File size: 2,682 Bytes
32da3e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | """Evaluation dataset utilities for multi-dataset support."""
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
from torch.utils.data import Dataset
from data.unified_dataloader import prepare_unified_dataloader
logger = logging.getLogger(__name__)
@dataclass
class EvalDatasetInfo:
"""Container for eval dataset info."""
dataset: Dataset
reference_npz: Optional[Union[str, List[str]]] # Only required when 'fid' in metrics
condition_type: str
metrics: List[str] = field(default_factory=lambda: ['fid'])
num_samples: Optional[int] = None # cap eval at this many samples; None -> full val set
data_dir: Optional[str] = None # passed to fd_evaluator for MIND raw-image lookup
def normalize_eval_datasets(datasets_cfg):
"""
Normalize eval.datasets config to dict of {name: dataset_config}.
Supported format:
eval.datasets = {mscoco: {...}, mjhq: {...}}
Returns dict of {name: dataset_config}. If no datasets are configured, returns an empty dict.
"""
result = {}
for name, cfg in datasets_cfg.items():
result[name] = cfg.copy()
# set target to name if not explicitly provided (for simpleeval different versions)
if 'target' not in result[name]:
result[name]['target'] = name
return result
def prepare_eval_datasets(
eval_datasets_config: Dict[str, dict],
image_size: int,
batch_size: int,
num_workers: int,
rank: int,
world_size: int,
) -> Dict[str, EvalDatasetInfo]:
"""
Prepare eval datasets from normalized config.
Returns dict of {name: EvalDatasetInfo}.
"""
eval_datasets = {}
for ds_name, ds_cfg in eval_datasets_config.items():
ds_cond_type = ds_cfg.get('condition_type', 'text')
result = prepare_unified_dataloader(
config=ds_cfg,
image_size=image_size,
batch_size=batch_size,
num_workers=num_workers,
rank=rank,
world_size=world_size,
condition_type=ds_cond_type,
shuffle=False,
)
eval_datasets[ds_name] = EvalDatasetInfo(
dataset=result.loader.dataset,
reference_npz=ds_cfg.get('reference_npz'),
condition_type=ds_cond_type,
metrics=ds_cfg.get('metrics', ['fid']),
num_samples=ds_cfg.get('num_samples'),
data_dir=ds_cfg.get('data_dir'),
)
logger.info(f"Eval dataset loaded: {ds_name}, {len(result.loader.dataset)} samples")
return eval_datasets
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