first commit

ldm/extras.py

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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
+
+@contextmanager
+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