File size: 2,576 Bytes
4c4a6cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | """Shared training helpers."""
from __future__ import annotations
import logging
import os
import random
import sys
from datetime import datetime
import torch
import torch.nn as nn
import yaml
def setup_logger(log_path: str):
log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s")
root = logging.getLogger()
root.setLevel(logging.INFO)
root.handlers.clear()
fh = logging.FileHandler(log_path, encoding="utf-8")
fh.setFormatter(log_formatter)
root.addHandler(fh)
sh = logging.StreamHandler(sys.stdout)
sh.setFormatter(log_formatter)
root.addHandler(sh)
def set_seed(seed: int):
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
class AverageMeter:
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / max(self.count, 1)
class CustomDataParallel(nn.DataParallel):
def __getattr__(self, key):
try:
return super().__getattr__(key)
except AttributeError:
return getattr(self.module, key)
def load_yaml_config(path: str) -> dict:
with open(path) as f:
return yaml.safe_load(f)
def save_checkpoint(state, is_best, out_dir, filename="checkpoint.pth.tar"):
os.makedirs(out_dir, exist_ok=True)
path = os.path.join(out_dir, filename)
torch.save(state, path)
if is_best:
best = os.path.join(out_dir, "checkpoint_best_loss.pth.tar")
torch.save(state, best)
logging.info(f"Saved checkpoint to {path} (best={is_best})")
def load_checkpoint_dict(path: str, map_location="cpu"):
ckpt = torch.load(path, map_location=map_location)
if isinstance(ckpt, dict) and "state_dict" in ckpt:
state = ckpt["state_dict"]
else:
state = ckpt
# strip module.
out = {}
for k, v in state.items():
out[k[7:] if k.startswith("module.") else k] = v
return out, ckpt if isinstance(ckpt, dict) else {"state_dict": state}
def adamw_trainable(model: nn.Module, lr: float, weight_decay: float = 0.01):
params = [p for p in model.parameters() if p.requires_grad]
return torch.optim.AdamW(params, lr=lr, weight_decay=weight_decay)
def exp_dir(root: str, exp_name: str, quality_level) -> str:
path = os.path.join(root, exp_name, str(quality_level))
os.makedirs(path, exist_ok=True)
return path
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