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import os
import contextlib
import joblib
from typing import Union
from loguru import _Logger, logger
from itertools import chain
import torch
from yacs.config import CfgNode as CN
from pytorch_lightning.utilities import rank_zero_only
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CN):
return yacs_cfg
return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}
def upper_config(dict_cfg):
if not isinstance(dict_cfg, dict):
return dict_cfg
return {k.upper(): upper_config(v) for k, v in dict_cfg.items()}
def log_on(condition, message, level):
if condition:
assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL']
logger.log(level, message)
def get_rank_zero_only_logger(logger: _Logger):
if rank_zero_only.rank == 0:
return logger
else:
for _level in logger._core.levels.keys():
level = _level.lower()
setattr(logger, level,
lambda x: None)
logger._log = lambda x: None
return logger
def setup_gpus(gpus: Union[str, int]) -> int:
""" A temporary fix for pytorch-lighting 1.3.x """
gpus = str(gpus)
gpu_ids = []
if ',' not in gpus:
n_gpus = int(gpus)
return n_gpus if n_gpus != -1 else torch.cuda.device_count()
else:
gpu_ids = [i.strip() for i in gpus.split(',') if i != '']
# setup environment variables
visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
if visible_devices is None:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids)
visible_devices = os.getenv('CUDA_VISIBLE_DEVICES')
logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}')
else:
logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.')
return len(gpu_ids)
def flattenList(x):
return list(chain(*x))
@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
"""Context manager to patch joblib to report into tqdm progress bar given as argument
Usage:
with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))
When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing)
ret_vals = Parallel(n_jobs=args.world_size)(
delayed(lambda x: _compute_cov_score(pid, *x))(param)
for param in tqdm(combinations(image_ids, 2),
desc=f'Computing cov_score of [{pid}]',
total=len(image_ids)*(len(image_ids)-1)/2))
Src: https://stackoverflow.com/a/58936697
"""
class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __call__(self, *args, **kwargs):
tqdm_object.update(n=self.batch_size)
return super().__call__(*args, **kwargs)
old_batch_callback = joblib.parallel.BatchCompletionCallBack
joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback
try:
yield tqdm_object
finally:
joblib.parallel.BatchCompletionCallBack = old_batch_callback
tqdm_object.close()
def detect_NaN(feat_0, feat_1):
logger.info(f'NaN detected in feature')
logger.info(f"#NaN in feat_0: {torch.isnan(feat_0).int().sum()}, #NaN in feat_1: {torch.isnan(feat_1).int().sum()}")
feat_0[torch.isnan(feat_0)] = 0
feat_1[torch.isnan(feat_1)] = 0
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