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Genius Patrick d992a99fc7 | 1 year ago | |
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README.md | 1 year ago | |
pvt_large_ascend.yaml | 1 year ago | |
pvt_medium_ascend.yaml | 1 year ago | |
pvt_small_ascend.yaml | 1 year ago | |
pvt_tiny_ascend.yaml | 1 year ago |
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
PVT is a general backbone network for dense prediction without convolution operation. PVT introduces a pyramid structure in Transformer to generate multi-scale feature maps for dense prediction tasks. PVT uses a gradual reduction strategy to control the size of the feature maps through the patch embedding layer, and proposes a spatial reduction attention (SRA) layer to replace the traditional multi head attention layer in the encoder, which greatly reduces the computing/memory overhead.[1]
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
PVT_tiny | D910x8-G | 74.81 | 92.18 | 13.23 | yaml | weights |
PVT_small | D910x8-G | 79.66 | 94.71 | 24.49 | yaml | weights |
PVT_medium | D910x8-G | 81.82 | 95.81 | 44.21 | yaml | weights |
PVT_large | D910x8-G | 81.75 | 95.70 | 61.36 | yaml | weights |
Please refer to the installation instruction in MindCV.
Please download the ImageNet-1K dataset for model training and validation.
It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
# distributed training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/pvt/pvt_tiny_ascend.yaml --data_dir /path/to/imagenet
If the script is executed by the root user, the
--allow-run-as-root
parameter must be added tompirun
.
Similarly, you can train the model on multiple GPU devices with the above mpirun
command.
For detailed illustration of all hyper-parameters, please refer to config.py.
Note: As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.
If you want to train or finetune the model on a smaller dataset without distributed training, please run:
# standalone training on a CPU/GPU/Ascend device
python train.py --config configs/pvt/pvt_tiny_ascend.yaml --data_dir /path/to/imagenet --distribute False
To validate the accuracy of the trained model, you can use validate.py
and parse the checkpoint path with --ckpt_path
.
python validate.py --model=pvt_tiny --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
To deploy online inference services with the trained model efficiently, please refer to the deployment tutorial.
[1]. Wang W, Xie E, Li X, et al. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 568-578.
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