GANcMRI / dnnlib /submission /run_context.py
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# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://nvlabs.github.io/stylegan2/license.html
"""Helpers for managing the run/training loop."""
import datetime
import json
import os
import pprint
import time
import types
from typing import Any
from . import submit
# Singleton RunContext
_run_context = None
class RunContext(object):
"""Helper class for managing the run/training loop.
The context will hide the implementation details of a basic run/training loop.
It will set things up properly, tell if run should be stopped, and then cleans up.
User should call update periodically and use should_stop to determine if run should be stopped.
Args:
submit_config: The SubmitConfig that is used for the current run.
config_module: (deprecated) The whole config module that is used for the current run.
"""
def __init__(self, submit_config: submit.SubmitConfig, config_module: types.ModuleType = None):
global _run_context
# Only a single RunContext can be alive
assert _run_context is None
_run_context = self
self.submit_config = submit_config
self.should_stop_flag = False
self.has_closed = False
self.start_time = time.time()
self.last_update_time = time.time()
self.last_update_interval = 0.0
self.progress_monitor_file_path = None
# vestigial config_module support just prints a warning
if config_module is not None:
print("RunContext.config_module parameter support has been removed.")
# write out details about the run to a text file
self.run_txt_data = {"task_name": submit_config.task_name, "host_name": submit_config.host_name, "start_time": datetime.datetime.now().isoformat(sep=" ")}
with open(os.path.join(submit_config.run_dir, "run.txt"), "w") as f:
pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
def __enter__(self) -> "RunContext":
return self
def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
self.close()
def update(self, loss: Any = 0, cur_epoch: Any = 0, max_epoch: Any = None) -> None:
"""Do general housekeeping and keep the state of the context up-to-date.
Should be called often enough but not in a tight loop."""
assert not self.has_closed
self.last_update_interval = time.time() - self.last_update_time
self.last_update_time = time.time()
if os.path.exists(os.path.join(self.submit_config.run_dir, "abort.txt")):
self.should_stop_flag = True
def should_stop(self) -> bool:
"""Tell whether a stopping condition has been triggered one way or another."""
return self.should_stop_flag
def get_time_since_start(self) -> float:
"""How much time has passed since the creation of the context."""
return time.time() - self.start_time
def get_time_since_last_update(self) -> float:
"""How much time has passed since the last call to update."""
return time.time() - self.last_update_time
def get_last_update_interval(self) -> float:
"""How much time passed between the previous two calls to update."""
return self.last_update_interval
def close(self) -> None:
"""Close the context and clean up.
Should only be called once."""
if not self.has_closed:
# update the run.txt with stopping time
self.run_txt_data["stop_time"] = datetime.datetime.now().isoformat(sep=" ")
with open(os.path.join(self.submit_config.run_dir, "run.txt"), "w") as f:
pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False)
self.has_closed = True
# detach the global singleton
global _run_context
if _run_context is self:
_run_context = None
@staticmethod
def get():
import dnnlib
if _run_context is not None:
return _run_context
return RunContext(dnnlib.submit_config)