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Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer

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当使用脚本训练时 python scripts/train.py +experiment=gkt_nuscenes_vehicle_kernel_7x1.yaml data.dataset_dir= data.labels_dir= 训练完毕后,指标较低 ![{0d6e6989-cc7a-44e5-8ac2-8915041c8fe0}](https://user-images.githubusercontent.com/83942224/204687183-57fa4727-fe91-43cf-8c83-ce45a49e981e.png) 使用github上模型进行验证时 python scripts/eval.py +experiment=gkt_nuscenes_vehicle_kernel_7x1.yaml data.dataset_dir= data.labels_dir= experiment.ckptt 报错 optimizer_loop.optim_progress.optimizer.step.total.completed = self._loaded_checkpoint["global_step"] KeyError: 'global_step'

This problem occurs in torch\distributed\rendezvous.py ![image](https://user-images.githubusercontent.com/67987511/235866380-5d3db58b-5c6a-4d2a-8eec-25111a1c267d.png) But in this code I can't find any '}' to solve the problem ![image](https://user-images.githubusercontent.com/67987511/235866526-be5160e3-cc77-4a82-9d20-a74862707650.png) I try to print (hostname, port, world_size, start_daemon, timeout), But...

我修改了gkt_nuscenes_vehicle_kernel_7x1_setting1.yaml中BEV h的尺寸,可是训练过程中,val 指标特别低,基本不变 ,不知道为什么 data: bev: # h: 400 # TODO 程序跑不通 h: 200 w: 200 # h_meters: 100.0 # TODO h_meters: 50.0 w_meters: 50.0 # python scripts/train.py +experiment=gkt_nuscenes_vehicle_kernel_7x1_setting1.yaml \...

你好,我们在测试的时候,使用了不同的图像尺寸作为输入。但是和训练时的尺寸图像尺寸不同的时候,模型结果会差很多。请问可能的原因是什么。会不会是因为GKT的网络参数依赖于图像尺寸。

Hi, thank you for your open source project! However, when I run eval.py , there`s an error like " KeyError:'state_dict' ", i thought it would be something wrong with 'state_dict'...

Hi, as the title written, I can only find `generate_data.py` in `GKT/segmentation/script`. Where can I find the script that generate labels? Thanks!

![image](https://user-images.githubusercontent.com/88700988/197555972-c287e6c1-e4cb-4dc0-9160-db731ae4fb39.png)

Hi! Thanks a lot for the open-source project! However, I failed to reproduce the experimental results, i.e. the high FPS compared with BEVFormer. May I ask if you have made...

How soon will the code be open sourced