# 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. """ Code to train model. """ import functools import logging import os from typing import Dict import torch import torch.distributed as dist from torch.cuda.amp import GradScaler from torch.nn.parallel import DistributedDataParallel as DDP from torchtyping import TensorType from nerfactory.cameras.rays import RayBundle from nerfactory.data.dataloader import ( EvalDataloader, setup_dataset_eval, setup_dataset_train, ) from nerfactory.graphs.base import setup_graph from nerfactory.optimizers.optimizers import setup_optimizers from nerfactory.utils import profiler, writer from nerfactory.utils.config import Config from nerfactory.utils.decorators import check_main_thread from nerfactory.utils.writer import EventName, TimeWriter from nerfactory.viewer.server import viewer_utils logging.getLogger("PIL").setLevel(logging.WARNING) class Trainer: """Training class Args: config (Config): The configuration object. local_rank (int, optional): Local rank of the process. Defaults to 0. world_size (int, optional): World size of the process. Defaults to 1. """ def __init__(self, config: Config, local_rank: int = 0, world_size: int = 1): self.config = config self.local_rank = local_rank self.world_size = world_size self.device = "cpu" if world_size == 0 else f"cuda:{local_rank}" self.mixed_precision = self.config.trainer.mixed_precision if self.device == "cpu": self.mixed_precision = False logging.warning("Mixed precision is disabled for CPU training.") # dataset variables self.dataset_inputs_train = None self.dataloader_train = None self.dataloader_eval = None # model variables self.graph = None self.optimizers = None self.start_step = 0 # logging variables writer.setup_event_writers(config.logging, max_iter=config.trainer.max_num_iterations) profiler.setup_profiler(config.logging) # visualizer variable self.visualizer_state = viewer_utils.VisualizerState(config.viewer) self.grad_scaler = GradScaler(enabled=self.mixed_precision) def setup(self, test_mode=False): """Setup the Trainer by calling other setup functions. Args: test_mode (bool, optional): Whether to setup for testing. Defaults to False. """ self.dataset_inputs_train, self.dataloader_train = setup_dataset_train(self.config.data, device=self.device) _, self.dataloader_eval = setup_dataset_eval(self.config.data, test_mode=test_mode, device=self.device) self.graph = setup_graph(self.config.graph, self.dataset_inputs_train, device=self.device) self.optimizers = setup_optimizers(self.config.optimizers, self.graph.get_param_groups()) if self.config.trainer.resume_train.load_dir: self._load_checkpoint() if self.world_size > 1: self.graph = DDP(self.graph, device_ids=[self.local_rank]) dist.barrier(device_ids=[self.local_rank]) self.graph.register_callbacks() @classmethod def get_aggregated_loss(cls, loss_dict: Dict[str, torch.Tensor]) -> torch.Tensor: """Returns the aggregated losses and the scalar for calling .backwards() on. # TODO: move this out to another file/class/etc. """ # TODO(ethan): add loss weightings here from a config # e.g. weighted_losses = map(lambda k: some_weight_dict[k] * loss_dict[k], loss_dict.keys()) weighted_losses = loss_dict.values() return functools.reduce(torch.add, weighted_losses) def train(self) -> None: """Train the model.""" self.visualizer_state.init_scene( image_dataset=self.dataloader_train.image_sampler.dataset, dataset_inputs=self.dataset_inputs_train ) with TimeWriter(writer, EventName.TOTAL_TRAIN_TIME): num_iterations = self.config.trainer.max_num_iterations iter_dataloader_train = iter(self.dataloader_train) for step in range(self.start_step, self.start_step + num_iterations): with TimeWriter(writer, EventName.ITER_LOAD_TIME, step=step): ray_indices, batch = next(iter_dataloader_train) with TimeWriter(writer, EventName.ITER_TRAIN_TIME, step=step) as t: loss_metric_dict = self.train_iteration(ray_indices, batch, step) writer.put_scalar(name=EventName.RAYS_PER_SEC, scalar=ray_indices.shape[0] / t.duration, step=step) if step != 0 and step % self.config.logging.steps_per_log == 0: writer.put_dict(name="Loss/train-loss_dict", scalar_dict=loss_metric_dict, step=step) if step != 0 and self.config.trainer.steps_per_save and step % self.config.trainer.steps_per_save == 0: self._save_checkpoint(self.config.trainer.model_dir, step) if step % self.config.trainer.steps_per_test == 0: self.eval_with_dataloader(self.dataloader_eval, step=step) self._write_out_storage(step) self.visualizer_state.update_scene(step, self.graph) self._write_out_storage(num_iterations) def _write_out_storage(self, step: int) -> None: """Perform writes only during appropriate time steps Args: step (int): Current training step. """ if ( step % self.config.logging.steps_per_log == 0 or (self.config.trainer.steps_per_save and step % self.config.trainer.steps_per_save == 0) or step % self.config.trainer.steps_per_test == 0 or step == self.config.trainer.max_num_iterations ): writer.write_out_storage() def _load_checkpoint(self) -> None: """Helper function to load graph and optimizer from prespecified checkpoint""" load_config = self.config.trainer.resume_train load_path = os.path.join(load_config.load_dir, f"step-{load_config.load_step:09d}.ckpt") assert os.path.exists(load_path), f"Checkpoint {load_path} does not exist" loaded_state = torch.load(load_path, map_location="cpu") self.start_step = loaded_state["step"] + 1 # load the checkpoints for graph and optimizer self.graph.load_graph(loaded_state) self.optimizers.load_optimizers(loaded_state) self.grad_scaler.load_state_dict(loaded_state["scaler"]) logging.info("done loading checkpoint from %s", load_path) @check_main_thread def _save_checkpoint(self, output_dir: str, step: int) -> None: """Save the model and optimizers Args: output_dir: directory to save the checkpoint step: number of steps in training for given checkpoint """ if not os.path.exists(output_dir): os.makedirs(output_dir) ckpt_path = os.path.join(output_dir, f"step-{step:09d}.ckpt") torch.save( { "step": step, "model": self.graph.module.state_dict() if hasattr(self.graph, "module") else self.graph.state_dict(), "optimizers": {k: v.state_dict() for (k, v) in self.optimizers.optimizers.items()}, "scalers": self.grad_scaler.state_dict(), }, ckpt_path, ) @profiler.time_function def train_iteration(self, ray_indices: TensorType["num_rays", 3], batch: dict, step: int) -> Dict[str, float]: """Run one iteration with a batch of inputs. Args: ray_indices: Contains camera, row, and col indicies for target rays. batch: Batch of training data. step: Current training step. Returns: Dict[str, float]: Dictionary of model losses and metrics. """ self.optimizers.zero_grad_all() with torch.autocast(device_type=ray_indices.device.type, enabled=self.mixed_precision): _, loss_dict, metrics_dict = self.graph.forward(ray_indices, batch=batch) loss = sum(loss_dict.values()) self.grad_scaler.scale(loss).backward() self.optimizers.optimizer_scaler_step_all(self.grad_scaler) self.grad_scaler.update() self.optimizers.scheduler_step_all(step) if self.graph.callbacks: for func_ in self.graph.callbacks: func_.after_step(step) # Merging loss and metrics dict into a single output. loss_dict["loss"] = loss loss_dict.update(metrics_dict) return loss_dict @profiler.time_function def test_image(self, camera_ray_bundle: RayBundle, batch: dict, step: int = None) -> float: """Test a specific image. Args: camera_ray_bundle: Bundle of test rays. batch: Batch of data. step: Current training step. Returns: float: PSNR """ self.graph.eval() image_idx = int(camera_ray_bundle.camera_indices[0, 0]) outputs = self.graph.get_outputs_for_camera_ray_bundle(camera_ray_bundle) psnr = self.graph.log_test_image_outputs(image_idx, step, batch, outputs) self.graph.train() return psnr def eval_with_dataloader(self, dataloader: EvalDataloader, step: int = None) -> None: """Run evaluation with a given dataloader. Args: dataloader: Evaluation dataloader. step: Current training iteration. """ for camera_ray_bundle, batch in dataloader: self.test_image(camera_ray_bundle, batch, step=step)