Comments (4)
Hi Tim. Are you using a SSD drive?
from sparse-to-dense.pytorch.
Yes (and 16 GB RAM), however, a rather slow SSD.
iostat does not show high throughputs to SSD, my best guess is that most of the data is cached in RAM.
For a batch-size of 16, the average CPU time is 0.250 and GPU time is 0.16 during training.
from sparse-to-dense.pytorch.
The pre-loading of training data is already implemented in the code (which is quite a standard thing to do and I'll be surprised if any one is not doing this). The GPU utilization is rather high on my side, and the training should take roughly 6-9 hours, if I remember correctly.
If you don't have a fast SSD, you can try to reduce the amount of data augmentation in the code to speed things up.
from sparse-to-dense.pytorch.
Ok, thanks for the tips!
I'll close this for now :)
from sparse-to-dense.pytorch.
Related Issues (20)
- An issue with "resume" mode HOT 2
- [NYU] Different Scaling in Training and Validation HOT 4
- Implementing SLAM
- How is the loss calculated for KITTI dataset ?
- Is there an easy way to run inference on a different dataset HOT 2
- Apply the pretrained model to other datasets HOT 2
- No rgb image normalization during pre-process HOT 1
- Different sparse input when each sample input is loaded HOT 3
- The low download speed in NYU and KITTI
- License for repo
- pose information for processed data
- Benchmark on KITTI vs NYU Depth v2
- Request for pretrained model with depth-only modality
- Failed to reproduce the RGB based problem, whereas the RGBd problem works fine for me.
- How can I use this Git from Windows OS HOT 1
- Using another model
- Scaling factor cancels out for depth values
- The principle of implementing a simple Visual Odometry (VO) algorithm
- Output for custom image
- replace the method of "misc.imresize(img, self.size, self.interpolation, 'F')" HOT 2
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