131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import os
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import random
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import re
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import numpy as np
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import torch
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from diffusion.utils.logger import get_root_logger
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# from tools.download import find_model
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def save_checkpoint(
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work_dir,
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epoch,
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model,
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model_ema=None,
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optimizer=None,
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lr_scheduler=None,
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generator=torch.Generator(device="cpu").manual_seed(42),
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keep_last=False,
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step=None,
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add_symlink=False,
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):
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os.makedirs(work_dir, exist_ok=True)
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state_dict = dict(state_dict=model.state_dict())
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if model_ema is not None:
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state_dict["state_dict_ema"] = model_ema.state_dict()
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if optimizer is not None:
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state_dict["optimizer"] = optimizer.state_dict()
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if lr_scheduler is not None:
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state_dict["scheduler"] = lr_scheduler.state_dict()
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if epoch is not None:
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state_dict["epoch"] = epoch
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file_path = os.path.join(work_dir, f"epoch_{epoch}.pth")
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if step is not None:
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file_path = file_path.split(".pth")[0] + f"_step_{step}.pth"
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rng_state = {
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"torch": torch.get_rng_state(),
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"torch_cuda": torch.cuda.get_rng_state_all(),
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"numpy": np.random.get_state(),
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"python": random.getstate(),
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"generator": generator.get_state(),
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}
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state_dict["rng_state"] = rng_state
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logger = get_root_logger()
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torch.save(state_dict, file_path)
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logger.info(f"Saved checkpoint of epoch {epoch} to {file_path.format(epoch)}.")
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if keep_last:
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for i in range(epoch):
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previous_ckgt = file_path.format(i)
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if os.path.exists(previous_ckgt):
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os.remove(previous_ckgt)
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if add_symlink:
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link_path = os.path.join(os.path.dirname(file_path), "latest.pth")
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if os.path.exists(link_path) or os.path.islink(link_path):
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os.remove(link_path)
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os.symlink(os.path.abspath(file_path), link_path)
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return file_path
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def load_checkpoint(
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checkpoint,
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model,
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model_ema=None,
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optimizer=None,
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lr_scheduler=None,
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load_ema=False,
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resume_optimizer=True,
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resume_lr_scheduler=True,
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null_embed_path=None,
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):
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assert isinstance(checkpoint, str)
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logger = get_root_logger()
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ckpt_file = checkpoint
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checkpoint = find_model(ckpt_file)
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state_dict_keys = ["pos_embed", "base_model.pos_embed", "model.pos_embed"]
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for key in state_dict_keys:
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if key in checkpoint["state_dict"]:
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del checkpoint["state_dict"][key]
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if "state_dict_ema" in checkpoint and key in checkpoint["state_dict_ema"]:
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del checkpoint["state_dict_ema"][key]
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break
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if load_ema:
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state_dict = checkpoint["state_dict_ema"]
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else:
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state_dict = checkpoint.get("state_dict", checkpoint) # to be compatible with the official checkpoint
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null_embed = torch.load(null_embed_path, map_location="cpu", weights_only=False)
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state_dict["y_embedder.y_embedding"] = null_embed["uncond_prompt_embeds"][0]
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rng_state = checkpoint.get("rng_state", None)
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missing, unexpect = model.load_state_dict(state_dict, strict=False)
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if model_ema is not None:
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model_ema.load_state_dict(checkpoint["state_dict_ema"], strict=False)
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if optimizer is not None and resume_optimizer:
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optimizer.load_state_dict(checkpoint["optimizer"])
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if lr_scheduler is not None and resume_lr_scheduler:
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lr_scheduler.load_state_dict(checkpoint["scheduler"])
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epoch = 0
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if optimizer is not None:
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epoch = checkpoint.get("epoch", re.match(r".*epoch_(\d*).*.pth", ckpt_file).group()[0])
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logger.info(
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f"Resume checkpoint of epoch {epoch} from {ckpt_file}. Load ema: {load_ema}, "
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f"resume optimizer： {resume_optimizer}, resume lr scheduler: {resume_lr_scheduler}."
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)
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return epoch, missing, unexpect, rng_state
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logger.info(f"Load checkpoint from {ckpt_file}. Load ema: {load_ema}.")
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return epoch, missing, unexpect, rng_state
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