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7f921f4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | # Copyright 2023-2025 Marigold Team, ETH Zürich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# More information about Marigold:
# https://marigoldmonodepth.github.io
# https://marigoldcomputervision.github.io
# Efficient inference pipelines are now part of diffusers:
# https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage
# https://huggingface.co/docs/diffusers/api/pipelines/marigold
# Examples of trained models and live demos:
# https://huggingface.co/prs-eth
# Related projects:
# https://rollingdepth.github.io/
# https://marigolddepthcompletion.github.io/
# Citation (BibTeX):
# https://github.com/prs-eth/Marigold#-citation
# If you find Marigold useful, we kindly ask you to cite our papers.
# --------------------------------------------------------------------------
import logging
import os
import sys
import wandb
from tabulate import tabulate
from torch.utils.tensorboard import SummaryWriter
def config_logging(cfg_logging, out_dir=None):
file_level = cfg_logging.get("file_level", 10)
console_level = cfg_logging.get("console_level", 10)
log_formatter = logging.Formatter(cfg_logging["format"])
root_logger = logging.getLogger()
root_logger.handlers.clear()
root_logger.setLevel(min(file_level, console_level))
if out_dir is not None:
_logging_file = os.path.join(
out_dir, cfg_logging.get("filename", "logging.log")
)
file_handler = logging.FileHandler(_logging_file)
file_handler.setFormatter(log_formatter)
file_handler.setLevel(file_level)
root_logger.addHandler(file_handler)
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(log_formatter)
console_handler.setLevel(console_level)
root_logger.addHandler(console_handler)
# Avoid pollution by packages
logging.getLogger("PIL").setLevel(logging.INFO)
logging.getLogger("matplotlib").setLevel(logging.INFO)
class MyTrainingLogger:
"""Tensorboard + wandb logger"""
writer: SummaryWriter
is_initialized = False
def __init__(self) -> None:
pass
def set_dir(self, tb_log_dir):
if self.is_initialized:
raise ValueError("Do not initialize writer twice")
self.writer = SummaryWriter(tb_log_dir)
self.is_initialized = True
def log_dict(self, scalar_dict, global_step, walltime=None):
for k, v in scalar_dict.items():
self.writer.add_scalar(k, v, global_step=global_step, walltime=walltime)
return
# global instance
tb_logger = MyTrainingLogger()
# -------------- wandb tools --------------
def init_wandb(enable: bool, **kwargs):
if enable:
run = wandb.init(sync_tensorboard=True, **kwargs)
else:
run = wandb.init(mode="disabled")
return run
def log_slurm_job_id(step):
global tb_logger
_jobid = os.getenv("SLURM_JOB_ID")
if _jobid is None:
_jobid = -1
tb_logger.writer.add_scalar("job_id", int(_jobid), global_step=step)
logging.debug(f"Slurm job_id: {_jobid}")
def load_wandb_job_id(out_dir):
with open(os.path.join(out_dir, "WANDB_ID"), "r") as f:
wandb_id = f.read()
return wandb_id
def save_wandb_job_id(run, out_dir):
with open(os.path.join(out_dir, "WANDB_ID"), "w+") as f:
f.write(run.id)
def eval_dict_to_text(val_metrics: dict, dataset_name: str, sample_list_path: str):
eval_text = f"Evaluation metrics:\n\
on dataset: {dataset_name}\n\
over samples in: {sample_list_path}\n"
eval_text += tabulate([val_metrics.keys(), val_metrics.values()])
return eval_text
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