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Compare optical flow extracted from a generated video against optical flow extracted from a ground-truth reference video.

Both flows are produced by the same ptlflow model (default dpflow/things). The resulting per-pixel / per-frame / temporal metric set is identical to synthetic_optical_flow — only the way the reference flow is constructed differs.

Classes

fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.GtOpticalFlowMetric 

GtOpticalFlowMetric(model_name: str = 'dpflow', ckpt: str = 'things', min_mag: float = 0.5, max_mag_pct: float = 80.0, grid_size: int = 8)

Bases: BaseMetric

Per-pixel / per-frame / temporal flow comparison vs. a reference video.

The headline score is pixel_epe_mean_mean (lower is better); every other scalar lives in details so downstream consumers can pick whichever one they care about.

Source code in fastvideo/eval/metrics/optical_flow/gt_optical_flow/metric.py 
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def __init__(
    self,
    model_name: str = "dpflow",
    ckpt: str = "things",
    min_mag: float = 0.5,
    max_mag_pct: float = 80.0,
    grid_size: int = 8,
) -> None:
    super().__init__()
    self.model_name = model_name
    self.ckpt = ckpt
    self.min_mag = min_mag
    self.max_mag_pct = max_mag_pct
    self.grid_size = grid_size
    self._model = None
    # One frame-pair per DPFlow forward: the cost volume is ~4 GB
    # per pair at 1080p, so batching OOMs. Bump for low-res inputs.
    self._chunk_size = 1

Functions