flyingshan/learning-polar-encodings-for-arbitrary-oriented-ship-detection-in-sar-images

Code for paper "Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images"

learning-polar-encodings-for-arbitrary-oriented-ship-detection-in-sar-images's Issues

请问7×7conv在哪里

你好，请问7×7卷积在哪里？代码中并未体现。感谢回复。

assertion error

Thanks for you code, when i train the model, this error appears:
in decoder.py in polar_decode assert num_targets >0
This happens in the evaluation process, can you give some suggestions?

about your model

Hi, thank you for your work.
Can you share these two files MODEL_TO_EVALUATE.pth and MODEL_TO_DRAW_RESULTS.pth?
Looking forward for your answer.

ModuleNotFoundError: No module named 'polyiou'

ModuleNotFoundError: No module named 'polyiou',Is there a file missing here?

ValueError: Points cannot contain NaN

您好，请问在运行大些的数据集(如HRSID,RSDD-SAR)时为什么总会出现这个问题：仿佛是模型不太稳定。
Traceback (most recent call last):
File "/home/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images-master/train.py", line 199, in run_epoch
loss = criterion(pr_decs, data_dict)
File "/root/miniconda3/envs/gagale/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images-master/loss.py", line 126, in forward
wh_loss, iou_loss = self.L_wh(pr_decs['wh'], gt_batch['reg_mask'], gt_batch['ind'], gt_batch['wh'])#wh_loss 和 iou_loss 分别代表宽度（width）和高度（height）的损失，由 IoUWeightedSmoothL1Loss 计算得出。
File "/root/miniconda3/envs/gagale/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images-master/polar.py", line 453, in forward
ious_all_lists = self._calculate_ious(output, mask, ind, target)
File "/home/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images-master/polar.py", line 429, in _calculate_ious
pred_bboxes = self._polar_to_bboxes(valid_pred) # [num_obj, 8]
File "/home/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images-master/polar.py", line 354, in _polar_to_bboxes
mbb = MinimumBoundingBox(target_pts)
File "/home/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images-master/MBB.py", line 109, in MinimumBoundingBox
hull_ordered = [points[index] for index in ConvexHull(points).vertices]
File "qhull.pyx", line 2431, in scipy.spatial.qhull.ConvexHull.init
File "qhull.pyx", line 283, in scipy.spatial.qhull._Qhull.init
ValueError: Points cannot contain NaN

TypeError: 'float' object is not iterable

I used the data you provided and run the code "python main.py --data_dir data/ssdd --num_epoch 120 --batch_size 8 --dataset ssdd --phase train --K 100" It seems that there are some data problems. And not all data could cause this.

Traceback (most recent call last):
File "main.py", line 59, in
ctrbox_obj.train_network(args)
File "/data/rocket/SGDD/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images/train.py", line 145, in train_network
criterion=criterion)
File "/data/rocket/SGDD/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images/train.py", line 190, in run_epoch
loss = criterion(pr_decs, data_dict)
File "/root/anaconda3/envs/mmdetection/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/data/rocket/SGDD/Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images/loss.py", line 126, in forward
wh_loss, iou_loss = self.L_wh(pr_decs['wh'], gt_batch['reg_mask'], gt_batch['ind'], gt_batch['wh'])
TypeError: 'float' object is not iterable

Thansk a lot

Could you share the annotation file of RSSDD dataset？

Could you share me your annotation file with c_x, c_y, w, h, α ？ My email :[email protected] Thanks!

error code

Thanks for you code, when i eval the model, this error appears:
Traceback (most recent call last):
File "main.py", line 65, in
ctrbox_obj.evaluation(args, down_ratio=down_ratio)
File "E:\Polar-master\eval.py", line 40, in evaluation

File "E:\Polar-master\func_utils.py", line 97, in write_results
pts0, scores0 = decode_prediction(predictions, dsets, args, img_id, down_ratio)
ValueError: too many values to unpack (expected 2)

This happens in the evaluation process too, can you give some suggestions?