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44963e7 | 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 | """Stage 1: SLAT-Interior VAE Pre-training."""
import os
import sys
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from accelerate import Accelerator
from omegaconf import OmegaConf
from tqdm import tqdm
def main():
# Load config
config_path = sys.argv[1] if len(sys.argv) > 1 else "configs/vae_pretrain.yaml"
config = OmegaConf.load(config_path)
# Initialize accelerator
accelerator = Accelerator(
mixed_precision="bf16",
gradient_accumulation_steps=config.training.gradient_accumulation,
)
device = accelerator.device
# Build model
from interiorfusion.models.slat_vae import SLATInteriorVAE
model = SLATInteriorVAE(
latent_dim=config.model.latent_dim,
base_resolution=config.model.base_resolution,
)
# Optimizer
optimizer = torch.optim.AdamW(
model.parameters(),
lr=config.optimizer.lr,
weight_decay=config.optimizer.weight_decay,
betas=tuple(config.optimizer.betas),
)
# Scheduler
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer,
T_0=config.scheduler.warmup_steps,
T_mult=2,
)
# Data loader
from interiorfusion.data.dataset import InteriorFusionDataset
dataset = InteriorFusionDataset(
root=config.data.dataset,
split="train",
resolution=config.model.base_resolution,
)
dataloader = DataLoader(
dataset,
batch_size=config.training.batch_size,
shuffle=True,
num_workers=config.data.num_workers,
pin_memory=config.data.pin_memory,
)
# Prepare with accelerator
model, optimizer, dataloader, scheduler = accelerator.prepare(
model, optimizer, dataloader, scheduler
)
# Training loop
global_step = 0
for epoch in range(1000):
model.train()
epoch_loss = 0.0
for batch in tqdm(dataloader, desc=f"Epoch {epoch}"):
with accelerator.accumulate(model):
# Forward
occupancy = batch["occupancy"] # [B, 1, N, N, N]
materials = batch["materials"] # [B, 4, N, N, N]
depth = batch["depth"] # [B, 1, N, N, N]
normal = batch["normal"] # [B, 3, N, N, N]
# Encode
z, mu, logvar = model.encode(occupancy, materials)
# Decode
pred_shape, pred_material = model.decode(z)
# Decode depth and normal from shape
pred_depth = model.predict_depth(pred_shape)
pred_normal = model.predict_normal(pred_shape)
# Losses
loss_recon = F.l1_loss(pred_shape, occupancy) + \
F.l1_loss(pred_material, materials)
loss_kl = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
loss_kl = loss_kl * config.loss.kl_divergence.weight
loss_depth = F.l1_loss(pred_depth, depth) * config.loss.depth_consistency.weight
loss_normal = (1 - F.cosine_similarity(
pred_normal, normal, dim=1
).mean()) * config.loss.normal_consistency.weight
loss = loss_recon + loss_kl + loss_depth + loss_normal
# Backward
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
epoch_loss += loss.item()
# Logging
if global_step % 100 == 0:
accelerator.print(
f"Step {global_step}: "
f"loss={loss.item():.4f} "
f"recon={loss_recon.item():.4f} "
f"kl={loss_kl.item():.4f} "
f"depth={loss_depth.item():.4f} "
f"normal={loss_normal.item():.4f}"
)
# Checkpoint
if global_step % 5000 == 0:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unwrapped_model = accelerator.unwrap_model(model)
checkpoint_path = f"checkpoints/vae_step{global_step}.pt"
os.makedirs("checkpoints", exist_ok=True)
torch.save({
"model": unwrapped_model.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"step": global_step,
"config": OmegaConf.to_container(config),
}, checkpoint_path)
print(f"Saved checkpoint: {checkpoint_path}")
# Early stopping on step limit
if global_step >= config.training.max_steps:
accelerator.print("Reached max steps. Training complete.")
return
avg_loss = epoch_loss / len(dataloader)
accelerator.print(f"Epoch {epoch} complete. Avg loss: {avg_loss:.4f}")
if __name__ == "__main__":
main()
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