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ca3f598 | 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 | from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Subset
from meshai_train.dataset import PreprocessedMeshDataset, collate_preprocessed
from meshai_train.models import MeshAIHybridTrainBundle
TRAIN_VERSION = "v4.0-real-preprocessed"
def hybrid_loss(out: dict[str, torch.Tensor], batch: dict[str, torch.Tensor]) -> tuple[torch.Tensor, dict[str, float]]:
voxel_loss = nn.functional.mse_loss(out["voxel_pred"], batch["voxel_tgt"])
bridge_loss = nn.functional.mse_loss(out["bridge_out"], out["tex_latent"].detach())
tex_reg = out["tex_latent"].pow(2).mean() * 1e-4
total = voxel_loss + bridge_loss + tex_reg
return total, {
"voxel": float(voxel_loss.item()),
"bridge": float(bridge_loss.item()),
"tex_reg": float(tex_reg.item()),
}
def save_real_checkpoint(
path: Path,
*,
epoch: int,
global_step: int,
model: MeshAIHybridTrainBundle,
extra: dict[str, Any] | None = None,
) -> None:
payload = {
"version": TRAIN_VERSION,
"epoch": epoch,
"global_step": global_step,
"geometry": model.geometry.state_dict(),
"texture": model.texture.state_dict(),
"bridge": model.bridge.state_dict(),
"extra": extra or {},
}
torch.save(payload, path)
def load_real_checkpoint(path: Path, model: MeshAIHybridTrainBundle, device: str) -> int:
state = torch.load(path, map_location=device, weights_only=False)
if state.get("version") != TRAIN_VERSION:
return 0
model.geometry.load_state_dict(state["geometry"])
model.texture.load_state_dict(state["texture"])
model.bridge.load_state_dict(state["bridge"])
return int(state.get("global_step", 0))
def start_real_training(
*,
monitor: Any,
checkpoint_dir: Path,
output_dir: Path,
token: str | None,
epochs: int,
resume_from: Path | None,
validation_every: int,
val_ratio: float,
limit: int | None,
hf_repo: str,
data_root: Path | None,
log_fn: Any,
log_vram_fn: Any,
clear_gpu_fn: Any,
load_val_uids_fn: Any,
checkpoint_every: int = 100,
) -> None:
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32
log_fn(f"Pipeline surumu: {TRAIN_VERSION} (gercek preprocessed latent + render)")
log_fn(f"Veri: {hf_repo}" + (f" | limit={limit}" if limit else ""))
if torch.cuda.is_available():
log_fn(f"GPU: {torch.cuda.get_device_name(0)}")
log_vram_fn("startup")
dataset = PreprocessedMeshDataset(
token=token,
data_root=data_root,
hf_repo=hf_repo,
limit=limit,
)
val_uids = load_val_uids_fn()
val_idx = [i for i, o in enumerate(dataset.objects) if str(o.get("uid")) in val_uids]
if not val_idx:
val_count = max(1, int(len(dataset) * val_ratio))
val_idx = list(range(val_count))
train_idx = [i for i in range(len(dataset)) if i not in set(val_idx)]
if not train_idx:
train_idx = list(range(len(dataset)))
val_idx = train_idx[:1]
train_loader = DataLoader(
Subset(dataset, train_idx),
batch_size=2 if device == "cuda" else 1,
shuffle=True,
pin_memory=device == "cuda",
collate_fn=collate_preprocessed,
)
val_loader = DataLoader(
Subset(dataset, val_idx),
batch_size=2 if device == "cuda" else 1,
shuffle=False,
pin_memory=device == "cuda",
collate_fn=collate_preprocessed,
)
log_fn(f"Veri: {len(train_idx)} train + {len(val_idx)} val (gercek latent/render).")
model = MeshAIHybridTrainBundle().to(device=device, dtype=dtype)
opt = torch.optim.AdamW(model.parameters(), lr=1e-4, fused=False)
latest = checkpoint_dir / "latest_model.pt"
global_step = 0
if resume_from and resume_from.exists():
global_step = load_real_checkpoint(resume_from, model, device)
log_fn(f"Resume: {resume_from} step={global_step}")
def _eval() -> tuple[float, float]:
model.eval()
losses: list[float] = []
with torch.no_grad():
for batch in val_loader:
batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
out = model(batch["geom_in"], batch["views"])
loss, _ = hybrid_loss(out, batch)
losses.append(float(loss.item()))
model.train()
mean = float(np.mean(losses)) if losses else float("nan")
return mean, mean
for epoch in range(1, epochs + 1):
log_fn(f"--- Epoch {epoch}/{epochs} ---")
model.train()
epoch_losses: list[float] = []
for batch in train_loader:
batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
opt.zero_grad(set_to_none=True)
out = model(batch["geom_in"], batch["views"])
loss, parts = hybrid_loss(out, batch)
if not torch.isfinite(loss):
monitor.note_nan_skip("hybrid")
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step()
global_step += 1
epoch_losses.append(float(loss.item()))
monitor.note_step(global_step, "geometry", parts["voxel"])
monitor.last["train_loss_texture"] = round(parts["bridge"], 6)
if global_step <= 3 or global_step % 25 == 0:
log_fn(
f"step={global_step} loss={float(loss.item()):.6f} "
f"voxel={parts['voxel']:.6f} bridge={parts['bridge']:.6f}"
)
if checkpoint_every > 0 and global_step % checkpoint_every == 0:
save_real_checkpoint(latest, epoch=epoch, global_step=global_step, model=model)
log_fn(
f"CHECKPOINT_SAVED step={global_step} -> {latest} "
f"({latest.stat().st_size // 1024} KB)"
)
if global_step % validation_every == 0:
val_geom, val_tex = _eval()
monitor.note_validation(global_step, val_geom, val_tex)
log_vram_fn(f"step_{global_step}")
val_geom, val_tex = _eval()
geom_mean = float(np.mean(epoch_losses)) if epoch_losses else float("nan")
monitor.note_epoch_end(epoch, epochs, geom_mean, val_tex, val_geom, val_tex)
save_real_checkpoint(latest, epoch=epoch, global_step=global_step, model=model)
clear_gpu_fn()
log_fn(f"Epoch {epoch} kaydedildi -> {latest} ({latest.stat().st_size // 1024} KB)")
log_fn(f"CHECKPOINT_SAVED step={global_step} epoch={epoch} -> {latest}")
monitor.finish(ok=True)
log_fn("Gercek preprocessed egitim tamamlandi.")
log_fn("Not: Tam TRELLIS/Hunyuan agirlik fine-tune sonraki adim; bu asama MeshAI hibrit latent koprusu.")
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