Comments (1)
I've converted the coco_dataset into the voc style, the flag txt in ImageSets for each class and rewrite the label and label_1c for coco which generates labels' txt.
I think it's not easy like this
Actually, the data split for coco is also released in folder "data". Just change the dataset config and number of classes, everything should be good.
Though you give the
process_coco.py
, it dosen't work, and i think it misses the flag txt for each classAnd the batchsize setting:
batch=64 subdivisions=8will fill with memory and raise the
out of memory
error.Now I change the batch_size setting:
batch=8 subdivisions=8even though the smallest batch_size:
batch=4 subdivisions=4It can forward successfuly, but i met the same
out of memory
in backward:THCudaCheck FAIL file=/pytorch/torch/lib/THC/generic/THCStorage.cu line=58 error=2 : out of memory Traceback (most recent call last): File "train_meta.py", line 344, in <module> train(epoch) File "train_meta.py", line 242, in train loss.backward() File "/home/aringsan/anaconda2/envs/pytorch2/lib/python2.7/site-packages/torch/autograd/variable.py", line 167, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph, retain_variables) File "/home/aringsan/anaconda2/envs/pytorch2/lib/python2.7/site-packages/torch/autograd/__init__.py", line 99, in backward variables, grad_variables, retain_graph) RuntimeError: cuda runtime error (2) : out of memory at /pytorch/torch/lib/THC/generic/THCStorage.cu:58I want to know your coco_setting and your hardware, I use 4 Titan XP whose memory is 12G
Thanks
Hello, I also encounter the same wth you, have you solved it?
Thanks very much!
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Related Issues (20)
- 能否共享一下t-SNE的代码?并分享一下如何使用。感谢
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- ???
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