File size: 8,921 Bytes
e51b27b | 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 | from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
from pet_vlm_dataset import PETSUVRDataset, collate_pet_suvr
from train_pet_foundation import PETSUVRFoundationModel, build_encoder
class PETTextDataset(PETSUVRDataset):
def __init__(self, text_csv: str | Path, output_size: tuple[int, int, int]) -> None:
super().__init__(text_csv, output_size=output_size)
def __getitem__(self, index: int) -> dict[str, object]:
item = super().__getitem__(index)
row = self.manifest.iloc[index]
item["text"] = str(row["region_text"])
item["low_regions"] = str(row["low_regions"])
item["high_regions"] = str(row["high_regions"])
return item
def collate_pet_text(batch: list[dict[str, object]]) -> dict[str, object]:
out = collate_pet_suvr(batch)
out["low_regions"] = [item["low_regions"] for item in batch]
out["high_regions"] = [item["high_regions"] for item in batch]
return out
class PETTextAlignmentModel(nn.Module):
def __init__(self, pet_model: PETSUVRFoundationModel, text_model_name: str, embed_dim: int) -> None:
super().__init__()
try:
from transformers import AutoModel
except ImportError as exc:
raise ImportError("PET-text alignment requires `transformers`. Install it before training Stage 2.") from exc
self.pet_model = pet_model
for p in self.pet_model.parameters():
p.requires_grad = False
self.pet_model.eval()
self.text_model = AutoModel.from_pretrained(text_model_name)
for p in self.text_model.parameters():
p.requires_grad = False
self.text_model.eval()
text_dim = int(getattr(self.text_model.config, "hidden_size"))
self.pet_head = nn.Sequential(nn.LayerNorm(embed_dim), nn.Linear(embed_dim, embed_dim))
self.text_head = nn.Sequential(nn.LayerNorm(text_dim), nn.Linear(text_dim, embed_dim))
self.temperature = nn.Parameter(torch.tensor(0.07))
def encode_pet(self, image: torch.Tensor) -> torch.Tensor:
with torch.no_grad():
pet_feat = self.pet_model.pet_encoder(image)
pet_z = torch.nn.functional.normalize(self.pet_model.pet_projector(pet_feat), dim=-1)
return torch.nn.functional.normalize(self.pet_head(pet_z), dim=-1)
def encode_text(self, tokens: dict[str, torch.Tensor]) -> torch.Tensor:
with torch.no_grad():
output = self.text_model(**tokens)
pooled = getattr(output, "pooler_output", None)
if pooled is None:
mask = tokens["attention_mask"].unsqueeze(-1).to(output.last_hidden_state.dtype)
pooled = (output.last_hidden_state * mask).sum(dim=1) / mask.sum(dim=1).clamp_min(1.0)
return torch.nn.functional.normalize(self.text_head(pooled), dim=-1)
def forward(self, image: torch.Tensor, tokens: dict[str, torch.Tensor]) -> torch.Tensor:
pet_z = self.encode_pet(image)
text_z = self.encode_text(tokens)
return pet_z @ text_z.T / self.temperature.clamp_min(0.01)
def load_pet_model(checkpoint: Path, args: argparse.Namespace, device: torch.device) -> PETSUVRFoundationModel:
ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False)
saved_args = ckpt.get("args", {})
for name in ("backbone", "embed_dim", "freeze_encoder"):
if getattr(args, name, None) is None and name in saved_args:
setattr(args, name, saved_args[name])
if args.output_size is None:
args.output_size = tuple(saved_args.get("output_size", (96, 96, 96)))
sample = PETSUVRDataset(args.train_csv, output_size=tuple(args.output_size))[0]
encoder = build_encoder(args)
model = PETSUVRFoundationModel(encoder, int(sample["suvr"].numel()), args.embed_dim or 256, bool(args.freeze_encoder))
model.load_state_dict(ckpt["model"], strict=True)
return model.to(device)
def run_epoch(
model: PETTextAlignmentModel,
loader: DataLoader,
tokenizer,
device: torch.device,
optimizer: torch.optim.Optimizer | None,
max_length: int,
) -> float:
train = optimizer is not None
model.train(train)
model.pet_model.eval()
model.text_model.eval()
total = 0.0
count = 0
for batch in loader:
image = batch["image"].to(device, non_blocking=True)
tokens = tokenizer(batch["text"], padding=True, truncation=True, max_length=max_length, return_tensors="pt")
tokens = {k: v.to(device) for k, v in tokens.items()}
logits = model(image, tokens)
labels = torch.arange(logits.shape[0], device=device)
loss = 0.5 * (
nn.functional.cross_entropy(logits, labels)
+ nn.functional.cross_entropy(logits.T, labels)
)
if train:
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
total += float(loss.detach()) * image.shape[0]
count += image.shape[0]
return total / max(count, 1)
def main() -> None:
parser = argparse.ArgumentParser(description="Train controlled PET-to-region-text contrastive alignment.")
parser.add_argument("--pet-checkpoint", type=Path, required=True)
parser.add_argument("--train-csv", type=Path, required=True)
parser.add_argument("--val-csv", type=Path, required=True)
parser.add_argument("--text-model", default="emilyalsentzer/Bio_ClinicalBERT")
parser.add_argument("--backbone", choices=["small_cnn", "medicalnet", "brainiac", "brainfm", "swinunetr", "sam_med3d"], default=None)
parser.add_argument("--medicalnet-weights", type=Path, default=Path("pretrained/medicalnet/resnet_50_23dataset.pth"))
parser.add_argument("--brainiac-weights", type=Path, default=Path("pretrained/brainiac/backbone.safetensors"))
parser.add_argument("--brainfm-weights", type=Path, default=Path("pretrained/brainfm/assets/brainfm_pretrained.pth"))
parser.add_argument("--brainfm-code-root", type=Path, default=Path("pretrained/brainfm"))
parser.add_argument("--swinunetr-weights", type=Path, default=Path("pretrained/swinunetr/model_swinvit.pt"))
parser.add_argument("--sam-med3d-weights", type=Path, default=Path("pretrained/sam-med3d/sam_med3d_turbo.pth"))
parser.add_argument("--output-size", type=int, nargs=3, default=None)
parser.add_argument("--embed-dim", type=int, default=None)
parser.add_argument("--freeze-encoder", action=argparse.BooleanOptionalAction, default=None)
parser.add_argument("--epochs", type=int, default=20)
parser.add_argument("--batch-size", type=int, default=8)
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--num-workers", type=int, default=2)
parser.add_argument("--max-length", type=int, default=96)
parser.add_argument("--out", type=Path, default=Path("runs/vlm/pet_text_alignment.pt"))
parser.add_argument("--best-out", type=Path, default=None)
args = parser.parse_args()
from transformers import AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pet_model = load_pet_model(args.pet_checkpoint, args, device)
tokenizer = AutoTokenizer.from_pretrained(args.text_model)
model = PETTextAlignmentModel(pet_model, args.text_model, args.embed_dim or 256).to(device)
train_set = PETTextDataset(args.train_csv, output_size=tuple(args.output_size))
val_set = PETTextDataset(args.val_csv, output_size=tuple(args.output_size))
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=collate_pet_text)
val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=collate_pet_text)
optimizer = torch.optim.AdamW([p for p in model.parameters() if p.requires_grad], lr=args.lr)
best = float("inf")
best_out = args.best_out or args.out.with_name(args.out.stem + "_best.pt")
args.out.parent.mkdir(parents=True, exist_ok=True)
for epoch in range(1, args.epochs + 1):
train_loss = run_epoch(model, train_loader, tokenizer, device, optimizer, args.max_length)
val_loss = run_epoch(model, val_loader, tokenizer, device, None, args.max_length)
print(f"epoch={epoch} train_loss={train_loss:.6f} val_loss={val_loss:.6f}", flush=True)
if val_loss < best:
best = val_loss
torch.save({"model": model.state_dict(), "args": vars(args), "best_val_loss": best, "epoch": epoch}, best_out)
print(f"saved_best {best_out} val_loss={best:.6f}", flush=True)
torch.save({"model": model.state_dict(), "args": vars(args), "best_val_loss": best}, args.out)
print(f"saved {args.out}", flush=True)
if __name__ == "__main__":
main()
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