TNT
This implements training of TNT on the ImageNet dataset, mainly modified from CV-Backbones/tnt_pytorch.
TNT Detail
TNT divides the image patches into sub-patches. With the structure of visual sentences and visual words, TNT can better perform the image classification task.
Requirements
- Install PyTorch (pytorch.org)
pip install -r requirements.txtNote:Install the torchvision that corresponds to the torch version- Download the ImageNet dataset
- Then, and move validation images to labeled subfolders, using the following shell script
- Please add the following shapes into /usr/local/Ascend/ascend-toolkit/5.0.3/arm64-linux/opp/op_impl/built-in/ai_core/tbe/impl/dynamic/transpose.py for better performance.
- [2, 25088, 4, 16, 6], [25088, 16, 2, 4, 6]
Training
To train a model, run main.py with the desired model architecture and the path to the ImageNet dataset:
# training 1p accuracy
bash ./test/train_full_1p.sh --data_path=real_data_path
# training 1p performance
bash ./test/train_performance_1p.sh --data_path=real_data_path
# training 8p accuracy
bash ./test/train_full_8p.sh --data_path=real_data_path
# training 8p performance
bash ./test/train_performance_8p.sh --data_path=real_data_path
#test 8p accuracy
bash test/train_eval_8p.sh --data_path=real_data_path --pth_path=real_pre_train_model_path
# finetuning 1p
bash test/train_finetune_1p.sh --data_path=real_data_path --pth_path=real_pre_train_model_path
Log path: test/output/devie_id/train_${device_id}.log # training detail log test/output/devie_id/WideReesnet50_2_bs8192_8p_perf.log # 8p training performance result log test/output/devie_id/WideReesnet50_2_bs8192_8p_acc.log # 8p training accuracy result log
TNT training result
| Acc@1 | FPS | Npu_nums | Epochs | AMP_Type |
|---|---|---|---|---|
| - | 57 | 1 | 1 | O1 |
| 71.4% | 410 | 8 | 79 | O1 |
自测结果说明：
- 项目交付精度要求：Acc@1 81.5%，目前在gpu中Acc@1为81.2%，已达目标精度的99%以上。
由于跑完310个epoch训练时间过长，本项目中测试npu上79个epoch后Acc@1为71.4%，此时gpu的Acc@1为71.7%，Atlas（NPU）精度大于竞品V（GPU）精度的99%（即71.7% * 99% = 71.0%），说明精度已经对齐，符合验收标准。
Statement
For details about the public address of the code in this repository, you can get from the file public_address_statement.md