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Allows MPS inference. Fix visualization args

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This commit is contained in:
JunkyByte
2023-07-25 16:28:48 +02:00
parent 03f3c41e07
commit d6df5d248f
2 changed files with 7 additions and 7 deletions
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2
cotracker/predictor.py
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@@ -17,7 +17,7 @@ from cotracker.models.build_cotracker import (
class CoTrackerPredictor(torch.nn.Module): class CoTrackerPredictor(torch.nn.Module):
def __init__( def __init__(
self, checkpoint="cotracker/checkpoints/cotracker_stride_4_wind_8.pth", device=None self, checkpoint="cotracker/checkpoints/cotracker_stride_4_wind_8.pth"
): ):
super().__init__() super().__init__()
self.interp_shape = (384, 512) self.interp_shape = (384, 512)

12
demo.py
View File

@@ -14,6 +14,9 @@ from PIL import Image
from cotracker.utils.visualizer import Visualizer, read_video_from_path from cotracker.utils.visualizer import Visualizer, read_video_from_path
from cotracker.predictor import CoTrackerPredictor from cotracker.predictor import CoTrackerPredictor
DEFAULT_DEVICE = ('cuda' if torch.cuda.is_available() else
'mps' if torch.backends.mps.is_available() else
'cpu')
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
@@ -55,11 +58,8 @@ if __name__ == "__main__":
segm_mask = torch.from_numpy(segm_mask)[None, None] segm_mask = torch.from_numpy(segm_mask)[None, None]
model = CoTrackerPredictor(checkpoint=args.checkpoint) model = CoTrackerPredictor(checkpoint=args.checkpoint)
if torch.cuda.is_available(): model = model.to(DEFAULT_DEVICE)
model = model.cuda() video = video.to(DEFAULT_DEVICE)
video = video.cuda()
else:
print("CUDA is not available!")
pred_tracks, pred_visibility = model( pred_tracks, pred_visibility = model(
video, video,
@@ -73,4 +73,4 @@ if __name__ == "__main__":
# save a video with predicted tracks # save a video with predicted tracks
seq_name = args.video_path.split("/")[-1] seq_name = args.video_path.split("/")[-1]
vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3) vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3)
vis.visualize(video, pred_tracks, query_frame=args.grid_query_frame) vis.visualize(video, pred_tracks, pred_visibility, query_frame=args.grid_query_frame)