'assert (boxes1[:, 2:] >= boxes1[:, :2]).all()' happened when training

Thanks for amazing work I have questions when training with your code, assert (boxes1[:, 2:] >= boxes1[:, :2]).all() happened in function generalized_box_iou After reading the code i find that boxes1 is the predictd bbox from a MLP layer, which i think the above assertion may happen during early training time, and then break the training. I wonder if there are Mechanism that can make sure to avoid this happen

My Environment:

Provide your environment information using the following command: Collecting environment information… PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.0

OS: Ubuntu 16.04.5 LTS GCC version: (Ubuntu 4.9.3-13ubuntu2) 4.9.3 CMake version: version 3.16.2

Python version: 3.6 Is CUDA available: Yes CUDA runtime version: 10.0.130 GPU models and configuration: GPU 0: TITAN Xp GPU 1: TITAN Xp GPU 2: TITAN Xp GPU 3: TITAN Xp

Nvidia driver version: 410.48 cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5

Versions of relevant libraries: [pip3] numpy==1.17.1 [pip3] torch==1.4.0 [pip3] torchfile==0.1.0 [pip3] torchvision==0.5.0 [conda] mkl 2019.4 243 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main [conda] pytorch 1.4.0 py3.6_cuda10.0.130_cudnn7.6.3_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch [conda] torch 1.0.0 <pip> [conda] torchfile 0.1.0 <pip> [conda] torchvision 0.5.0 py36_cu100 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch