csuhan / redet Goto Github PK

Hi, thanks for the excellent work and the source code.

I have some questions about the role of "with_module" in the config. i.e., in the first stage, since 'with_module=False', you use 'delta2dbbox_v3' to get the new_rois in rbbox_head.py, while use 'delta2dbbox' for the second stage.

I found that the difference between 'delta2dbbox_v3' and 'delta2dbbox' is the calculation of the 'gangle'. That is, in the first stage, you just use to calc the gangle:
gangle = dangle + Rroi_angle

But in the second stage, you use:
gangle = (2 * np.pi) * dangle + Rroi_angle gangle = gangle % ( 2 * np.pi)

Thanks!

i tried to train the model but got this error

from mmdet.datasets import get_dataset

ImportError: cannot import name 'get_dataset'

i also run sh compile.sh and pip3 install setup.py but error still

No matching distribution found for e2cnn

学长你好，我想自定义一个数据集来跑这个模型，我把数据集的标注转成了coco格式如下所示。然后修改了config文件的数据集路径，为什么还是会报错。错误如下：
"images":[],
"categories":[],
"annotations":[
{
"area":2054.1579212663173,
"category_id":4,
"segmentation":"polygon",
"iscrowd":0,
"bbox":[
645.6300334536613,
304.6851641094081,
24.166563779603734,
85
],
"image_id":1,
"id":1
},

Traceback (most recent call last):
File "tools/train.py", line 95, in
main()
File "tools/train.py", line 91, in main
logger=logger)
File "/home/localchao/LHX/jyz/ReDet/mmdet/apis/train.py", line 61, in train_detector
_non_dist_train(model, dataset, cfg, validate=validate)
File "/home/localchao/LHX/jyz/ReDet/mmdet/apis/train.py", line 197, in _non_dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/mmcv-0.2.13-py3.7-linux-x86_64.egg/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/mmcv-0.2.13-py3.7-linux-x86_64.egg/mmcv/runner/runner.py", line 260, in train
for i, data_batch in enumerate(data_loader):
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 819, in next
return self._process_data(data)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data
data.reraise()
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/_utils.py", line 385, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
data = fetcher.fetch(index)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/localchao/LHX/jyz/ReDet/mmdet/datasets/custom.py", line 191, in getitem
data = self.prepare_train_img(idx)
File "/home/localchao/LHX/jyz/ReDet/mmdet/datasets/custom.py", line 229, in prepare_train_img
ann = self.get_ann_info(idx)
File "/home/localchao/LHX/jyz/ReDet/mmdet/datasets/coco.py", line 43, in get_ann_info
return self._parse_ann_info(ann_info, self.with_mask)
File "/home/localchao/LHX/jyz/ReDet/mmdet/datasets/coco.py", line 91, in _parse_ann_info
gt_masks.append(self.coco.annToMask(ann))
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/pycocotools-2.0.2-py3.7-linux-x86_64.egg/pycocotools/coco.py", line 441, in annToMask
rle = self.annToRLE(ann)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/pycocotools-2.0.2-py3.7-linux-x86_64.egg/pycocotools/coco.py", line 428, in annToRLE
elif type(segm['counts']) == list:
TypeError: string indices must be integers

请问为什么已经加载了您ReResNet的预训练模型，还会出现不匹配问题呢？

2021-07-05 20:38:56,774 - INFO - load model from: work_dirs/ReResNet_pretrain/re_resnet50_c8_batch256-25b16846.pth
2021-07-05 20:38:56,790 - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: head.fc.weight, head.fc.bias

missing keys in source state_dict: layer4.2.conv1.filter, layer1.1.conv1.filter, layer3.0.downsample.0.filter, layer1.0.conv2.filter, layer3.0.conv3.filter, layer4.1.conv3.filter, layer1.0.conv1.filter, layer3.3.conv1.filter, layer2.2.conv1.filter, layer3.3.conv3.filter, layer3.1.conv3.filter, layer3.4.conv1.filter, layer3.5.conv2.filter, layer2.1.conv2.filter, layer2.0.downsample.0.filter, layer2.1.conv1.filter, layer3.2.conv3.filter, layer4.2.conv3.filter, layer3.0.conv1.filter, layer3.4.conv2.filter, layer2.3.conv2.filter, layer3.0.conv2.filter, layer4.0.conv2.filter, layer2.0.conv3.filter, layer3.2.conv2.filter, layer4.0.downsample.0.filter, layer3.5.conv1.filter, layer4.2.conv2.filter, layer2.0.conv2.filter, layer2.2.conv3.filter, layer1.2.conv2.filter, layer2.2.conv2.filter, layer3.3.conv2.filter, layer2.3.conv1.filter, layer2.1.conv3.filter, layer1.0.downsample.0.filter, layer3.5.conv3.filter, conv1.filter, layer4.1.conv1.filter, layer4.1.conv2.filter, layer4.0.conv3.filter, layer1.1.conv2.filter, layer4.0.conv1.filter, layer1.2.conv3.filter, layer2.0.conv1.filter, layer3.4.conv3.filter, layer3.1.conv1.filter, layer1.2.conv1.filter, layer1.0.conv3.filter, layer3.1.conv2.filter, layer1.1.conv3.filter, layer3.2.conv1.filter, layer2.3.conv3.filter

I tried testing your model but I got error, can you help me with this?

This is what error I got.

!python tools/test.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth --out work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl

Traceback (most recent call last): File "tools/test.py", line 12, in <module> from mmdet.apis import init_dist File "/content/ReDet/mmdet/apis/__init__.py", line 2, in <module> from .train import train_detector File "/content/ReDet/mmdet/apis/train.py", line 10, in <module> from mmdet import datasets File "/content/ReDet/mmdet/datasets/__init__.py", line 1, in <module> from .DOTA import DOTADataset, DOTADataset_v3 File "/content/ReDet/mmdet/datasets/DOTA.py", line 1, in <module> from .coco import CocoDataset File "/content/ReDet/mmdet/datasets/coco.py", line 2, in <module> from pycocotools.coco import COCO File "/usr/local/lib/python3.7/site-packages/pycocotools-2.0.2-py3.7-linux-x86_64.egg/pycocotools/coco.py", line 49, in <module> import matplotlib.pyplot as plt File "/usr/local/lib/python3.7/site-packages/matplotlib-3.4.2-py3.7-linux-x86_64.egg/matplotlib/pyplot.py", line 2500, in <module> switch_backend(rcParams["backend"]) File "/usr/local/lib/python3.7/site-packages/matplotlib-3.4.2-py3.7-linux-x86_64.egg/matplotlib/pyplot.py", line 277, in switch_backend class backend_mod(matplotlib.backend_bases._Backend): File "/usr/local/lib/python3.7/site-packages/matplotlib-3.4.2-py3.7-linux-x86_64.egg/matplotlib/pyplot.py", line 278, in backend_mod locals().update(vars(importlib.import_module(backend_name))) File "/usr/local/lib/python3.7/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) ModuleNotFoundError: No module named 'ipykernel'

P.S. I am running this in google colab

I have installed ReDet following official instructions.

Env:
docker: pytorch/pytorch:1.3-cuda10.1-cudnn7-devel

cmd
python demo_large_image.py

Segmentation fault (core dumped)

gdb info:

[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".

Program received signal SIGSEGV, Segmentation fault.
__GI___libc_free (mem=0xa64fa1f51c57fbdf) at malloc.c:2958

hello, sir! ！I have run the multi-scale test code on DOTAv1.5 , it took more than a day to complete the test，and i also run the single-scale code on DOTAv1.5，it also took a lot of time.
I would like to ask which structure has the most influence in the network and what is the specific reason for the slow test, and I have tried to modify the confidence and IOU threshold, but nothing worked.
Do you have any suggestions, thank you , sir !!!!!!

打搅您了，真的十分抱歉
我想问一下，您有DOTA1.5的test集的标签文件吗？

你好，我完全按照:
Hello, I follow exactly:
Please refer to INSTALL.md for installation and dataset preparation.

Please see GETTING_STARTED.md for the basic usage.

操作到
Operation to
python tools/test.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py \ work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth \ --out work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl
之后
after that

出现了错误
Something went wrong

在执行tools/test.py
While executing tools/test.py

model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)

的时候报错如下：
The error is reported as follows:

(redet) root@liuyi:~/Documents/ReDet-master# python tools/test.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth --out work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl
ReResNet Orientation: 8 Fix Params: False
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
The model and loaded state dict do not match exactly

missing keys in source state_dict: backbone.layer3.2.conv3.filter, backbone.layer3.4.conv3.filter, backbone.layer4.2.conv3.filter, backbone.layer2.3.conv2.filter, neck.lateral_convs.3.conv.expanded_bias, neck.fpn_convs.1.conv.expanded_bias, neck.fpn_convs.2.conv.expanded_bias, neck.fpn_convs.3.conv.filter, backbone.layer3.2.conv2.filter, backbone.layer4.0.conv1.filter, backbone.layer2.1.conv2.filter, backbone.layer3.3.conv2.filter, backbone.layer4.0.conv3.filter, backbone.layer2.0.conv3.filter, backbone.layer3.5.conv2.filter, backbone.layer2.0.downsample.0.filter, backbone.layer3.4.conv2.filter, backbone.layer4.0.downsample.0.filter, backbone.layer4.1.conv3.filter, backbone.layer2.0.conv1.filter, backbone.layer3.3.conv3.filter, backbone.layer2.2.conv1.filter, backbone.layer2.1.conv3.filter, backbone.layer3.0.conv2.filter, backbone.layer2.0.conv2.filter, backbone.conv1.filter, backbone.layer3.2.conv1.filter, neck.lateral_convs.1.conv.filter, neck.fpn_convs.3.conv.expanded_bias, backbone.layer4.0.conv2.filter, backbone.layer3.0.conv3.filter, backbone.layer2.2.conv2.filter, neck.lateral_convs.0.conv.expanded_bias, neck.fpn_convs.1.conv.filter, backbone.layer4.2.conv1.filter, backbone.layer3.1.conv1.filter, backbone.layer2.3.conv1.filter, neck.lateral_convs.2.conv.expanded_bias, neck.lateral_convs.3.conv.filter, neck.lateral_convs.0.conv.filter, backbone.layer4.1.conv2.filter, backbone.layer3.5.conv1.filter, backbone.layer2.2.conv3.filter, backbone.layer4.2.conv2.filter, backbone.layer2.3.conv3.filter, neck.lateral_convs.1.conv.expanded_bias, backbone.layer3.3.conv1.filter, neck.fpn_convs.0.conv.expanded_bias, backbone.layer3.1.conv3.filter, neck.lateral_convs.2.conv.filter, neck.fpn_convs.2.conv.filter, backbone.layer3.1.conv2.filter, backbone.layer4.1.conv1.filter, backbone.layer3.5.conv3.filter, backbone.layer2.1.conv1.filter, neck.fpn_convs.0.conv.filter, backbone.layer3.0.downsample.0.filter, backbone.layer3.0.conv1.filter, backbone.layer3.4.conv1.filter

completed: 0, elapsed: 0s
writing results to work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl

But I have put ReDet_re50_refpn_1x_dota15-7f2d6dda.pth in the correct position.
How can I solve it?
--------------------------------------------------------环境-------------------------------------------------------------
torch 1.3.1
torchvision 0.4.2
mmcv 0.2.13
mmdet 0.6.0+unknown /root/Documents/ReDet-master

and
Package Version Location

addict 2.4.0
certifi 2020.12.5
cffi 1.14.5
chardet 4.0.0
cycler 0.10.0
Cython 0.29.23
e2cnn 0.1.7
idna 2.10
kiwisolver 1.3.1
matplotlib 3.4.1
mkl-fft 1.3.0
mkl-random 1.2.1
mkl-service 2.3.0
mmcv 0.2.13
mmdet 0.6.0+unknown /root/Documents/ReDet-master
numpy 1.20.1
olefile 0.46
opencv-python 4.5.1.48
pandas 1.2.4
Pillow 8.2.0
pip 21.0.1
pycocotools 2.0.2
pycparser 2.20
pyparsing 3.0.0b2
python-dateutil 2.8.1
pytz 2021.1
PyYAML 5.4.1
requests 2.25.1
scipy 1.6.3
setuptools 52.0.0.post20210125
Shapely 1.8a1
six 1.15.0
terminaltables 3.1.0
torch 1.3.1
torchvision 0.4.2
tqdm 4.60.0
urllib3 1.26.4
wheel 0.36.2

hello~~~i want to ask why the size of the model I trained is about 257M, It's more than twice the size of the model on the website~

My training orders are: bash ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_dota15.py 4

您好，我在re_resnet.py文件中看到了101和152的backbone，请问~这两种情况可以提供预训练模型吗？精度可以达到多少呢？谢谢！！

学长你好，我想自定义一个数据集来跑这个模型，我把数据集的标注转成了coco格式如下所示。然后修改了config文件的数据集路径，但在计算loss时还是报错了。错误如下：
"images":[略],
"categories":[略],
"annotations":[
{
"area":3366.6824649275077,
"category_id":3,
"segmentation":[
[
356.7924715069118,
54.362633790420304,
337.6467271719448,
29.497233060353793,
252.64484276405284,
94.9465843308558,
271.79058709901983,
119.81198506092231
]
],
"iscrowd":0,
"bbox":[
304.7186571354823,
74.65460906063805,
31.3822828934849,
107.27971818858487
],
"image_id":18,
"id":1
}；

Traceback (most recent call last):
File "tools/train.py", line 95, in
main()
File "tools/train.py", line 91, in main
logger=logger)
File "/home/localchao/LHX/jyz/ReDet/mmdet/apis/train.py", line 61, in train_detector
_non_dist_train(model, dataset, cfg, validate=validate)
File "/home/localchao/LHX/jyz/ReDet/mmdet/apis/train.py", line 197, in _non_dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/mmcv-0.2.13-py3.7-linux-x86_64.egg/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/mmcv-0.2.13-py3.7-linux-x86_64.egg/mmcv/runner/runner.py", line 264, in train
self.model, data_batch, train_mode=True, **kwargs)
File "/home/localchao/LHX/jyz/ReDet/mmdet/apis/train.py", line 39, in batch_processor
losses = model(**data)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
return self.module(*inputs[0], **kwargs[0])
File "/home/localchao/anaconda3/envs/CG-Net/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/localchao/LHX/jyz/ReDet/mmdet/models/detectors/base_new.py", line 95, in forward
return self.forward_train(img, img_meta, **kwargs)
File "/home/localchao/LHX/jyz/ReDet/mmdet/models/detectors/ReDet.py", line 143, in forward_train
*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
File "/home/localchao/LHX/jyz/ReDet/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss
gt_bboxes_ignore=gt_bboxes_ignore)
File "/home/localchao/LHX/jyz/ReDet/mmdet/models/anchor_heads/anchor_head.py", line 177, in loss
sampling=self.sampling)
File "/home/localchao/LHX/jyz/ReDet/mmdet/core/anchor/anchor_target.py", line 63, in anchor_target
unmap_outputs=unmap_outputs)
File "/home/localchao/LHX/jyz/ReDet/mmdet/core/utils/misc.py", line 24, in multi_apply
return tuple(map(list, zip(*map_results)))
File "/home/localchao/LHX/jyz/ReDet/mmdet/core/anchor/anchor_target.py", line 108, in anchor_target_single
cfg.allowed_border)
File "/home/localchao/LHX/jyz/ReDet/mmdet/core/anchor/anchor_target.py", line 173, in anchor_inside_flags
(flat_anchors[:, 2] < img_w + allowed_border) &
RuntimeError: Expected object of scalar type Byte but got scalar type Bool for argument #2 'other' in call to _th_and

Why is there no map in the test results?

Looking forward for your reply ... thanks

您好：
请问cascade mask rcnn的旋转目标检测的代码是哪个。

另外想问一下配置文件中的h-obb代表什么意思。例如configs/DOTA1_5/faster_rcnn_h-obb_r50_fpn_1x_dota1_5.py中的h-obb是什么意思。

First of all, thank you very much for publicing code. I have some questions about the Rotated RoI Align module. The rroi is expressed as (x,y,w,h,angle), how is angle defined here, I can't understand the angle defined in the polygonToRotRectangle_batch function?

如果原始图片为1024*1024的话，还有必要运行prepare_dota1_5.py这个脚本吗，为什么我在运行之后，没有得到相应的json文件。

Dir sir!
Thanks for your great jobs!
I have ran your test code with the corresponding weights and the mAP is correct on dota1.0/task1 (mAP=80.03).
However, the test time is more than 11 hours with GPU 1080ti.
Do you have any suggestions?

How to use RedeT (Re50) as a backbone for other detection frameworks like DETR?

when I use the commend "python demo_large_image.py" DOTA dataset , I meet the follow error,how I can do to solve it. Thank you!
Traceback (most recent call last):
File "/home/zyt/PycharmProjects/ReDet-master/demo_large_image.py", line 149, in
roitransformer.inference_single_vis(img_path, out_path, (1024, 1024), (3072, 3072))
NameError: name 'roitransformer' is not defined
P2714__1__3513___1426.png

Hi @csuhan, thanks for open-sourcing the code
I needed bbox AP but unable to figure out how to achieve that. I saw tools/coco_eval.py is something related to that but still not able to get it correctly

Can you guide me on how to get bbox AP for the trainval_split data for DOTAv1 dataset (as I think testing bbox mAP is not achievable considering the test evaluation is done on DOTA evaluation server, that only provides class mAP)

Looking forward for your reply ... thanks

(pytorch) ll@amax-Server:/media/data1/liuliang/ReDet$ python tools/parse_results.py --config configs/ReDet/ReDet_re50_refpn_1x_dota15.py --type OBB
/home/ll/anaconda3/envs/pytorch/lib/python3.7/site-packages/mpl_toolkits/mplot3d/init.py:1: MatplotlibDeprecationWarning:
The deprecated function was deprecated in Matplotlib 3.4 and will be removed two minor releases later.
from .axes3d import Axes3D
ReResNet Orientation: 8 Fix Params: False
loading annotations into memory...
Done (t=0.08s)
creating index...
index created!
(pytorch) ll@amax-Server:/media/data1/liuliang/ReDet$

I am following the Install.md file as given, and I install the CudaToolKit along with PyTorch (same line of code):

conda install pytorch=1.3.1 torchvision cudatoolkit=10.0 -c pytorch -y

However, when I try compiling the bash script after cloning the repo, I get the error:

bash compile.sh

This gives:

OSError: CUDA_HOME environment variable is not set

I am in a Conda environment called Redet, and these steps pretty much reproduce the same error in all my machines. Is there anything wrong with the install steps?

I did try to set CUDA_HOME manually, but it would not work with the torch_cpp APIs. Any solution?

Thanks for all your great work.

Thanks for your work, csuhan.
I try to use your code on my server with 3090, which only support cuda 11. So I follow the instruction of https://github.com/csuhan/ReDet/issues/1 with pytorch 1.7.0 and cuda 11.0.
But when I run the bash compile.sh, I met a problem as follow:
/usr/local/cuda-11.0/bin/nvcc -I/home/wangqx/anaconda3/envs/redet_torch17_py38/lib/python3.8/site-packages/torch/include -I/home/wangqx/anaconda3/envs/redet_torch17_py38/lib/python3.8/site-packages/torch/include/torch/csrc/api/include -I/home/wangqx/anaconda3/envs/redet_torch17_py38/lib/python3.8/site-packages/torch/include/TH -I/home/wangqx/anaconda3/envs/redet_torch17_py38/lib/python3.8/site-packages/torch/include/THC -I/usr/local/cuda-11.0/include -I/home/wangqx/anaconda3/envs/redet_torch17_py38/include/python3.8 -c -c /home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/ops/roi_pool/src/roi_pool_kernel.cu -o /home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/ops/roi_pool/build/temp.linux-x86_64-3.8/src/roi_pool_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=roi_pool_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=sm_86 -std=c++14
nvcc fatal : Unsupported gpu architecture 'compute_86'

Then I use the command TORCH_CUDA_ARCH_LIST=7.0 bash compile.sh to solve this problem. But the code still can not work and raise this this error:

File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/apis/inference.py", line 66, in inference_detector
return _inference_single(model, imgs, img_transform, device)
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/apis/inference.py", line 93, in _inference_single
result = model(return_loss=False, rescale=True, **data)
File "/home/wangqx/anaconda3/envs/redet_torch17_py38/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/models/detectors/base_new.py", line 97, in forward
return self.forward_test(img, img_meta, **kwargs)
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/models/detectors/base_new.py", line 86, in forward_test
return self.simple_test(imgs[0], img_metas[0], **kwargs)
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/models/detectors/ReDet.py", line 239, in simple_test
proposal_list = self.simple_test_rpn(
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/models/detectors/test_mixins.py", line 12, in simple_test_rpn
proposal_list = self.rpn_head.get_bboxes(*proposal_inputs)
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/models/anchor_heads/anchor_head.py", line 216, in get_bboxes
proposals = self.get_bboxes_single(cls_score_list, bbox_pred_list,
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/models/anchor_heads/rpn_head.py", line 92, in get_bboxes_single
proposals, _ = nms(proposals, cfg.nms_thr)
File "/home/wangqx/airplane_detection_qx/ReDet_torch17_py38/mmdet/ops/nms/nms_wrapper.py", line 49, in nms
inds = nms_cuda.nms(dets_th, iou_thr)
RuntimeError: CUDA error: no kernel image is available for execution on the device

RuntimeError: Expected object of scalar type Byte but got scalar type Bool for argument #2

in: mmdet/core/anchor/anchor_target.py

def anchor_inside_flags(flat_anchors, valid_flags, img_shape,
                        allowed_border=0):
    img_h, img_w = img_shape[:2]
    allowed_border=-1
    if allowed_border >= 0:
        inside_flags = valid_flags & \
            (flat_anchors[:, 0] >= -allowed_border) & \
            (flat_anchors[:, 1] >= -allowed_border) & \
            (flat_anchors[:, 2] < img_w + allowed_border) & \
            (flat_anchors[:, 3] < img_h + allowed_border)
    else:
        inside_flags = valid_flags
    return inside_flags

when I use the commend "python tools/train.py configs/ReDet/ReDet_re50_refpn_1x_dota1py" train DOTA dataset I meet the follow situation,if that mean I have meet some error? how I can do to solve it. Thank you!

2021-03-30 22:59:04,147 - INFO - Distributed training: False
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
loading annotations into memory...
Done (t=0.32s)
creating index...
index created!
2021-03-30 22:59:44,364 - INFO - Start running, host: zyt@zyt-Z97-HD3, work_dir: /home/zyt/PycharmProjects/ReDet-master/tools/work_dirs/ReDet_re50_refpn_1x_dota1
2021-03-30 22:59:44,364 - INFO - workflow: [('train', 1)], max: 12 epochs
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
2021-03-30 23:00:27,707 - INFO - Epoch [1][50/1249] lr: 0.00399, eta: 3:35:14, time: 0.865, data_time: 0.086, memory: 3688, loss_rpn_cls: 0.4150, loss_rpn_bbox: 0.2032, s0.rbbox_loss_cls: 0.5068, s0.rbbox_acc: 93.5293, s0.rbbox_loss_bbox: 0.2453, s1.rbbox_loss_cls: 0.4625, s1.rbbox_acc: 94.3530, s1.rbbox_loss_bbox: 0.0571, loss: 1.8898
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.

when I run test.py,I meet this problem,how can I solve this problem?
Evaluating bbox...
Loading and preparing results...
DONE (t=1.43s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=7.60s).
Accumulating evaluation results...
DONE (t=1.90s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

thanks for your effort and sharing the code .. excuse me i have problems in setup the project in 2060 rtx with cuda 11.1 trying to do all solved issues but cannot run the train.py file

在运行 python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] 这条语句的时候，显示错误：ImportError: cannot import name 'deform_conv_cuda'，请问楼主该怎么解决呢，已经执行过pip install deform_conv_cuda和setup.py develop

loss都能正常下降，但是每个epoch训练后并没有产生checkpoint的.pth文件，log却记录的很清晰，能回答一下吗，谢谢。
我的命令行是这样的：CUDA_VISIBLE_DEVICES=2 python tools/train.py /work/plane_code/ReDet/configs/ReDet/ReDet_re50_refpn_1x_dota15_ms.py --work_dir /work/checkpoint/new__pkl --validate

Hi, csuhan.
when I tested the code, an error occurred.
Traceback (most recent call last):
File "tools/test.py", line 12, in
from mmdet.apis import init_dist
File "/root/code/remote-sensing-detection/ReDet-master/mmdet/apis/init.py", line 2, in
from .train import train_detector
File "/root/code/remote-sensing-detection/ReDet-master/mmdet/apis/train.py", line 14, in
from mmdet.models import RPN
File "/root/code/remote-sensing-detection/ReDet-master/mmdet/models/init.py", line 1, in
from .backbones import * # noqa: F401,F403
File "/root/code/remote-sensing-detection/ReDet-master/mmdet/models/backbones/init.py", line 2, in
from .re_resnet import ReResNet
File "/root/code/remote-sensing-detection/ReDet-master/mmdet/models/backbones/re_resnet.py", line 5, in
import e2cnn.nn as enn
File "/usr/local/anaconda3/envs/pytorch1.4+10.1/lib/python3.6/site-packages/e2cnn-0.1.7-py3.6.egg/e2cnn/init.py", line 17, in
from e2cnn import group
File "/usr/local/anaconda3/envs/pytorch1.4+10.1/lib/python3.6/site-packages/e2cnn-0.1.7-py3.6.egg/e2cnn/group/init.py", line 4, in
from .group import Group
File "/usr/local/anaconda3/envs/pytorch1.4+10.1/lib/python3.6/site-packages/e2cnn-0.1.7-py3.6.egg/e2cnn/group/group.py", line 2
from future import annotations
^
SyntaxError: future feature annotations is not defined
how can I solve this error?

Hi @csuhan, thanks for the code. I ran test.py on pretrained model weights but I am getting below output on dota1.
Also, I would like to mention that the test.py shows the below output and then starts running on testing images but not sure where I am making the mistake.

Can you help me with this ... Thanks!

I executed this command: python tools/test.py configs/ReDet/ReDet_re50_refpn_1x_dota1_ms.py models/pretrained_weights/dota1_ms_baseline_weights.pth --out work_dirs/pretrained_results/results.pkl

dota1_ms_baseline_weights.pth I downloaded from here

missing keys in source state_dict: backbone.layer4.0.conv1.filter, neck.lateral_convs.2.conv.filter, backbone.layer2.0.conv2.filter, backbone.layer3.4.conv1.filter, backbone.layer4.0.downsample.0.filter, backbone.layer2.0.downsample.0.filter, backbone.layer2.3.conv1.filter, neck.lateral_convs.3.conv.expanded_bias, backbone.layer2.1.conv2.filter, backbone.layer2.3.conv3.filter, backbone.layer3.2.conv2.filter, neck.lateral_convs.3.conv.filter, backbone.layer3.3.conv3.filter, neck.fpn_convs.3.conv.filter, neck.fpn_convs.2.conv.filter, backbone.layer2.1.conv1.filter, neck.fpn_convs.1.conv.filter, backbone.layer2.1.conv3.filter, neck.fpn_convs.3.conv.expanded_bias, neck.fpn_convs.2.conv.expanded_bias, backbone.conv1.filter, backbone.layer2.0.conv3.filter, backbone.layer4.0.conv2.filter, neck.lateral_convs.1.conv.expanded_bias, neck.fpn_convs.1.conv.expanded_bias, backbone.layer3.2.conv1.filter, backbone.layer3.1.conv1.filter, backbone.layer3.2.conv3.filter, backbone.layer4.2.conv3.filter, neck.lateral_convs.0.conv.filter, backbone.layer2.0.conv1.filter, backbone.layer4.2.conv2.filter, backbone.layer2.2.conv1.filter, backbone.layer3.5.conv3.filter, backbone.layer2.2.conv2.filter, neck.fpn_convs.0.conv.expanded_bias, backbone.layer3.0.downsample.0.filter, backbone.layer3.4.conv2.filter, backbone.layer4.1.conv2.filter, backbone.layer3.0.conv2.filter, backbone.layer2.3.conv2.filter, backbone.layer2.2.conv3.filter, backbone.layer3.5.conv1.filter, backbone.layer3.4.conv3.filter, backbone.layer3.5.conv2.filter, neck.fpn_convs.0.conv.filter, backbone.layer3.0.conv3.filter, neck.lateral_convs.1.conv.filter, backbone.layer3.3.conv2.filter, neck.lateral_convs.0.conv.expanded_bias, backbone.layer4.1.conv3.filter, backbone.layer3.0.conv1.filter, backbone.layer3.3.conv1.filter, neck.lateral_convs.2.conv.expanded_bias, backbone.layer3.1.conv3.filter, backbone.layer4.0.conv3.filter, backbone.layer4.1.conv1.filter, backbone.layer3.1.conv2.filter, backbone.layer4.2.conv1.filter

I run it using the command:

CUDA_VISIBLE_DEVICES=1,2,3,4 ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_dota1.py 4

I used 4 gpus, and batchsize is 8.
I found that the number of iterations in each epoch is different from your log file. Mine is 1695, but yours is 1550. Why? I did not make any changes to the DOTA dataset other than run "prepare_dota1.py". Looking forward to your reply.

你好,我使用sh文件中的多GPU测试的命令行进行测试,想要加快测试速度,毕竟一张卡的多尺度测试有点慢..

测试使用了两张显卡,测试过程没有任何问题. 但是当所有的图片测试完成之后(测试进度条走完), 其中一张卡就会不再运行程序(显存占用为0), 而另外一张卡的显存依旧在占用. 最终的results.pkl文件始终生成不出来, 程序就卡住.

这个过程反复测试了3次,每次都遇到这种情况.

当我采用一张卡进行测试, 运行test.py的时候都很正常, 可以正常的生成results.pkl文件

请问您是否遇到这种情况..

万分感谢!

当我使用这行代码训练的时候 ./tools/dist_train.sh /home/ReDet/configs/ReDet/ReDet_re50_refpn_1x_dota15.py 4
产生如下错误：RuntimeError: Expected object of scalar type Byte but got scalar type Bool for argument #2 'other' in call to _th_and
以及 subprocess.CalledProcessError: Command '['/home/anaconda3/envs/redet/bin/python', '-u', './tools/train.py', '--local_rank=3', '/home/ReDet/configs/ReDet/ReDet_re50_refpn_1x_dota15.py', '--launcher', 'pytorch']' returned non-zero exit status 1.
如果能得到耐心的解答，不胜感激

My dataset has only horizontal box and no mask information. Can I use ReDet ?
thanks

Hi,

I tried your code with the 30xx GPU series which only works for Cuda toolkit >=11.0 (using the mvcc library adapted with Cuda 11.0). Unfortunately, your code can't work with Cuda 11.0. I think the reason is from the "compile.sh" file. Can you have some guides to deal with the Cuda 11.0.

Thanks.

are there any refrence about reresnet?i need to import e2cnn.where i can touch the e2cnn module.i think the reresnet is interesting as a backbone for rotation object.i want to utilize it on single-stage framework.

我的权重与配置文件与GETTING_STARTED.md相同，而且源代码会将权重文件的路径自动设置在tools/configs下，我又拷贝出来一份放在了tools/configs下面，但是会出现模型尺寸不匹配的错误。

The model and loaded state dict do not match exactly

missing keys in source state_dict: backbone.layer4.2.conv3.filter, neck.lateral_convs.3.conv.filter, backbone.layer2.0.downsample.0.filter, backbone.layer3.0.conv2.filter, backbone.layer3.0.conv3.filter, neck.lateral_convs.2.conv.filter, backbone.layer4.1.conv3.filter, neck.lateral_convs.1.conv.filter, backbone.layer3.1.conv1.filter, neck.fpn_convs.0.conv.expanded_bias, backbone.layer2.3.conv2.filter, neck.fpn_convs.1.conv.expanded_bias, backbone.layer4.0.downsample.0.filter, backbone.layer3.4.conv3.filter, backbone.layer3.5.conv2.filter, neck.lateral_convs.0.conv.expanded_bias, backbone.layer2.1.conv3.filter, backbone.layer3.2.conv1.filter, backbone.layer3.5.conv1.filter, backbone.layer3.2.conv2.filter, neck.fpn_convs.2.conv.filter, backbone.layer4.1.conv1.filter, backbone.layer3.2.conv3.filter, backbone.layer4.2.conv2.filter, neck.lateral_convs.2.conv.expanded_bias, backbone.layer4.1.conv2.filter, neck.lateral_convs.0.conv.filter, backbone.layer3.3.conv2.filter, neck.lateral_convs.3.conv.expanded_bias, neck.fpn_convs.2.conv.expanded_bias, backbone.layer2.0.conv2.filter, neck.fpn_convs.1.conv.filter, backbone.layer4.0.conv1.filter, backbone.layer3.5.conv3.filter, backbone.layer3.0.conv1.filter, backbone.layer3.1.conv3.filter, backbone.layer2.0.conv3.filter, backbone.layer2.2.conv1.filter, backbone.layer4.0.conv2.filter, backbone.layer3.3.conv3.filter, backbone.layer3.3.conv1.filter, neck.fpn_convs.3.conv.filter, backbone.layer4.2.conv1.filter, backbone.layer2.2.conv2.filter, backbone.layer3.4.conv1.filter, neck.fpn_convs.3.conv.expanded_bias, backbone.layer4.0.conv3.filter, backbone.layer3.1.conv2.filter, backbone.layer2.3.conv3.filter, backbone.layer2.1.conv1.filter, neck.lateral_convs.1.conv.expanded_bias, backbone.layer2.1.conv2.filter, neck.fpn_convs.0.conv.filter, backbone.layer2.3.conv1.filter, backbone.layer3.0.downsample.0.filter, backbone.layer2.0.conv1.filter, backbone.layer2.2.conv3.filter, backbone.layer3.4.conv2.filter, backbone.conv1.filter

[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 6/6, 4.5 task/s, elapsed: 1s, ETA: 0s
writing results to /home/user/code/ReDet-master/work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl

I have followed the Install.md file as given. There is not any error in the Install procedure. But when I run 'CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_mydataset.py 4 --validate', I get the following error,

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

ReResNet Orientation: 8 Fix Params: False
ReResNet Orientation: 8 Fix Params: False
ReResNet Orientation: 8 Fix Params: False
ReResNet Orientation: 8 Fix Params: False
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
2021-07-07 20:19:27,048 - INFO - Distributed training: True
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/tmp/pip-req-build-4baxydiv/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
loading annotations into memory...
Done (t=0.06s)
creating index...
index created!
loading annotations into memory...
Done (t=0.06s)
creating index...
index created!
2021-07-07 20:20:58,958 - INFO - load model from: work_dirs/ReResNet_pretrain/re_resnet50_c8_batch256-25b16846.pth
2021-07-07 20:20:59,018 - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: head.fc.weight, head.fc.bias

missing keys in source state_dict: conv1.filter, layer1.0.conv1.filter, layer1.0.conv2.filter, layer1.0.conv3.filter, layer1.0.downsample.0.filter, layer1.1.conv1.filter, layer1.1.conv2.filter, layer1.1.conv3.filter, layer1.2.conv1.filter, layer1.2.conv2.filter, layer1.2.conv3.filter, layer2.0.conv1.filter, layer2.0.conv2.filter, layer2.0.conv3.filter, layer2.0.downsample.0.filter, layer2.1.conv1.filter, layer2.1.conv2.filter, layer2.1.conv3.filter, layer2.2.conv1.filter, layer2.2.conv2.filter, layer2.2.conv3.filter, layer2.3.conv1.filter, layer2.3.conv2.filter, layer2.3.conv3.filter, layer3.0.conv1.filter, layer3.0.conv2.filter, layer3.0.conv3.filter, layer3.0.downsample.0.filter, layer3.1.conv1.filter, layer3.1.conv2.filter, layer3.1.conv3.filter, layer3.2.conv1.filter, layer3.2.conv2.filter, layer3.2.conv3.filter, layer3.3.conv1.filter, layer3.3.conv2.filter, layer3.3.conv3.filter, layer3.4.conv1.filter, layer3.4.conv2.filter, layer3.4.conv3.filter, layer3.5.conv1.filter, layer3.5.conv2.filter, layer3.5.conv3.filter, layer4.0.conv1.filter, layer4.0.conv2.filter, layer4.0.conv3.filter, layer4.0.downsample.0.filter, layer4.1.conv1.filter, layer4.1.conv2.filter, layer4.1.conv3.filter, layer4.2.conv1.filter, layer4.2.conv2.filter, layer4.2.conv3.filter

loading annotations into memory...
loading annotations into memory...
Done (t=0.07s)
creating index...
index created!
Done (t=0.07s)
creating index...
index created!
Traceback (most recent call last):
File "./tools/train.py", line 95, in
main()
File "./tools/train.py", line 91, in main
logger=logger)
File "/home/ljtang/ReDet/mmdet/apis/train.py", line 59, in train_detector
Traceback (most recent call last):
File "./tools/train.py", line 95, in
_dist_train(model, dataset, cfg, validate=validate)
File "/home/ljtang/ReDet/mmdet/apis/train.py", line 144, in _dist_train
Traceback (most recent call last):
File "./tools/train.py", line 95, in
model = MMDistributedDataParallel(model.cuda())
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 300, in init
main()
File "./tools/train.py", line 91, in main
main()logger=logger)

File "/home/ljtang/ReDet/mmdet/apis/train.py", line 59, in train_detector
File "./tools/train.py", line 91, in main
self._ddp_init_helper()
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 318, in _ddp_init_helper
_dist_train(model, dataset, cfg, validate=validate)
logger=logger)
File "/home/ljtang/ReDet/mmdet/apis/train.py", line 144, in _dist_train
File "/home/ljtang/ReDet/mmdet/apis/train.py", line 59, in train_detector
self._module_copies = replicate(self.module, self.device_ids, detach=True)
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/replicate.py", line 96, in replicate
_dist_train(model, dataset, cfg, validate=validate)
File "/home/ljtang/ReDet/mmdet/apis/train.py", line 144, in _dist_train
model = MMDistributedDataParallel(model.cuda())
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 300, in init
param_copies = _broadcast_coalesced_reshape(params, devices, detach)
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/replicate.py", line 75, in _broadcast_coalesced_reshape
model = MMDistributedDataParallel(model.cuda())
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 300, in init
self._ddp_init_helper()
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 318, in _ddp_init_helper
return comm.broadcast_coalesced(tensors, devices)
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/cuda/comm.py", line 39, in broadcast_coalesced
self._ddp_init_helper()
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 318, in _ddp_init_helper
return torch._C._broadcast_coalesced(tensors, devices, buffer_size)
RuntimeError: all tensors must be on devices[0]
self._module_copies = replicate(self.module, self.device_ids, detach=True)
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/replicate.py", line 96, in replicate
self._module_copies = replicate(self.module, self.device_ids, detach=True)
param_copies = _broadcast_coalesced_reshape(params, devices, detach)
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/replicate.py", line 96, in replicate
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/replicate.py", line 75, in _broadcast_coalesced_reshape
return comm.broadcast_coalesced(tensors, devices)param_copies = _broadcast_coalesced_reshape(params, devices, detach)

File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/cuda/comm.py", line 39, in broadcast_coalesced
File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/nn/parallel/replicate.py", line 75, in _broadcast_coalesced_reshape
return torch._C._broadcast_coalesced(tensors, devices, buffer_size)
return comm.broadcast_coalesced(tensors, devices)
RuntimeError: File "/home/ljtang/miniconda3/envs/dbpp/lib/python3.7/site-packages/torch/cuda/comm.py", line 39, in broadcast_coalesced
all tensors must be on devices[0]
return torch._C._broadcast_coalesced(tensors, devices, buffer_size)
RuntimeError: all tensors must be on devices[0]

您好，打搅您了
请问处理hrsc2016数据集的results.pkl结果的代码是哪个呀?
Parse the results.pkl to the format needed for DOTA evaluation

CUDA_VISIBLE_DEVICES=1,2 ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_dota1.py 2

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

ReResNet Orientation: 8 Fix Params: False
ReResNet Orientation: 8 Fix Params: False
2021-05-19 21:40:15,266 - INFO - Distributed training: True
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
2021-05-19 21:40:42,380 - INFO - load model from: /home/neo/desktop/ReDet/tools/work_dirs/ReResNet_pretrain/re_resnet50_c8_batch256-12933bc2.pth
loading annotations into memory...
2021-05-19 21:40:42,542 - WARNING - The model and loaded state dict do not match exactly

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layer2.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer3.1.bn3.batch_norm_[8].running_mean, layer2.2.bn1.batch_norm_[8].weight, layer3.1.bn3.batch_norm_[8].weight, layer1.0.downsample.1.batch_norm_[8].weight, layer3.3.bn1.batch_norm_[8].weight, layer2.2.conv2.filter, layer3.0.bn3.batch_norm_[8].running_mean, layer4.0.bn2.batch_norm_[8].running_mean, layer3.3.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer1.0.conv3.filter, layer2.2.bn3.batch_norm_[8].running_mean, layer1.0.conv3.weights, layer3.1.bn1.batch_norm_[8].running_mean, layer4.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer2.1.bn3.batch_norm_[8].running_var, layer4.0.bn3.batch_norm_[8].running_mean, layer2.0.downsample.1.batch_norm_[8].running_var, layer3.1.bn2.indices_8, layer3.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer4.2.bn3.batch_norm_[8].running_mean, layer3.0.bn3.batch_norm_[8].bias, layer3.4.bn3.indices_8, layer3.2.conv3.weights, layer1.2.bn1.batch_norm_[8].weight, layer4.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer4.0.bn3.indices_8, layer1.0.bn1.indices_8, layer3.4.conv2.filter, layer3.3.bn2.indices_8, layer1.0.downsample.1.batch_norm_[8].running_var, layer3.0.bn2.batch_norm_[8].weight, layer3.5.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer3.5.bn1.batch_norm_[8].running_mean, layer4.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer3.1.bn3.indices_8, layer4.0.downsample.0.filter, layer1.1.bn1.batch_norm_[8].running_var, layer2.3.bn1.indices_8, layer4.2.conv3.filter, layer2.0.bn1.batch_norm_[8].running_mean, layer4.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer2.0.downsample.1.batch_norm_[8].weight, layer2.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer3.5.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer2.2.bn3.indices_8, layer1.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer2.3.bn3.indices_8, bn1.indices_8, layer2.3.bn3.batch_norm_[8].bias, layer2.3.bn3.batch_norm_[8].running_mean, layer3.0.bn1.batch_norm_[8].running_mean, layer4.2.bn2.batch_norm_[8].weight, layer1.0.conv1.weights, conv1.filter, layer3.1.bn3.batch_norm_[8].running_var, layer3.4.bn1.batch_norm_[8].running_mean, layer2.0.bn3.indices_8, layer2.1.conv1.filter, layer2.3.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer4.0.bn1.batch_norm_[8].weight, layer1.0.bn3.indices_8, layer2.0.downsample.0.filter, layer2.1.conv2.weights, layer3.0.bn1.batch_norm_[8].weight, layer3.3.bn2.batch_norm_[8].running_var, layer3.1.conv2.weights, layer4.2.bn3.batch_norm_[8].weight, layer2.0.conv2.weights, layer2.2.bn1.batch_norm_[8].running_mean, layer3.0.conv2.weights, layer3.2.bn3.batch_norm_[8].running_var, layer2.1.bn3.indices_8, layer2.2.bn3.batch_norm_[8].bias, layer2.3.conv2.filter, layer4.0.bn1.batch_norm_[8].running_var, layer2.3.bn1.batch_norm_[8].running_var, layer2.2.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer1.1.bn3.indices_8, layer3.3.bn3.batch_norm_[8].running_mean, layer2.2.bn2.indices_8, layer1.2.conv1.filter, layer1.0.bn2.batch_norm_[8].bias, layer2.0.conv3.weights, layer3.3.bn3.batch_norm_[8].weight, layer3.3.conv2.weights, layer4.0.bn1.indices_8, layer1.0.conv2.filter, layer2.3.bn3.batch_norm_[8].weight, layer3.5.bn2.indices_8, layer2.2.conv1.filter, layer3.5.bn3.batch_norm_[8].bias, layer3.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, layer4.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis

Done (t=1.70s)
creating index...
loading annotations into memory...
index created!
Done (t=1.77s)
creating index...
index created!
2021-05-19 21:48:10,559 - INFO - Start running, host: neo@neo, work_dir: /home/neo/desktop/ReDet/work_dirs/ReDet_re50_refpn_1x_dota1
2021-05-19 21:48:10,559 - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
File "./tools/train.py", line 95, in
main()
File "./tools/train.py", line 91, in main
logger=logger)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 59, in train_detector
_dist_train(model, dataset, cfg, validate=validate)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 171, in _dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 255, in train
self.model.train()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 727, in train
self._freeze_stages()
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 693, in _freeze_stages
m.eval()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1080, in eval
return self.train(False)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 386, in train
_filter, _bias = self.expand_parameters()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 303, in expand_parameters
_filter = self.basisexpansion(self.weights)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 334, in forward
_filter = self._expand_block(weights, io_pair).reshape(out_indices[2], in_indices[2], self.S)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 301, in _expand_block
_filter = block_expansion(coefficients)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py", line 99, in forward
return torch.einsum('boi...,kb->koi...', self.sampled_basis, weights) #.transpose(1, 2).contiguous()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/functional.py", line 201, in einsum
return torch._C._VariableFunctions.einsum(equation, operands)
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/THC/THCBlas.cu:331
Traceback (most recent call last):
File "./tools/train.py", line 95, in
main()
File "./tools/train.py", line 91, in main
logger=logger)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 59, in train_detector
_dist_train(model, dataset, cfg, validate=validate)
File "/home/neo/desktop/ReDet/mmdet/apis/train.py", line 171, in _dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 255, in train
self.model.train()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 727, in train
self._freeze_stages()
File "/home/neo/desktop/ReDet/mmdet/models/backbones/re_resnet.py", line 693, in _freeze_stages
m.eval()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1080, in eval
return self.train(False)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1064, in train
module.train(mode)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 386, in train
_filter, _bias = self.expand_parameters()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/r2convolution.py", line 303, in expand_parameters
_filter = self.basisexpansion(self.weights)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 334, in forward
_filter = self._expand_block(weights, io_pair).reshape(out_indices[2], in_indices[2], self.S)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_blocks.py", line 301, in _expand_block
_filter = block_expansion(coefficients)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/e2cnn-0.1.7-py3.7.egg/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py", line 99, in forward
return torch.einsum('boi...,kb->koi...', self.sampled_basis, weights) #.transpose(1, 2).contiguous()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/functional.py", line 201, in einsum
return torch._C._VariableFunctions.einsum(equation, operands)
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/THC/THCBlas.cu:331
Traceback (most recent call last):
File "/home/neo/anaconda3/envs/redet/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/distributed/launch.py", line 253, in
main()
File "/home/neo/anaconda3/envs/redet/lib/python3.7/site-packages/torch/distributed/launch.py", line 249, in main
cmd=cmd)
subprocess.CalledProcessError: Command '['/home/neo/anaconda3/envs/redet/bin/python', '-u', './tools/train.py', '--local_rank=1', 'configs/ReDet/ReDet_re50_refpn_1x_dota1.py', '--launcher', 'pytorch']' returned non-zero exit status 1.
`

Hi, csuhan! I run this algorithm with RTX3080*2,the env is as follows：

_libgcc_mutex 0.1 conda_forge https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
_openmp_mutex 4.5 1_gnu https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
addict 2.4.0 pypi_0 pypi
blas 1.0 mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
bzip2 1.0.8 h7f98852_4 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ca-certificates 2020.12.5 ha878542_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
certifi 2020.12.5 py37h89c1867_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
chardet 4.0.0 pypi_0 pypi
cudatoolkit 11.1.1 h6406543_8 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cycler 0.10.0 pypi_0 pypi
cython 0.29.23 py37hcd2ae1e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
e2cnn 0.1.7 pypi_0 pypi
ffmpeg 4.3 hf484d3e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
freetype 2.10.4 h0708190_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gmp 6.2.1 h58526e2_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gnutls 3.6.13 h85f3911_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
idna 2.10 pypi_0 pypi
intel-openmp 2021.2.0 h06a4308_610 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
jpeg 9b h024ee3a_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
kiwisolver 1.3.1 pypi_0 pypi
lame 3.100 h7f98852_1001 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lcms2 2.12 h3be6417_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
ld_impl_linux-64 2.35.1 hea4e1c9_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libffi 3.3 h58526e2_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgcc-ng 9.3.0 h2828fa1_19 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgomp 9.3.0 h2828fa1_19 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libiconv 1.16 h516909a_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libpng 1.6.37 h21135ba_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libstdcxx-ng 9.3.0 h6de172a_19 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libtiff 4.1.0 h2733197_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libuv 1.41.0 h7f98852_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lz4-c 1.9.3 h9c3ff4c_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
matplotlib 3.4.2 pypi_0 pypi
mkl 2021.2.0 h06a4308_296 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl-service 2.3.0 py37h27cfd23_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_fft 1.3.0 py37h42c9631_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_random 1.2.1 py37ha9443f7_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mmcv 0.2.13 pypi_0 pypi
mmdet 0.6.0+unknown dev_0
ncurses 6.2 h58526e2_4 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
nettle 3.6 he412f7d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ninja 1.10.2 h4bd325d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
numpy 1.20.1 py37h93e21f0_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
numpy-base 1.20.1 py37h7d8b39e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
olefile 0.46 pyh9f0ad1d_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
opencv-python 4.5.2.52 pypi_0 pypi
openh264 2.1.1 h780b84a_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openssl 1.1.1k h7f98852_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pillow 6.2.2 pypi_0 pypi
pip 21.1.1 pyhd8ed1ab_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pycocotools 2.0.2 pypi_0 pypi
pyparsing 2.4.7 pypi_0 pypi
python 3.7.10 hffdb5ce_100_cpython https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-dateutil 2.8.1 pypi_0 pypi
python_abi 3.7 1_cp37m https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytorch 1.8.0 py3.7_cuda11.1_cudnn8.0.5_0
pyyaml 5.4.1 pypi_0 pypi
readline 8.1 h46c0cb4_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
requests 2.25.1 pypi_0 pypi
scipy 1.6.3 pypi_0 pypi
setuptools 49.6.0 py37h89c1867_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
shapely 1.7.1 pypi_0 pypi
six 1.16.0 pyh6c4a22f_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
sqlite 3.35.5 h74cdb3f_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
terminaltables 3.1.0 pypi_0 pypi
tk 8.6.10 h21135ba_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
torchvision 0.9.0 py37_cu111
tqdm 4.60.0 pypi_0 pypi
typing_extensions 3.7.4.3 py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
urllib3 1.26.4 pypi_0 pypi
wheel 0.36.2 pyhd3deb0d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xz 5.2.5 h516909a_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zlib 1.2.11 h516909a_1010 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zstd 1.4.9 ha95c52a_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge

**### The questions are:
1.I used the pretrain pth,but got
"unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, ",
how can I fix this?
2.When I input"python tools/train.py /home/neo/desktop/ReDet-master/configs/ReDet/ReDet_re50_refpn_1x_dota1.py", all worked correctly!
But when I input"CUDA_VISIBLE_DEVICES=1,2 ./tools/dist_train.sh configs/ReDet/ReDet_re50_refpn_1x_dota1.py 2", I got "RuntimeError: cublas runtime error : the GPU program failed".

Any suggestions would be appreciative.**

我在训练模型时总是报有这样的错误，但是最后也可以训练出结果，可以拿去测试，请问这个是怎么回事呀？

2021-06-23 09:19:23,956 - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, backbone.bn1.batch_norm_[8].weight, backbone.bn1.batch_norm_[8].bias, backbone.bn1.batch_norm_[8].running_mean, backbone.bn1.batch_norm_[8].running_var, backbone.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv1.weights, backbone.layer1.0.conv1.filter, backbone.layer1.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn1.indices_8, backbone.layer1.0.bn1.batch_norm_[8].weight, backbone.layer1.0.bn1.batch_norm_[8].bias, backbone.layer1.0.bn1.batch_norm_[8].running_mean, backbone.layer1.0.bn1.batch_norm_[8].running_var, backbone.layer1.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv2.weights, backbone.layer1.0.conv2.filter, backbone.layer1.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn2.indices_8, backbone.layer1.0.bn2.batch_norm_[8].weight, backbone.layer1.0.bn2.batch_norm_[8].bias, backbone.layer1.0.bn2.batch_norm_[8].running_mean, backbone.layer1.0.bn2.batch_norm_[8].running_var, backbone.layer1.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv3.weights, backbone.layer1.0.conv3.filter, backbone.layer1.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn3.indices_8, backbone.layer1.0.bn3.batch_norm_[8].weight, backbone.layer1.0.bn3.batch_norm_[8].bias, backbone.layer1.0.bn3.batch_norm_[8].running_mean, backbone.layer1.0.bn3.batch_norm_[8].running_var, backbone.layer1.0.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.0.downsample.0.weights, backbone.layer1.0.downsample.0.filter, backbone.layer1.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.downsample.1.indices_8, backbone.layer1.0.downsample.1.batch_norm_[8].weight, backbone.layer1.0.downsample.1.batch_norm_[8].bias, backbone.layer1.0.downsample.1.batch_norm_[8].running_mean, backbone.layer1.0.downsample.1.batch_norm_[8].running_var, backbone.layer1.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv1.weights, backbone.layer1.1.conv1.filter, backbone.layer1.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn1.indices_8, backbone.layer1.1.bn1.batch_norm_[8].weight, backbone.layer1.1.bn1.batch_norm_[8].bias, backbone.layer1.1.bn1.batch_norm_[8].running_mean, backbone.layer1.1.bn1.batch_norm_

感谢您

are there any refrence about reresnet?i need to import e2cnn.where i can touch the e2cnn module.i think the reresnet is interesting as a backbone for rotation object.i want to utilize it on single-stage framework.

作者你好，非常感谢分享代码，在阅读代码过程中，我好像没有找到您的RiROI代码所在的位置，能够告知您RiROI代码所在py文件，非常感谢。

i tried to train datasets but got
Traceback (most recent call last): File "train.py", line 95, in <module> main() File "train.py", line 73, in main cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) File "/path/mmdetection/mmdet/models/builder.py", line 58, in build_detector cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) File "/usr/local/lib/python3.6/dist-packages/mmcv/utils/registry.py", line 210, in build return self.build_func(*args, **kwargs, registry=self) File "/usr/local/lib/python3.6/dist-packages/mmcv/cnn/builder.py", line 26, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/usr/local/lib/python3.6/dist-packages/mmcv/utils/registry.py", line 44, in build_from_cfg f'{obj_type} is not in the {registry.name} registry') KeyError: 'ReDet is not in the models registry'

Hello, I have now processed the DOTA dataset and obtained the .json files. But I got this error:

!python tools/test.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth --out work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl 

ReResNet Orientation: 8	Fix Params: False
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
Traceback (most recent call last):
  File "tools/test.py", line 208, in <module>
    main()
  File "tools/test.py", line 168, in main
    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
  File "/usr/local/lib/python3.7/site-packages/mmcv-0.2.13-py3.7-linux-x86_64.egg/mmcv/runner/checkpoint.py", line 172, in load_checkpoint
    checkpoint = torch.load(filename, map_location=map_location)
  File "/usr/local/lib/python3.7/site-packages/torch/serialization.py", line 426, in load
    return _load(f, map_location, pickle_module, **pickle_load_args)
  File "/usr/local/lib/python3.7/site-packages/torch/serialization.py", line 603, in _load
    magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, 'v'.

My data directory is in:

./ReDet/data
          ---dota15
                --train
                --val
                --test
          ---dota15_1024
                --test1024
                --trainval1024

when I use the commend "python tools/train.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py" train DOTA dataset I meet the follow error,how I can do to solve it. Thank you!

ReResNet Orientation: 8 Fix Params: False
2021-03-22 11:10:38,503 - INFO - Distributed training: False
/pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
/pytorch/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
2021-03-22 11:11:08,697 - INFO - load model from: work_dirs/ReResNet_pretrain/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth
2021-03-22 11:11:08,767 - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.weights, backbone.conv1.basisexpansion.block_expansion('irrep_0', 'regular').sampled_basis, backbone.bn1.indices_8, backbone.bn1.batch_norm_[8].weight, backbone.bn1.batch_norm_[8].bias, backbone.bn1.batch_norm_[8].running_mean, backbone.bn1.batch_norm_[8].running_var, backbone.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv1.weights, backbone.layer1.0.conv1.filter, backbone.layer1.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn1.indices_8, backbone.layer1.0.bn1.batch_norm_[8].weight, backbone.layer1.0.bn1.batch_norm_[8].bias, backbone.layer1.0.bn1.batch_norm_[8].running_mean, backbone.layer1.0.bn1.batch_norm_[8].running_var, backbone.layer1.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv2.weights, backbone.layer1.0.conv2.filter, backbone.layer1.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn2.indices_8, backbone.layer1.0.bn2.batch_norm_[8].weight, backbone.layer1.0.bn2.batch_norm_[8].bias, backbone.layer1.0.bn2.batch_norm_[8].running_mean, backbone.layer1.0.bn2.batch_norm_[8].running_var, backbone.layer1.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.0.conv3.weights, backbone.layer1.0.conv3.filter, backbone.layer1.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.bn3.indices_8, backbone.layer1.0.bn3.batch_norm_[8].weight, backbone.layer1.0.bn3.batch_norm_[8].bias, backbone.layer1.0.bn3.batch_norm_[8].running_mean, backbone.layer1.0.bn3.batch_norm_[8].running_var, backbone.layer1.0.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.0.downsample.0.weights, backbone.layer1.0.downsample.0.filter, backbone.layer1.0.downsample.0.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.0.downsample.1.indices_8, backbone.layer1.0.downsample.1.batch_norm_[8].weight, backbone.layer1.0.downsample.1.batch_norm_[8].bias, backbone.layer1.0.downsample.1.batch_norm_[8].running_mean, backbone.layer1.0.downsample.1.batch_norm_[8].running_var, backbone.layer1.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv1.weights, backbone.layer1.1.conv1.filter, backbone.layer1.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn1.indices_8, backbone.layer1.1.bn1.batch_norm_[8].weight, backbone.layer1.1.bn1.batch_norm_[8].bias, backbone.layer1.1.bn1.batch_norm_[8].running_mean, backbone.layer1.1.bn1.batch_norm_[8].running_var, backbone.layer1.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv2.weights, backbone.layer1.1.conv2.filter, backbone.layer1.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn2.indices_8, backbone.layer1.1.bn2.batch_norm_[8].weight, backbone.layer1.1.bn2.batch_norm_[8].bias, backbone.layer1.1.bn2.batch_norm_[8].running_mean, backbone.layer1.1.bn2.batch_norm_[8].running_var, backbone.layer1.1.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.1.conv3.weights, backbone.layer1.1.conv3.filter, backbone.layer1.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.1.bn3.indices_8, backbone.layer1.1.bn3.batch_norm_[8].weight, backbone.layer1.1.bn3.batch_norm_[8].bias, backbone.layer1.1.bn3.batch_norm_[8].running_mean, backbone.layer1.1.bn3.batch_norm_[8].running_var, backbone.layer1.1.bn3.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv1.weights, backbone.layer1.2.conv1.filter, backbone.layer1.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn1.indices_8, backbone.layer1.2.bn1.batch_norm_[8].weight, backbone.layer1.2.bn1.batch_norm_[8].bias, backbone.layer1.2.bn1.batch_norm_[8].running_mean, backbone.layer1.2.bn1.batch_norm_[8].running_var, backbone.layer1.2.bn1.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv2.weights, backbone.layer1.2.conv2.filter, backbone.layer1.2.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn2.indices_8, backbone.layer1.2.bn2.batch_norm_[8].weight, backbone.layer1.2.bn2.batch_norm_[8].bias, backbone.layer1.2.bn2.batch_norm_[8].running_mean, backbone.layer1.2.bn2.batch_norm_[8].running_var, backbone.layer1.2.bn2.batch_norm_[8].num_batches_tracked, backbone.layer1.2.conv3.weights, backbone.layer1.2.conv3.filter, backbone.layer1.2.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer1.2.bn3.indices_8, backbone.layer1.2.bn3.batch_norm_[8].weight, backbone.layer1.2.bn3.batch_norm_[8].bias, backbone.layer1.2.bn3.batch_norm_[8].running_mean, backbone.layer1.2.bn3.batch_norm_[8].running_var, backbone.layer1.2.bn3.batch_norm_[8].num_batches_tracked, backbone.layer2.0.conv1.weights, backbone.layer2.0.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.bn1.indices_8, backbone.layer2.0.bn1.batch_norm_[8].weight, backbone.layer2.0.bn1.batch_norm_[8].bias, backbone.layer2.0.bn1.batch_norm_[8].running_mean, backbone.layer2.0.bn1.batch_norm_[8].running_var, backbone.layer2.0.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.0.conv2.weights, backbone.layer2.0.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.bn2.indices_8, backbone.layer2.0.bn2.batch_norm_[8].weight, backbone.layer2.0.bn2.batch_norm_[8].bias, backbone.layer2.0.bn2.batch_norm_[8].running_mean, backbone.layer2.0.bn2.batch_norm_[8].running_var, backbone.layer2.0.bn2.batch_norm_[8].num_batches_tracked, backbone.layer2.0.conv3.weights, backbone.layer2.0.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.0.bn3.indices_8, backbone.layer2.0.bn3.batch_norm_[8].weight, 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backbone.layer2.1.bn1.batch_norm_[8].running_var, backbone.layer2.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.1.conv2.weights, backbone.layer2.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.1.bn2.indices_8, backbone.layer2.1.bn2.batch_norm_[8].weight, backbone.layer2.1.bn2.batch_norm_[8].bias, backbone.layer2.1.bn2.batch_norm_[8].running_mean, backbone.layer2.1.bn2.batch_norm_[8].running_var, backbone.layer2.1.bn2.batch_norm_[8].num_batches_tracked, backbone.layer2.1.conv3.weights, backbone.layer2.1.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.1.bn3.indices_8, backbone.layer2.1.bn3.batch_norm_[8].weight, backbone.layer2.1.bn3.batch_norm_[8].bias, backbone.layer2.1.bn3.batch_norm_[8].running_mean, backbone.layer2.1.bn3.batch_norm_[8].running_var, backbone.layer2.1.bn3.batch_norm_[8].num_batches_tracked, backbone.layer2.2.conv1.weights, backbone.layer2.2.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.2.bn1.indices_8, backbone.layer2.2.bn1.batch_norm_[8].weight, backbone.layer2.2.bn1.batch_norm_[8].bias, backbone.layer2.2.bn1.batch_norm_[8].running_mean, backbone.layer2.2.bn1.batch_norm_[8].running_var, backbone.layer2.2.bn1.batch_norm_[8].num_batches_tracked, backbone.layer2.2.conv2.weights, backbone.layer2.2.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.2.bn2.indices_8, backbone.layer2.2.bn2.batch_norm_[8].weight, backbone.layer2.2.bn2.batch_norm_[8].bias, backbone.layer2.2.bn2.batch_norm_[8].running_mean, backbone.layer2.2.bn2.batch_norm_[8].running_var, backbone.layer2.2.bn2.batch_norm_[8].num_batches_tracked, backbone.layer2.2.conv3.weights, backbone.layer2.2.conv3.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer2.2.bn3.indices_8, backbone.layer2.2.bn3.batch_norm_[8].weight, 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backbone.layer3.0.downsample.1.batch_norm_[8].running_mean, backbone.layer3.0.downsample.1.batch_norm_[8].running_var, backbone.layer3.0.downsample.1.batch_norm_[8].num_batches_tracked, backbone.layer3.1.conv1.weights, backbone.layer3.1.conv1.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer3.1.bn1.indices_8, backbone.layer3.1.bn1.batch_norm_[8].weight, backbone.layer3.1.bn1.batch_norm_[8].bias, backbone.layer3.1.bn1.batch_norm_[8].running_mean, backbone.layer3.1.bn1.batch_norm_[8].running_var, backbone.layer3.1.bn1.batch_norm_[8].num_batches_tracked, backbone.layer3.1.conv2.weights, backbone.layer3.1.conv2.basisexpansion.block_expansion('regular', 'regular').sampled_basis, backbone.layer3.1.bn2.indices_8, backbone.layer3.1.bn2.batch_norm_[8].weight, backbone.layer3.1.bn2.batch_norm_[8].bias, backbone.layer3.1.bn2.batch_norm_[8].running_mean, backbone.layer3.1.bn2.batch_norm_[8].running_var, backbone.layer3.1.bn2.batch_norm_[8].num_batches_tracked, 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loading annotations into memory...
Done (t=2.77s)
creating index...
index created!
2021-03-22 11:11:13,786 - INFO - Start running, host: why@why, work_dir: /home/why/DL/ReDet-master/work_dirs/ReDet_re50_refpn_1x_dota15
2021-03-22 11:11:13,787 - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
File "tools/train.py", line 95, in
main()
File "tools/train.py", line 91, in main
logger=logger)
File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 61, in train_detector
_non_dist_train(model, dataset, cfg, validate=validate)
File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 197, in _non_dist_train
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 358, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/mmcv/runner/runner.py", line 264, in train
self.model, data_batch, train_mode=True, **kwargs)
File "/home/why/DL/ReDet-master/mmdet/apis/train.py", line 39, in batch_processor
losses = model(**data)
File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
return self.module(*inputs[0], **kwargs[0])
File "/home/why/anaconda3/envs/redet/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/home/why/DL/ReDet-master/mmdet/models/detectors/base_new.py", line 95, in forward
return self.forward_train(img, img_meta, **kwargs)
File "/home/why/DL/ReDet-master/mmdet/models/detectors/ReDet.py", line 143, in forward_train
*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
File "/home/why/DL/ReDet-master/mmdet/models/anchor_heads/rpn_head.py", line 51, in loss
gt_bboxes_ignore=gt_bboxes_ignore)
File "/home/why/DL/ReDet-master/mmdet/models/anchor_heads/anchor_head.py", line 177, in loss
sampling=self.sampling)
File "/home/why/DL/ReDet-master/mmdet/core/anchor/anchor_target.py", line 63, in anchor_target
unmap_outputs=unmap_outputs)
File "/home/why/DL/ReDet-master/mmdet/core/utils/misc.py", line 24, in multi_apply
return tuple(map(list, zip(*map_results)))
File "/home/why/DL/ReDet-master/mmdet/core/anchor/anchor_target.py", line 108, in anchor_target_single
cfg.allowed_border)
File "/home/why/DL/ReDet-master/mmdet/core/anchor/anchor_target.py", line 173, in anchor_inside_flags
(flat_anchors[:, 2] < img_w + allowed_border) &
RuntimeError: Expected object of scalar type Byte but got scalar type Bool for argument #2 'other' in call to _th_and

用DOTAv1.5试了下你们的模型，总体感觉：你们的paper上没有系统的去评价模型的FPS，虽然总体的参数量下降了，但是训练速度太慢了，模型的推理速度也挺慢的。请问对于这样的模型，你们有什么提升速度上个的技巧？理论上这样的一个模块可以移植到EfficientNet，然后用来做Backbone?