metric

VBench Background Consistency — CLIP ViT-B/32 temporal feature similarity.

Measures background stability via cosine similarity of CLIP features between consecutive frames and the first frame.

fastvideo.eval.metrics.vbench.background_consistency.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.vbench.background_consistency.metric.BaseMetric.to

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

Move metric (and its internal models) to device.

fastvideo.eval.metrics.vbench.background_consistency.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.vbench.background_consistency.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.vbench.background_consistency.metric.BaseMetric.reset

reset() -> None

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

fastvideo.eval.metrics.vbench.background_consistency.metric.BaseMetric.accumulate

accumulate(sample: dict) -> None

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

fastvideo.eval.metrics.vbench.background_consistency.metric.BaseMetric.finalize

finalize() -> MetricResult

Compute the corpus-level result from buffered state.

fastvideo.eval.metrics.vbench.background_consistency.metric.BaseMetric.merge_from

merge_from(other: BaseMetric) -> None

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

fastvideo.eval.metrics.vbench.background_consistency.metric.register

register(name: str)

Decorator to register a metric class.

Usage::

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

fastvideo.eval.metrics.vbench.background_consistency.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.vbench.background_consistency.metric.consistency_score

consistency_score(features: torch.Tensor) -> float

VBench-style temporal consistency from (T, D) L2-normalized features.

For each frame t > 0, computes: sim = (cos(f[t], f[t-1]) + cos(f[t], f[0])) / 2, clamped >= 0

Returns the mean similarity across all t > 0.

fastvideo.eval.metrics.vbench.background_consistency.metric.BackgroundConsistencyMetric

BackgroundConsistencyMetric()

@register("vbench.background_consistency")
class BackgroundConsistencyMetric(BaseMetric):
name = "vbench.background_consistency"
requires_reference = False
higher_is_better = True
needs_gpu = True
dependencies = ["clip"]
backbone = "clip_vit_b32"

def __init__(self) -> None:
    super().__init__()
    self._model: Any = None

def to(self, device):
    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
    import clip
    from fastvideo.eval.models import get_cache_dir
    model, _ = clip.load(
        "ViT-B/32",
        device=self.device,
        download_root=str(get_cache_dir() / "clip"),
    )
    model.eval()
    self._model = model

@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    video = sample["video"]  # (T, C, H, W)
    frames = _clip_transform(video.to(self.device))

    chunk = self._chunk_size or 64
    feats = []
    for i in range(0, frames.shape[0], chunk):
        f = self._model.encode_image(frames[i:i + chunk]).float()
        f = F.normalize(f, dim=-1, p=2)
        feats.append(f)
    all_feats = torch.cat(feats, dim=0)  # (T, D)
    return MetricResult(
        name=self.name,
        score=consistency_score(all_feats),
        details={},
    )</code></pre></details>
fastvideo.eval.metrics.vbench.background_consistency.metric.BackgroundConsistencyMetric.to
to(device)
Source
def to(self, device):
    super().to(device)
    if self._model is not None:
        self._model = self._model.to(self.device)
    return self
fastvideo.eval.metrics.vbench.background_consistency.metric.BackgroundConsistencyMetric.setup
setup() -> None
Source
def setup(self) -> None:
    if self._model is not None:
        return
    import clip
    from fastvideo.eval.models import get_cache_dir
    model, _ = clip.load(
        "ViT-B/32",
        device=self.device,
        download_root=str(get_cache_dir() / "clip"),
    )
    model.eval()
    self._model = model
fastvideo.eval.metrics.vbench.background_consistency.metric.BackgroundConsistencyMetric.compute
compute(sample: dict) -> MetricResult
Source
@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    video = sample["video"]  # (T, C, H, W)
    frames = _clip_transform(video.to(self.device))
chunk = self._chunk_size or 64
feats = &#91;&#93;
for i in range(0, frames.shape&#91;0&#93;, chunk):
    f = self._model.encode_image(frames&#91;i:i + chunk&#93;).float()
    f = F.normalize(f, dim=-1, p=2)
    feats.append(f)
all_feats = torch.cat(feats, dim=0)  # (T, D)
return MetricResult(
    name=self.name,
    score=consistency_score(all_feats),
    details=&#123;&#125;,
)</code></pre></details>