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32da3e8 | 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 | """Checkpoint save/load utilities for Stage 1 and Stage 2 training."""
from __future__ import annotations
import os
from typing import Optional, Tuple
import torch
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim.lr_scheduler import LambdaLR
def save_stage1_checkpoint(
path: str,
step: int,
epoch: int,
model: DDP,
ema_model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
scheduler: Optional[LambdaLR],
disc: torch.nn.Module,
disc_optimizer: torch.optim.Optimizer,
disc_scheduler: Optional[LambdaLR],
) -> None:
"""Save Stage 1 training checkpoint (model + discriminator)."""
state = {
"step": step,
"epoch": epoch,
"model": model.module.state_dict(),
"ema": ema_model.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict() if scheduler is not None else None,
"disc": disc.state_dict(),
"disc_optimizer": disc_optimizer.state_dict(),
"disc_scheduler": disc_scheduler.state_dict() if disc_scheduler is not None else None,
}
os.makedirs(os.path.dirname(path), exist_ok=True)
torch.save(state, path)
def load_stage1_checkpoint(
path: str,
model: DDP,
ema_model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
scheduler: Optional[LambdaLR],
disc: torch.nn.Module,
disc_optimizer: torch.optim.Optimizer,
disc_scheduler: Optional[LambdaLR],
) -> Tuple[int, int]:
"""Load Stage 1 training checkpoint. Returns (epoch, step)."""
checkpoint = torch.load(path, map_location="cpu")
model.module.load_state_dict(checkpoint["model"])
ema_model.load_state_dict(checkpoint["ema"])
optimizer.load_state_dict(checkpoint["optimizer"])
if scheduler is not None and checkpoint.get("scheduler") is not None:
scheduler.load_state_dict(checkpoint["scheduler"])
disc.load_state_dict(checkpoint["disc"])
disc_optimizer.load_state_dict(checkpoint["disc_optimizer"])
if disc_scheduler is not None and checkpoint.get("disc_scheduler") is not None:
disc_scheduler.load_state_dict(checkpoint["disc_scheduler"])
return checkpoint.get("epoch", 0), checkpoint.get("step", 0)
def save_stage2_checkpoint(
path: str,
step: int,
epoch: int,
model: DDP,
ema_model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
scheduler: Optional[LambdaLR],
) -> None:
"""Save Stage 2 training checkpoint."""
state = {
"step": step,
"epoch": epoch,
"model": model.module.state_dict(),
"ema": ema_model.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict() if scheduler is not None else None,
}
os.makedirs(os.path.dirname(path), exist_ok=True)
torch.save(state, path)
def load_stage2_checkpoint(
path: str,
model: DDP,
ema_model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
scheduler: Optional[LambdaLR],
) -> Tuple[int, int]:
"""Load Stage 2 training checkpoint. Returns (epoch, step)."""
checkpoint = torch.load(path, map_location="cpu")
model.module.load_state_dict(checkpoint["model"])
ema_model.load_state_dict(checkpoint["ema"])
optimizer.load_state_dict(checkpoint["optimizer"])
if scheduler is not None and checkpoint.get("scheduler") is not None:
scheduler.load_state_dict(checkpoint["scheduler"])
return checkpoint.get("epoch", 0), checkpoint.get("step", 0)
__all__ = [
"save_stage1_checkpoint",
"load_stage1_checkpoint",
"save_stage2_checkpoint",
"load_stage2_checkpoint",
]
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