timswim
commented
3 years ago Hmm, found the upper limit of the resolution, it is 1000x 512. Lack of + -100MB. Excuse for troubling. P.S. I use 1080Ti with 11Gb too.
Closed timswim closed 3 years ago
timswim
commented
3 years ago Hmm, found the upper limit of the resolution, it is 1000x 512. Lack of + -100MB. Excuse for troubling. P.S. I use 1080Ti with 11Gb too.
Hi, thanks for your work. Running train.py gives error :
/media/data/ObjectDetectionExperiments/Projects/3_SemanticSegment/FDA-master/my_env/lib/python3.5/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='elementwise_mean' instead. warnings.warn(warning.format(ret)) Traceback (most recent call last): File "/media/data/ObjectDetectionExperiments/Projects/3_SemanticSegment/FDA-master/train.py", line 135, in
main()
File "/media/data/ObjectDetectionExperiments/Projects/3_SemanticSegment/FDA-master/train.py", line 94, in main
trg_seg_score = model(trg_img, lbl=trg_lbl, weight=class_weights, ita=args.ita) # forward pass
File "/media/data/ObjectDetectionExperiments/Projects/3_SemanticSegment/FDA-master/my_env/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/media/data/ObjectDetectionExperiments/Projects/3_SemanticSegment/FDA-master/model/deeplab.py", line 181, in forward
self.loss_seg = self.CrossEntropy2d(x, lbl, weight=weight)
File "/media/data/ObjectDetectionExperiments/Projects/3_SemanticSegment/FDA-master/model/deeplab.py", line 237, in CrossEntropy2d
predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
RuntimeError: CUDA error: out of memory
If i use in data/init.py image size = (512, 256), train is running without error and take ~5Gb GPU. Why it happen?