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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_local.config import Csv, config
from df_local.model import init_model
from df_local.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
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