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Hi, when l use the example python api to inference a video with 15mins, it consume large GPU memory and report torch.cuda.OutOfMemoryError. I find the script "load_video" extract 1 frame per second, which need 900 frames for 15min video and cost large memory.
So is there any suggested number of input frames for long videos? for example, one video 30 frames?
The text was updated successfully, but these errors were encountered:
sample some frames per second...
def load_video(video_file, skip=1):
from decord import VideoReader
vr = VideoReader(video_file)
# Get video frame rate
fps = vr.get_avg_fps()
# Calculate frame indices for 1fps
frame_indices = [int(fps * i) for i in range(int(len(vr) / fps))]
frames = vr.get_batch(frame_indices).asnumpy()
ls = []
if skip == 1 or skip is None:
ls = [Image.fromarray(frames[i]) for i in range(int(len(vr) / fps))]
elif skip > 1:
ls = [Image.fromarray(frames[i]) for i in range(int(len(vr) / fps))][::skip] # sample frame
elif skip < 0:
ls = [Image.fromarray(frames[i]) for i in range(int(len(vr) / fps))][:-skip] # skip<0 and get 0 to -skip frame
return ls
Hi, when l use the example python api to inference a video with 15mins, it consume large GPU memory and report torch.cuda.OutOfMemoryError. I find the script "load_video" extract 1 frame per second, which need 900 frames for 15min video and cost large memory.
So is there any suggested number of input frames for long videos? for example, one video 30 frames?
The text was updated successfully, but these errors were encountered: