Closed hello-eternity closed 1 year ago
I have the same problem. I spent a lot of time trying to find a solution. But unfortunately I did not find anything. Please help me if anyone has solved this problem?
Please add --if_extract
for first inference.
Please add
--if_extract
for first inference.
By applying --if_extract
this time I have two warnings that do not allow full execution.
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UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode)) /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:3385: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details. warnings.warn("Default grid_sample and affine_grid behavior has changed " ^C
Please add
--if_extract
for first inference.By applying
--if_extract
this time I have two warnings that do not allow full execution. ــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــUserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. "See the documentation of nn.Upsample for details.".format(mode)) /usr/local/lib/python3.7/site-packages/torch/nn/functional.py:3385: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details. warnings.warn("Default grid_sample and affine_grid behavior has changed " ^C
This happened because your RAM isn't enough, so it decided to terminate Either get more RAM or make your videos shorter, may be 2 seconds long and then test
I hope it works for you
For RAM saving, you can save the intermediate frames into .jpg
directly after generation instead of merging after the full video generation. Then you can merge the frames after the reenactment inferencing.
Your post helped me a lot. Thx!!!
The error log is like this when running the python inference.py --config configs/inference.yaml --video_source=./docs/demo/videos/RD_Radio34_003_512.mp4 --image_source=./docs/demo/images/100.jpg --cross_id --output_dir=./docs/demo/output