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.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric`

BaseMetric()

Abstract base class for all eval metrics.

Two execution shapes:

Per-sample (is_set_metric=False, default) — implement :meth:compute. The Evaluator calls it once per input sample and returns one :class:MetricResult per sample.
Set-vs-set (is_set_metric=True) — implement :meth:accumulate (called once per sample to buffer features) and :meth:finalize (called once after all samples to compute the corpus-level result). Use :meth:reset to clear buffers and :meth:merge_from to fold multi-GPU per-worker state together.

Optionally override :meth:setup to eagerly load models. Metrics that chunk along the time dim for memory hardcode their own chunk size in __init__ (see optical_flow for the canonical example). Eval always processes one video per :meth:Evaluator.evaluate call; compute / accumulate receive a single sample, not a batch.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.to`

to(device: str | torch.device) -> BaseMetric

Move metric (and its internal models) to device.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.setup`

setup() -> None

Eagerly load models. Called once by :class:EvalWorker.

Default is a no-op; metrics with no eager state (pixel math, closed-form ops) inherit this. Override only if your metric needs to load weights.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.compute`

compute(sample: dict) -> MetricResult

Per-sample metrics: compute the score for one sample.

sample["video"] is (T, C, H, W) float in [0, 1]. sample["reference"] (if used) has the same shape. Return self._skip(sample, reason) for missing inputs.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.reset`

reset() -> None

Clear accumulator state at the start of each evaluate() call.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.accumulate`

accumulate(sample: dict) -> None

Buffer per-sample features for a corpus-level metric.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.finalize`

finalize() -> MetricResult

Compute the corpus-level result from buffered state.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.BaseMetric.merge_from`

merge_from(other: BaseMetric) -> None

Multi-GPU: fold another worker's accumulator state into this one.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.aggregate_temporal`

aggregate_temporal(per_frame: list[dict[str, float]]) -> dict[str, float | int | None]

Aggregate per-frame metric dicts into mean/std/max/auc/onset summaries.

Port of mhuo's compute_temporal_metrics.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.compute_frame_metrics`

compute_frame_metrics(flow_gt: np.ndarray, flow_gen: np.ndarray, grid_size: int = 8, min_mag: float = 0.5, max_mag_pct: float = 80.0) -> dict[str, float]

Per-frame comparison metrics between two HxWx2 flow fields.

Port of mhuo's compute_frame_metrics — see ptlflow_validation.py.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.extract_video_flows`

extract_video_flows(model, video: torch.Tensor, *, chunk: int, device: torch.device) -> list[np.ndarray]

Run model on every consecutive frame pair in video.

Returns a list of HxWx2 flow arrays of length T - 1.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.load_ptlflow_model`

load_ptlflow_model(model_name: str, ckpt: str, device: torch.device)

Load a ptlflow model on device in eval mode.

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.register`

register(name: str)

Decorator to register a metric class.

Usage::

@register("ssim")
class SSIMMetric(BaseMetric):
    ...

`fastvideo.eval.metrics.optical_flow.gt_optical_flow.metric.MetricResult`

MetricResult(name: str, score: float | None, details: dict[str, Any] = dict())

Standard result container returned by all metrics.

score is None when the metric was skipped (e.g. missing required input). Check details["skipped"] for the reason.

`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)

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

@register("optical_flow.gt_optical_flow")
class GtOpticalFlowMetric(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.
    """

    name = "optical_flow.gt_optical_flow"
    requires_reference = True
    higher_is_better = False
    needs_gpu = True
    backbone = "optical_flow"
    dependencies = ["ptlflow"]

    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

    def to(self, device: str | torch.device) -> GtOpticalFlowMetric:
        super().to(device)
        if self._model is not None:
            self._model = self._model.to(self.device)
        return self

    def setup(self) -> None:
        if self._model is not None:
            return
        self._model = load_ptlflow_model(self.model_name, self.ckpt, self.device)

    def compute(self, sample: dict) -> MetricResult:
        if self._model is None:
            self.setup()

        gen_video = sample["video"].float()  # (T, C, H, W)
        ref_video = sample["reference"].float()
        n = min(gen_video.shape[0], ref_video.shape[0])
        gen_video, ref_video = gen_video[:n], ref_video[:n]
        if n < 2:
            raise ValueError("Need at least 2 frames to compute optical flow")

        chunk = self._chunk_size or 16
        gen_flows = extract_video_flows(
            self._model,
            gen_video,
            chunk=chunk,
            device=self.device,
        )
        ref_flows = extract_video_flows(
            self._model,
            ref_video,
            chunk=chunk,
            device=self.device,
        )
        per_frame = [
            compute_frame_metrics(
                rf,
                gf,
                grid_size=self.grid_size,
                min_mag=self.min_mag,
                max_mag_pct=self.max_mag_pct,
            ) for rf, gf in zip(ref_flows, gen_flows, strict=False)
        ]
        summary = aggregate_temporal(per_frame)
        score = summary.get("pixel_epe_mean_mean")
        details = dict(summary)
        details["per_frame_metrics"] = per_frame
        return MetricResult(
            name=self.name,
            score=float(score) if score is not None else None,
            details=details,
        )

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

to(device: str | torch.device) -> GtOpticalFlowMetric

Source

def to(self, device: str | torch.device) -> GtOpticalFlowMetric:
    super().to(device)
    if self._model is not None:
        self._model = self._model.to(self.device)
    return self

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

setup() -> None

Source

def setup(self) -> None:
    if self._model is not None:
        return
    self._model = load_ptlflow_model(self.model_name, self.ckpt, self.device)

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

compute(sample: dict) -> MetricResult

Source

def compute(self, sample: dict) -> MetricResult:
    if self._model is None:
        self.setup()

    gen_video = sample["video"].float()  # (T, C, H, W)
    ref_video = sample["reference"].float()
    n = min(gen_video.shape[0], ref_video.shape[0])
    gen_video, ref_video = gen_video[:n], ref_video[:n]
    if n < 2:
        raise ValueError("Need at least 2 frames to compute optical flow")

    chunk = self._chunk_size or 16
    gen_flows = extract_video_flows(
        self._model,
        gen_video,
        chunk=chunk,
        device=self.device,
    )
    ref_flows = extract_video_flows(
        self._model,
        ref_video,
        chunk=chunk,
        device=self.device,
    )
    per_frame = [
        compute_frame_metrics(
            rf,
            gf,
            grid_size=self.grid_size,
            min_mag=self.min_mag,
            max_mag_pct=self.max_mag_pct,
        ) for rf, gf in zip(ref_flows, gen_flows, strict=False)
    ]
    summary = aggregate_temporal(per_frame)
    score = summary.get("pixel_epe_mean_mean")
    details = dict(summary)
    details["per_frame_metrics"] = per_frame
    return MetricResult(
        name=self.name,
        score=float(score) if score is not None else None,
        details=details,
    )