wehub-resource-sync
246 lines
8.1 KiB
Python
Executable File
246 lines
8.1 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import logging
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import os
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import re
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from collections import OrderedDict
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from datetime import datetime
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import numpy as np
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import pytz
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import torch.distributed as dist
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from mmcv.utils.logging import logger_initialized
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from termcolor import colored
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from .dist_utils import is_local_master
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def get_root_logger(
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log_file=None, log_level=logging.INFO, name=colored("[Sana]", attrs=["bold"]), timezone="Asia/Shanghai"
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):
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"""Get root logger.
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Args:
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log_file (str, optional): File path of log. Defaults to None.
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log_level (int, optional): The level of logger.
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Defaults to logging.INFO.
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name (str): logger name
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Returns:
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:obj:`logging.Logger`: The obtained logger
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"""
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if log_file is None:
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log_file = "/dev/null"
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logger = get_logger(name=name, log_file=log_file, log_level=log_level, timezone=timezone)
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return logger
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class TimezoneFormatter(logging.Formatter):
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def __init__(self, fmt=None, datefmt=None, tz=None):
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super().__init__(fmt, datefmt)
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self.tz = pytz.timezone(tz) if tz else None
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def formatTime(self, record, datefmt=None):
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dt = datetime.fromtimestamp(record.created, self.tz)
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if datefmt:
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s = dt.strftime(datefmt)
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else:
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s = dt.isoformat()
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return s
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def get_logger(name, log_file=None, log_level=logging.INFO, timezone="UTC"):
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"""Initialize and get a logger by name.
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If the logger has not been initialized, this method will initialize the
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logger by adding one or two handlers, otherwise the initialized logger will
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be directly returned. During initialization, a StreamHandler will always be
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added. If `log_file` is specified and the process rank is 0, a FileHandler
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will also be added.
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Args:
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name (str): Logger name.
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log_file (str | None): The log filename. If specified, a FileHandler
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will be added to the logger.
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log_level (int): The logger level. Note that only the process of
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rank 0 is affected, and other processes will set the level to
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"Error" thus be silent most of the time.
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timezone (str): Timezone for the log timestamps.
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Returns:
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logging.Logger: The expected logger.
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"""
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logger = logging.getLogger(name)
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logger.propagate = False # disable root logger to avoid duplicate logging
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if name in logger_initialized:
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return logger
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# handle hierarchical names
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# e.g., logger "a" is initialized, then logger "a.b" will skip the
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# initialization since it is a child of "a".
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for logger_name in logger_initialized:
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if name.startswith(logger_name):
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return logger
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stream_handler = logging.StreamHandler()
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handlers = [stream_handler]
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if dist.is_available() and dist.is_initialized():
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rank = dist.get_rank()
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else:
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rank = 0
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# only rank 0 will add a FileHandler
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if rank == 0 and log_file is not None:
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os.makedirs(os.path.dirname(log_file), exist_ok=True)
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file_handler = logging.FileHandler(log_file, "a")
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handlers.append(file_handler)
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formatter = TimezoneFormatter(
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"%(asctime)s - %(name)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", tz=timezone
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)
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for handler in handlers:
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handler.setFormatter(formatter)
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handler.setLevel(log_level)
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logger.addHandler(handler)
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# only rank0 for each node will print logs
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log_level = log_level if is_local_master() else logging.ERROR
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logger.setLevel(log_level)
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logger_initialized[name] = True
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return logger
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def rename_file_with_creation_time(file_path):
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# Get the file creation time.
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creation_time = os.path.getctime(file_path)
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creation_time_str = datetime.fromtimestamp(creation_time).strftime("%Y-%m-%d_%H-%M-%S")
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# Build the new file name.
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dir_name, file_name = os.path.split(file_path)
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name, ext = os.path.splitext(file_name)
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new_file_name = f"{name}_{creation_time_str}{ext}"
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new_file_path = os.path.join(dir_name, new_file_name)
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# Rename the file.
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os.rename(file_path, new_file_path)
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# print(f"File renamed to: {new_file_path}")
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return new_file_path
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class TimezoneFormatter(logging.Formatter):
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def __init__(self, fmt=None, datefmt=None, tz=None):
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super().__init__(fmt, datefmt)
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self.tz = pytz.timezone(tz) if tz else None
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def formatTime(self, record, datefmt=None):
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dt = datetime.fromtimestamp(record.created, self.tz)
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if datefmt:
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s = dt.strftime(datefmt)
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else:
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s = dt.isoformat()
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return s
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class LogBuffer:
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def __init__(self):
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self.val_history = OrderedDict()
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self.n_history = OrderedDict()
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self.output = OrderedDict()
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self.ready = False
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def clear(self) -> None:
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self.val_history.clear()
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self.n_history.clear()
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self.clear_output()
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def clear_output(self) -> None:
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self.output.clear()
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self.ready = False
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def update(self, vars: dict, count: int = 1) -> None:
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assert isinstance(vars, dict)
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for key, var in vars.items():
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if key not in self.val_history:
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self.val_history[key] = []
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self.n_history[key] = []
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self.val_history[key].append(var)
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self.n_history[key].append(count)
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def average(self, n: int = 0) -> None:
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"""Average latest n values or all values."""
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assert n >= 0
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for key in self.val_history:
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values = np.array(self.val_history[key][-n:])
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nums = np.array(self.n_history[key][-n:])
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avg = np.sum(values * nums) / np.sum(nums)
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self.output[key] = avg
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self.ready = True
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def tracker(args, result_dict, label="", pattern="epoch_step", metric="FID"):
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if args.report_to == "wandb":
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import wandb
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wandb_name = f"[{args.log_metric}]_{args.name}"
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wandb.init(project=args.tracker_project_name, name=wandb_name, resume="allow", id=wandb_name, tags="metrics")
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run = wandb.run
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if pattern == "step":
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pattern = "sample_steps"
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elif pattern == "epoch_step":
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pattern = "step"
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custom_name = f"custom_{pattern}"
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run.define_metric(custom_name)
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# define which metrics will be plotted against it
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run.define_metric(f"{metric}_{label}", step_metric=custom_name)
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steps = []
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results = []
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def extract_value(regex, exp_name):
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match = re.search(regex, exp_name)
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if match:
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return match.group(1)
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else:
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return "unknown"
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for exp_name, result_value in result_dict.items():
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if pattern == "step":
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regex = r".*step(\d+)_scale.*"
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custom_x = extract_value(regex, exp_name)
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elif pattern == "sample_steps":
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regex = r".*step(\d+)_size.*"
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custom_x = extract_value(regex, exp_name)
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else:
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regex = rf"{pattern}(\d+(\.\d+)?)"
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custom_x = extract_value(regex, exp_name)
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custom_x = 1 if custom_x == "unknown" else custom_x
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assert custom_x != "unknown"
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steps.append(float(custom_x))
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results.append(result_value)
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sorted_data = sorted(zip(steps, results))
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steps, results = zip(*sorted_data)
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for step, result in sorted(zip(steps, results)):
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run.log({f"{metric}_{label}": result, custom_name: step})
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else:
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print(f"{args.report_to} is not supported")
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