How can you use Flow-guided video completion (FGVC) for a personal file? Operation is not specified on the various official sources for those who would like to use the FGVC freely from the Google Colab platform (https://colab.research.google.com/drive/1pb6FjWdwq_q445rG2NP0dubw7LKNUkqc?usp=sharing). I, as a test, I uploaded a video to Google Drive (of the same account from which I was running Google Colab's scripts) divided into various frames, located in a .zip folder called "demo1.zip". I then ran the first script in the sequence, called "Prepare environment", I activated video sharing via public link and I copied the link in the second script (immediately after the first word "wget –quiet") and in the first entry "rm" I entered "demo1.zip", in relation to the name of my video file. I proceeded like this after reading the description just above the run button of the second script: "We show a demo on a 15-frames sequence. To process your own data, simply upload the sequence and specify the path." Running the second script as well, this is successful and my video file is loaded. I then go to the fourth (and last) script which consists in processing the content through an AI to obtain the final product with an enlarged Field Of View (FOV => larger aspect ratio). After a few seconds of running, the process ends with an error:
File "video_completion.py", line 613, in <module>
main (args)
File "video_completion.py", line 576, in main
video_completion_sphere (args)
File "video_completion.py", line 383, in video_completion_sphere
RAFT_model = initialize_RAFT (args)
File "video_completion.py", line 78, in initialize_RAFT
model.load_state_dict (torch.load (args.model))
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 594, in load
return _load (opened_zipfile, map_location, pickle_module, ** pickle_load_args)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 853, in _load
result = unpickler.load ()
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 845, in persistent_load
load_tensor (data_type, size, key, _maybe_decode_ascii (location))
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 834, in load_tensor
loaded_storages [key] = restore_location (storage, location)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 175, in default_restore_location
result = fn (storage, location)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 151, in _cuda_deserialize
device = validate_cuda_device (location)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 135, in validate_cuda_device
raise RuntimeError ('Attempting to deserialize object on a CUDA'
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available () is False. If you are running on a CPU-only machine, please use torch.load with map_location = torch.device ('cpu') to map your storages to the CPU.
What's wrong with execution? Is there a way to fix and allow to finish the process with Google Colab? Let me know!