Comments (2)
lec0dex
commented on May 18, 2024
1
It only works with single frames. About RAM issues, this version loads all the frames into GPU RAM and some Numpy manipulations also loads it entirely into RAM.
You may try my fork, which loads and process frame by frame. Two considerations to take in account is the multiprocessing with Ray, as it consumes around 2GiB of RAM per task-cpu-core on a 720p frame. The other consideration is that the second step will consume far more memory as I were unable to optimize flowNN process and my per-batch approach to remediate that is experimental.
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Cioscos
commented on May 18, 2024
It only works with single frames. About RAM issues, this version loads all the frames into GPU RAM and some Numpy manipulations also loads it entirely into RAM.
You may try my fork, which loads and process frame by frame. Two considerations to take in account is the multiprocessing with Ray, as it consumes around 2GiB of RAM per task-cpu-core on a 720p frame. The other consideration is that the second step will consume far more memory as I were unable to optimize flowNN process and my per-batch approach to remediate that is experimental.
Ok, thx I will try ur repo
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Related Issues (20)
- Can we use the cv2.remap to replace get_flowNN color propagation function? HOT 2
- ' --Nonlocal ' in video_completion.py results in error HOT 2
- Object removal task evaluation code HOT 3
- RuntimeError: Error keeps saying "to have 3 channels, but got 4 channels instead" HOT 1
- The implementation of the homography warp before optical flow calculation HOT 4
- Inpainting doesn't seem to clean up intermediary files HOT 2
- server for weights.zip and data.zip seems unreachable HOT 5
- 对于较少背景移动时，去除文字水印，会出现填充拖影问题 HOT 1
- How can I train a new model on a different dataset? HOT 4
- Same problem result of '--Nonlocal' (as issue #52) HOT 2
- Where can I read about all those parameters such as edge guide, mixed precisions and so on? HOT 1
- Code Error in non-local optical flow concatenation HOT 1
- RAFT Model HOT 1
- edge_guide flag HOT 1
- test question HOT 2
- Inference is not as accurate as the provided video samples HOT 4
- Evaluation Metrics HOT 2
- code question
- How to train the network？ HOT 1
- Please Large FOV colab
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