File size: 12,800 Bytes
16d3ebf | 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 | """Custom AutoModel implementation for a basic V-JEPA2 fMRI encoder."""
from __future__ import annotations
import os
from pathlib import Path
from typing import Any, Iterable
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
try:
from .configuration_vjepa2_fmri_encoder import VJEPA2FMRIEncoderConfig
except ImportError:
from configuration_vjepa2_fmri_encoder import VJEPA2FMRIEncoderConfig
class RidgeDecoder(nn.Module):
def __init__(self, state_dict: dict[str, torch.Tensor]) -> None:
super().__init__()
self.register_buffer("mean", state_dict["steps.1.mean"])
self.register_buffer("std", state_dict["steps.1.std"])
self.register_buffer("coef", state_dict["steps.2.regressor._coef"])
self.register_buffer("intercept", state_dict["steps.2.regressor._intercept"])
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.reshape(x.shape[0], -1)
x = (x - self.mean.to(device=x.device)) / self.std.to(device=x.device)
coef = self.coef.to(device=x.device)
x = x.to(dtype=coef.dtype)
return x @ coef.T + self.intercept.to(device=x.device)
class HookedFeatureExtractor:
def __init__(self, layer_names: Iterable[str], ret_type: str = "chw", spatial_pool: int = 14) -> None:
self.layer_names = list(layer_names)
self.ret_type = ret_type
self.spatial_pool = int(spatial_pool)
@staticmethod
def _get_layer(model: nn.Module, layer_name: str) -> nn.Module:
layer: object = model
for part in layer_name.split("."):
layer = layer[int(part)] if part.isdigit() else getattr(layer, part)
if not isinstance(layer, nn.Module):
raise TypeError(f"{layer_name} did not resolve to a torch module")
return layer
@staticmethod
def _unwrap_output(output: Any) -> torch.Tensor:
if isinstance(output, (list, tuple)):
if len(output) == 0:
raise ValueError("Received an empty feature tuple.")
output = output[0]
if not torch.is_tensor(output):
raise TypeError(f"Expected tensor feature output, got {type(output)!r}")
return output
def __call__(self, model: nn.Module, videos: torch.Tensor, **model_kwargs) -> list[torch.Tensor]:
outputs: dict[str, torch.Tensor] = {}
hooks = [
self._get_layer(model, name).register_forward_hook(
lambda _module, _inputs, output, name=name: outputs.__setitem__(name, self._unwrap_output(output))
)
for name in self.layer_names
]
try:
model(videos, **model_kwargs)
finally:
for hook in hooks:
hook.remove()
return [self._process_feature(outputs[name]) for name in self.layer_names]
def _process_feature(self, feature: torch.Tensor) -> torch.Tensor:
batch, tokens, channels = feature.shape
feature = feature.reshape(batch, -1, 14, 14, channels).permute(0, 1, 4, 2, 3)
if self.spatial_pool > 1:
batch, frames, channels, height, width = feature.shape
new_height = height // self.spatial_pool
new_width = width // self.spatial_pool
feature = feature.reshape(
batch,
frames,
channels,
new_height,
self.spatial_pool,
new_width,
self.spatial_pool,
)
feature = feature.permute(0, 1, 2, 3, 5, 4, 6).mean(dim=(-2, -1))
if self.ret_type == "chw":
return feature.mean(dim=1)
if self.ret_type == "tchw":
return feature
raise ValueError(f"Unsupported ret_type: {self.ret_type}")
class LocalVJEPA2Backbone(nn.Module):
def __init__(self, size: str, image_size: int, normalize_input: bool, checkpoint_path: str) -> None:
super().__init__()
self.image_size = int(image_size)
self.normalize_input = bool(normalize_input)
self.register_buffer("image_mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 1, 3, 1, 1))
self.register_buffer("image_std", torch.tensor([0.229, 0.224, 0.225]).view(1, 1, 3, 1, 1))
hub_name = {
"large": "vjepa2_vit_large",
"huge": "vjepa2_vit_huge",
"giant": "vjepa2_vit_giant",
}[size]
backbone = torch.hub.load("facebookresearch/vjepa2", hub_name, pretrained=False)
backbone, predictor = backbone if isinstance(backbone, (list, tuple)) else (backbone, None)
state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
backbone.load_state_dict(_clean_backbone_key(state_dict["target_encoder"]), strict=False)
if predictor is not None and "predictor" in state_dict:
predictor.load_state_dict(_clean_backbone_key(state_dict["predictor"]), strict=False)
self.backbone = backbone
def forward(self, videos: torch.Tensor, normalize: bool | None = None) -> torch.Tensor:
if videos.ndim != 5:
raise ValueError(f"Expected video tensor shaped [B, T, C, H, W], got {tuple(videos.shape)}")
if videos.shape[2] != 3:
raise ValueError(f"Expected RGB video with 3 channels at dim 2, got {videos.shape[2]}")
videos = videos.float()
batch, frames, channels, height, width = videos.shape
if height != self.image_size or width != self.image_size:
videos = videos.reshape(batch * frames, channels, height, width)
videos = F.interpolate(
videos,
size=(self.image_size, self.image_size),
mode="bilinear",
align_corners=False,
)
videos = videos.reshape(batch, frames, channels, self.image_size, self.image_size)
normalize = self.normalize_input if normalize is None else bool(normalize)
if normalize:
videos = (videos - self.image_mean.to(device=videos.device, dtype=videos.dtype)) / self.image_std.to(
device=videos.device,
dtype=videos.dtype,
)
return self.backbone(videos.permute(0, 2, 1, 3, 4))
def _clean_backbone_key(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
cleaned = {}
for key, value in state_dict.items():
key = key.replace("module.", "").replace("backbone.", "")
cleaned[key] = value
return cleaned
class VJEPA2FMRIEncoderModel(PreTrainedModel):
config_class = VJEPA2FMRIEncoderConfig
base_model_prefix = "vjepa2_fmri_encoder"
main_input_name = "videos"
def __init__(self, config: VJEPA2FMRIEncoderConfig) -> None:
super().__init__(config)
self.decoders = nn.ModuleList()
self.register_buffer("decoding_units", torch.empty(0, dtype=torch.long))
self.extractor: HookedFeatureExtractor | None = None
self.vjepa: LocalVJEPA2Backbone | None = None
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str | os.PathLike[str],
*model_args: Any,
config: VJEPA2FMRIEncoderConfig | None = None,
load_vjepa: bool | None = None,
vjepa_size: str | None = None,
normalize_input: bool | None = None,
**kwargs: Any,
) -> "VJEPA2FMRIEncoderModel":
if model_args:
raise TypeError("Unexpected positional arguments for VJEPA2FMRIEncoderModel.from_pretrained")
revision = kwargs.pop("revision", None)
token = kwargs.pop("token", None)
cache_dir = kwargs.pop("cache_dir", None)
local_files_only = kwargs.pop("local_files_only", False)
for ignored in ("trust_remote_code", "state_dict", "ignore_mismatched_sizes", "adapter_kwargs", "weights_only"):
kwargs.pop(ignored, None)
if kwargs:
raise TypeError(f"Unsupported keyword argument(s): {', '.join(sorted(kwargs))}")
if config is None:
config = VJEPA2FMRIEncoderConfig.from_pretrained(
pretrained_model_name_or_path,
revision=revision,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
)
checkpoint_path = cls._resolve_file_path(
pretrained_model_name_or_path,
filename=config.checkpoint_filename,
revision=revision,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
)
checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
model = cls(config)
model.decoders = nn.ModuleList([RidgeDecoder(state_dict) for state_dict in checkpoint["decoders_state_dict"]])
model.register_buffer("decoding_units", checkpoint["decoding_units"].long())
for name, value in checkpoint.get("registered_attrs", {}).items():
if torch.is_tensor(value):
model.register_buffer(name, value)
load_vjepa = config.load_vjepa if load_vjepa is None else bool(load_vjepa)
vjepa_size = config.vjepa_size if vjepa_size is None else vjepa_size
normalize_input = config.normalize_input if normalize_input is None else bool(normalize_input)
if load_vjepa:
backbone_path = cls._resolve_file_path(
pretrained_model_name_or_path,
filename=config.backbone_filename,
revision=revision,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
)
extractor_config = checkpoint["extractor_config"]
model.extractor = HookedFeatureExtractor(
layer_names=cls._resolve_layer_names(extractor_config),
ret_type=extractor_config.get("ret_type", "chw"),
spatial_pool=extractor_config.get("spatial_pool", 14),
)
model.vjepa = LocalVJEPA2Backbone(
size=vjepa_size,
image_size=config.image_size,
normalize_input=normalize_input,
checkpoint_path=backbone_path,
)
model.eval()
return model
@staticmethod
def _resolve_file_path(
pretrained_model_name_or_path: str | os.PathLike[str],
*,
filename: str,
revision: str | None,
token: str | bool | None,
cache_dir: str | os.PathLike[str] | None,
local_files_only: bool,
) -> str:
path = Path(pretrained_model_name_or_path)
if path.exists():
file_path = path / filename if path.is_dir() else path
if not file_path.exists():
raise FileNotFoundError(f"Missing file: {file_path}")
return str(file_path)
from huggingface_hub import hf_hub_download
return hf_hub_download(
repo_id=str(pretrained_model_name_or_path),
filename=filename,
repo_type="model",
revision=revision,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
)
@staticmethod
def _resolve_layer_names(extractor_config: dict[str, Any]) -> list[str]:
layer_names = extractor_config.get("layer_names")
if layer_names is None:
layer_names = extractor_config.get("loi")
if layer_names is None:
raise KeyError("extractor_config must contain `layer_names` or `loi`.")
return list(layer_names)
def forward_features(self, features: list[torch.Tensor]) -> torch.Tensor:
if len(features) != len(self.decoders):
raise ValueError(f"Expected {len(self.decoders)} feature tensors, got {len(features)}")
outputs = [decoder(feature) for decoder, feature in zip(self.decoders, features)]
output = torch.stack(outputs, dim=-1)
index = self.decoding_units.to(output.device).unsqueeze(0).unsqueeze(-1)
index = index.expand(output.shape[0], -1, -1)
return output.gather(dim=2, index=index).squeeze(-1)
def forward(self, videos: torch.Tensor, normalize: bool | None = None) -> torch.Tensor:
if self.vjepa is None or self.extractor is None:
raise RuntimeError("This model was loaded with load_vjepa=False.")
features = self.extractor(self.vjepa, videos, normalize=normalize)
return self.forward_features(features)
def predict_fmri(self, videos: torch.Tensor, normalize: bool | None = None) -> torch.Tensor:
"""Predict z-scored fMRI beta responses for videos shaped [B, T, C, H, W]."""
return self(videos, normalize=normalize)
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