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| from pathlib import Path | |
| from omegaconf import OmegaConf | |
| import torch | |
| from ldm.util import instantiate_from_config | |
| import logging | |
| from contextlib import contextmanager | |
| from contextlib import contextmanager | |
| import logging | |
| def all_logging_disabled(highest_level=logging.CRITICAL): | |
| """ | |
| A context manager that will prevent any logging messages | |
| triggered during the body from being processed. | |
| :param highest_level: the maximum logging level in use. | |
| This would only need to be changed if a custom level greater than CRITICAL | |
| is defined. | |
| https://gist.github.com/simon-weber/7853144 | |
| """ | |
| # two kind-of hacks here: | |
| # * can't get the highest logging level in effect => delegate to the user | |
| # * can't get the current module-level override => use an undocumented | |
| # (but non-private!) interface | |
| previous_level = logging.root.manager.disable | |
| logging.disable(highest_level) | |
| try: | |
| yield | |
| finally: | |
| logging.disable(previous_level) | |
| def load_training_dir(train_dir, device, epoch="last"): | |
| """Load a checkpoint and config from training directory""" | |
| train_dir = Path(train_dir) | |
| ckpt = list(train_dir.rglob(f"*{epoch}.ckpt")) | |
| assert len(ckpt) == 1, f"found {len(ckpt)} matching ckpt files" | |
| config = list(train_dir.rglob(f"*-project.yaml")) | |
| assert len(ckpt) > 0, f"didn't find any config in {train_dir}" | |
| if len(config) > 1: | |
| print(f"found {len(config)} matching config files") | |
| config = sorted(config)[-1] | |
| print(f"selecting {config}") | |
| else: | |
| config = config[0] | |
| config = OmegaConf.load(config) | |
| return load_model_from_config(config, ckpt[0], device) | |
| def load_model_from_config(config, ckpt, device="cpu", verbose=False): | |
| """Loads a model from config and a ckpt | |
| if config is a path will use omegaconf to load | |
| """ | |
| if isinstance(config, (str, Path)): | |
| config = OmegaConf.load(config) | |
| with all_logging_disabled(): | |
| print(f"Loading model from {ckpt}") | |
| pl_sd = torch.load(ckpt, map_location="cpu") | |
| global_step = pl_sd["global_step"] | |
| sd = pl_sd["state_dict"] | |
| model = instantiate_from_config(config.model) | |
| m, u = model.load_state_dict(sd, strict=False) | |
| if len(m) > 0 and verbose: | |
| print("missing keys:") | |
| print(m) | |
| if len(u) > 0 and verbose: | |
| print("unexpected keys:") | |
| model.to(device) | |
| model.eval() | |
| model.cond_stage_model.device = device | |
| return model |