[feat]: add SwanLab tracker

[huarzone]

Summary

Adds a SwanLab tracker alongside the existing Weights & Biases tracker, so training runs can log metrics to SwanLab.

Changes

• New SwanlabTracker mirroring WandbTracker; the run config is passed through _sanitize_wandb_config so non-serializable values (torch.dtype, Path, Enum, tensors, ...) don't break swanlab.init.
• Trackers enum gains SWANLAB = "swanlab".
• initialize_trackers wires up the swanlab branch.

Enable with --trackers swanlab (or trackers: ["swanlab"] in YAML); requires pip install swanlab. The existing none / wandb behavior is unchanged, and trackers can still be combined via SequentialTracker.

Testing

• python -m py_compile fastvideo/training/trackers.py passes.

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces support for SwanLab tracking by implementing the SwanlabTracker class and registering it in the trackers configuration. The reviewer pointed out that SwanlabTracker lacks a video method implementation, which prevents validation videos from being logged to SwanLab, and provided a code suggestion to resolve this.

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[gemini-code-assist]

The SwanlabTracker does not implement the video method. During validation, _log_validation in training_pipeline.py calls self.tracker.video(...) to log validation videos. Since BaseTracker.video returns None, validation videos will not be logged to SwanLab. Implementing the video method using swanlab.Video will enable validation video tracking.

    def finish(self) -> None:
        self._swanlab.finish()

    def video(
        self,
        data: Any,
        *,
        caption: str | None = None,
        fps: int | None = None,
        format: str | None = None,
    ) -> Any:
        kwargs: dict[str, Any] = {}
        if caption is not None:
            kwargs["caption"] = caption
        if fps is not None:
            kwargs["fps"] = fps
        return self._swanlab.Video(data, **kwargs)