File size: 3,595 Bytes
32da3e8 | 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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | import argparse
import hashlib
import logging
import math
import os
import sys
import torch
import torch.distributed as dist
import wandb
from torchvision.utils import make_grid
def create_logger(logging_dir: str, logger_name: str) -> logging.Logger:
"""
Create a logger that writes to a log file and stdout.
Only rank 0 writes; other ranks get a dummy logger.
"""
rank = dist.get_rank() if dist.is_initialized() else 0
logger = logging.getLogger(logger_name) # use provided logger name
if rank == 0:
# Make sure log dir exists
os.makedirs(logging_dir, exist_ok=True)
# Clear any existing handlers so we can reconfigure
for h in list(logger.handlers):
logger.removeHandler(h)
logger.setLevel(logging.INFO)
logger.propagate = False # don't double-log via root
fmt = logging.Formatter(
'[\033[34m%(asctime)s\033[0m] %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
)
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler.setFormatter(fmt)
logger.addHandler(stream_handler)
file_handler = logging.FileHandler(os.path.join(logging_dir, "log.txt"))
file_handler.setFormatter(fmt)
logger.addHandler(file_handler)
else:
# Dummy logger: no handlers, no output
logger.setLevel(logging.CRITICAL + 1)
logger.propagate = False
for h in list(logger.handlers):
logger.removeHandler(h)
return logger
def is_main_process():
return dist.get_rank() == 0
def namespace_to_dict(namespace):
return {
k: namespace_to_dict(v) if isinstance(v, argparse.Namespace) else v
for k, v in vars(namespace).items()
}
def generate_run_id(exp_name):
# https://stackoverflow.com/questions/16008670/how-to-hash-a-string-into-8-digits
return str(int(hashlib.sha256(exp_name.encode('utf-8')).hexdigest(), 16) % 10 ** 8)
def initialize(args, entity, exp_name, project_name):
config_dict = namespace_to_dict(args)
if is_main_process():
if "WANDB_KEY" in os.environ:
wandb.login(key=os.environ["WANDB_KEY"])
else:
# assert already logged in
pass
wandb.init(
entity=entity,
project=project_name,
name=exp_name,
config=config_dict,
id=generate_run_id(exp_name),
resume="allow",
reinit=True,
)
def log(stats, step=None):
if is_main_process():
# print(f"WandB logging at step {step}: {stats}")
wandb.log({k: v for k, v in stats.items()}, step=step)
def log_image(sample, step=None):
if is_main_process():
sample = array2grid(sample)
wandb.log({"samples": wandb.Image(sample)}, step=step)
def log_images(images_dict, step=None):
"""Log multiple images to wandb.
Args:
images_dict: dict mapping name -> tensor grid (already in grid format from make_grid)
step: logging step
"""
if is_main_process():
log_dict = {}
for name, img in images_dict.items():
# Convert grid tensor to numpy for wandb
img = img.clamp(0, 1).mul(255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
log_dict[name] = wandb.Image(img)
wandb.log(log_dict, step=step)
def array2grid(x):
nrow = round(math.sqrt(x.size(0)))
x = make_grid(x, nrow=nrow, normalize=True, value_range=(0,1))
x = x.clamp(0, 1).mul(255).permute(1,2,0).to('cpu', torch.uint8).numpy()
return x
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