yimiandai/open-deepinfrared

Hi there, I'm Yi-Mian Dai (戴一冕 in Chinese)! 👋

I am a PhD student at Nanjing University of Aeronautics & Astronautics (NUAA).

🔭 Reseach-wise, I mainly focus on:

• Small Object Detection
• Neural Architecture Design

😄 I am open to:

• collaboration opportunities (anytime & anywhere & any type)
• postdoc position, research internships, or visiting scholar

📫 How to reach me:

• Email: yimian [dot] dai [at] gmail.com
• WeChat (微信) (Anoymous requests are not welcome)

Publications:

• Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard. Attentional Local Contrast Networks for Infrared Small Target Detection[J]. Accepted by IEEE Transactions on Geoscience and Remote Sensing. (code)
• Yimian Dai, Yiquan Wu, Fei Zhou, Kobus Barnard. Asymmetric Contextual Modulation for Infrared Small Target Detection[C]. Accepted by IEEE Winter Conference on Applications of Computer Vision, WACV 2021. (dataset, code)
• Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard. Attentional Feature Fusion[C]. Accepted by IEEE Winter Conference on Applications of Computer Vision, WACV 2021. (code)
• Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard. Attention as Activation[C]. Accepted by 25th International Conference on Pattern Recognition (ICPR 2020), Oral. (code)
• Yimian Dai, Yiquan Wu. Reweighted Infrared Patch-Tensor Model with Both Nonlocal and Local Priors for Single-Frame Small Target Detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8), 3752-3767. (code)

Software:

• iNSFC: An awesome LaTeX template for NSFC proposal
• NUAAletter: A LaTeX Letter template for NUAA

Services:

• Journal Reviewer
  • IEEE Transactions on Geoscience and Remote Sensing (TGRS)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)
  • IEEE Geoscience and Remote Sensing Letters (GRSL)
  • Infrared Physics & Technology

Miscellaneous:

• PhD Thesis (in Chinese)

open-deepinfrared's Issues

NotImplementedError: Input Error: Only 3D, 4D and 5D input Tensors supported (got 4D) for the modes: nearest | linear | bilinear | bicubic | trilinear (got nearest-exact)

尊敬的作者您好，非常感谢您的科研贡献，我在复现您的代码的时候遇到了输入图像读取报错。
我已经正常安装模型、搭建环境、并将数据集放入指定位置；
具体报错如下：
python tools/test_det.py > configs/oscar/sota/oscar_w_noco_head_r18_caffe_fpn_p2_gn-head_1x_sirst_det2noco.py \

work_dirs/oscar_w_noco_head_r18_caffe_fpn_p2_gn-head_1x_sirst_det2noco.pth --eval "mNoCoAP"

/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmdet/utils/setup_env.py:38: UserWarning: 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.
warnings.warn(
/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmdet/utils/setup_env.py:48: UserWarning: Setting MKL_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.
warnings.warn(
libpng warning: iCCP: known incorrect sRGB profile
load checkpoint from local path: work_dirs/oscar_w_noco_head_r18_caffe_fpn_p2_gn-head_1x_sirst_det2noco.pth
[ ] 0/256, elapsed: 0s, ETA:/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/nn/functional.py:3631: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn(
/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1634272068694/work/aten/src/ATen/native/TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/nn/functional.py:3631: UserWarning: Default upsampling behavior when mode=nearest-exact is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
warnings.warn(
Traceback (most recent call last):
File "tools/test_det.py", line 274, in
main()
File "tools/test_det.py", line 240, in main
outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmdet/apis/test.py", line 29, in single_gpu_test
result = model(return_loss=False, rescale=True, **data)
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmcv/parallel/data_parallel.py", line 51, in forward
return super().forward(*inputs, **kwargs)
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
return self.module(*inputs[0], **kwargs[0])
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 116, in new_func
return old_func(*args, **kwargs)
File "/home/sunxue/open-deepinfrared-master/deepir/models/detectors/oscar.py", line 66, in forward
return self.forward_test(img, img_metas, **kwargs)
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmdet/models/detectors/base.py", line 147, in forward_test
return self.simple_test(imgs[0], img_metas[0], **kwargs)
File "/home/sunxue/open-deepinfrared-master/deepir/models/detectors/oscar.py", line 132, in simple_test
results_list = self.bbox_head.simple_test(
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 360, in simple_test
return self.simple_test_bboxes(feats, img_metas, rescale=rescale)
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmdet/models/dense_heads/dense_test_mixins.py", line 37, in simple_test_bboxes
results_list = self.get_bboxes(
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 205, in new_func
return old_func(*args, **kwargs)
File "/home/sunxue/open-deepinfrared-master/deepir/models/dense_heads/oscar/oscar_w_noco_head.py", line 985, in get_bboxes
anchors = [F.interpolate(decoded_coarse_bbox_preds,
File "/home/sunxue/anaconda3/envs/py3.8_new/lib/python3.8/site-packages/torch/nn/functional.py", line 3752, in interpolate
raise NotImplementedError(
NotImplementedError: Input Error: Only 3D, 4D and 5D input Tensors supported (got 4D) for the modes: nearest | linear | bilinear | bicubic | trilinear (got nearest-exact)

train on own dataset

Hello, what do I need to change if I want to train on my own dataset?