Comments (4)
I have slove this problem ,and anthor problem about cuda complie setting.It run on my computer now. Thank you for sharing code!
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@Udonnoodle , Hi, I met a similar problem after I update the torch version to the newest one, the error is as follows: could you help me out?
My cuda is v7.5 and I use cudnnV7.0
Thanks a lot !
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I solved the problem by comment 'cudnn.benchmark=True', but I don't know why.
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cudnn.benchmark=True
on the doc, it says:
uses the inbuilt cudnn auto-tuner to find the fastest convolution algorithms.
If this is set to false, uses some in-built heuristics that might not always be fastest.
by default cudnn.benchmark is set to false. Setting to true will improve performance, at the expense of using more memory. The input shape should be the same for each batch, otherwise autotune will re-run for each batch, causing a huge slow-down.
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Related Issues (20)
- Segmentation fault (core dumped)
- when changed the trained network,it occured HOT 2
- things about net_mb_slow_-a_train_all.t7 HOT 6
- Invalid device function HOT 6
- Apply trained network to image of other resolutions HOT 3
- min() function with vol:cuda() HOT 1
- Read 0 blocks instead of 1 HOT 2
- Where is adcensus.StereoJoin_forward2? HOT 2
- UnicodeDecodeError: 'utf-8' codec can't decode byte 0x99 in position 22: invalid start byte HOT 1
- Training based on the pre-trained net using new data
- how to train with my own data
- What are the correct dimension and ndisp values for Middlebury datasets? HOT 1
- The 'Interpolation' of post-processing is only for kitti datasets but not for middlebury datasets
- Problem about the results compared with Middlebury displayed results
- Can not get same error rate which is referred on Kitti dataset HOT 1
- Predict on CPU
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- Different metrics to evaluate the dataset
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