mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 11:24:00 +08:00
Ethan Weber
146332cfb6
* in progress. camera_indices broken * faster dataloader. starting on pixel sampler logic * imagesampler and pixelsampler * lint and black * continuing merge * pytest working * fixed docs * docs * minor changes
278 lines
12 KiB
Python
278 lines
12 KiB
Python
"""
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Code to train model.
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"""
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import copy
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import logging
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import os
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from time import time
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from typing import Callable, Dict, List
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import torch
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import torch.distributed as dist
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from omegaconf import DictConfig
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from torch.nn.parallel import DistributedDataParallel as DDP
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from pyrad.nerf.dataset.utils import DatasetInputs, get_dataset_inputs_from_dataset_config
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from pyrad.nerf.image_sampler import CacheImageSampler
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from pyrad.nerf.optimizers import Optimizers
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from pyrad.nerf.pixel_sampler import PixelSampler
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from pyrad.utils import profiler, writer
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from pyrad.utils.callbacks import update_occupancy
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from pyrad.utils.decorators import check_main_thread
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from pyrad.utils.misc import get_dict_to_torch, instantiate_from_dict_config
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logging.getLogger("PIL").setLevel(logging.WARNING)
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class Trainer:
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"""Training class"""
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def __init__(self, config: DictConfig, local_rank: int = 0, world_size: int = 1):
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"""_summary_
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Args:
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config (DictConfig): _description_
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local_rank (int, optional): _description_. Defaults to 0.
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world_size (int, optional): _description_. Defaults to 1.
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cpu (bool, optional): Whether or not to use the CPU.
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"""
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self.config = config
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self.local_rank = local_rank
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self.world_size = world_size
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# dataset variables
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self.train_image_dataset = None
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self.train_image_sampler = None
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self.train_pixel_sampler = None
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self.val_image_dataset = None
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self.val_image_intrinsics = None
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self.val_image_camera_to_world = None
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# model variables
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self.graph = None
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self.optimizers = None
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self.start_step = 0
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# logging variables
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writer.setup_event_writers(config)
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profiler.setup_profiler(config.logging)
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self.device = "cpu" if self.world_size == 0 else f"cuda:{self.local_rank}"
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def setup(self, test_mode=False):
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"""Setup the Trainer by calling other setup functions."""
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dataset_inputs_train = get_dataset_inputs_from_dataset_config(**self.config.data.dataset, split="train")
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if test_mode:
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dataset_inputs_eval = get_dataset_inputs_from_dataset_config(**self.config.data.dataset, split="test")
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else:
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config_data_dataset_val = copy.deepcopy(self.config.data.dataset)
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config_data_dataset_val.downscale_factor = self.config.data.val_downscale_factor
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dataset_inputs_eval = get_dataset_inputs_from_dataset_config(**config_data_dataset_val, split="val")
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self.setup_dataset_train(dataset_inputs_train)
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self.setup_dataset_eval(dataset_inputs_eval)
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self.setup_graph(dataset_inputs_train)
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@profiler.time_function
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def setup_dataset_eval(self, dataset_inputs: DatasetInputs):
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"""Helper method to load test or val dataset based on test/train mode"""
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self.val_image_dataset = instantiate_from_dict_config(
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self.config.data.image_dataset,
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image_filenames=dataset_inputs.image_filenames,
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downscale_factor=dataset_inputs.downscale_factor,
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semantics=dataset_inputs.semantics,
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alpha_color=dataset_inputs.alpha_color,
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)
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self.val_image_intrinsics = dataset_inputs.intrinsics
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self.val_image_camera_to_world = dataset_inputs.camera_to_world
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@profiler.time_function
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def setup_dataset_train(self, dataset_inputs: DatasetInputs):
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"""_summary_"""
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self.train_image_dataset = instantiate_from_dict_config(
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self.config.data.image_dataset,
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image_filenames=dataset_inputs.image_filenames,
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downscale_factor=dataset_inputs.downscale_factor,
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semantics=dataset_inputs.semantics,
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alpha_color=dataset_inputs.alpha_color,
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) # ImageDataset
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self.train_image_sampler = CacheImageSampler(
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self.train_image_dataset,
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num_samples_to_collate=len(self.train_image_dataset)
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if self.config.data.image_sampler.num_images_to_sample_from == 0
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else self.config.data.image_sampler.num_images_to_sample_from,
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num_times_to_repeat_images=self.config.data.image_sampler.num_times_to_repeat_images,
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device=self.device if self.config.data.image_sampler.move_to_graph_device else "cpu",
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) # ImageSampler
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self.train_pixel_sampler = PixelSampler(
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num_rays_per_batch=self.config.data.pixel_sampler.num_rays_per_batch, keep_full_image=False
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) # PixelSampler
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@profiler.time_function
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def setup_graph(self, dataset_inputs: DatasetInputs):
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"""Setup the graph. The dataset inputs should be set with the training data.
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Args:
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dataset_inputs (DatasetInputs): The inputs which will be used to define the camera parameters.
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"""
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self.graph = instantiate_from_dict_config(
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self.config.graph.network,
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intrinsics=dataset_inputs.intrinsics,
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camera_to_world=dataset_inputs.camera_to_world,
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scene_bounds=dataset_inputs.scene_bounds,
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stuff_classes=dataset_inputs.semantics.stuff_classes,
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stuff_colors=dataset_inputs.semantics.stuff_colors,
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)
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self.graph.to(self.device)
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self.setup_optimizers() # NOTE(ethan): can this be before DDP?
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if self.config.graph.resume_train.load_dir:
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self.load_checkpoint(self.config.graph.resume_train)
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if self.world_size > 1:
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self.graph = DDP(self.graph, device_ids=[self.local_rank])
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dist.barrier(device_ids=[self.local_rank])
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def setup_optimizers(self):
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"""_summary_"""
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self.optimizers = Optimizers(self.config.graph.param_groups, self.graph.get_param_groups())
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def load_checkpoint(self, load_config: DictConfig) -> int:
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"""Load the checkpoint from the given path
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Args:
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load_path (str): path from which to load the model
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Returns:
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int: step iteration associated with the loaded checkpoint
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"""
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load_path = os.path.join(load_config.load_dir, f"step-{load_config.load_step:09d}.ckpt")
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assert os.path.exists(load_path), f"Checkpoint {load_path} does not exist"
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loaded_state = torch.load(load_path, map_location="cpu")
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self.graph.load_state_dict({key.replace("module.", ""): value for key, value in loaded_state["model"].items()})
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for k, v in loaded_state["optimizers"].items():
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self.optimizers.optimizers[k].load_state_dict(v)
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self.start_step = loaded_state["step"] + 1
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logging.info("done loading checkpoint from %s", load_path)
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@check_main_thread
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def save_checkpoint(self, output_dir: str, step: int) -> None:
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"""Save the model and optimizers
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Args:
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output_dir (str): directory to save the checkpoint
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step (int): number of steps in training for given checkpoint
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model (Graph): Graph model to be saved
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optimizers (Optimizers): Optimizers to be saved
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"""
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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ckpt_path = os.path.join(output_dir, f"step-{step:09d}.ckpt")
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torch.save(
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{
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"step": step,
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"model": self.graph.module.state_dict() if hasattr(self.graph, "module") else self.graph.state_dict(),
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"optimizers": {k: v.state_dict() for (k, v) in self.optimizers.optimizers.items()},
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},
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ckpt_path,
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)
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@classmethod
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def get_aggregated_loss(cls, loss_dict: Dict[str, torch.tensor]):
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"""Returns the aggregated losses and the scalar for calling .backwards() on.
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# TODO: move this out to another file/class/etc.
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"""
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loss_sum = 0.0
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for loss_name in loss_dict.keys():
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# TODO(ethan): add loss weightings here from a config
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loss_sum += loss_dict[loss_name]
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return loss_sum
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def train(self) -> None:
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"""_summary_"""
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train_start = time()
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num_iterations = self.config.graph.max_num_iterations
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iter_train_image_sampler = iter(self.train_image_sampler)
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for step in range(self.start_step, self.start_step + num_iterations):
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data_start = time()
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image_batch = next(iter_train_image_sampler)
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image_batch = get_dict_to_torch(image_batch, device=self.device)
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batch = self.train_pixel_sampler.sample(image_batch)
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writer.put_time(
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name=writer.EventName.ITER_LOAD_TIME,
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start_time=data_start,
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end_time=time(),
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step=step,
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avg_over_iters=True,
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)
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iter_start = time()
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loss_dict = self.train_iteration(batch, step, _callback=[update_occupancy])
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writer.put_time(
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name=writer.EventName.RAYS_PER_SEC,
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start_time=iter_start,
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end_time=time(),
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step=step,
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avg_over_iters=True,
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avg_over_batch=batch["indices"].shape[0],
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)
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writer.put_time(
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name=writer.EventName.ITER_TRAIN_TIME,
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start_time=iter_start,
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end_time=time(),
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step=step,
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avg_over_iters=True,
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update_eta=True,
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)
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if step != 0 and step % self.config.logging.steps_per_log == 0:
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writer.put_dict(name="Loss/train-loss_dict", scalar_dict=loss_dict, step=step)
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if step != 0 and self.config.graph.steps_per_save and step % self.config.graph.steps_per_save == 0:
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self.save_checkpoint(self.config.graph.model_dir, step)
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if step % self.config.graph.steps_per_test == 0:
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for image_idx in self.config.data.val_image_indices:
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_ = self.test_image(image_idx=image_idx, step=step)
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self._write_out_storage(step)
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writer.put_time(
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name=writer.EventName.TOTAL_TRAIN_TIME, start_time=train_start, end_time=time(), step=num_iterations
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)
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self._write_out_storage(num_iterations)
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def _write_out_storage(self, step):
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"""Perform writes only during appropriate time steps"""
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if (
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step % self.config.logging.steps_per_log == 0
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or (self.config.graph.steps_per_save and step % self.config.graph.steps_per_save == 0)
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or step % self.config.graph.steps_per_test == 0
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or step == self.config.graph.max_num_iterations
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):
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writer.write_out_storage()
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@profiler.time_function
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def train_iteration(self, batch: dict, step: int, _callback: List[Callable] = None):
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"""Run one iteration with a batch of inputs."""
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ray_indices = batch["indices"]
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_, loss_dict = self.graph.forward(ray_indices, batch=batch, step=step)
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loss = loss_dict["aggregated_loss"]
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self.optimizers.zero_grad_all()
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loss.backward()
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self.optimizers.optimizer_step_all()
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self.optimizers.scheduler_step_all(step)
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if _callback:
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for _func in _callback:
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_func(self.graph)
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return loss_dict
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@profiler.time_function
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def test_image(self, image_idx, step):
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"""Test a specific image."""
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self.graph.eval()
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intrinsics = self.val_image_intrinsics[image_idx].to(self.device)
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camera_to_world = self.val_image_camera_to_world[image_idx].to(self.device)
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chunk_size = self.config.data.val_num_rays_per_chunk
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training_camera_index = image_idx # TODO(ethan): change this because training and test should not be the same
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outputs = self.graph.get_outputs_for_camera(
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intrinsics, camera_to_world, chunk_size=chunk_size, training_camera_index=training_camera_index
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)
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batch = self.val_image_dataset[image_idx]
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batch = get_dict_to_torch(batch, device=self.device)
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psnr = self.graph.log_test_image_outputs(image_idx, step, batch, outputs)
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self.graph.train()
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return psnr
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