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1ef5ba8 | 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 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 | """LightningDataModule for FairTalking-Bench."""
from __future__ import annotations
from typing import Any, Dict, Optional
import pytorch_lightning as pl
import torch
from torch.utils.data import ConcatDataset, DataLoader
from .fairtalking_dataset import FairTalkingBenchDataset, HDTFIdPairedDataset, TalkingHeadBenchFakeTestDataset
from .ffpp_dataset import FFPPTestDataset
from .hdtf_paired_dataset import HDTFPairedTestDataset
from .hdtf_paired_train_dataset import HDTFPairedTrainDataset
from .mmdf_dataset import MMDFTestDataset
from .transforms import build_video_transform
def _collate_drop_none(batch):
"""Custom collate that:
1. Drops `None` items (datasets return None for irretrievable samples).
2. Only collates a key if EVERY item in the surviving batch has it.
This matters for optional cross-generator companion fields
(`alt_video` / `alt_audio` / `meta.alt_generator`) which datasets
attach only to *some* samples (fakes that found a paired alt).
CTA's training_step already gates on `"alt_video" in batch`, so
dropping the key when even one sample lacks it is safe and matches
the existing contract.
Without this guard, batches that contain a mix of "with-alt" and
"without-alt" samples (e.g. when concatenating ours+HDTF train data)
raise `KeyError: 'alt_video'` inside torch.stack — exactly the failure
you saw at training step 0.
"""
batch = [b for b in batch if b is not None]
if not batch:
return None
# Use the intersection of keys across the whole batch, not just batch[0].
common_keys = set(batch[0].keys())
for b in batch[1:]:
common_keys &= set(b.keys())
out: Dict[str, Any] = {}
for k in common_keys:
if k == "meta":
out[k] = [b[k] for b in batch]
elif isinstance(batch[0][k], torch.Tensor):
out[k] = torch.stack([b[k] for b in batch], dim=0)
elif isinstance(batch[0][k], (int, float)):
out[k] = torch.tensor([b[k] for b in batch])
else:
out[k] = [b[k] for b in batch]
return out
class FairTalkingDataModule(pl.LightningDataModule):
def __init__(self, data_cfg, aug_cfg_train=None, aug_cfg_eval=None, return_paired: bool = False):
super().__init__()
self.cfg = data_cfg
self.train_transform = build_video_transform(aug_cfg_train or data_cfg.aug, training=True)
self.eval_transform = build_video_transform(aug_cfg_eval or data_cfg.aug, training=False)
self.return_paired = return_paired
# ------------------------------------------------------------------
def _make_main(self, csv_name: str, training: bool, expand_fakes: str = "random"):
return FairTalkingBenchDataset(
root=self.cfg.root,
split_csv=csv_name,
audio_cache_dir=self.cfg.audio_cache_dir,
generators=list(self.cfg.generators),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
video_transform=self.train_transform if training else self.eval_transform,
return_paired=self.return_paired and training,
expand_fakes=expand_fakes,
)
def _make_extra_hdtf_train(self):
"""Build the supplementary HDTF-paired training dataset.
Activated when `cfg.extra_train_hdtf_paired` is true. Returns a
Dataset that yields the SAME sample dict shape as
FairTalkingBenchDataset, so the two can be ConcatDataset-merged
into one training loader without changing collate logic.
"""
# Generators available under <root>/<gen>/. Defaults match the 7
# generators present in /apdcephfs_gy5/.../HDTF-paird (no SadTalk).
gens = getattr(self.cfg, "extra_train_hdtf_generators", None)
if gens is None:
gens = ("AniPortrait", "Ditto", "EDTalk", "Float", "Hallo", "Joyvasa", "Sonic")
return HDTFPairedTrainDataset(
root=self.cfg.extra_train_hdtf_root,
generators=list(gens),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
audio_cache_dir=getattr(self.cfg, "extra_train_hdtf_audio_cache_dir", None),
video_transform=self.train_transform,
return_paired=self.return_paired,
include_real=bool(getattr(self.cfg, "extra_train_hdtf_include_real", True)),
real_subdir=getattr(self.cfg, "extra_train_hdtf_real_subdir", "Real"),
)
def _make_thb_fake_test(self):
"""Create TalkingHeadBench test dataset (fake + optional real)."""
# THB uses a different set of generator directory names than FairTalking.
# Prefer cfg.thb_generators when provided; otherwise fall back to cfg.generators.
gens = getattr(self.cfg, "thb_generators", None) or self.cfg.generators
# Optional THB real-video config. When provided, real videos from the
# requested split+subsets are added (label=0), enabling test/auc.
real_subsets = getattr(self.cfg, "thb_real_subsets", None)
real_subsets = list(real_subsets) if real_subsets else None
return TalkingHeadBenchFakeTestDataset(
root=self.cfg.thb_root if hasattr(self.cfg, 'thb_root') else self.cfg.root,
audio_cache_dir=self.cfg.thb_audio_cache_dir if hasattr(self.cfg, 'thb_audio_cache_dir') else self.cfg.audio_cache_dir,
generators=list(gens),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
video_transform=self.eval_transform,
real_root=getattr(self.cfg, "thb_real_root", None),
real_split_json=getattr(self.cfg, "thb_real_split_json", None),
real_subsets=real_subsets,
real_split_name=getattr(self.cfg, "thb_real_split_name", "Test"),
real_audio_cache_dir=getattr(self.cfg, "thb_real_audio_cache_dir", None),
)
def _make_ffpp_test(self):
"""Create standalone FaceForensics++ test dataset."""
gens = getattr(self.cfg, "ffpp_generators", None) or self.cfg.generators
return FFPPTestDataset(
root=self.cfg.root,
generators=list(gens),
compression=getattr(self.cfg, "ffpp_compression", "c23"),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
include_real=bool(getattr(self.cfg, "ffpp_include_real", True)),
real_rel_dir=getattr(self.cfg, "ffpp_real_rel_dir", None),
real_root=getattr(self.cfg, "ffpp_real_root", None),
max_fake_per_generator=getattr(self.cfg, "ffpp_max_fake_per_generator", None),
max_real=getattr(self.cfg, "ffpp_max_real", None),
audio_cache_dir=getattr(self.cfg, "audio_cache_dir", None),
video_transform=self.eval_transform,
)
def _make_hdtf_paired_test(self):
"""Create standalone HDTF-paired test dataset."""
gens = getattr(self.cfg, "hdtf_paired_generators", None) or self.cfg.generators
root = getattr(self.cfg, "hdtf_paired_root", self.cfg.root)
return HDTFPairedTestDataset(
root=root,
generators=list(gens),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
include_real=bool(getattr(self.cfg, "hdtf_paired_include_real", True)),
real_dir=getattr(self.cfg, "hdtf_paired_real_dir", None),
audio_cache_dir=getattr(self.cfg, "hdtf_paired_audio_cache_dir", None),
video_transform=self.eval_transform,
)
def _make_mmdf_test(self):
"""Create standalone MMDF test dataset.
Reads:
<mmdf_root>/test/fake/<gen>/*.mp4 (label=1)
<mmdf_root>/test/real/<gen>/*.mp4 (label=0, paired by basename)
Real path is hard-coded relative to mmdf_root inside MMDFTestDataset
— yaml does not need to (and cannot) override it.
"""
gens = getattr(self.cfg, "mmdf_generators", None) or ("aniportrait", "hunyuan", "megactor-s")
return MMDFTestDataset(
root=getattr(self.cfg, "mmdf_root", self.cfg.root),
generators=list(gens),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
include_real=bool(getattr(self.cfg, "mmdf_include_real", True)),
audio_cache_dir=getattr(self.cfg, "mmdf_audio_cache_dir", None),
max_fake_per_generator=getattr(self.cfg, "mmdf_max_fake_per_generator", None),
max_real_per_generator=getattr(self.cfg, "mmdf_max_real_per_generator", None),
video_transform=self.eval_transform,
)
def _make_mmdf_test(self):
"""Create standalone MMDF test dataset.
Reads:
<mmdf_root>/test/fake/<gen>/*.mp4 (label=1)
<mmdf_root>/test/real/<gen>/*.mp4 (label=0, paired by basename)
Real path is hard-coded relative to mmdf_root inside MMDFTestDataset
— yaml does not need to (and cannot) override it.
"""
gens = getattr(self.cfg, "mmdf_generators", None) or ("aniportrait", "hunyuan", "megactor-s")
return MMDFTestDataset(
root=getattr(self.cfg, "mmdf_root", self.cfg.root),
generators=list(gens),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
include_real=bool(getattr(self.cfg, "mmdf_include_real", True)),
audio_cache_dir=getattr(self.cfg, "mmdf_audio_cache_dir", None),
max_fake_per_generator=getattr(self.cfg, "mmdf_max_fake_per_generator", None),
max_real_per_generator=getattr(self.cfg, "mmdf_max_real_per_generator", None),
video_transform=self.eval_transform,
)
def _is_test_only_cfg(self) -> bool:
"""Return True when this data config is a pure test set (no train/val)."""
return (
bool(getattr(self.cfg, "use_ffpp_test", False))
or bool(getattr(self.cfg, "use_hdtf_paired_test", False))
or bool(getattr(self.cfg, "use_mmdf_test", False))
or getattr(self.cfg, "train_csv", None) in (None, "null")
)
def setup(self, stage: Optional[str] = None):
if stage in (None, "fit") and not self._is_test_only_cfg():
base_train = self._make_main(self.cfg.train_csv, training=True)
# Optional supplementary training data merged via ConcatDataset.
# Only HDTF-paired is wired here today; add more branches as needed.
extras = []
if bool(getattr(self.cfg, "extra_train_hdtf_paired", False)):
extras.append(self._make_extra_hdtf_train())
if extras:
self.train_ds = ConcatDataset([base_train] + extras)
# Friendly summary on rank 0.
try:
sizes = ", ".join(
f"{type(d).__name__}={len(d)}" for d in [base_train] + extras
)
print(f"[datamodule] train ConcatDataset sizes: {sizes}; total={len(self.train_ds)}")
except Exception:
pass
else:
self.train_ds = base_train
# Val intentionally NOT touched — always uses the base val.csv.
self.val_ds = self._make_main(self.cfg.val_csv, training=False, expand_fakes="random")
if stage in (None, "test"):
# Standalone FF++ test set
if getattr(self.cfg, "use_ffpp_test", False):
self.test_ds = self._make_ffpp_test()
# Standalone HDTF-paired test set
elif getattr(self.cfg, "use_hdtf_paired_test", False):
self.test_ds = self._make_hdtf_paired_test()
# Standalone MMDF test set
elif getattr(self.cfg, "use_mmdf_test", False):
self.test_ds = self._make_mmdf_test()
# TalkingHeadBench fake test set
elif getattr(self.cfg, "use_thb_test", False):
self.test_ds = self._make_thb_fake_test()
else:
self.test_ds = self._make_main(self.cfg.test_csv, training=False, expand_fakes="all")
# ------------------------------------------------------------------
def _loader(self, ds, shuffle: bool):
nw = int(self.cfg.num_workers)
kwargs = dict(
batch_size=self.cfg.batch_size,
shuffle=shuffle,
num_workers=nw,
pin_memory=self.cfg.pin_memory,
collate_fn=_collate_drop_none,
drop_last=shuffle,
)
if nw > 0:
kwargs["persistent_workers"] = self.cfg.persistent_workers
kwargs["prefetch_factor"] = self.cfg.prefetch_factor
return DataLoader(ds, **kwargs)
def train_dataloader(self): return self._loader(self.train_ds, shuffle=True)
def val_dataloader(self): return self._loader(self.val_ds, shuffle=False)
def test_dataloader(self): return self._loader(self.test_ds, shuffle=False)
def thb_fake_test_loader(self):
"""Build a DataLoader specifically for TalkingHeadBench fake test set."""
ds = self._make_thb_fake_test()
return self._loader(ds, shuffle=False)
# ------------------------------------------------------------------
def hdtf_loader(self, subset: str, mode: str = "pair"):
"""Build a DataLoader for Protocol 4 (HDTF id-paired)."""
csv_map = {
"A": self.cfg.hdtf_subset_a_csv,
"B": self.cfg.hdtf_subset_b_csv,
"C": self.cfg.hdtf_subset_c_csv,
}
ds = HDTFIdPairedDataset(
root=self.cfg.root,
subset_csv=csv_map[subset],
audio_cache_dir=self.cfg.audio_cache_dir,
generators=list(self.cfg.generators),
num_frames=self.cfg.num_frames,
frame_stride=self.cfg.frame_stride,
frame_size=self.cfg.frame_size,
audio_seconds=self.cfg.audio_seconds,
audio_sample_rate=self.cfg.audio_sample_rate,
mode=mode,
video_transform=self.eval_transform,
)
return DataLoader(
ds,
batch_size=1 if mode == "tuple" else self.cfg.batch_size,
shuffle=False,
num_workers=int(self.cfg.num_workers),
pin_memory=self.cfg.pin_memory,
collate_fn=_collate_drop_none,
)
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