mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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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>
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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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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Optimizers class.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Union
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from omegaconf import DictConfig
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from torch.cuda.amp.grad_scaler import GradScaler
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from torch.nn.parameter import Parameter
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from nerfactory.utils import writer
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def setup_optimizers(
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config: Union[DictConfig, Dict[str, Any]], param_groups: Dict[str, List[Parameter]]
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) -> "Optimizers":
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"""Helper to set up the optimizers
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Args:
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config: The optimizer configuration object.
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param_groups: A dictionary of parameter groups to optimize.
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Returns:
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The optimizers object.
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"""
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return Optimizers(config, param_groups)
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class Optimizers:
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"""A set of optimizers.
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Args:
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config: The optimizer configuration object.
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param_groups: A dictionary of parameter groups to optimize.
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"""
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def __init__(self, config: Union[DictConfig, Dict[str, Any]], param_groups: Dict[str, List[Parameter]]):
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self.config = config
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self.optimizers = {}
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self.schedulers = {}
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for param_group_name, params in param_groups.items():
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lr_init = config[param_group_name]["optimizer"].lr
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self.optimizers[param_group_name] = config[param_group_name]["optimizer"].setup(params=params)
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if config[param_group_name]["scheduler"]:
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self.schedulers[param_group_name] = config[param_group_name]["scheduler"].setup(
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optimizer=self.optimizers[param_group_name], lr_init=lr_init
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)
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def optimizer_step(self, param_group_name: str) -> None:
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"""Fetch and step corresponding optimizer.
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Args:
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param_group_name: name of optimizer to step forward
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"""
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self.optimizers[param_group_name].step()
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def scheduler_step(self, param_group_name: str) -> None:
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"""Fetch and step corresponding scheduler.
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Args:
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param_group_name: name of scheduler to step forward
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"""
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if self.config.param_group_name.scheduler: # type: ignore
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self.schedulers[param_group_name].step()
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def zero_grad_all(self) -> None:
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"""Zero the gradients for all optimizer parameters."""
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for _, optimizer in self.optimizers.items():
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optimizer.zero_grad()
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def optimizer_scaler_step_all(self, grad_scaler: GradScaler) -> None:
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"""Take an optimizer step using a grad scaler.
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Args:
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grad_scaler: GradScaler to use
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"""
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for _, optimizer in self.optimizers.items():
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grad_scaler.step(optimizer)
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def optimizer_step_all(self):
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"""Run step for all optimizers."""
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for _, optimizer in self.optimizers.items():
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# note that they key is the parameter name
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optimizer.step()
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def scheduler_step_all(self, step: int) -> None:
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"""Run step for all schedulers.
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Args:
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step: the current step
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"""
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for param_group_name, scheduler in self.schedulers.items():
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scheduler.step()
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# TODO(ethan): clean this up. why is there indexing into a list?
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lr = scheduler.get_last_lr()[0]
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writer.put_scalar(name=f"learning_rate/{param_group_name}", scalar=lr, step=step)
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def load_optimizers(self, loaded_state: Dict[str, Any]) -> None:
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"""Helper to load the optimizer state from previous checkpoint
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Args:
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loaded_state: the state from the previous checkpoint
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"""
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for k, v in loaded_state.items():
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self.optimizers[k].load_state_dict(v)
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