||||
|
|
||||||
|
或通过shell脚本开始训练：
|
||||||
|
|
||||||
|
```shell
|
||||||
|
Ascend:
|
||||||
|
# 分布式训练示例（8卡）
|
||||||
|
sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
|
||||||
|
# 单机训练
|
||||||
|
sh run_standalone_train.sh DEVICE_ID DATA_PATH
|
||||||
|
```
|
||||||
|
|
||||||
|
### 样例
|
||||||
|
|
||||||
|
```shell
|
||||||
|
# Ascend分布式训练示例（8卡）
|
||||||
|
sh scripts/run_distribute_train.sh RANK_TABLE_FILE /dataset/train
|
||||||
|
# Ascend单机训练示例
|
||||||
|
sh scripts/run_standalone_train.sh 0 /dataset/train
|
||||||
|
```
|
||||||
|
|
||||||
|
您可以在日志中找到检查点文件和结果。
|
||||||
|
|
||||||
|
## 评估过程
|
||||||
|
|
||||||
|
### 用法
|
||||||
|
|
||||||
|
您可以通过python脚本开始训练：
|
||||||
|
|
||||||
|
```shell
|
||||||
|
python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt
|
||||||
|
```
|
||||||
|
|
||||||
|
或通过shell脚本开始训练：
|
||||||
|
|
||||||
|
```shell
|
||||||
|
# 评估
|
||||||
|
sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH PLATFORM
|
||||||
|
```
|
||||||
|
|
||||||
|
PLATFORM is Ascend, default is Ascend.
|
||||||
|
|
||||||
|
#### 样例
|
||||||
|
|
||||||
|
```shell
|
||||||
|
# 检查点评估
|
||||||
|
sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext152_100.ckpt Ascend
|
||||||
|
|
||||||
|
#或者直接使用脚本运行
|
||||||
|
python eval.py --data_dir /opt/npu/pvc/dataset/storage/imagenet/val/ --platform Ascend --pretrained /root/test/resnext152_64x4d/outputs_demo/best_acc_0.ckpt
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 结果
|
||||||
|
|
||||||
|
评估结果保存在脚本路径下。您可以在日志中找到类似以下的结果。
|
||||||
|
|
||||||
|
```log
|
||||||
|
acc=80.08%(TOP1)
|
||||||
|
acc=94.71%(TOP5)
|
||||||
|
```
|
||||||
|
|
||||||
|
## 模型导出
|
||||||
|
|
||||||
|
```shell
|
||||||
|
python export.py --device_target [PLATFORM] --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT]
|
||||||
|
```
|
||||||
|
|
||||||
|
`EXPORT_FORMAT` 可选 ["AIR", "ONNX", "MINDIR"].
|
||||||
|
|
||||||
|
# 模型描述
|
||||||
|
|
||||||
|
## 性能
|
||||||
|
|
||||||
|
### 训练性能
|
||||||
|
|
||||||
|
| 参数 | ResNeXt152 | |
|
||||||
|
| -------------------------- | ---------------------------------------------------------- | ------------------------- |
|
||||||
|
| 资源 | Ascend 910；CPU：2.60GHz，192核；内存：755GB | |
|
||||||
|
| 上传日期 | 2021-6-30 | |
|
||||||
|
| MindSpore版本 | 1.2 | |
|
||||||
|
| 数据集 | ImageNet | |
|
||||||
|
| 训练参数 | src/config.py | |
|
||||||
|
| 优化器 | Momentum | |
|
||||||
|
| 损失函数 | Softmax交叉熵 | |
|
||||||
|
| 损失 | 1.2892 | |
|
||||||
|
| 准确率 | 80.08%(TOP1) | |
|
||||||
|
| 总时长 | 7.8小时 （8卡） | |
|
||||||
|
| 调优检查点 | 192 M（.ckpt文件） | |
|
||||||
|
|
||||||
|
#### 推理性能
|
||||||
|
|
||||||
|
| 参数 | | | |
|
||||||
|
| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
|
||||||
|
| 资源 | | | Ascend 910 |
|
||||||
|
| 上传日期 | | | 2021-6-20 |
|
||||||
|
| MindSpore版本 | | | 1.2 |
|
||||||
|
| 数据集 | | | ImageNet， 1.2万 |
|
||||||
|
| batch_size | | | 1 |
|
||||||
|
| 输出 | | | 概率 |
|
||||||
|
| 准确率 | | | acc=80.08%(TOP1) |
|
||||||
|
|
||||||
|
# 随机情况说明
|
||||||
|
|
||||||
|
dataset.py中设置了“create_dataset”函数内的种子，同时还使用了train.py中的随机种子。
|
||||||
|
|
||||||
|
# ModelZoo主页
|
||||||
|
|
||||||
|
请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。
|
||||||
|
|
@ -0,0 +1,265 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""Eval"""
|
||||||
|
import os
|
||||||
|
import time
|
||||||
|
import argparse
|
||||||
|
import datetime
|
||||||
|
import glob
|
||||||
|
import numpy as np
|
||||||
|
import mindspore.nn as nn
|
||||||
|
|
||||||
|
from mindspore import Tensor, context
|
||||||
|
from mindspore.context import ParallelMode
|
||||||
|
from mindspore.communication.management import init, get_rank, get_group_size, release
|
||||||
|
from mindspore.ops import operations as P
|
||||||
|
from mindspore.ops import functional as F
|
||||||
|
from mindspore.common import dtype as mstype
|
||||||
|
|
||||||
|
from src.utils.logging import get_logger
|
||||||
|
from src.utils.auto_mixed_precision import auto_mixed_precision
|
||||||
|
from src.utils.var_init import load_pretrain_model
|
||||||
|
from src.image_classification import get_network
|
||||||
|
from src.dataset import classification_dataset
|
||||||
|
from src.config import config
|
||||||
|
|
||||||
|
|
||||||
|
class ParameterReduce(nn.Cell):
|
||||||
|
"""ParameterReduce"""
|
||||||
|
def __init__(self):
|
||||||
|
super(ParameterReduce, self).__init__()
|
||||||
|
self.cast = P.Cast()
|
||||||
|
self.reduce = P.AllReduce()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
one = self.cast(F.scalar_to_array(1.0), mstype.float32)
|
||||||
|
out = x * one
|
||||||
|
ret = self.reduce(out)
|
||||||
|
return ret
|
||||||
|
|
||||||
|
|
||||||
|
def parse_args(cloud_args=None):
|
||||||
|
"""parse_args"""
|
||||||
|
parser = argparse.ArgumentParser('mindspore classification test')
|
||||||
|
parser.add_argument('--platform',
|
||||||
|
type=str,
|
||||||
|
default='Ascend',
|
||||||
|
choices=('Ascend', 'GPU'),
|
||||||
|
help='run platform')
|
||||||
|
# dataset related
|
||||||
|
parser.add_argument('--data_dir',
|
||||||
|
type=str,
|
||||||
|
default='/opt/npu/datasets/classification/val',
|
||||||
|
help='eval data dir')
|
||||||
|
parser.add_argument('--per_batch_size',
|
||||||
|
default=32,
|
||||||
|
type=int,
|
||||||
|
help='batch size for per npu')
|
||||||
|
# network related
|
||||||
|
parser.add_argument('--graph_ckpt',
|
||||||
|
type=int,
|
||||||
|
default=1, help='graph ckpt or feed ckpt')
|
||||||
|
parser.add_argument('--pretrained',
|
||||||
|
default='',
|
||||||
|
type=str,
|
||||||
|
help='fully path of pretrained model to load. '
|
||||||
|
'If it is a direction, it will test all ckpt')
|
||||||
|
|
||||||
|
# logging related
|
||||||
|
parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
|
||||||
|
parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
|
||||||
|
|
||||||
|
# roma obs
|
||||||
|
parser.add_argument('--train_url', type=str, default="", help='train url')
|
||||||
|
|
||||||
|
args, _ = parser.parse_known_args()
|
||||||
|
args = merge_args(args, cloud_args)
|
||||||
|
args.image_size = config['image_size']
|
||||||
|
args.num_classes = config['num_classes']
|
||||||
|
args.rank = config['rank']
|
||||||
|
args.group_size = config['group_size']
|
||||||
|
args.image_size = list(map(int, args.image_size.split(',')))
|
||||||
|
|
||||||
|
# init distributed
|
||||||
|
if args.is_distributed:
|
||||||
|
if args.platform == "Ascend":
|
||||||
|
init()
|
||||||
|
elif args.platform == "GPU":
|
||||||
|
init("nccl")
|
||||||
|
args.rank = get_rank()
|
||||||
|
args.group_size = get_group_size()
|
||||||
|
else:
|
||||||
|
args.rank = 0
|
||||||
|
args.group_size = 1
|
||||||
|
|
||||||
|
args.outputs_dir = os.path.join(args.log_path,
|
||||||
|
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
||||||
|
|
||||||
|
args.logger = get_logger(args.outputs_dir, args.rank)
|
||||||
|
return args
|
||||||
|
|
||||||
|
|
||||||
|
def get_top5_acc(top5_arg, gt_class):
|
||||||
|
sub_count = 0
|
||||||
|
for top5, gt_file in zip(top5_arg, gt_class):
|
||||||
|
if gt_file in top5:
|
||||||
|
sub_count += 1
|
||||||
|
return sub_count
|
||||||
|
|
||||||
|
def merge_args(args, cloud_args):
|
||||||
|
"""merge_args"""
|
||||||
|
args_dict = vars(args)
|
||||||
|
if isinstance(cloud_args, dict):
|
||||||
|
for key in cloud_args.keys():
|
||||||
|
val = cloud_args[key]
|
||||||
|
if key in args_dict and val:
|
||||||
|
arg_type = type(args_dict[key])
|
||||||
|
if arg_type is not type(None):
|
||||||
|
val = arg_type(val)
|
||||||
|
args_dict[key] = val
|
||||||
|
return args
|
||||||
|
|
||||||
|
|
||||||
|
def get_result(args, model, top1_correct, top5_correct, img_tot):
|
||||||
|
"""calculate top1 and top5 value."""
|
||||||
|
results = [[top1_correct], [top5_correct], [img_tot]]
|
||||||
|
args.logger.info('before results={}'.format(results))
|
||||||
|
if args.is_distributed:
|
||||||
|
model_md5 = model.replace('/', '')
|
||||||
|
tmp_dir = '/cache'
|
||||||
|
if not os.path.exists(tmp_dir):
|
||||||
|
os.mkdir(tmp_dir)
|
||||||
|
top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(args.rank, model_md5)
|
||||||
|
top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(args.rank, model_md5)
|
||||||
|
img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(args.rank, model_md5)
|
||||||
|
np.save(top1_correct_npy, top1_correct)
|
||||||
|
np.save(top5_correct_npy, top5_correct)
|
||||||
|
np.save(img_tot_npy, img_tot)
|
||||||
|
while True:
|
||||||
|
rank_ok = True
|
||||||
|
for other_rank in range(args.group_size):
|
||||||
|
top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
|
||||||
|
top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
|
||||||
|
img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
|
||||||
|
if not os.path.exists(top1_correct_npy) or not os.path.exists(top5_correct_npy) or \
|
||||||
|
not os.path.exists(img_tot_npy):
|
||||||
|
rank_ok = False
|
||||||
|
if rank_ok:
|
||||||
|
break
|
||||||
|
|
||||||
|
top1_correct_all = 0
|
||||||
|
top5_correct_all = 0
|
||||||
|
img_tot_all = 0
|
||||||
|
for other_rank in range(args.group_size):
|
||||||
|
top1_correct_npy = '/cache/top1_rank_{}_{}.npy'.format(other_rank, model_md5)
|
||||||
|
top5_correct_npy = '/cache/top5_rank_{}_{}.npy'.format(other_rank, model_md5)
|
||||||
|
img_tot_npy = '/cache/img_tot_rank_{}_{}.npy'.format(other_rank, model_md5)
|
||||||
|
top1_correct_all += np.load(top1_correct_npy)
|
||||||
|
top5_correct_all += np.load(top5_correct_npy)
|
||||||
|
img_tot_all += np.load(img_tot_npy)
|
||||||
|
results = [[top1_correct_all], [top5_correct_all], [img_tot_all]]
|
||||||
|
results = np.array(results)
|
||||||
|
else:
|
||||||
|
results = np.array(results)
|
||||||
|
|
||||||
|
args.logger.info('after results={}'.format(results))
|
||||||
|
return results
|
||||||
|
|
||||||
|
|
||||||
|
def test(cloud_args=None):
|
||||||
|
"""test"""
|
||||||
|
args = parse_args(cloud_args)
|
||||||
|
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
|
||||||
|
device_target=args.platform, save_graphs=False)
|
||||||
|
if os.getenv('DEVICE_ID', "not_set").isdigit():
|
||||||
|
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
|
||||||
|
|
||||||
|
# init distributed
|
||||||
|
if args.is_distributed:
|
||||||
|
parallel_mode = ParallelMode.DATA_PARALLEL
|
||||||
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
|
||||||
|
gradients_mean=True)
|
||||||
|
|
||||||
|
args.logger.save_args(args)
|
||||||
|
|
||||||
|
# network
|
||||||
|
args.logger.important_info('start create network')
|
||||||
|
if os.path.isdir(args.pretrained):
|
||||||
|
models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
|
||||||
|
print(models)
|
||||||
|
if args.graph_ckpt:
|
||||||
|
f_key = (lambda x: -1 *
|
||||||
|
int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0]))
|
||||||
|
else:
|
||||||
|
f_key = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
|
||||||
|
args.models = sorted(models, key=f_key)
|
||||||
|
else:
|
||||||
|
args.models = [args.pretrained,]
|
||||||
|
|
||||||
|
for model in args.models:
|
||||||
|
de_dataset = classification_dataset(args.data_dir, image_size=args.image_size,
|
||||||
|
per_batch_size=args.per_batch_size,
|
||||||
|
max_epoch=1, rank=args.rank, group_size=args.group_size,
|
||||||
|
mode='eval')
|
||||||
|
eval_dataloader = de_dataset.create_tuple_iterator(output_numpy=True, num_epochs=1)
|
||||||
|
network = get_network(num_classes=args.num_classes, platform=args.platform)
|
||||||
|
|
||||||
|
load_pretrain_model(model, network, args)
|
||||||
|
|
||||||
|
img_tot = 0
|
||||||
|
top1_correct = 0
|
||||||
|
top5_correct = 0
|
||||||
|
if args.platform == "Ascend":
|
||||||
|
network.to_float(mstype.float16)
|
||||||
|
else:
|
||||||
|
auto_mixed_precision(network)
|
||||||
|
network.set_train(False)
|
||||||
|
t_end = time.time()
|
||||||
|
it_name = 0
|
||||||
|
for data, gt_classes in eval_dataloader:
|
||||||
|
output = network(Tensor(data, mstype.float32))
|
||||||
|
output = output.asnumpy()
|
||||||
|
|
||||||
|
top1_output = np.argmax(output, (-1))
|
||||||
|
top5_output = np.argsort(output)[:, -5:]
|
||||||
|
|
||||||
|
t1_correct = np.equal(top1_output, gt_classes).sum()
|
||||||
|
top1_correct += t1_correct
|
||||||
|
top5_correct += get_top5_acc(top5_output, gt_classes)
|
||||||
|
img_tot += args.per_batch_size
|
||||||
|
|
||||||
|
if args.rank == 0 and it_name == 0:
|
||||||
|
t_end = time.time()
|
||||||
|
it_name = 1
|
||||||
|
if args.rank == 0:
|
||||||
|
time_used = time.time() - t_end
|
||||||
|
fps = (img_tot - args.per_batch_size) * args.group_size / time_used
|
||||||
|
args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
|
||||||
|
results = get_result(args, model, top1_correct, top5_correct, img_tot)
|
||||||
|
top1_correct = results[0, 0]
|
||||||
|
top5_correct = results[1, 0]
|
||||||
|
img_tot = results[2, 0]
|
||||||
|
acc1 = 100.0 * top1_correct / img_tot
|
||||||
|
acc5 = 100.0 * top5_correct / img_tot
|
||||||
|
args.logger.info('after allreduce eval: top1_correct={}, tot={},'
|
||||||
|
'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
|
||||||
|
args.logger.info('after allreduce eval: top5_correct={}, tot={},'
|
||||||
|
'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
|
||||||
|
if args.is_distributed:
|
||||||
|
release()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test()
|
||||||
|
|
@ -0,0 +1,53 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
resnext export mindir.
|
||||||
|
"""
|
||||||
|
import argparse
|
||||||
|
import numpy as np
|
||||||
|
from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
|
||||||
|
from src.config import config
|
||||||
|
from src.image_classification import get_network
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description='checkpoint export')
|
||||||
|
parser.add_argument("--device_id", type=int, default=0, help="Device id")
|
||||||
|
parser.add_argument("--batch_size", type=int, default=1, help="batch size")
|
||||||
|
parser.add_argument("--ckpt_file", type=str, required=True,
|
||||||
|
help="Checkpoint file path.")
|
||||||
|
parser.add_argument('--width', type=int, default=224, help='input width')
|
||||||
|
parser.add_argument('--height', type=int, default=224, help='input height')
|
||||||
|
parser.add_argument("--file_name", type=str,
|
||||||
|
default="resnext152", help="output file name.")
|
||||||
|
parser.add_argument("--file_format", type=str,
|
||||||
|
choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
|
||||||
|
parser.add_argument("--device_target", type=str, default="Ascend",
|
||||||
|
choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
|
||||||
|
if args.device_target == "Ascend":
|
||||||
|
context.set_context(device_id=args.device_id)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
net = get_network(num_classes=config['num_classes'],
|
||||||
|
platform=args.device_target)
|
||||||
|
|
||||||
|
param_dict = load_checkpoint(args.ckpt_file)
|
||||||
|
load_param_into_net(net, param_dict)
|
||||||
|
input_shp = [args.batch_size, 3, args.height, args.width]
|
||||||
|
input_array = Tensor(
|
||||||
|
np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
|
||||||
|
export(net, input_array, file_name=args.file_name,
|
||||||
|
file_format=args.file_format)
|
||||||
|
|
@ -0,0 +1,57 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
|
||||||
|
DATA_DIR=$2
|
||||||
|
export RANK_TABLE_FILE=$1
|
||||||
|
export RANK_SIZE=8
|
||||||
|
export HCCL_CONNECT_TIMEOUT=600
|
||||||
|
echo "hccl connect time out has changed to 600 second"
|
||||||
|
PATH_CHECKPOINT=""
|
||||||
|
if [ $# == 3 ]
|
||||||
|
then
|
||||||
|
PATH_CHECKPOINT=$3
|
||||||
|
fi
|
||||||
|
|
||||||
|
cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
|
||||||
|
echo "the number of logical core" $cores
|
||||||
|
avg_core_per_rank=`expr $cores \/ $RANK_SIZE`
|
||||||
|
core_gap=`expr $avg_core_per_rank \- 1`
|
||||||
|
echo "avg_core_per_rank" $avg_core_per_rank
|
||||||
|
echo "core_gap" $core_gap
|
||||||
|
for((i=0;i<RANK_SIZE;i++))
|
||||||
|
do
|
||||||
|
start=`expr $i \* $avg_core_per_rank`
|
||||||
|
export DEVICE_ID=$i
|
||||||
|
export RANK_ID=$i
|
||||||
|
export DEPLOY_MODE=0
|
||||||
|
export GE_USE_STATIC_MEMORY=1
|
||||||
|
end=`expr $start \+ $core_gap`
|
||||||
|
cmdopt=$start"-"$end
|
||||||
|
|
||||||
|
rm -rf LOG_less$i
|
||||||
|
mkdir ./LOG_less$i
|
||||||
|
cp *.py ./LOG_less$i
|
||||||
|
cd ./LOG_less$i || exit
|
||||||
|
echo "start training for rank $i, device $DEVICE_ID"
|
||||||
|
|
||||||
|
env > env_less.log
|
||||||
|
taskset -c $cmdopt python ../train.py \
|
||||||
|
--is_distribute=1 \
|
||||||
|
--device_id=$DEVICE_ID \
|
||||||
|
--pretrained=$PATH_CHECKPOINT \
|
||||||
|
--data_dir=$DATA_DIR > log_less.txt 2>&1 &
|
||||||
|
cd ../
|
||||||
|
done
|
||||||
|
|
@ -0,0 +1,29 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
|
||||||
|
export DEVICE_ID=$1
|
||||||
|
DATA_DIR=$2
|
||||||
|
PATH_CHECKPOINT=$3
|
||||||
|
PLATFORM=Ascend
|
||||||
|
if [ $# == 4 ]
|
||||||
|
then
|
||||||
|
PLATFORM=$4
|
||||||
|
fi
|
||||||
|
|
||||||
|
python eval.py \
|
||||||
|
--pretrained=$PATH_CHECKPOINT \
|
||||||
|
--platform=$PLATFORM \
|
||||||
|
--data_dir=$DATA_DIR > log.txt 2>&1 &
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
|
||||||
|
export DEVICE_ID=$1
|
||||||
|
DATA_DIR=$2
|
||||||
|
PATH_CHECKPOINT=""
|
||||||
|
if [ $# == 3 ]
|
||||||
|
then
|
||||||
|
PATH_CHECKPOINT=$3
|
||||||
|
fi
|
||||||
|
|
||||||
|
python train.py \
|
||||||
|
--is_distribute=0 \
|
||||||
|
--device_id=$DEVICE_ID \
|
||||||
|
--pretrained=$PATH_CHECKPOINT \
|
||||||
|
--data_dir=$DATA_DIR > log.txt 2>&1 &
|
||||||
|
|
||||||
|
|
@ -0,0 +1,16 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""resnet"""
|
||||||
|
from .resnet import *
|
||||||
|
|
@ -0,0 +1,302 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
ResNet based ResNext
|
||||||
|
"""
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from mindspore.ops.operations import TensorAdd, Split, Concat
|
||||||
|
from mindspore.ops import operations as P
|
||||||
|
from mindspore.common.initializer import TruncatedNormal
|
||||||
|
|
||||||
|
from src.utils.cunstom_op import SEBlock, GroupConv
|
||||||
|
|
||||||
|
|
||||||
|
__all__ = ['ResNet', 'resnext50', 'resnext101', 'resnext152']
|
||||||
|
|
||||||
|
|
||||||
|
def weight_variable(shape, factor=0.1):
|
||||||
|
return TruncatedNormal(0.02)
|
||||||
|
|
||||||
|
|
||||||
|
def conv7x7(in_channels, out_channels, stride=1, padding=3, has_bias=False, groups=1):
|
||||||
|
return nn.Conv2d(in_channels, out_channels, kernel_size=7, stride=stride, has_bias=has_bias,
|
||||||
|
padding=padding, pad_mode="pad", group=groups)
|
||||||
|
|
||||||
|
|
||||||
|
def conv3x3(in_channels, out_channels, stride=1, padding=1, has_bias=False, groups=1):
|
||||||
|
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, has_bias=has_bias,
|
||||||
|
padding=padding, pad_mode="pad", group=groups)
|
||||||
|
|
||||||
|
|
||||||
|
def conv1x1(in_channels, out_channels, stride=1, padding=0, has_bias=False, groups=1):
|
||||||
|
return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, has_bias=has_bias,
|
||||||
|
padding=padding, pad_mode="pad", group=groups)
|
||||||
|
|
||||||
|
|
||||||
|
class _DownSample(nn.Cell):
|
||||||
|
"""
|
||||||
|
Downsample for ResNext-ResNet.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
in_channels (int): Input channels.
|
||||||
|
out_channels (int): Output channels.
|
||||||
|
stride (int): Stride size for the 1*1 convolutional layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tensor, output tensor.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>>DownSample(32, 64, 2)
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, in_channels, out_channels, stride):
|
||||||
|
super(_DownSample, self).__init__()
|
||||||
|
self.conv = conv1x1(in_channels, out_channels,
|
||||||
|
stride=stride, padding=0)
|
||||||
|
self.bn = nn.BatchNorm2d(out_channels)
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
out = self.conv(x)
|
||||||
|
out = self.bn(out)
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
class BasicBlock(nn.Cell):
|
||||||
|
"""
|
||||||
|
ResNet basic block definition.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
in_channels (int): Input channels.
|
||||||
|
out_channels (int): Output channels.
|
||||||
|
stride (int): Stride size for the first convolutional layer. Default: 1.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tensor, output tensor.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>>BasicBlock(32, 256, stride=2)
|
||||||
|
"""
|
||||||
|
expansion = 1
|
||||||
|
|
||||||
|
def __init__(self, in_channels, out_channels, stride=1, down_sample=None, use_se=False,
|
||||||
|
platform="Ascend", **kwargs):
|
||||||
|
super(BasicBlock, self).__init__()
|
||||||
|
self.conv1 = conv3x3(in_channels, out_channels, stride=stride)
|
||||||
|
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||||
|
self.relu = P.ReLU()
|
||||||
|
self.conv2 = conv3x3(out_channels, out_channels, stride=1)
|
||||||
|
self.bn2 = nn.BatchNorm2d(out_channels)
|
||||||
|
|
||||||
|
self.use_se = use_se
|
||||||
|
if self.use_se:
|
||||||
|
self.se = SEBlock(out_channels)
|
||||||
|
|
||||||
|
self.down_sample_flag = False
|
||||||
|
if down_sample is not None:
|
||||||
|
self.down_sample = down_sample
|
||||||
|
self.down_sample_flag = True
|
||||||
|
|
||||||
|
self.add = TensorAdd()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
"""describe network construct"""
|
||||||
|
identity = x
|
||||||
|
out = self.conv1(x)
|
||||||
|
out = self.bn1(out)
|
||||||
|
out = self.relu(out)
|
||||||
|
out = self.conv2(out)
|
||||||
|
out = self.bn2(out)
|
||||||
|
|
||||||
|
if self.use_se:
|
||||||
|
out = self.se(out)
|
||||||
|
|
||||||
|
if self.down_sample_flag:
|
||||||
|
identity = self.down_sample(x)
|
||||||
|
|
||||||
|
out = self.add(out, identity)
|
||||||
|
out = self.relu(out)
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
class Bottleneck(nn.Cell):
|
||||||
|
"""
|
||||||
|
ResNet Bottleneck block definition.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
in_channels (int): Input channels.
|
||||||
|
out_channels (int): Output channels.
|
||||||
|
stride (int): Stride size for the initial convolutional layer. Default: 1.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tensor, the ResNet unit's output.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>>Bottleneck(3, 256, stride=2)
|
||||||
|
"""
|
||||||
|
expansion = 4
|
||||||
|
|
||||||
|
def __init__(self, in_channels, out_channels, stride=1, down_sample=None,
|
||||||
|
base_width=64, groups=1, use_se=False, platform="Ascend", **kwargs):
|
||||||
|
super(Bottleneck, self).__init__()
|
||||||
|
|
||||||
|
width = int(out_channels * (base_width / 64.0)) * groups
|
||||||
|
self.groups = groups
|
||||||
|
self.conv1 = conv1x1(in_channels, width, stride=1)
|
||||||
|
self.bn1 = nn.BatchNorm2d(width)
|
||||||
|
self.relu = P.ReLU()
|
||||||
|
|
||||||
|
self.conv3x3s = nn.CellList()
|
||||||
|
|
||||||
|
if platform == "GPU":
|
||||||
|
self.conv2 = nn.Conv2d(
|
||||||
|
width, width, 3, stride, pad_mode='pad', padding=1, group=groups)
|
||||||
|
else:
|
||||||
|
self.conv2 = GroupConv(
|
||||||
|
width, width, 3, stride, pad=1, groups=groups)
|
||||||
|
|
||||||
|
self.op_split = Split(axis=1, output_num=self.groups)
|
||||||
|
self.op_concat = Concat(axis=1)
|
||||||
|
|
||||||
|
self.bn2 = nn.BatchNorm2d(width)
|
||||||
|
self.conv3 = conv1x1(width, out_channels * self.expansion, stride=1)
|
||||||
|
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
|
||||||
|
|
||||||
|
self.use_se = use_se
|
||||||
|
if self.use_se:
|
||||||
|
self.se = SEBlock(out_channels * self.expansion)
|
||||||
|
|
||||||
|
self.down_sample_flag = False
|
||||||
|
if down_sample is not None:
|
||||||
|
self.down_sample = down_sample
|
||||||
|
self.down_sample_flag = True
|
||||||
|
|
||||||
|
self.cast = P.Cast()
|
||||||
|
self.add = TensorAdd()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
"""describe network construct"""
|
||||||
|
identity = x
|
||||||
|
out = self.conv1(x)
|
||||||
|
out = self.bn1(out)
|
||||||
|
out = self.relu(out)
|
||||||
|
|
||||||
|
out = self.conv2(out)
|
||||||
|
out = self.bn2(out)
|
||||||
|
out = self.relu(out)
|
||||||
|
out = self.conv3(out)
|
||||||
|
out = self.bn3(out)
|
||||||
|
|
||||||
|
if self.use_se:
|
||||||
|
out = self.se(out)
|
||||||
|
|
||||||
|
if self.down_sample_flag:
|
||||||
|
identity = self.down_sample(x)
|
||||||
|
|
||||||
|
out = self.add(out, identity)
|
||||||
|
out = self.relu(out)
|
||||||
|
return out
|
||||||
|
|
||||||
|
|
||||||
|
class ResNet(nn.Cell):
|
||||||
|
"""
|
||||||
|
ResNet architecture.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
block (cell): Block for network.
|
||||||
|
layers (list): Numbers of block in different layers.
|
||||||
|
width_per_group (int): Width of every group.
|
||||||
|
groups (int): Groups number.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tuple, output tensor tuple.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>>ResNet()
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, block, layers, width_per_group=64, groups=1, use_se=False, platform="Ascend"):
|
||||||
|
super(ResNet, self).__init__()
|
||||||
|
self.in_channels = 64
|
||||||
|
self.groups = groups
|
||||||
|
self.base_width = width_per_group
|
||||||
|
|
||||||
|
self.conv = conv7x7(3, self.in_channels, stride=2, padding=3)
|
||||||
|
self.bn = nn.BatchNorm2d(self.in_channels)
|
||||||
|
self.relu = P.ReLU()
|
||||||
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
|
||||||
|
|
||||||
|
self.layer1 = self._make_layer(
|
||||||
|
block, 64, layers[0], use_se=use_se, platform=platform)
|
||||||
|
self.layer2 = self._make_layer(
|
||||||
|
block, 128, layers[1], stride=2, use_se=use_se, platform=platform)
|
||||||
|
self.layer3 = self._make_layer(
|
||||||
|
block, 256, layers[2], stride=2, use_se=use_se, platform=platform)
|
||||||
|
self.layer4 = self._make_layer(
|
||||||
|
block, 512, layers[3], stride=2, use_se=use_se, platform=platform)
|
||||||
|
|
||||||
|
self.out_channels = 512 * block.expansion
|
||||||
|
self.cast = P.Cast()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
"""describe network construct"""
|
||||||
|
x = self.conv(x)
|
||||||
|
x = self.bn(x)
|
||||||
|
x = self.relu(x)
|
||||||
|
x = self.maxpool(x)
|
||||||
|
x = self.layer1(x)
|
||||||
|
x = self.layer2(x)
|
||||||
|
x = self.layer3(x)
|
||||||
|
x = self.layer4(x)
|
||||||
|
|
||||||
|
return x
|
||||||
|
|
||||||
|
def _make_layer(self, block, out_channels, blocks_num, stride=1, use_se=False, platform="Ascend"):
|
||||||
|
"""_make_layer"""
|
||||||
|
down_sample = None
|
||||||
|
if stride != 1 or self.in_channels != out_channels * block.expansion:
|
||||||
|
down_sample = _DownSample(self.in_channels,
|
||||||
|
out_channels * block.expansion,
|
||||||
|
stride=stride)
|
||||||
|
|
||||||
|
layers = []
|
||||||
|
layers.append(block(self.in_channels,
|
||||||
|
out_channels,
|
||||||
|
stride=stride,
|
||||||
|
down_sample=down_sample,
|
||||||
|
base_width=self.base_width,
|
||||||
|
groups=self.groups,
|
||||||
|
use_se=use_se,
|
||||||
|
platform=platform))
|
||||||
|
self.in_channels = out_channels * block.expansion
|
||||||
|
for _ in range(1, blocks_num):
|
||||||
|
layers.append(block(self.in_channels, out_channels, base_width=self.base_width,
|
||||||
|
groups=self.groups, use_se=use_se, platform=platform))
|
||||||
|
|
||||||
|
return nn.SequentialCell(layers)
|
||||||
|
|
||||||
|
def get_out_channels(self):
|
||||||
|
return self.out_channels
|
||||||
|
|
||||||
|
|
||||||
|
def resnext50(platform="Ascend"):
|
||||||
|
return ResNet(Bottleneck, [3, 4, 6, 3], width_per_group=4, groups=32, platform=platform)
|
||||||
|
|
||||||
|
|
||||||
|
def resnext101(platform="Ascend"):
|
||||||
|
return ResNet(Bottleneck, [3, 4, 23, 3], width_per_group=4, groups=64, platform=platform)
|
||||||
|
|
||||||
|
|
||||||
|
def resnext152(platform="Ascend"):
|
||||||
|
return ResNet(Bottleneck, [3, 8, 36, 3], width_per_group=4, groups=64, platform=platform)
|
||||||
|
|
@ -0,0 +1,39 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""config"""
|
||||||
|
config = {
|
||||||
|
"image_size": '224,224',
|
||||||
|
"num_classes": 1000,
|
||||||
|
"lr": 0.4,
|
||||||
|
"lr_scheduler": 'cosine_annealing',
|
||||||
|
"lr_epochs": '30,60,90,120',
|
||||||
|
"lr_gamma": 0.1,
|
||||||
|
"eta_min": 0,
|
||||||
|
"T_max": 150,
|
||||||
|
"max_epoch": 150,
|
||||||
|
"warmup_epochs": 1,
|
||||||
|
"weight_decay": 0.0001,
|
||||||
|
"momentum": 0.9,
|
||||||
|
"is_dynamic_loss_scale": 0,
|
||||||
|
"loss_scale": 1024,
|
||||||
|
"label_smooth": 1,
|
||||||
|
"label_smooth_factor": 0.1,
|
||||||
|
"ckpt_interval": 2,
|
||||||
|
"ckpt_save_max": 30,
|
||||||
|
"ckpt_path": 'outputs_demo/',
|
||||||
|
"is_save_on_master": 1,
|
||||||
|
"rank": 0,
|
||||||
|
"group_size": 1
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,41 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
"""
|
||||||
|
from mindspore.nn.loss.loss import _Loss
|
||||||
|
from mindspore.ops import operations as P
|
||||||
|
from mindspore.ops import functional as F
|
||||||
|
from mindspore import Tensor
|
||||||
|
from mindspore.common import dtype as mstype
|
||||||
|
import mindspore.nn as nn
|
||||||
|
|
||||||
|
class CrossEntropy(_Loss):
|
||||||
|
"""
|
||||||
|
the redefined loss function with SoftmaxCrossEntropyWithLogits.
|
||||||
|
"""
|
||||||
|
def __init__(self, smooth_factor=0., num_classes=1000):
|
||||||
|
super(CrossEntropy, self).__init__()
|
||||||
|
self.onehot = P.OneHot()
|
||||||
|
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||||
|
self.off_value = Tensor(1.0 * smooth_factor / (num_classes -1), mstype.float32)
|
||||||
|
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||||
|
self.mean = P.ReduceMean(False)
|
||||||
|
|
||||||
|
def construct(self, logit, label):
|
||||||
|
one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
|
||||||
|
loss = self.ce(logit, one_hot_label)
|
||||||
|
loss = self.mean(loss, 0)
|
||||||
|
return loss
|
||||||
|
|
@ -0,0 +1,158 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
dataset processing.
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
from mindspore.common import dtype as mstype
|
||||||
|
import mindspore.dataset as de
|
||||||
|
import mindspore.dataset.transforms.c_transforms as C
|
||||||
|
import mindspore.dataset.vision.c_transforms as V_C
|
||||||
|
from PIL import Image, ImageFile
|
||||||
|
from src.utils.sampler import DistributedSampler
|
||||||
|
|
||||||
|
ImageFile.LOAD_TRUNCATED_IMAGES = True
|
||||||
|
|
||||||
|
|
||||||
|
class TxtDataset():
|
||||||
|
"""
|
||||||
|
create txt dataset.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
Returns:
|
||||||
|
de_dataset.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, root, txt_name):
|
||||||
|
super(TxtDataset, self).__init__()
|
||||||
|
self.imgs = []
|
||||||
|
self.labels = []
|
||||||
|
fin = open(txt_name, "r")
|
||||||
|
for line in fin:
|
||||||
|
img_name, label = line.strip().split(' ')
|
||||||
|
self.imgs.append(os.path.join(root, img_name))
|
||||||
|
self.labels.append(int(label))
|
||||||
|
fin.close()
|
||||||
|
|
||||||
|
def __getitem__(self, index):
|
||||||
|
img = Image.open(self.imgs[index]).convert('RGB')
|
||||||
|
return img, self.labels[index]
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return len(self.imgs)
|
||||||
|
|
||||||
|
|
||||||
|
def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
|
||||||
|
mode='train',
|
||||||
|
input_mode='folder',
|
||||||
|
root='',
|
||||||
|
num_parallel_workers=None,
|
||||||
|
shuffle=None,
|
||||||
|
sampler=None,
|
||||||
|
class_indexing=None,
|
||||||
|
drop_remainder=True,
|
||||||
|
transform=None,
|
||||||
|
target_transform=None):
|
||||||
|
"""
|
||||||
|
A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
|
||||||
|
If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
|
||||||
|
are written into a textfile.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
|
||||||
|
Or path of the textfile that contains every image's path of the dataset.
|
||||||
|
image_size (Union(int, sequence)): Size of the input images.
|
||||||
|
per_batch_size (int): the batch size of evey step during training.
|
||||||
|
max_epoch (int): the number of epochs.
|
||||||
|
rank (int): The shard ID within num_shards (default=None).
|
||||||
|
group_size (int): Number of shards that the dataset should be divided
|
||||||
|
into (default=None).
|
||||||
|
mode (str): "train" or others. Default: " train".
|
||||||
|
input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
|
||||||
|
root (str): the images path for "input_mode="txt"". Default: " ".
|
||||||
|
num_parallel_workers (int): Number of workers to read the data. Default: None.
|
||||||
|
shuffle (bool): Whether or not to perform shuffle on the dataset
|
||||||
|
(default=None, performs shuffle).
|
||||||
|
sampler (Sampler): Object used to choose samples from the dataset. Default: None.
|
||||||
|
class_indexing (dict): A str-to-int mapping from folder name to index
|
||||||
|
(default=None, the folder names will be sorted
|
||||||
|
alphabetically and each class will be given a
|
||||||
|
unique index starting from 0).
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> from src.dataset import classification_dataset
|
||||||
|
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
|
||||||
|
>>> data_dir = "/path/to/imagefolder_directory"
|
||||||
|
>>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
|
||||||
|
>>> per_batch_size=64, max_epoch=100,
|
||||||
|
>>> rank=0, group_size=4)
|
||||||
|
>>> # Path of the textfile that contains every image's path of the dataset.
|
||||||
|
>>> data_dir = "/path/to/dataset/images/train.txt"
|
||||||
|
>>> images_dir = "/path/to/dataset/images"
|
||||||
|
>>> de_dataset = classification_dataset(data_dir, image_size=[224, 244],
|
||||||
|
>>> per_batch_size=64, max_epoch=100,
|
||||||
|
>>> rank=0, group_size=4,
|
||||||
|
>>> input_mode="txt", root=images_dir)
|
||||||
|
"""
|
||||||
|
|
||||||
|
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
|
||||||
|
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
|
||||||
|
|
||||||
|
if transform is None:
|
||||||
|
if mode == 'train':
|
||||||
|
transform_img = [
|
||||||
|
V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
|
||||||
|
V_C.RandomHorizontalFlip(prob=0.5),
|
||||||
|
V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
|
||||||
|
V_C.Normalize(mean=mean, std=std),
|
||||||
|
V_C.HWC2CHW()
|
||||||
|
]
|
||||||
|
else:
|
||||||
|
transform_img = [
|
||||||
|
V_C.Decode(),
|
||||||
|
V_C.Resize((256, 256)),
|
||||||
|
V_C.CenterCrop(image_size),
|
||||||
|
V_C.Normalize(mean=mean, std=std),
|
||||||
|
V_C.HWC2CHW()
|
||||||
|
]
|
||||||
|
else:
|
||||||
|
transform_img = transform
|
||||||
|
|
||||||
|
if target_transform is None:
|
||||||
|
transform_label = [C.TypeCast(mstype.int32)]
|
||||||
|
else:
|
||||||
|
transform_label = target_transform
|
||||||
|
|
||||||
|
if input_mode == 'folder':
|
||||||
|
de_dataset = de.ImageFolderDataset(data_dir, num_parallel_workers=num_parallel_workers,
|
||||||
|
shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
|
||||||
|
num_shards=group_size, shard_id=rank)
|
||||||
|
else:
|
||||||
|
dataset = TxtDataset(root, data_dir)
|
||||||
|
sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
|
||||||
|
de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
|
||||||
|
|
||||||
|
de_dataset = de_dataset.map(operations=transform_img, input_columns="image",
|
||||||
|
num_parallel_workers=num_parallel_workers)
|
||||||
|
de_dataset = de_dataset.map(operations=transform_label, input_columns="label",
|
||||||
|
num_parallel_workers=num_parallel_workers)
|
||||||
|
|
||||||
|
columns_to_project = ["image", "label"]
|
||||||
|
de_dataset = de_dataset.project(columns=columns_to_project)
|
||||||
|
|
||||||
|
de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
|
||||||
|
de_dataset = de_dataset.repeat(max_epoch)
|
||||||
|
|
||||||
|
return de_dataset
|
||||||
|
|
@ -0,0 +1,95 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""Evaluation callback when training"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import stat
|
||||||
|
import time
|
||||||
|
from mindspore import save_checkpoint
|
||||||
|
from mindspore import log as logger
|
||||||
|
from mindspore.train.callback import Callback
|
||||||
|
|
||||||
|
|
||||||
|
class EvalCallBack(Callback):
|
||||||
|
"""
|
||||||
|
Evaluation callback when training.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
eval_function (function): evaluation function.
|
||||||
|
eval_param_dict (dict): evaluation parameters' configure dict.
|
||||||
|
interval (int): run evaluation interval, default is 1.
|
||||||
|
eval_start_epoch (int): evaluation start epoch, default is 1.
|
||||||
|
save_best_ckpt (bool): Whether to save best checkpoint, default is True.
|
||||||
|
best_ckpt_name (str): bast checkpoint name, default is `best.ckpt`.
|
||||||
|
metrics_name (str): evaluation metrics name, default is `acc`.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> EvalCallBack(eval_function, eval_param_dict)
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, eval_function, eval_param_dict, interval=1, eval_start_epoch=1, save_best_ckpt=True,
|
||||||
|
ckpt_directory="./", best_ckpt_name="best.ckpt", metrics_name="acc"):
|
||||||
|
super(EvalCallBack, self).__init__()
|
||||||
|
self.eval_param_dict = eval_param_dict
|
||||||
|
self.eval_function = eval_function
|
||||||
|
self.eval_start_epoch = eval_start_epoch
|
||||||
|
if interval < 1:
|
||||||
|
raise ValueError("interval should >= 1.")
|
||||||
|
self.interval = interval
|
||||||
|
self.save_best_ckpt = save_best_ckpt
|
||||||
|
self.best_res = 0
|
||||||
|
self.best_epoch = 0
|
||||||
|
if not os.path.isdir(ckpt_directory):
|
||||||
|
os.makedirs(ckpt_directory)
|
||||||
|
self.bast_ckpt_path = os.path.join(ckpt_directory, best_ckpt_name)
|
||||||
|
self.metrics_name = metrics_name
|
||||||
|
|
||||||
|
def remove_ckpoint_file(self, file_name):
|
||||||
|
"""Remove the specified checkpoint file from this checkpoint manager and also from the directory."""
|
||||||
|
try:
|
||||||
|
os.chmod(file_name, stat.S_IWRITE)
|
||||||
|
os.remove(file_name)
|
||||||
|
except OSError:
|
||||||
|
logger.warning("OSError, failed to remove the older ckpt file %s.", file_name)
|
||||||
|
except ValueError:
|
||||||
|
logger.warning("ValueError, failed to remove the older ckpt file %s.", file_name)
|
||||||
|
|
||||||
|
def epoch_end(self, run_context):
|
||||||
|
"""Callback when epoch end."""
|
||||||
|
cb_params = run_context.original_args()
|
||||||
|
cur_epoch = cb_params.cur_epoch_num
|
||||||
|
if cur_epoch >= self.eval_start_epoch and (cur_epoch - self.eval_start_epoch) % self.interval == 0:
|
||||||
|
eval_start = time.time()
|
||||||
|
res = self.eval_function(self.eval_param_dict)
|
||||||
|
eval_cost = time.time() - eval_start
|
||||||
|
print("epoch: {}, {}: {}, eval_cost:{:.2f}".format(cur_epoch, self.metrics_name, res, eval_cost),
|
||||||
|
flush=True)
|
||||||
|
if res >= self.best_res:
|
||||||
|
self.best_res = res
|
||||||
|
self.best_epoch = cur_epoch
|
||||||
|
print("update best result: {}".format(res), flush=True)
|
||||||
|
if self.save_best_ckpt:
|
||||||
|
if os.path.exists(self.bast_ckpt_path):
|
||||||
|
self.remove_ckpoint_file(self.bast_ckpt_path)
|
||||||
|
save_checkpoint(cb_params.train_network, self.bast_ckpt_path)
|
||||||
|
print("update best checkpoint at: {}".format(self.bast_ckpt_path), flush=True)
|
||||||
|
|
||||||
|
def end(self, run_context):
|
||||||
|
print("End training, the best {0} is: {1}, the best {0} epoch is {2}".format(self.metrics_name,
|
||||||
|
self.best_res,
|
||||||
|
self.best_epoch), flush=True)
|
||||||
|
|
@ -0,0 +1,42 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
common architecture.
|
||||||
|
"""
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from src.utils.cunstom_op import GlobalAvgPooling
|
||||||
|
|
||||||
|
__all__ = ['CommonHead']
|
||||||
|
|
||||||
|
class CommonHead(nn.Cell):
|
||||||
|
"""
|
||||||
|
common architecture definition.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
num_classes (int): Number of classes.
|
||||||
|
out_channels (int): Output channels.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tensor, output tensor.
|
||||||
|
"""
|
||||||
|
def __init__(self, num_classes, out_channels):
|
||||||
|
super(CommonHead, self).__init__()
|
||||||
|
self.avgpool = GlobalAvgPooling()
|
||||||
|
self.fc = nn.Dense(out_channels, num_classes, has_bias=True).add_flags_recursive(fp16=True)
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
x = self.avgpool(x)
|
||||||
|
x = self.fc(x)
|
||||||
|
return x
|
||||||
|
|
@ -0,0 +1,104 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
Image classifiation.
|
||||||
|
"""
|
||||||
|
import math
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from mindspore.common import initializer as init
|
||||||
|
import src.backbone as backbones
|
||||||
|
import src.head as heads
|
||||||
|
from src.utils.var_init import default_recurisive_init, KaimingNormal
|
||||||
|
|
||||||
|
|
||||||
|
class ImageClassificationNetwork(nn.Cell):
|
||||||
|
"""
|
||||||
|
architecture of image classification network.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
Returns:
|
||||||
|
Tensor, output tensor.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, backbone, head, include_top=True, activation="None"):
|
||||||
|
super(ImageClassificationNetwork, self).__init__()
|
||||||
|
self.backbone = backbone
|
||||||
|
self.include_top = include_top
|
||||||
|
self.need_activation = False
|
||||||
|
if self.include_top:
|
||||||
|
self.head = head
|
||||||
|
if activation != "None":
|
||||||
|
self.need_activation = True
|
||||||
|
if activation == "Sigmoid":
|
||||||
|
self.activation = P.Sigmoid()
|
||||||
|
elif activation == "Softmax":
|
||||||
|
self.activation = P.Softmax()
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(
|
||||||
|
f"The activation {activation} not in [Sigmoid, Softmax].")
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
x = self.backbone(x)
|
||||||
|
if self.include_top:
|
||||||
|
x = self.head(x)
|
||||||
|
if self.need_activation:
|
||||||
|
x = self.activation(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class Resnet(ImageClassificationNetwork):
|
||||||
|
"""
|
||||||
|
Resnet architecture.
|
||||||
|
Args:
|
||||||
|
backbone_name (string): backbone.
|
||||||
|
num_classes (int): number of classes, Default is 1000.
|
||||||
|
Returns:
|
||||||
|
Resnet.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, backbone_name, num_classes=1000, platform="Ascend", include_top=True, activation="None"):
|
||||||
|
self.backbone_name = backbone_name
|
||||||
|
backbone = backbones.__dict__[self.backbone_name](platform=platform)
|
||||||
|
out_channels = backbone.get_out_channels()
|
||||||
|
head = heads.CommonHead(num_classes=num_classes,
|
||||||
|
out_channels=out_channels)
|
||||||
|
super(Resnet, self).__init__(backbone, head, include_top, activation)
|
||||||
|
|
||||||
|
default_recurisive_init(self)
|
||||||
|
|
||||||
|
for cell in self.cells_and_names():
|
||||||
|
if isinstance(cell, nn.Conv2d):
|
||||||
|
cell.weight.set_data(init.initializer(
|
||||||
|
KaimingNormal(a=math.sqrt(5), mode='fan_out',
|
||||||
|
nonlinearity='relu'),
|
||||||
|
cell.weight.shape, cell.weight.dtype))
|
||||||
|
elif isinstance(cell, nn.BatchNorm2d):
|
||||||
|
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
|
||||||
|
cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
|
||||||
|
|
||||||
|
# Zero-initialize the last BN in each residual branch,
|
||||||
|
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
||||||
|
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
||||||
|
for cell in self.cells_and_names():
|
||||||
|
if isinstance(cell, backbones.resnet.Bottleneck):
|
||||||
|
cell.bn3.gamma.set_data(init.initializer(
|
||||||
|
'zeros', cell.bn3.gamma.shape))
|
||||||
|
elif isinstance(cell, backbones.resnet.BasicBlock):
|
||||||
|
cell.bn2.gamma.set_data(init.initializer(
|
||||||
|
'zeros', cell.bn2.gamma.shape))
|
||||||
|
|
||||||
|
|
||||||
|
def get_network(**kwargs):
|
||||||
|
return Resnet('resnext152', **kwargs)
|
||||||
|
|
@ -0,0 +1,142 @@
|
||||||
|
# Copyright 2021 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
|
||||||
|
from collections import Counter
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
|
||||||
|
"""
|
||||||
|
Applies liner Increasing to generate learning rate array in warmup stage.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
current_step(int): current step in warmup stage.
|
||||||
|
warmup_steps(int): all steps in warmup stage.
|
||||||
|
base_lr(float): init learning rate.
|
||||||
|
init_lr(float): end learning rate
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
float, learning rate.
|
||||||
|
"""
|
||||||
|
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, T_max, eta_min=0):
|
||||||
|
"""
|
||||||
|
Applies cosine decay to generate learning rate array with warmup.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
lr(float): init learning rate
|
||||||
|
steps_per_epoch(int): steps of one epoch
|
||||||
|
warmup_epochs(int): number of warmup epochs
|
||||||
|
max_epoch(int): total epoch of training
|
||||||
|
T_max(int): max epoch in decay.
|
||||||
|
eta_min(float): end learning rate
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
lr_each_step = []
|
||||||
|
for i in range(total_steps):
|
||||||
|
last_epoch = i // steps_per_epoch
|
||||||
|
if i < warmup_steps:
|
||||||
|
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||||
|
else:
|
||||||
|
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2
|
||||||
|
lr_each_step.append(lr)
|
||||||
|
|
||||||
|
return np.array(lr_each_step).astype(np.float32)
|
||||||
|
|
||||||
|
|
||||||
|
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
|
||||||
|
"""
|
||||||
|
Applies step decay to generate learning rate array with warmup.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
lr(float): init learning rate
|
||||||
|
lr_epochs(list): learning rate decay epoches list
|
||||||
|
steps_per_epoch(int): steps of one epoch
|
||||||
|
warmup_epochs(int): number of warmup epochs
|
||||||
|
max_epoch(int): total epoch of training
|
||||||
|
gamma(float): attenuation constants.
|
||||||
|
|
||||||
|
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)
|
||||||
|
milestones = lr_epochs
|
||||||
|
milestones_steps = []
|
||||||
|
for milestone in milestones:
|
||||||
|
milestones_step = milestone * steps_per_epoch
|
||||||
|
milestones_steps.append(milestones_step)
|
||||||
|
|
||||||
|
lr_each_step = []
|
||||||
|
lr = base_lr
|
||||||
|
milestones_steps_counter = Counter(milestones_steps)
|
||||||
|
for i in range(total_steps):
|
||||||
|
if i < warmup_steps:
|
||||||
|
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||||
|
else:
|
||||||
|
lr = lr * gamma**milestones_steps_counter[i]
|
||||||
|
lr_each_step.append(lr)
|
||||||
|
|
||||||
|
return np.array(lr_each_step).astype(np.float32)
|
||||||
|
|
||||||
|
|
||||||
|
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
|
||||||
|
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
|
||||||
|
|
||||||
|
|
||||||
|
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
|
||||||
|
lr_epochs = []
|
||||||
|
for i in range(1, max_epoch):
|
||||||
|
if i % epoch_size == 0:
|
||||||
|
lr_epochs.append(i)
|
||||||
|
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
|
||||||
|
|
||||||
|
|
||||||
|
def get_lr(args):
|
||||||
|
"""generate learning rate array."""
|
||||||
|
if args.lr_scheduler == 'exponential':
|
||||||
|
lr = warmup_step_lr(args.lr,
|
||||||
|
args.lr_epochs,
|
||||||
|
args.steps_per_epoch,
|
||||||
|
args.warmup_epochs,
|
||||||
|
args.max_epoch,
|
||||||
|
gamma=args.lr_gamma,
|
||||||
|
)
|
||||||
|
elif args.lr_scheduler == 'cosine_annealing':
|
||||||
|
lr = warmup_cosine_annealing_lr(args.lr,
|
||||||
|
args.steps_per_epoch,
|
||||||
|
args.warmup_epochs,
|
||||||
|
args.max_epoch,
|
||||||
|
args.T_max,
|
||||||
|
args.eta_min)
|
||||||
|
else:
|
||||||
|
raise NotImplementedError(args.lr_scheduler)
|
||||||
|
return lr
|
||||||
|
|
@ -0,0 +1,132 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""evaluation metric."""
|
||||||
|
|
||||||
|
from mindspore.communication.management import GlobalComm
|
||||||
|
from mindspore.ops import operations as P
|
||||||
|
import mindspore.nn as nn
|
||||||
|
import mindspore.common.dtype as mstype
|
||||||
|
|
||||||
|
|
||||||
|
class ClassifyCorrectCell(nn.Cell):
|
||||||
|
r"""
|
||||||
|
Cell that returns correct count of the prediction in classification network.
|
||||||
|
This Cell accepts a network as arguments.
|
||||||
|
It returns orrect count of the prediction to calculate the metrics.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
network (Cell): The network Cell.
|
||||||
|
|
||||||
|
Inputs:
|
||||||
|
- **data** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
|
||||||
|
- **label** (Tensor) - Tensor of shape :math:`(N, \ldots)`.
|
||||||
|
|
||||||
|
Outputs:
|
||||||
|
Tuple, containing a scalar correct count of the prediction
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> # For a defined network Net without loss function
|
||||||
|
>>> net = Net()
|
||||||
|
>>> eval_net = nn.ClassifyCorrectCell(net)
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, network):
|
||||||
|
super(ClassifyCorrectCell, self).__init__(auto_prefix=False)
|
||||||
|
self._network = network
|
||||||
|
self.argmax = P.Argmax()
|
||||||
|
self.equal = P.Equal()
|
||||||
|
self.cast = P.Cast()
|
||||||
|
self.reduce_sum = P.ReduceSum()
|
||||||
|
self.allreduce = P.AllReduce(P.ReduceOp.SUM, GlobalComm.WORLD_COMM_GROUP)
|
||||||
|
|
||||||
|
def construct(self, data, label):
|
||||||
|
outputs = self._network(data)
|
||||||
|
y_pred = self.argmax(outputs)
|
||||||
|
y_pred = self.cast(y_pred, mstype.int32)
|
||||||
|
y_correct = self.equal(y_pred, label)
|
||||||
|
y_correct = self.cast(y_correct, mstype.float32)
|
||||||
|
y_correct = self.reduce_sum(y_correct)
|
||||||
|
total_correct = self.allreduce(y_correct)
|
||||||
|
return (total_correct,)
|
||||||
|
|
||||||
|
|
||||||
|
class DistAccuracy(nn.Metric):
|
||||||
|
r"""
|
||||||
|
Calculates the accuracy for classification data in distributed mode.
|
||||||
|
The accuracy class creates two local variables, correct number and total number that are used to compute the
|
||||||
|
frequency with which predictions matches labels. This frequency is ultimately returned as the accuracy: an
|
||||||
|
idempotent operation that simply divides correct number by total number.
|
||||||
|
|
||||||
|
.. math::
|
||||||
|
|
||||||
|
\text{accuracy} =\frac{\text{true_positive} + \text{true_negative}}
|
||||||
|
|
||||||
|
{\text{true_positive} + \text{true_negative} + \text{false_positive} + \text{false_negative}}
|
||||||
|
|
||||||
|
Args:
|
||||||
|
eval_type (str): Metric to calculate the accuracy over a dataset, for classification (single-label).
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> y_correct = Tensor(np.array([20]))
|
||||||
|
>>> metric = nn.DistAccuracy(batch_size=3, device_num=8)
|
||||||
|
>>> metric.clear()
|
||||||
|
>>> metric.update(y_correct)
|
||||||
|
>>> accuracy = metric.eval()
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, batch_size, device_num):
|
||||||
|
super(DistAccuracy, self).__init__()
|
||||||
|
self.clear()
|
||||||
|
self.batch_size = batch_size
|
||||||
|
self.device_num = device_num
|
||||||
|
|
||||||
|
def clear(self):
|
||||||
|
"""Clears the internal evaluation result."""
|
||||||
|
self._correct_num = 0
|
||||||
|
self._total_num = 0
|
||||||
|
|
||||||
|
def update(self, *inputs):
|
||||||
|
"""
|
||||||
|
Updates the internal evaluation result :math:`y_{pred}` and :math:`y`.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
inputs: Input `y_correct`. `y_correct` is a `scalar Tensor`.
|
||||||
|
`y_correct` is the right prediction count that gathered from all devices
|
||||||
|
it's a scalar in float type
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the number of the input is not 1.
|
||||||
|
"""
|
||||||
|
|
||||||
|
if len(inputs) != 1:
|
||||||
|
raise ValueError('Distribute accuracy needs 1 input (y_correct), but got {}'.format(len(inputs)))
|
||||||
|
y_correct = self._convert_data(inputs[0])
|
||||||
|
self._correct_num += y_correct
|
||||||
|
self._total_num += self.batch_size * self.device_num
|
||||||
|
|
||||||
|
def eval(self):
|
||||||
|
"""
|
||||||
|
Computes the accuracy.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Float, the computed result.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
RuntimeError: If the sample size is 0.
|
||||||
|
"""
|
||||||
|
|
||||||
|
if self._total_num == 0:
|
||||||
|
raise RuntimeError('Accuracy can not be calculated, because the number of samples is 0.')
|
||||||
|
return self._correct_num / self._total_num
|
||||||
|
|
@ -0,0 +1,53 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""Auto mixed precision."""
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from mindspore.ops import functional as F
|
||||||
|
from mindspore._checkparam import Validator as validator
|
||||||
|
from mindspore.common import dtype as mstype
|
||||||
|
|
||||||
|
|
||||||
|
class OutputTo(nn.Cell):
|
||||||
|
"Cast cell output back to float16 or float32"
|
||||||
|
|
||||||
|
def __init__(self, op, to_type=mstype.float16):
|
||||||
|
super(OutputTo, self).__init__(auto_prefix=False)
|
||||||
|
self._op = op
|
||||||
|
validator.check_type_name('to_type', to_type, [mstype.float16, mstype.float32], None)
|
||||||
|
self.to_type = to_type
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
return F.cast(self._op(x), self.to_type)
|
||||||
|
|
||||||
|
|
||||||
|
def auto_mixed_precision(network):
|
||||||
|
"""Do keep batchnorm fp32."""
|
||||||
|
cells = network.name_cells()
|
||||||
|
change = False
|
||||||
|
network.to_float(mstype.float16)
|
||||||
|
for name in cells:
|
||||||
|
subcell = cells[name]
|
||||||
|
if subcell == network:
|
||||||
|
continue
|
||||||
|
elif name == 'fc':
|
||||||
|
network.insert_child_to_cell(name, OutputTo(subcell, mstype.float32))
|
||||||
|
change = True
|
||||||
|
elif isinstance(subcell, (nn.BatchNorm2d, nn.BatchNorm1d)):
|
||||||
|
network.insert_child_to_cell(name, OutputTo(subcell.to_float(mstype.float32), mstype.float16))
|
||||||
|
change = True
|
||||||
|
else:
|
||||||
|
auto_mixed_precision(subcell)
|
||||||
|
if isinstance(network, nn.SequentialCell) and change:
|
||||||
|
network.cell_list = list(network.cells())
|
||||||
|
|
@ -0,0 +1,105 @@
|
||||||
|
# Copyright 2021 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 operations
|
||||||
|
"""
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from mindspore.ops import operations as P
|
||||||
|
from mindspore.common import dtype as mstype
|
||||||
|
|
||||||
|
|
||||||
|
class GlobalAvgPooling(nn.Cell):
|
||||||
|
"""
|
||||||
|
global average pooling feature map.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
mean (tuple): means for each channel.
|
||||||
|
"""
|
||||||
|
def __init__(self):
|
||||||
|
super(GlobalAvgPooling, self).__init__()
|
||||||
|
self.mean = P.ReduceMean(False)
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
x = self.mean(x, (2, 3))
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
class SEBlock(nn.Cell):
|
||||||
|
"""
|
||||||
|
squeeze and excitation block.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
channel (int): number of feature maps.
|
||||||
|
reduction (int): weight.
|
||||||
|
"""
|
||||||
|
def __init__(self, channel, reduction=16):
|
||||||
|
super(SEBlock, self).__init__()
|
||||||
|
|
||||||
|
self.avg_pool = GlobalAvgPooling()
|
||||||
|
self.fc1 = nn.Dense(channel, channel // reduction)
|
||||||
|
self.relu = P.ReLU()
|
||||||
|
self.fc2 = nn.Dense(channel // reduction, channel)
|
||||||
|
self.sigmoid = P.Sigmoid()
|
||||||
|
self.reshape = P.Reshape()
|
||||||
|
self.shape = P.Shape()
|
||||||
|
self.sum = P.Sum()
|
||||||
|
self.cast = P.Cast()
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
"""describe network construct"""
|
||||||
|
b, c = self.shape(x)
|
||||||
|
y = self.avg_pool(x)
|
||||||
|
|
||||||
|
y = self.reshape(y, (b, c))
|
||||||
|
y = self.fc1(y)
|
||||||
|
y = self.relu(y)
|
||||||
|
y = self.fc2(y)
|
||||||
|
y = self.sigmoid(y)
|
||||||
|
y = self.reshape(y, (b, c, 1, 1))
|
||||||
|
return x * y
|
||||||
|
|
||||||
|
class GroupConv(nn.Cell):
|
||||||
|
"""
|
||||||
|
group convolution operation.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
in_channels (int): Input channels of feature map.
|
||||||
|
out_channels (int): Output channels of feature map.
|
||||||
|
kernel_size (int): Size of convolution kernel.
|
||||||
|
stride (int): Stride size for the group convolution layer.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tensor, output tensor.
|
||||||
|
"""
|
||||||
|
def __init__(self, in_channels, out_channels, kernel_size, stride, pad_mode="pad", pad=0, groups=1, has_bias=False):
|
||||||
|
super(GroupConv, self).__init__()
|
||||||
|
assert in_channels % groups == 0 and out_channels % groups == 0
|
||||||
|
self.groups = groups
|
||||||
|
self.convs = nn.CellList()
|
||||||
|
self.op_split = P.Split(axis=1, output_num=self.groups)
|
||||||
|
self.op_concat = P.Concat(axis=1)
|
||||||
|
self.cast = P.Cast()
|
||||||
|
for _ in range(groups):
|
||||||
|
self.convs.append(nn.Conv2d(in_channels//groups, out_channels//groups,
|
||||||
|
kernel_size=kernel_size, stride=stride, has_bias=has_bias,
|
||||||
|
padding=pad, pad_mode=pad_mode, group=1))
|
||||||
|
|
||||||
|
def construct(self, x):
|
||||||
|
features = self.op_split(x)
|
||||||
|
outputs = ()
|
||||||
|
for i in range(self.groups):
|
||||||
|
outputs = outputs + (self.convs[i](self.cast(features[i], mstype.float32)),)
|
||||||
|
out = self.op_concat(outputs)
|
||||||
|
return out
|
||||||
|
|
@ -0,0 +1,82 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
get logger.
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
class LOGGER(logging.Logger):
|
||||||
|
"""
|
||||||
|
set up logging file.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
logger_name (string): logger name.
|
||||||
|
log_dir (string): path of logger.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
string, logger path
|
||||||
|
"""
|
||||||
|
def __init__(self, logger_name, rank=0):
|
||||||
|
super(LOGGER, self).__init__(logger_name)
|
||||||
|
if rank % 8 == 0:
|
||||||
|
console = logging.StreamHandler(sys.stdout)
|
||||||
|
console.setLevel(logging.INFO)
|
||||||
|
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||||
|
console.setFormatter(formatter)
|
||||||
|
self.addHandler(console)
|
||||||
|
|
||||||
|
def setup_logging_file(self, log_dir, rank=0):
|
||||||
|
"""set up log file"""
|
||||||
|
self.rank = rank
|
||||||
|
if not os.path.exists(log_dir):
|
||||||
|
os.makedirs(log_dir, exist_ok=True)
|
||||||
|
log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
|
||||||
|
self.log_fn = os.path.join(log_dir, log_name)
|
||||||
|
fh = logging.FileHandler(self.log_fn)
|
||||||
|
fh.setLevel(logging.INFO)
|
||||||
|
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||||
|
fh.setFormatter(formatter)
|
||||||
|
self.addHandler(fh)
|
||||||
|
|
||||||
|
def info(self, msg, *args, **kwargs):
|
||||||
|
if self.isEnabledFor(logging.INFO):
|
||||||
|
self._log(logging.INFO, msg, args, **kwargs)
|
||||||
|
|
||||||
|
def save_args(self, args):
|
||||||
|
self.info('Args:')
|
||||||
|
args_dict = vars(args)
|
||||||
|
for key in args_dict.keys():
|
||||||
|
self.info('--> %s: %s', key, args_dict[key])
|
||||||
|
self.info('')
|
||||||
|
|
||||||
|
def important_info(self, msg, *args, **kwargs):
|
||||||
|
if self.isEnabledFor(logging.INFO) and self.rank == 0:
|
||||||
|
line_width = 2
|
||||||
|
important_msg = '\n'
|
||||||
|
important_msg += ('*'*70 + '\n')*line_width
|
||||||
|
important_msg += ('*'*line_width + '\n')*2
|
||||||
|
important_msg += '*'*line_width + ' '*8 + msg + '\n'
|
||||||
|
important_msg += ('*'*line_width + '\n')*2
|
||||||
|
important_msg += ('*'*70 + '\n')*line_width
|
||||||
|
self.info(important_msg, *args, **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def get_logger(path, rank):
|
||||||
|
logger = LOGGER("mindversion", rank)
|
||||||
|
logger.setup_logging_file(path, rank)
|
||||||
|
return logger
|
||||||
|
|
@ -0,0 +1,36 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
optimizer parameters.
|
||||||
|
"""
|
||||||
|
def get_param_groups(network):
|
||||||
|
"""get param groups"""
|
||||||
|
decay_params = []
|
||||||
|
no_decay_params = []
|
||||||
|
for x in network.trainable_params():
|
||||||
|
parameter_name = x.name
|
||||||
|
if parameter_name.endswith('.bias'):
|
||||||
|
# all bias not using weight decay
|
||||||
|
no_decay_params.append(x)
|
||||||
|
elif parameter_name.endswith('.gamma'):
|
||||||
|
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||||
|
no_decay_params.append(x)
|
||||||
|
elif parameter_name.endswith('.beta'):
|
||||||
|
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||||
|
no_decay_params.append(x)
|
||||||
|
else:
|
||||||
|
decay_params.append(x)
|
||||||
|
|
||||||
|
return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
|
||||||
|
|
@ -0,0 +1,53 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
choose samples from the dataset
|
||||||
|
"""
|
||||||
|
import math
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
class DistributedSampler():
|
||||||
|
"""
|
||||||
|
sampling the dataset.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
Returns:
|
||||||
|
num_samples, number of samples.
|
||||||
|
"""
|
||||||
|
def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
|
||||||
|
self.dataset = dataset
|
||||||
|
self.rank = rank
|
||||||
|
self.group_size = group_size
|
||||||
|
self.dataset_length = len(self.dataset)
|
||||||
|
self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
|
||||||
|
self.total_size = self.num_samples * self.group_size
|
||||||
|
self.shuffle = shuffle
|
||||||
|
self.seed = seed
|
||||||
|
|
||||||
|
def __iter__(self):
|
||||||
|
if self.shuffle:
|
||||||
|
self.seed = (self.seed + 1) & 0xffffffff
|
||||||
|
np.random.seed(self.seed)
|
||||||
|
indices = np.random.permutation(self.dataset_length).tolist()
|
||||||
|
else:
|
||||||
|
indices = list(range(len(self.dataset_length)))
|
||||||
|
|
||||||
|
indices += indices[:(self.total_size - len(indices))]
|
||||||
|
indices = indices[self.rank::self.group_size]
|
||||||
|
return iter(indices)
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return self.num_samples
|
||||||
|
|
||||||
|
|
@ -0,0 +1,228 @@
|
||||||
|
# Copyright 2021 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.
|
||||||
|
# ============================================================================
|
||||||
|
"""
|
||||||
|
Initialize.
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import math
|
||||||
|
from functools import reduce
|
||||||
|
import numpy as np
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from mindspore.common import initializer as init
|
||||||
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
||||||
|
|
||||||
|
def _calculate_gain(nonlinearity, param=None):
|
||||||
|
r"""
|
||||||
|
Return the recommended gain value for the given nonlinearity function.
|
||||||
|
|
||||||
|
The values are as follows:
|
||||||
|
================= ====================================================
|
||||||
|
nonlinearity gain
|
||||||
|
================= ====================================================
|
||||||
|
Linear / Identity :math:`1`
|
||||||
|
Conv{1,2,3}D :math:`1`
|
||||||
|
Sigmoid :math:`1`
|
||||||
|
Tanh :math:`\frac{5}{3}`
|
||||||
|
ReLU :math:`\sqrt{2}`
|
||||||
|
Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
|
||||||
|
================= ====================================================
|
||||||
|
|
||||||
|
Args:
|
||||||
|
nonlinearity: the non-linear function
|
||||||
|
param: optional parameter for the non-linear function
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
|
||||||
|
"""
|
||||||
|
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
|
||||||
|
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
|
||||||
|
return 1
|
||||||
|
if nonlinearity == 'tanh':
|
||||||
|
return 5.0 / 3
|
||||||
|
if nonlinearity == 'relu':
|
||||||
|
return math.sqrt(2.0)
|
||||||
|
if nonlinearity == 'leaky_relu':
|
||||||
|
if param is None:
|
||||||
|
negative_slope = 0.01
|
||||||
|
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
|
||||||
|
negative_slope = param
|
||||||
|
else:
|
||||||
|
raise ValueError("negative_slope {} not a valid number".format(param))
|
||||||
|
return math.sqrt(2.0 / (1 + negative_slope ** 2))
|
||||||
|
|
||||||
|
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
|
||||||
|
|
||||||
|
def _assignment(arr, num):
|
||||||
|
"""Assign the value of `num` to `arr`."""
|
||||||
|
if arr.shape == ():
|
||||||
|
arr = arr.reshape((1))
|
||||||
|
arr[:] = num
|
||||||
|
arr = arr.reshape(())
|
||||||
|
else:
|
||||||
|
if isinstance(num, np.ndarray):
|
||||||
|
arr[:] = num[:]
|
||||||
|
else:
|
||||||
|
arr[:] = num
|
||||||
|
return arr
|
||||||
|
|
||||||
|
def _calculate_in_and_out(arr):
|
||||||
|
"""
|
||||||
|
Calculate n_in and n_out.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
arr (Array): Input array.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
|
||||||
|
"""
|
||||||
|
dim = len(arr.shape)
|
||||||
|
if dim < 2:
|
||||||
|
raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
|
||||||
|
|
||||||
|
n_in = arr.shape[1]
|
||||||
|
n_out = arr.shape[0]
|
||||||
|
|
||||||
|
if dim > 2:
|
||||||
|
counter = reduce(lambda x, y: x * y, arr.shape[2:])
|
||||||
|
n_in *= counter
|
||||||
|
n_out *= counter
|
||||||
|
return n_in, n_out
|
||||||
|
|
||||||
|
def _select_fan(array, mode):
|
||||||
|
mode = mode.lower()
|
||||||
|
valid_modes = ['fan_in', 'fan_out']
|
||||||
|
if mode not in valid_modes:
|
||||||
|
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
|
||||||
|
|
||||||
|
fan_in, fan_out = _calculate_in_and_out(array)
|
||||||
|
return fan_in if mode == 'fan_in' else fan_out
|
||||||
|
|
||||||
|
class KaimingInit(init.Initializer):
|
||||||
|
r"""
|
||||||
|
Base Class. Initialize the array with He kaiming algorithm.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
a: the negative slope of the rectifier used after this layer (only
|
||||||
|
used with ``'leaky_relu'``)
|
||||||
|
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
|
||||||
|
preserves the magnitude of the variance of the weights in the
|
||||||
|
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
|
||||||
|
backwards pass.
|
||||||
|
nonlinearity: the non-linear function, recommended to use only with
|
||||||
|
``'relu'`` or ``'leaky_relu'`` (default).
|
||||||
|
"""
|
||||||
|
def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
|
||||||
|
super(KaimingInit, self).__init__()
|
||||||
|
self.mode = mode
|
||||||
|
self.gain = _calculate_gain(nonlinearity, a)
|
||||||
|
def _initialize(self, arr):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class KaimingUniform(KaimingInit):
|
||||||
|
r"""
|
||||||
|
Initialize the array with He kaiming uniform algorithm. The resulting tensor will
|
||||||
|
have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
|
||||||
|
|
||||||
|
.. math::
|
||||||
|
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
|
||||||
|
|
||||||
|
Input:
|
||||||
|
arr (Array): The array to be assigned.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Array, assigned array.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> w = np.empty(3, 5)
|
||||||
|
>>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
|
||||||
|
"""
|
||||||
|
|
||||||
|
def _initialize(self, arr):
|
||||||
|
fan = _select_fan(arr, self.mode)
|
||||||
|
bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
|
||||||
|
data = np.random.uniform(-bound, bound, arr.shape)
|
||||||
|
|
||||||
|
_assignment(arr, data)
|
||||||
|
|
||||||
|
|
||||||
|
class KaimingNormal(KaimingInit):
|
||||||
|
r"""
|
||||||
|
Initialize the array with He kaiming normal algorithm. The resulting tensor will
|
||||||
|
have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
|
||||||
|
|
||||||
|
.. math::
|
||||||
|
\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
|
||||||
|
|
||||||
|
Input:
|
||||||
|
arr (Array): The array to be assigned.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Array, assigned array.
|
||||||
|
|
||||||
|
Examples:
|
||||||
|
>>> w = np.empty(3, 5)
|
||||||
|
>>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
|
||||||
|
"""
|
||||||
|
|
||||||
|
def _initialize(self, arr):
|
||||||
|
fan = _select_fan(arr, self.mode)
|
||||||
|
std = self.gain / math.sqrt(fan)
|
||||||
|
data = np.random.normal(0, std, arr.shape)
|
||||||
|
|
||||||
|
_assignment(arr, data)
|
||||||
|
|
||||||
|
|
||||||
|
def default_recurisive_init(custom_cell):
|
||||||
|
"""default_recurisive_init"""
|
||||||
|
for _, cell in custom_cell.cells_and_names():
|
||||||
|
if isinstance(cell, nn.Conv2d):
|
||||||
|
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||||
|
cell.weight.shape,
|
||||||
|
cell.weight.dtype))
|
||||||
|
if cell.bias is not None:
|
||||||
|
fan_in, _ = _calculate_in_and_out(cell.weight)
|
||||||
|
bound = 1 / math.sqrt(fan_in)
|
||||||
|
cell.bias.set_data(init.initializer(init.Uniform(bound),
|
||||||
|
cell.bias.shape,
|
||||||
|
cell.bias.dtype))
|
||||||
|
elif isinstance(cell, nn.Dense):
|
||||||
|
cell.weight.set_data(init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||||
|
cell.weight.shape,
|
||||||
|
cell.weight.dtype))
|
||||||
|
if cell.bias is not None:
|
||||||
|
fan_in, _ = _calculate_in_and_out(cell.weight)
|
||||||
|
bound = 1 / math.sqrt(fan_in)
|
||||||
|
cell.bias.set_data(init.initializer(init.Uniform(bound),
|
||||||
|
cell.bias.shape,
|
||||||
|
cell.bias.dtype))
|
||||||
|
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
def load_pretrain_model(ckpt_file, network, args):
|
||||||
|
"""load pretrain model."""
|
||||||
|
if os.path.isfile(ckpt_file):
|
||||||
|
param_dict = load_checkpoint(ckpt_file)
|
||||||
|
param_dict_new = {}
|
||||||
|
for key, values in param_dict.items():
|
||||||
|
if key.startswith('moments.'):
|
||||||
|
continue
|
||||||
|
elif key.startswith('network.'):
|
||||||
|
param_dict_new[key[8:]] = values
|
||||||
|
else:
|
||||||
|
param_dict_new[key] = values
|
||||||
|
load_param_into_net(network, param_dict_new)
|
||||||
|
args.logger.info('load model {} success'.format(ckpt_file))
|
||||||
|
|
@ -0,0 +1,331 @@
|
||||||
|
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
# ============================================================================
|
||||||
|
"""train ImageNet."""
|
||||||
|
import os
|
||||||
|
import time
|
||||||
|
import ast
|
||||||
|
import argparse
|
||||||
|
import datetime
|
||||||
|
|
||||||
|
import mindspore.nn as nn
|
||||||
|
from mindspore import Tensor, context
|
||||||
|
from mindspore.context import ParallelMode
|
||||||
|
from mindspore.nn.optim import Momentum
|
||||||
|
from mindspore.communication.management import init, get_rank, get_group_size
|
||||||
|
from mindspore.train.callback import ModelCheckpoint
|
||||||
|
from mindspore.train.callback import CheckpointConfig, Callback
|
||||||
|
from mindspore.train.model import Model
|
||||||
|
from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
|
||||||
|
from mindspore.common import set_seed
|
||||||
|
|
||||||
|
from src.dataset import classification_dataset
|
||||||
|
from src.crossentropy import CrossEntropy
|
||||||
|
from src.lr_generator import get_lr
|
||||||
|
from src.utils.logging import get_logger
|
||||||
|
from src.utils.optimizers__init__ import get_param_groups
|
||||||
|
from src.utils.var_init import load_pretrain_model
|
||||||
|
from src.image_classification import get_network
|
||||||
|
from src.config import config
|
||||||
|
from src.eval_callback import EvalCallBack
|
||||||
|
set_seed(1)
|
||||||
|
|
||||||
|
class BuildTrainNetwork(nn.Cell):
|
||||||
|
"""build training network"""
|
||||||
|
def __init__(self, network, criterion):
|
||||||
|
super(BuildTrainNetwork, self).__init__()
|
||||||
|
self.network = network
|
||||||
|
self.criterion = criterion
|
||||||
|
|
||||||
|
def construct(self, input_data, label):
|
||||||
|
output = self.network(input_data)
|
||||||
|
loss = self.criterion(output, label)
|
||||||
|
return loss
|
||||||
|
|
||||||
|
class ProgressMonitor(Callback):
|
||||||
|
"""monitor loss and time"""
|
||||||
|
def __init__(self, args):
|
||||||
|
super(ProgressMonitor, self).__init__()
|
||||||
|
self.me_epoch_start_time = 0
|
||||||
|
self.me_epoch_start_step_num = 0
|
||||||
|
self.args = args
|
||||||
|
self.ckpt_history = []
|
||||||
|
|
||||||
|
def begin(self, run_context):
|
||||||
|
self.args.logger.info('start network train...')
|
||||||
|
|
||||||
|
def epoch_begin(self, run_context):
|
||||||
|
pass
|
||||||
|
|
||||||
|
def epoch_end(self, run_context, *me_args):
|
||||||
|
"""describe network construct"""
|
||||||
|
cb_params = run_context.original_args()
|
||||||
|
me_step = cb_params.cur_step_num - 1
|
||||||
|
|
||||||
|
real_epoch = me_step // self.args.steps_per_epoch
|
||||||
|
time_used = time.time() - self.me_epoch_start_time
|
||||||
|
fps_mean = (self.args.per_batch_size * (me_step-self.me_epoch_start_step_num))
|
||||||
|
fps_mean = fps_mean * self.args.group_size
|
||||||
|
fps_mean = fps_mean / time_used
|
||||||
|
self.args.logger.info('epoch[{}], iter[{}], loss:{}, '
|
||||||
|
'mean_fps:{:.2f}'
|
||||||
|
'imgs/sec'.format(real_epoch,
|
||||||
|
me_step,
|
||||||
|
cb_params.net_outputs,
|
||||||
|
fps_mean))
|
||||||
|
|
||||||
|
if self.args.rank_save_ckpt_flag:
|
||||||
|
import glob
|
||||||
|
ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
|
||||||
|
for ckpt in ckpts:
|
||||||
|
ckpt_fn = os.path.basename(ckpt)
|
||||||
|
if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
|
||||||
|
continue
|
||||||
|
if ckpt in self.ckpt_history:
|
||||||
|
continue
|
||||||
|
self.ckpt_history.append(ckpt)
|
||||||
|
self.args.logger.info('epoch[{}], iter[{}], loss:{}, '
|
||||||
|
'ckpt:{},'
|
||||||
|
'ckpt_fn:{}'.format(real_epoch,
|
||||||
|
me_step,
|
||||||
|
cb_params.net_outputs,
|
||||||
|
ckpt,
|
||||||
|
ckpt_fn))
|
||||||
|
|
||||||
|
|
||||||
|
self.me_epoch_start_step_num = me_step
|
||||||
|
self.me_epoch_start_time = time.time()
|
||||||
|
|
||||||
|
def step_begin(self, run_context):
|
||||||
|
pass
|
||||||
|
|
||||||
|
def step_end(self, run_context, *me_args):
|
||||||
|
pass
|
||||||
|
|
||||||
|
def end(self, run_context):
|
||||||
|
self.args.logger.info('end network train...')
|
||||||
|
|
||||||
|
|
||||||
|
def parse_args(cloud_args=None):
|
||||||
|
"""parameters"""
|
||||||
|
parser = argparse.ArgumentParser('mindspore classification training')
|
||||||
|
parser.add_argument('--platform', type=str, default='Ascend',
|
||||||
|
choices=('Ascend', 'GPU'), help='run platform')
|
||||||
|
|
||||||
|
# dataset related
|
||||||
|
parser.add_argument('--data_dir', type=str, default='', help='train data dir')
|
||||||
|
parser.add_argument('--per_batch_size', default=128, type=int, help='batch size for per gpu')
|
||||||
|
# network related
|
||||||
|
parser.add_argument('--pretrained',
|
||||||
|
default='',
|
||||||
|
type=str,
|
||||||
|
help='model_path, local pretrained model to load')
|
||||||
|
|
||||||
|
# distributed related
|
||||||
|
parser.add_argument('--is_distributed', type=int, default=1, help='if multi device')
|
||||||
|
# roma obs
|
||||||
|
parser.add_argument('--train_url', type=str, default="", help='train url')
|
||||||
|
#new argument
|
||||||
|
parser.add_argument("--eval_interval", type=int, default=1,
|
||||||
|
help="Evaluation interval when run_eval is True, default is 1.")
|
||||||
|
parser.add_argument("--eval_start_epoch", type=int, default=120,
|
||||||
|
help="Evaluation start epoch when run_eval is True, default is 120.")
|
||||||
|
parser.add_argument("--save_best_ckpt", type=ast.literal_eval, default=True,
|
||||||
|
help="Save best checkpoint when run_eval is True, default is True.")
|
||||||
|
#dataset of eval dataset
|
||||||
|
parser.add_argument('--eval_data_dir',
|
||||||
|
type=str,
|
||||||
|
default='/opt/npu/pvc/dataset/storage/imagenet/val',
|
||||||
|
help='eval data dir')
|
||||||
|
parser.add_argument('--eval_per_batch_size',
|
||||||
|
default=32,
|
||||||
|
type=int,
|
||||||
|
help='batch size for per npu')
|
||||||
|
parser.add_argument("--run_eval",
|
||||||
|
type=ast.literal_eval,
|
||||||
|
default=True,
|
||||||
|
help="Run evaluation when training, default is True.")
|
||||||
|
#best ckpt
|
||||||
|
parser.add_argument('--eval_log_path',
|
||||||
|
type=str,
|
||||||
|
default='eval_outputs/',
|
||||||
|
help='path to save log')
|
||||||
|
parser.add_argument('--eval_is_distributed',
|
||||||
|
type=int,
|
||||||
|
default=0,
|
||||||
|
help='if multi device')
|
||||||
|
args, _ = parser.parse_known_args()
|
||||||
|
args = merge_args(args, cloud_args)
|
||||||
|
args.image_size = config['image_size']
|
||||||
|
args.num_classes = config['num_classes']
|
||||||
|
args.lr = config['lr']
|
||||||
|
args.lr_scheduler = config['lr_scheduler']
|
||||||
|
args.lr_epochs = config['lr_epochs']
|
||||||
|
args.lr_gamma = config['lr_gamma']
|
||||||
|
args.eta_min = config['eta_min']
|
||||||
|
args.T_max = config['T_max']
|
||||||
|
args.max_epoch = config['max_epoch']
|
||||||
|
args.warmup_epochs = config['warmup_epochs']
|
||||||
|
args.weight_decay = config['weight_decay']
|
||||||
|
args.momentum = config['momentum']
|
||||||
|
args.is_dynamic_loss_scale = config['is_dynamic_loss_scale']
|
||||||
|
args.loss_scale = config['loss_scale']
|
||||||
|
args.label_smooth = config['label_smooth']
|
||||||
|
args.label_smooth_factor = config['label_smooth_factor']
|
||||||
|
args.ckpt_interval = config['ckpt_interval']
|
||||||
|
args.ckpt_save_max = config['ckpt_save_max']
|
||||||
|
args.ckpt_path = config['ckpt_path']
|
||||||
|
args.is_save_on_master = config['is_save_on_master']
|
||||||
|
args.rank = config['rank']
|
||||||
|
args.group_size = config['group_size']
|
||||||
|
args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
|
||||||
|
args.image_size = list(map(int, args.image_size.split(',')))
|
||||||
|
|
||||||
|
context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
|
||||||
|
device_target=args.platform, save_graphs=False)
|
||||||
|
# init distributed
|
||||||
|
if args.is_distributed:
|
||||||
|
init()
|
||||||
|
args.rank = get_rank()
|
||||||
|
args.group_size = get_group_size()
|
||||||
|
else:
|
||||||
|
args.rank = 0
|
||||||
|
args.group_size = 1
|
||||||
|
|
||||||
|
if args.is_dynamic_loss_scale == 1:
|
||||||
|
args.loss_scale = 1 # for dynamic loss scale can not set loss scale in momentum opt
|
||||||
|
|
||||||
|
# select for master rank save ckpt or all rank save, compatible for model parallel
|
||||||
|
args.rank_save_ckpt_flag = 0
|
||||||
|
if args.is_save_on_master:
|
||||||
|
if args.rank == 0:
|
||||||
|
args.rank_save_ckpt_flag = 1
|
||||||
|
else:
|
||||||
|
args.rank_save_ckpt_flag = 1
|
||||||
|
|
||||||
|
# logger
|
||||||
|
args.outputs_dir = os.path.join(args.ckpt_path,
|
||||||
|
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
||||||
|
args.logger = get_logger(args.outputs_dir, args.rank)
|
||||||
|
return args
|
||||||
|
|
||||||
|
def merge_args(args, cloud_args):
|
||||||
|
"""dictionary"""
|
||||||
|
args_dict = vars(args)
|
||||||
|
if isinstance(cloud_args, dict):
|
||||||
|
for key in cloud_args.keys():
|
||||||
|
val = cloud_args[key]
|
||||||
|
if key in args_dict and val:
|
||||||
|
arg_type = type(args_dict[key])
|
||||||
|
if arg_type is not type(None):
|
||||||
|
val = arg_type(val)
|
||||||
|
args_dict[key] = val
|
||||||
|
return args
|
||||||
|
|
||||||
|
def apply_eval(eval_param):
|
||||||
|
eval_model = eval_param["model"]
|
||||||
|
eval_ds = eval_param["dataset"]
|
||||||
|
metrics_name = eval_param["metrics_name"]
|
||||||
|
res = eval_model.eval(eval_ds)
|
||||||
|
return res[metrics_name]
|
||||||
|
|
||||||
|
def train(cloud_args=None):
|
||||||
|
"""training process"""
|
||||||
|
args = parse_args(cloud_args)
|
||||||
|
if os.getenv('DEVICE_ID', "not_set").isdigit():
|
||||||
|
context.set_context(device_id=int(os.getenv('DEVICE_ID')))
|
||||||
|
|
||||||
|
# init distributed
|
||||||
|
if args.is_distributed:
|
||||||
|
parallel_mode = ParallelMode.DATA_PARALLEL
|
||||||
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
|
||||||
|
gradients_mean=True)
|
||||||
|
# dataloader
|
||||||
|
de_dataset = classification_dataset(args.data_dir, args.image_size,
|
||||||
|
args.per_batch_size, 1,
|
||||||
|
args.rank, args.group_size, num_parallel_workers=8)
|
||||||
|
de_dataset.map_model = 4 # !!!important
|
||||||
|
args.steps_per_epoch = de_dataset.get_dataset_size()
|
||||||
|
|
||||||
|
|
||||||
|
#eval_dataset
|
||||||
|
args.logger.save_args(args)
|
||||||
|
# network
|
||||||
|
args.logger.important_info('start create network')
|
||||||
|
# get network and init
|
||||||
|
network = get_network(num_classes=args.num_classes, platform=args.platform)
|
||||||
|
load_pretrain_model(args.pretrained, network, args)
|
||||||
|
# lr scheduler
|
||||||
|
lr = get_lr(args)
|
||||||
|
# optimizer
|
||||||
|
opt = Momentum(params=get_param_groups(network),
|
||||||
|
learning_rate=Tensor(lr),
|
||||||
|
momentum=args.momentum,
|
||||||
|
weight_decay=args.weight_decay,
|
||||||
|
loss_scale=args.loss_scale)
|
||||||
|
# loss
|
||||||
|
if not args.label_smooth:
|
||||||
|
args.label_smooth_factor = 0.0
|
||||||
|
loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
|
||||||
|
if args.is_dynamic_loss_scale == 1:
|
||||||
|
loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536,
|
||||||
|
scale_factor=2,
|
||||||
|
scale_window=2000)
|
||||||
|
else:
|
||||||
|
loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
|
||||||
|
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager,
|
||||||
|
metrics={'acc'}, amp_level="O3")
|
||||||
|
# checkpoint save
|
||||||
|
progress_cb = ProgressMonitor(args)
|
||||||
|
callbacks = [progress_cb,]
|
||||||
|
#eval dataset
|
||||||
|
if args.eval_data_dir is None or (not os.path.isdir(args.eval_data_dir)):
|
||||||
|
raise ValueError("{} is not a existing path.".format(args.eval_data_dir))
|
||||||
|
#code like eval.py
|
||||||
|
#if run eval
|
||||||
|
if args.run_eval:
|
||||||
|
if args.eval_data_dir is None or (not os.path.isdir(args.eval_data_dir)):
|
||||||
|
raise ValueError("{} is not a existing path.".format(args.eval_data_dir))
|
||||||
|
eval_de_dataset = classification_dataset(args.eval_data_dir,
|
||||||
|
image_size=args.image_size,
|
||||||
|
per_batch_size=args.eval_per_batch_size,
|
||||||
|
max_epoch=1,
|
||||||
|
rank=args.rank,
|
||||||
|
group_size=args.group_size,
|
||||||
|
mode='eval')
|
||||||
|
eval_param_dict = {"model": model, "dataset": eval_de_dataset, "metrics_name": "acc"}
|
||||||
|
eval_callback = EvalCallBack(apply_eval,
|
||||||
|
eval_param_dict,
|
||||||
|
interval=args.eval_interval,
|
||||||
|
eval_start_epoch=args.eval_start_epoch,
|
||||||
|
save_best_ckpt=args.save_best_ckpt,
|
||||||
|
ckpt_directory=args.ckpt_path,
|
||||||
|
best_ckpt_name="best_acc.ckpt",
|
||||||
|
metrics_name="acc"
|
||||||
|
)
|
||||||
|
callbacks.append(eval_callback)
|
||||||
|
if args.rank_save_ckpt_flag:
|
||||||
|
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
|
||||||
|
keep_checkpoint_max=args.ckpt_save_max)
|
||||||
|
save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
|
||||||
|
ckpt_cb = ModelCheckpoint(config=ckpt_config,
|
||||||
|
directory=save_ckpt_path,
|
||||||
|
prefix='{}'.format(args.rank))
|
||||||
|
callbacks.append(ckpt_cb)
|
||||||
|
|
||||||
|
model.train(args.max_epoch, de_dataset, callbacks=callbacks, dataset_sink_mode=True)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
train()
|
||||||
Loading…
Reference in New Issue