UniMatch

The UniMatch implements a wrapper for the UniMatch model, which estimates optical flow in videos using a multi-scale transformer-based approach.

boolean

default:"True"

If True, inference call is run on the local VM, else offloaded onto GRID-Cortex. Defaults to False.

This model is currently not available via Cortex.

str

required

The link to the video or the path to the video/image directory.

str

default:"video"

The mode of input, either ‘video’ or ‘image’.

list

The optical flow maps for the input video or images.

from grid.model.perception.optical_flow.gmflow import UniMatch
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

video_input =  "https://huggingface.co/datasets/pranay-ar/test/resolve/main/all_ego.mp4"

model = UniMatch(use_local = False)
result = model.run(video_input, mode='video')

License
Source

This code is licensed under the Apache 2.0 License.

⌘I

from grid.model.perception.optical_flow.gmflow import UniMatch
car = AirGenCar()

# We will be capturing an image from the AirGen simulator 
# and run model inference on it.

video_input =  "https://huggingface.co/datasets/pranay-ar/test/resolve/main/all_ego.mp4"

model = UniMatch(use_local = False)
result = model.run(video_input, mode='video')

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