XCiT: Cross-Covariance Image Transformers
XCiT: Cross-Covariance Image Transformers
Introduction
XCiT models propose a “transposed” version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries. The resulting cross-covariance attention (XCA) has linear complexity in the number of tokens, and allows efficient processing of high-resolution images. Our cross-covariance image transformer (XCiT) – built upon XCA – combines the accuracy of conventional transformers with the scalability of convolutional architectures.
Figure 1. Architecture of XCiT [1]
Results
Our reproduced model performance on ImageNet-1K is reported as follows.
Model |
Context |
Top-1 (%) |
Top-5 (%) |
Params (M) |
Recipe |
Download |
xcit_tiny_12_p16_224 |
D910x8-G |
77.67 |
93.79 |
7.00 |
yaml |
weights |
Notes
- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.
Quick Start
Preparation
Installation
Please refer to the installation instruction in MindCV.
Dataset Preparation
Please download the ImageNet-1K dataset for model training and validation.
Training
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/xcit/xcit_tiny_12_p16_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 to mpirun
.
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/xcit/xcit_tiny_12_p16_ascend.yaml --data_dir /path/to/dataset --distribute False
Validation
To validate the accuracy of the trained model, you can use validate.py
and parse the checkpoint path with --ckpt_path
.
python validate.py -c configs/xcit/xcit_tiny_12_p16_224_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
Deployment
Please refer to the deployment tutorial in MindCV.
References
[1] Ali A, Touvron H, Caron M, et al. Xcit: Cross-covariance image transformers[J]. Advances in neural information processing systems, 2021, 34: 20014-20027.