VBench Color — GRiT dense captioning for color accuracy.

Detects the target object via GRiT and checks if the expected color keyword appears in the object's caption. Score = frames_with_correct_color / frames_with_object_detected.

`fastvideo.eval.metrics.vbench.color.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.color.metric.BaseMetric.to`

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

Move metric (and its internal models) to device.

`fastvideo.eval.metrics.vbench.color.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.color.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.color.metric.BaseMetric.reset`

reset() -> None

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

`fastvideo.eval.metrics.vbench.color.metric.BaseMetric.accumulate`

accumulate(sample: dict) -> None

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

`fastvideo.eval.metrics.vbench.color.metric.BaseMetric.finalize`

finalize() -> MetricResult

Compute the corpus-level result from buffered state.

`fastvideo.eval.metrics.vbench.color.metric.BaseMetric.merge_from`

merge_from(other: BaseMetric) -> None

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

`fastvideo.eval.metrics.vbench.color.metric.register`

register(name: str)

Decorator to register a metric class.

Usage::

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

`fastvideo.eval.metrics.vbench.color.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.color.metric.ColorMetric`

ColorMetric()

Source

@register("vbench.color")
class ColorMetric(BaseMetric):

    name = "vbench.color"
    requires_reference = False
    higher_is_better = True
    needs_gpu = True
    dependencies = ["detectron2"]

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

    def setup(self) -> None:
        if self._model is not None:
            return
        from fastvideo.eval.metrics.vbench._grit_helper import load_grit_model
        self._model = load_grit_model(self.device)

    @torch.no_grad()
    def compute(self, sample: dict) -> MetricResult:
        from fastvideo.eval.metrics.vbench._grit_helper import prepare_frames

        video = sample["video"]  # (T, C, H, W)
        aux = sample.get("auxiliary_info") or {}
        if "color" not in aux:
            return self._skip(sample, "missing 'color' in auxiliary_info")

        prompt = sample.get("text_prompt") or ""
        color_key = aux["color"]
        # Parse object name: remove "a ", "an ", and the color word
        object_key = prompt.replace("a ", "").replace("an ", "").replace(color_key, "").strip()

        frames_np = prepare_frames(video)

        preds = []
        for frame in frames_np:
            ret = self._model.run_caption_tensor(frame)
            cur_pred = []
            if len(ret[0]) < 1:
                cur_pred.append(["", ""])
            else:
                for cap_det in ret[0]:
                    cur_pred.append([cap_det[0], cap_det[2][0]])
            preds.append(cur_pred)

        # Score: matching VBench's check_generate logic
        cur_object = 0
        cur_object_color = 0
        for frame_pred in preds:
            object_flag = False
            color_flag = False
            for pred in frame_pred:
                if object_key == pred[1]:
                    for cq in _COLOR_KEYWORDS:
                        if cq in pred[0]:
                            object_flag = True
                    if color_key in pred[0]:
                        color_flag = True
            if color_flag:
                cur_object_color += 1
            if object_flag:
                cur_object += 1

        score = cur_object_color / cur_object if cur_object > 0 else 0.0
        return MetricResult(
            name=self.name,
            score=float(score),
            details={
                "object_detected": cur_object,
                "color_correct": cur_object_color
            },
        )

`fastvideo.eval.metrics.vbench.color.metric.ColorMetric.setup`

setup() -> None

Source

def setup(self) -> None:
    if self._model is not None:
        return
    from fastvideo.eval.metrics.vbench._grit_helper import load_grit_model
    self._model = load_grit_model(self.device)

`fastvideo.eval.metrics.vbench.color.metric.ColorMetric.compute`

compute(sample: dict) -> MetricResult

Source

@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    from fastvideo.eval.metrics.vbench._grit_helper import prepare_frames

    video = sample["video"]  # (T, C, H, W)
    aux = sample.get("auxiliary_info") or {}
    if "color" not in aux:
        return self._skip(sample, "missing 'color' in auxiliary_info")

    prompt = sample.get("text_prompt") or ""
    color_key = aux["color"]
    # Parse object name: remove "a ", "an ", and the color word
    object_key = prompt.replace("a ", "").replace("an ", "").replace(color_key, "").strip()

    frames_np = prepare_frames(video)

    preds = []
    for frame in frames_np:
        ret = self._model.run_caption_tensor(frame)
        cur_pred = []
        if len(ret[0]) < 1:
            cur_pred.append(["", ""])
        else:
            for cap_det in ret[0]:
                cur_pred.append([cap_det[0], cap_det[2][0]])
        preds.append(cur_pred)

    # Score: matching VBench's check_generate logic
    cur_object = 0
    cur_object_color = 0
    for frame_pred in preds:
        object_flag = False
        color_flag = False
        for pred in frame_pred:
            if object_key == pred[1]:
                for cq in _COLOR_KEYWORDS:
                    if cq in pred[0]:
                        object_flag = True
                if color_key in pred[0]:
                    color_flag = True
        if color_flag:
            cur_object_color += 1
        if object_flag:
            cur_object += 1

    score = cur_object_color / cur_object if cur_object > 0 else 0.0
    return MetricResult(
        name=self.name,
        score=float(score),
        details={
            "object_detected": cur_object,
            "color_correct": cur_object_color
        },
    )