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87e9895 | 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 | """fMRI (frozen MinD-3D encoder features) -> TRELLIS.2 DINOv3 conditioning tokens."""
import os
import json
import math
import time
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
FEAT_DIR = "/home/hubin/data/fMRI-Shape/mapper_feats"
TOKEN_DIR = "/home/hubin/data/fMRI-Shape/trellis2_cond/tokens"
TOKEN_FRAMES = [0, 24, 48, 72, 96, 120, 144, 168]
N_TOKENS, TOKEN_DIM = 1029, 1024
N_FMRI_FRAMES, N_USED_FRAMES, FMRI_TOKENS = 10, 6, 257
TEST_FRAMES = [2, 3, 4, 5, 6, 7]
class Mapper(nn.Module):
def __init__(self, dim, depth, heads, dropout, mean_token):
super().__init__()
self.in_proj = nn.Sequential(nn.LayerNorm(1024), nn.Linear(1024, dim))
self.frame_emb = nn.Embedding(N_FMRI_FRAMES, dim)
self.pos_emb = nn.Parameter(torch.zeros(FMRI_TOKENS, dim))
self.queries = nn.Parameter(torch.randn(N_TOKENS, dim) * 0.02)
layer = nn.TransformerDecoderLayer(dim, heads, 4 * dim, dropout, activation="gelu",
batch_first=True, norm_first=True)
self.decoder = nn.TransformerDecoder(layer, depth)
self.out = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, TOKEN_DIM))
nn.init.zeros_(self.out[1].weight)
nn.init.zeros_(self.out[1].bias)
self.register_buffer("mean_token", mean_token)
def forward(self, feats, frame_idx, mem_drop=0.0):
# feats: [B, F, 257, 1024], frame_idx: [B, F]
b, f = frame_idx.shape
mem = self.in_proj(feats) + self.pos_emb + self.frame_emb(frame_idx)[:, :, None]
mem = mem.flatten(1, 2)
if self.training and mem_drop > 0:
keep = int(mem.shape[1] * (1 - mem_drop))
idx = torch.rand(b, mem.shape[1], device=mem.device).argsort(1)[:, :keep]
mem = torch.gather(mem, 1, idx[..., None].expand(-1, -1, mem.shape[-1]))
x = self.decoder(self.queries.expand(b, -1, -1), mem)
return F.layer_norm(self.out(x) + self.mean_token, (TOKEN_DIM,))
def load_tokens(ids, frame):
k = TOKEN_FRAMES.index(frame)
return np.stack([np.load(f"{TOKEN_DIR}/{i}.npy", mmap_mode="r")[k] for i in ids])
def pooled(x):
return F.normalize(x.float().mean(1), dim=-1)
@torch.no_grad()
def evaluate(model, feats, frames, targets, bs=16):
model.eval()
preds = []
for s in range(0, len(feats), bs):
fi = torch.tensor(frames, device="cuda").expand(min(bs, len(feats) - s), -1)
with torch.autocast("cuda", dtype=torch.bfloat16):
preds.append(model(feats[s:s + bs][:, frames].float(), fi).float())
model.train()
preds = torch.cat(preds)
return preds, token_metrics(preds, targets)
def token_metrics(preds, targets):
targets = targets.float()
cos = F.cosine_similarity(preds, targets, dim=-1).mean().item()
mse = F.mse_loss(preds, targets).item()
sim = pooled(preds) @ pooled(targets).T
rank = (sim > sim.diag()[:, None]).sum(1)
return {"cos": cos, "mse": mse, "top1": (rank < 1).float().mean().item(),
"top5": (rank < 5).float().mean().item(), "n": len(preds)}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--out_dir", default="/home/hubin/trellis_work/outputs/mapper_v0")
parser.add_argument("--sub_id", default="0001")
parser.add_argument("--target_frame", type=int, default=24)
parser.add_argument("--dim", type=int, default=768)
parser.add_argument("--depth", type=int, default=6)
parser.add_argument("--heads", type=int, default=12)
parser.add_argument("--dropout", type=float, default=0.1)
parser.add_argument("--mem_drop", type=float, default=0.2)
parser.add_argument("--feat_noise", type=float, default=0.1)
parser.add_argument("--contrastive", type=float, default=0.1)
parser.add_argument("--tau", type=float, default=0.05)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--wd", type=float, default=0.05)
parser.add_argument("--bs", type=int, default=32)
parser.add_argument("--steps", type=int, default=6000)
parser.add_argument("--n_val", type=int, default=100)
parser.add_argument("--eval_every", type=int, default=500)
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
os.makedirs(args.out_dir, exist_ok=True)
torch.manual_seed(args.seed)
rng = np.random.default_rng(args.seed)
train_ids = open(f"{FEAT_DIR}/train_ids.txt").read().split()
test_ids = open(f"{FEAT_DIR}/test_ids.txt").read().split()
train_feats = torch.from_numpy(np.load(f"{FEAT_DIR}/sub{args.sub_id}_train_feats.npy")).cuda()
test_feats = torch.from_numpy(np.load(f"{FEAT_DIR}/sub{args.sub_id}_test_feats.npy")).cuda()
train_tok = torch.from_numpy(load_tokens(train_ids, args.target_frame)).cuda()
test_tok = torch.from_numpy(load_tokens(test_ids, args.target_frame)).cuda()
perm = rng.permutation(len(train_ids))
val_idx = torch.from_numpy(np.sort(perm[:args.n_val])).cuda()
fit_idx = torch.from_numpy(np.sort(perm[args.n_val:])).cuda()
fit_cats = np.array([train_ids[i].split("/")[0] for i in fit_idx.tolist()])
mean_token = train_tok[fit_idx].float().mean(0)
ref = {}
for name, idx in [("val", val_idx), ("test", None)]:
tgt = train_tok[idx] if idx is not None else test_tok
ids = [train_ids[i] for i in idx.tolist()] if idx is not None else test_ids
mean_pred = F.layer_norm(mean_token, (TOKEN_DIM,)).expand(len(tgt), -1, -1)
cat_means = {c: train_tok[fit_idx[torch.from_numpy(np.where(fit_cats == c)[0]).cuda()]].float().mean(0)
for c in set(fit_cats)}
cat_pred = torch.stack([F.layer_norm(cat_means[i.split("/")[0]], (TOKEN_DIM,)) for i in ids])
ref[name] = {"train_mean": token_metrics(mean_pred, tgt),
"category_mean_oracle": token_metrics(cat_pred, tgt)}
print(json.dumps(ref, indent=1), flush=True)
model = Mapper(args.dim, args.depth, args.heads, args.dropout, mean_token).cuda()
print(f"params {sum(p.numel() for p in model.parameters()) / 1e6:.1f}M", flush=True)
opt = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wd, betas=(0.9, 0.98))
warmup = 300
sched = torch.optim.lr_scheduler.LambdaLR(
opt, lambda s: min(1, (s + 1) / warmup) * 0.5 * (1 + math.cos(math.pi * min(s, args.steps) / args.steps)))
log, best = [], (-1, None)
t0 = time.time()
for step in range(1, args.steps + 1):
bidx = fit_idx[torch.randint(len(fit_idx), (args.bs,), device="cuda")]
frames = torch.rand(args.bs, N_FMRI_FRAMES, device="cuda").argsort(1)[:, :N_USED_FRAMES].sort(1).values
feats = torch.gather(train_feats[bidx], 1, frames[:, :, None, None].expand(-1, -1, FMRI_TOKENS, 1024)).float()
feats = feats + args.feat_noise * torch.randn_like(feats)
tgt = train_tok[bidx].float()
with torch.autocast("cuda", dtype=torch.bfloat16):
pred = model(feats, frames, args.mem_drop)
pred = pred.float()
loss_tok = F.mse_loss(pred, tgt)
logits = pooled(pred) @ pooled(tgt).T / args.tau
labels = torch.arange(args.bs, device="cuda")
loss_con = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.T, labels)) / 2
loss = loss_tok + args.contrastive * loss_con
opt.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step()
sched.step()
if step % 100 == 0:
print(f"step {step} loss_tok {loss_tok.item():.4f} loss_con {loss_con.item():.3f} "
f"lr {sched.get_last_lr()[0]:.2e} {time.time() - t0:.0f}s", flush=True)
if step % args.eval_every == 0 or step == args.steps:
_, val_m = evaluate(model, train_feats[val_idx], TEST_FRAMES, train_tok[val_idx])
log.append({"step": step, "val": val_m})
print(f" [val] {val_m}", flush=True)
if val_m["cos"] > best[0]:
best = (val_m["cos"], step)
torch.save(model.state_dict(), f"{args.out_dir}/best.pt")
model.load_state_dict(torch.load(f"{args.out_dir}/best.pt"))
test_pred, test_m = evaluate(model, test_feats, TEST_FRAMES, test_tok)
train_pool = pooled(train_tok[fit_idx])
nn_idx = (pooled(test_pred) @ train_pool.T).argmax(1)
nn_ids = [train_ids[fit_idx[i]] for i in nn_idx.tolist()]
nn_cat_acc = float(np.mean([a.split("/")[0] == b.split("/")[0] for a, b in zip(nn_ids, test_ids)]))
np.save(f"{args.out_dir}/test_pred_tokens.npy", test_pred.half().cpu().numpy())
result = {"args": vars(args), "best_step": best[1], "reference": ref, "test": test_m,
"test_nn_category_acc": nn_cat_acc, "test_nn_ids": dict(zip(test_ids, nn_ids)), "log": log}
with open(f"{args.out_dir}/result.json", "w") as f:
json.dump(result, f, indent=1)
print("TEST", test_m, "nn_category_acc", nn_cat_acc, "best_step", best[1], flush=True)
if __name__ == "__main__":
main()
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