import glob import os import re from typing import List, Optional, Tuple, Union import numpy as np import torch from loguru import logger from torch import nn from df.config import Csv, config from df.model import init_model from df.utils import check_finite_module from libdf import DF def get_epoch(cp) -> int: return int(os.path.basename(cp).split(".")[0].split("_")[-1]) def load_model( cp_dir: Optional[str], df_state: DF, jit: bool = False, mask_only: bool = False, train_df_only: bool = False, extension: str = "ckpt", epoch: Union[str, int, None] = "latest", ) -> Tuple[nn.Module, int]: if mask_only and train_df_only: raise ValueError("Only one of `mask_only` `train_df_only` can be enabled") model = init_model(df_state, run_df=mask_only is False, train_mask=train_df_only is False) if jit: model = torch.jit.script(model) blacklist: List[str] = config("CP_BLACKLIST", [], Csv(), save=False, section="train") # type: ignore if cp_dir is not None: epoch = read_cp( model, "model", cp_dir, blacklist=blacklist, extension=extension, epoch=epoch ) epoch = 0 if epoch is None else epoch else: epoch = 0 return model, epoch def read_cp( obj: Union[torch.optim.Optimizer, nn.Module], name: str, dirname: str, epoch: Union[str, int, None] = "latest", extension="ckpt", blacklist=[], log: bool = True, ): checkpoints = [] if isinstance(epoch, str): assert epoch in ("best", "latest") if epoch == "best": checkpoints = glob.glob(os.path.join(dirname, f"{name}*.{extension}.best")) if len(checkpoints) == 0: logger.warning("Could not find `best` checkpoint. Checking for default...") if len(checkpoints) == 0: checkpoints = glob.glob(os.path.join(dirname, f"{name}*.{extension}")) checkpoints += glob.glob(os.path.join(dirname, f"{name}*.{extension}.best")) if len(checkpoints) == 0: return None if isinstance(epoch, int): latest = next((x for x in checkpoints if get_epoch(x) == epoch), None) if latest is None: logger.error(f"Could not find checkpoint of epoch {epoch}") exit(1) else: latest = max(checkpoints, key=get_epoch) epoch = get_epoch(latest) if log: logger.info("Found checkpoint {} with epoch {}".format(latest, epoch)) latest = torch.load(latest, map_location="cpu") latest = {k.replace("clc", "df"): v for k, v in latest.items()} if blacklist: reg = re.compile("".join(f"({b})|" for b in blacklist)[:-1]) len_before = len(latest) latest = {k: v for k, v in latest.items() if reg.search(k) is None} if len(latest) < len_before: logger.info("Filtered checkpoint modules: {}".format(blacklist)) if isinstance(obj, nn.Module): while True: try: missing, unexpected = obj.load_state_dict(latest, strict=False) except RuntimeError as e: e_str = str(e) logger.warning(e_str) if "size mismatch" in e_str: latest = {k: v for k, v in latest.items() if k not in e_str} continue raise e break for key in missing: logger.warning(f"Missing key: '{key}'") for key in unexpected: if key.endswith(".h0"): continue logger.warning(f"Unexpected key: {key}") return epoch obj.load_state_dict(latest) def write_cp( obj: Union[torch.optim.Optimizer, nn.Module], name: str, dirname: str, epoch: int, extension="ckpt", metric: Optional[float] = None, cmp="min", ): check_finite_module(obj) n_keep = config("n_checkpoint_history", default=3, cast=int, section="train") n_keep_best = config("n_best_checkpoint_history", default=5, cast=int, section="train") if metric is not None: assert cmp in ("min", "max") metric = float(metric) # Make sure it is not an integer # Each line contains a previous best with entries: (epoch, metric) with open(os.path.join(dirname, ".best"), "a+") as prev_best_f: prev_best_f.seek(0) # "a+" creates a file in read/write mode without truncating lines = prev_best_f.readlines() if len(lines) == 0: prev_best = float("inf" if cmp == "min" else "-inf") else: prev_best = float(lines[-1].strip().split(" ")[1]) cmp = "__lt__" if cmp == "min" else "__gt__" if getattr(metric, cmp)(prev_best): logger.info(f"Saving new best checkpoint at epoch {epoch} with metric: {metric}") prev_best_f.seek(0, os.SEEK_END) np.savetxt(prev_best_f, np.array([[float(epoch), metric]])) cp_name = os.path.join(dirname, f"{name}_{epoch}.{extension}.best") torch.save(obj.state_dict(), cp_name) cleanup(name, dirname, extension + ".best", nkeep=n_keep_best) cp_name = os.path.join(dirname, f"{name}_{epoch}.{extension}") logger.info(f"Writing checkpoint {cp_name} with epoch {epoch}") torch.save(obj.state_dict(), cp_name) cleanup(name, dirname, extension, nkeep=n_keep) def cleanup(name: str, dirname: str, extension: str, nkeep=5): if nkeep < 0: return checkpoints = glob.glob(os.path.join(dirname, f"{name}*.{extension}")) if len(checkpoints) == 0: return checkpoints = sorted(checkpoints, key=get_epoch, reverse=True) for cp in checkpoints[nkeep:]: logger.debug("Removing old checkpoint: {}".format(cp)) os.remove(cp) def check_patience( dirname: str, max_patience: int, new_metric: float, cmp: str = "min", raise_: bool = True ): cmp = "__lt__" if cmp == "min" else "__gt__" new_metric = float(new_metric) # Make sure it is not an integer prev_patience, prev_metric = read_patience(dirname) if prev_patience is None or getattr(new_metric, cmp)(prev_metric): # We have a better new_metric, reset patience write_patience(dirname, 0, new_metric) else: # We don't have a better metric, decrement patience new_patience = prev_patience + 1 write_patience(dirname, new_patience, prev_metric) if new_patience >= max_patience: if raise_: raise ValueError( f"No improvements on validation metric ({new_metric}) for {max_patience} epochs. " "Stopping." ) else: return False return True def read_patience(dirname: str) -> Tuple[Optional[int], float]: fn = os.path.join(dirname, ".patience") if not os.path.isfile(fn): return None, 0.0 patience, metric = np.loadtxt(fn) return int(patience), float(metric) def write_patience(dirname: str, new_patience: int, metric: float): return np.savetxt(os.path.join(dirname, ".patience"), [new_patience, metric]) def test_check_patience(): import tempfile with tempfile.TemporaryDirectory() as d: check_patience(d, 3, 1.0) check_patience(d, 3, 1.0) check_patience(d, 3, 1.0) assert check_patience(d, 3, 1.0, raise_=False) is False with tempfile.TemporaryDirectory() as d: check_patience(d, 3, 1.0) check_patience(d, 3, 0.9) check_patience(d, 3, 1.0) check_patience(d, 3, 1.0) assert check_patience(d, 3, 1.0, raise_=False) is False with tempfile.TemporaryDirectory() as d: check_patience(d, 3, 1.0, cmp="max") check_patience(d, 3, 1.9, cmp="max") check_patience(d, 3, 1.0, cmp="max") check_patience(d, 3, 1.0, cmp="max") assert check_patience(d, 3, 1.0, cmp="max", raise_=False) is False