shaoxiongduan
412c95b1a0
Mirrors FastVideo's VideoGenerator → Worker layering, in-process:
Evaluator (user-facing dispatcher)
└── EvalWorker × N (single-GPU, owns metric replicas)
EvalWorker (new) holds metric replicas on one device and scores one
sample at a time. Evaluator builds num_gpus workers eagerly in __init__
(every metric loaded on every replica) and exposes a single evaluate()
method that handles both shapes:
ev.evaluate(video=..., ...) → one sample, runs on worker 0
ev.evaluate([s1, s2, ...]) → fan out across workers
Removed (dead code or moved to runner in a follow-up):
- Evaluator.calibrate, _compute_chunked, _evaluate_multi_gpu,
_gpu_metrics, evaluate_dataset, B>1 input path
- BaseMetric.batch_unit and trial_forward (kept _chunk_size as a plain
default class attr; metrics use it for internal time-dim chunking)
- memory.is_batch_too_large, slice_sample (clear_cache stays)
Per-metric batch_unit/trial_forward overrides remain as harmless
class-attr cruft pending a mechanical removal pass.
Net: evaluator.py 447 → 135 LOC; metrics/base.py 92 → 66; memory.py
40 → 14; +78 LOC for worker.py.
15 lines
284 B
Python
15 lines
284 B
Python
"""Small GPU-memory helper used by :class:`EvalWorker`."""
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from __future__ import annotations
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import gc
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import torch
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def clear_cache() -> None:
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"""Free GPU cache + run garbage collection."""
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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