mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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56661b5d9a
* updated three.js version. only send camera information when it moves * changing code back to updating camera constantly because packets seem to be lost * interrupting function during rendering * small linting fixes * added logic for interrupting rendering + separted out visualizer logic from trainer Co-authored-by: evonneng <evonne_ng@berkeley.edu>
164 lines
4.4 KiB
Python
164 lines
4.4 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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"""Structured config classes"""
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from dataclasses import MISSING, dataclass
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from typing import Any, Dict, Optional
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from omegaconf import DictConfig
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@dataclass
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class MachineConfig:
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"""Configuration of machine setup"""
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seed: int = MISSING
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num_gpus: int = MISSING
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num_machines: int = MISSING
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machine_rank: int = MISSING
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dist_url: str = MISSING
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@dataclass
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class LoggingConfig:
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"""Configuration of loggers and profilers"""
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steps_per_log: int = MISSING
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max_buffer_size: int = MISSING
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writer: Dict[str, Any] = MISSING
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enable_profiler: bool = MISSING
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@dataclass
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class ResumeTrainConfig:
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"""Configuration for loading previous checkpoints"""
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load_dir: Optional[str] = None
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load_step: Optional[int] = None
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@dataclass
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class TrainerConfig:
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"""Configuration for training regimen"""
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model_dir: str = MISSING
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steps_per_save: int = MISSING
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steps_per_test: int = MISSING
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max_num_iterations: int = MISSING
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# additional optional parameters here
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resume_train: Optional[ResumeTrainConfig] = None
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@dataclass
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class DatasetInputsConfig:
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"""Configuration for dataset specification"""
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data_directory: str = MISSING
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dataset_format: str = MISSING
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downscale_factor: int = MISSING
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alpha_color: str = MISSING
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@dataclass
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class ImageDatasetConfig:
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"""Configuration for type of dataset to instantiate"""
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_target_: str = MISSING
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@dataclass
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class DataConfig:
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"""Configuration for train/eval datasets"""
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dataset_inputs_train: DatasetInputsConfig = MISSING
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image_dataset_train: ImageDatasetConfig = MISSING
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dataloader_train: Dict[str, Any] = MISSING
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dataset_inputs_eval: DatasetInputsConfig = MISSING
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image_dataset_eval: ImageDatasetConfig = MISSING
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dataloader_eval: Dict[str, Any] = MISSING
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# additional optional parameters here
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pixel_sampler: Optional[Dict[str, Any]] = None
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@dataclass
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class GraphConfig:
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"""Configuration for graph instantiation"""
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_target_: str = MISSING
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collider_config: Dict[str, Any] = MISSING
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num_coarse_samples: int = MISSING
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num_importance_samples: int = MISSING
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loss_coefficients: Dict[str, Any] = MISSING
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# additional optional parameters here
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field_implementation: Optional[str] = "torch"
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@dataclass
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class ViewerConfig:
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"""Configuration for viewer instantiation"""
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enable: bool = MISSING
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zmq_url: str = MISSING
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min_render_image_height: int = MISSING
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max_render_image_height: int = MISSING
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num_rays_per_chunk: int = MISSING
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@dataclass
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class Config:
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"""Full config contents"""
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machine: MachineConfig = MISSING
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logging: LoggingConfig = MISSING
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trainer: TrainerConfig = MISSING
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experiment_name: str = MISSING
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method_name: str = MISSING
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data: DataConfig = MISSING
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graph: GraphConfig = MISSING
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optimizers: Dict[str, Any] = MISSING
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viewer: Dict[str, Any] = MISSING
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# additional optional parameters here
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hydra: Optional[Dict[str, Any]] = None
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def setup_config(config: DictConfig) -> Config:
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"""helper that creates a typed config from the DictConfig
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Args:
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config (DictConfig): configuration to convert
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Returns:
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Config: typed version of the input configuration
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"""
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machine = MachineConfig(**config.machine)
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logging = LoggingConfig(**config.logging)
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trainer = TrainerConfig(**config.trainer)
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experiment_name = config.experiment_name
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method_name = config.method_name
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data = DataConfig(**config.data)
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graph = GraphConfig(**config.graph)
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optimizers = config.optimizers
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viewer = ViewerConfig(**config.viewer)
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return Config(
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machine=machine,
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logging=logging,
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trainer=trainer,
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experiment_name=experiment_name,
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method_name=method_name,
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data=data,
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graph=graph,
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optimizers=optimizers,
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viewer=viewer,
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)
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