fixed a bug of ops/torch/rrsda; update readme

INSTALL.md

@@ -15,8 +15,9 @@ If you are using slurm clusters, it is recommended to create a slurm config file
 
 ```bash
 export CLUSTER_ID=[YOUR_CLUSTER_ALIAS]
-vim configs/slurm/${CLUSTER_ID}.yaml
+cp configs/slurm/sz10.yaml configs/slurm/${CLUSTER_ID}.yaml && vim configs/slurm/${CLUSTER_ID}.yaml
 ```
+hence you can launch experiments in any available cluster consistently with `+slurm=${CLUSTER_ID}`.
 
 ## Dataset Preparation
 

README.md

@@ -3,23 +3,23 @@
 Official PyTorch implementation of **BiFormer**, from the following paper:
 
 [BiFormer: Vision Transformer with Bi-Level Routing Attention](https://arxiv.org/abs/2303.08810). CVPR 2023.\
-[Lei Zhu](https://github.com/rayleizhu), [Xinjiang Wang](https://www.linkedin.com/in/wang-xinjiang-784a3462), [Zhanghan Ke](https://zhke.io/), [Wayne Zhang](http://www.statfe.com/), and [Rynson Lau](https://www.cs.cityu.edu.hk/~rynson/)
+[Lei Zhu](https://github.com/rayleizhu), [Xinjiang Wang](https://scholar.google.com/citations?user=q4lnWaoAAAAJ&hl=en), [Zhanghan Ke](https://zhke.io/), [Wayne Zhang](http://www.statfe.com/), and [Rynson Lau](https://www.cs.cityu.edu.hk/~rynson/)
 
 --- 
 <p align="left">
 <img src="assets/teaser.png" width=60% height=60% 
 class="center">
 </p>
 
+
 <!-- ✅ ⬜️  -->
 
 ## News
 
-* 2023-03-24: For better readability, BRA and BiFormer-STL has been refactored. See [ops/bra_nchw.py](ops/bra_nchw.py) and [models/biformer_stl_nchw.py](models/biformer_stl_nchw.py). We still keep the [legacy (and a little bit messy) implementation](ops/bra_legacy.py) for compatiability of previously released checkpoints.
-
 * 2023-03-24: For better memory and computation efficieny, we are diving into the optimization of BRA with CUDA. Please stay tuned.
   - Collaborations and contributions are welcome, especially if you are an expert in CUDA/[cutlass](https://github.com/NVIDIA/cutlass). There is a chance to co-author a paper.
 
+* 2023-03-24: For better readability, BRA and BiFormer-STL has been refactored. See [ops/bra_nchw.py](ops/bra_nchw.py) and [models/biformer_stl_nchw.py](models/biformer_stl_nchw.py). We still keep the [legacy (and a little bit messy) implementation](ops/bra_legacy.py) for compatiability of previously released checkpoints.
 
 ## Results and Pre-trained Models
 
@@ -98,7 +98,7 @@ This project is released under the MIT license. Please see the [LICENSE](LICENSE
 
 ## Citation
 If you find this repository helpful, please consider citing:
-```
+```bibtex
 @Article{zhu2022biformer,
   author  = {Lei Zhu and Xinjiang Wang and Zhanghan Ke and Wayne Zhang and Rynson Lau},
   title   = {BiFormer: Vision Transformer with Bi-Level Routing Attention},

ops/torch/rrsda.py

@@ -115,6 +115,6 @@ def regional_routing_attention_torch(
 
     # remove paddings if needed
     if auto_pad and (q_pad_b > 0 or q_pad_r > 0):
-        output = output[:, :, :-q_pad_b, :-q_pad_r]
+        output = output[:, :, :Hq, :Wq]
 
     return output, attn