mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-11 02:24:38 +08:00
7e9a56fac4
* starting docs for pipelines * working on an example * clearing output * starting to implement config changes and add documentation * wip: removed cicular imports; porting basic config into python; todo: get python setups workin * circular imports again * wip: pausing at dataloader sort of working * break for today * running vanilla * wip need to get all the config docs working * laptop about to die * instant ngp working * added all configs for all methods; not tested yet: [mipnerf360, nerfw, semantic_nerf] * unpacking later * done with pylinting * mostly done. just need to do docs and one test * added import order to docs * fixing near/far plane and other config things * making loggers also instantiable via configs * docs * lint * making --help work with dcargs * moving configs back into flat * convert list to tuples; fixed post init function with mutable params * lint * made run_eval and benchmarking scripts compatible with new config/dcargs * lint * added option to load configs from presaved config Co-authored-by: evonneng <evonne_ng@berkeley.edu>
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
# Copyright 2022 The Plenoptix Team. 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.
|
|
|
|
"""
|
|
Profiler base class and functionality
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import logging
|
|
import time
|
|
from typing import Callable
|
|
|
|
from nerfactory.configs import base as cfg
|
|
from nerfactory.utils import comms
|
|
from nerfactory.utils.decorators import (
|
|
check_main_thread,
|
|
check_profiler_enabled,
|
|
decorate_all,
|
|
)
|
|
|
|
PROFILER = []
|
|
|
|
|
|
def time_function(func: Callable) -> Callable:
|
|
"""Decorator: time a function call"""
|
|
|
|
def wrapper(*args, **kwargs):
|
|
start = time.time()
|
|
ret = func(*args, **kwargs)
|
|
if PROFILER:
|
|
class_str = func.__qualname__
|
|
PROFILER[0].update_time(class_str, start, time.time())
|
|
return ret
|
|
|
|
return wrapper
|
|
|
|
|
|
def flush_profiler(config: cfg.LoggingConfig):
|
|
"""Method that checks if profiler is enabled before flushing"""
|
|
if config.enable_profiler and PROFILER:
|
|
PROFILER[0].print_profile()
|
|
|
|
|
|
def setup_profiler(config: cfg.LoggingConfig):
|
|
"""Initialization of profilers"""
|
|
if comms.is_main_process():
|
|
PROFILER.append(Profiler(config))
|
|
|
|
|
|
@decorate_all([check_profiler_enabled, check_main_thread])
|
|
class Profiler:
|
|
"""Profiler class"""
|
|
|
|
def __init__(self, config: cfg.LoggingConfig):
|
|
self.config = config
|
|
self.profiler_dict = {}
|
|
|
|
def update_time(self, func_name: str, start_time: float, end_time: float):
|
|
"""update the profiler dictionary with running averages of durations
|
|
|
|
Args:
|
|
func_name (str): the function name that is being profiled
|
|
start_time (float): the start time when function is called
|
|
end_time (float): the end time when function terminated
|
|
"""
|
|
val = end_time - start_time
|
|
func_dict = self.profiler_dict.get(func_name, {"val": 0, "step": 0})
|
|
prev_val = func_dict["val"]
|
|
prev_step = func_dict["step"]
|
|
self.profiler_dict[func_name] = {"val": (prev_val * prev_step + val) / (prev_step + 1), "step": prev_step + 1}
|
|
|
|
def print_profile(self):
|
|
"""helper to print out the profiler stats"""
|
|
logging.info("Printing profiling stats, from longest to shortest duration in seconds")
|
|
sorted_keys = sorted(
|
|
self.profiler_dict.keys(),
|
|
key=lambda k: self.profiler_dict[k]["val"],
|
|
reverse=True,
|
|
)
|
|
for k in sorted_keys:
|
|
val = f"{self.profiler_dict[k]['val']:0.4f}"
|
|
print(f"{k:<20}: {val:<20}")
|