import glob import logging import os import shutil import sys import numpy as np import random import time from absl import app import gin from internal import configs from internal import datasets from internal import image from internal import models from internal import train_utils from internal import utils from internal import vis from internal import checkpoints import torch import accelerate import tensorboardX from tqdm import tqdm from tqdm.contrib.logging import logging_redirect_tqdm from torch.utils._pytree import tree_map configs.define_common_flags() TIME_PRECISION = 1000 # Internally represent integer times in milliseconds. def main(unused_argv): config = configs.load_config() config.exp_path = os.path.join("exp", config.exp_name) config.checkpoint_dir = os.path.join(config.exp_path, 'checkpoints') utils.makedirs(config.exp_path) with utils.open_file(os.path.join(config.exp_path, 'config.gin'), 'w') as f: f.write(gin.config_str()) # accelerator for DDP accelerator = accelerate.Accelerator() # setup logger logging.basicConfig( format="%(asctime)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S", force=True, handlers=[logging.StreamHandler(sys.stdout), logging.FileHandler(os.path.join(config.exp_path, 'log_train.txt'))], level=logging.INFO, ) sys.excepthook = utils.handle_exception logger = accelerate.logging.get_logger(__name__) logger.info(config) logger.info(accelerator.state, main_process_only=False) config.world_size = accelerator.num_processes config.global_rank = accelerator.process_index if config.batch_size % accelerator.num_processes != 0: config.batch_size -= config.batch_size % accelerator.num_processes != 0 logger.info('turn batch size to', config.batch_size) # Set random seed. accelerate.utils.set_seed(config.seed, device_specific=True) # setup model and optimizer model = models.Model(config=config) optimizer, lr_fn = train_utils.create_optimizer(config, model) # load dataset dataset = datasets.load_dataset('train', config.data_dir, config) test_dataset = datasets.load_dataset('test', config.data_dir, config) dataloader = torch.utils.data.DataLoader(np.arange(len(dataset)), num_workers=8, shuffle=True, batch_size=1, collate_fn=dataset.collate_fn, persistent_workers=True, ) test_dataloader = torch.utils.data.DataLoader(np.arange(len(test_dataset)), num_workers=4, shuffle=False, batch_size=1, persistent_workers=True, collate_fn=test_dataset.collate_fn, ) if config.rawnerf_mode: postprocess_fn = test_dataset.metadata['postprocess_fn'] else: postprocess_fn = lambda z, _=None: z # use accelerate to prepare. model, dataloader, optimizer = accelerator.prepare(model, dataloader, optimizer) if config.resume_from_checkpoint: init_step = checkpoints.restore_checkpoint(config.checkpoint_dir, accelerator, logger) else: init_step = 0 module = accelerator.unwrap_model(model) dataiter = iter(dataloader) test_dataiter = iter(test_dataloader) num_params = train_utils.tree_len(list(model.parameters())) logger.info(f'Number of parameters being optimized: {num_params}') if (dataset.size > module.num_glo_embeddings and module.num_glo_features > 0): raise ValueError(f'Number of glo embeddings {module.num_glo_embeddings} ' f'must be at least equal to number of train images ' f'{dataset.size}') # metric handler metric_harness = image.MetricHarness() # tensorboard if accelerator.is_main_process: summary_writer = tensorboardX.SummaryWriter(config.exp_path) # function to convert image for tensorboard tb_process_fn = lambda x: x.transpose(2, 0, 1) if len(x.shape) == 3 else x[None] if config.rawnerf_mode: for name, data in zip(['train', 'test'], [dataset, test_dataset]): # Log shutter speed metadata in TensorBoard for debug purposes. for key in ['exposure_idx', 'exposure_values', 'unique_shutters']: summary_writer.add_text(f'{name}_{key}', str(data.metadata[key]), 0) logger.info("Begin training...") step = init_step + 1 total_time = 0 total_steps = 0 reset_stats = True if config.early_exit_steps is not None: num_steps = config.early_exit_steps else: num_steps = config.max_steps init_step = 0 with logging_redirect_tqdm(): tbar = tqdm(range(init_step + 1, num_steps + 1), desc='Training', initial=init_step, total=num_steps, disable=not accelerator.is_main_process) for step in tbar: try: batch = next(dataiter) except StopIteration: dataiter = iter(dataloader) batch = next(dataiter) batch = accelerate.utils.send_to_device(batch, accelerator.device) if reset_stats and accelerator.is_main_process: stats_buffer = [] train_start_time = time.time() reset_stats = False # use lr_fn to control learning rate learning_rate = lr_fn(step) for param_group in optimizer.param_groups: param_group['lr'] = learning_rate # fraction of training period train_frac = np.clip((step - 1) / (config.max_steps - 1), 0, 1) # Indicates whether we need to compute output normal or depth maps in 2D. compute_extras = (config.compute_disp_metrics or config.compute_normal_metrics) optimizer.zero_grad() with accelerator.autocast(): renderings, ray_history = model( True, batch, train_frac=train_frac, compute_extras=compute_extras, zero_glo=False) losses = {} # supervised by data data_loss, stats = train_utils.compute_data_loss(batch, renderings, config) losses['data'] = data_loss # interlevel loss in MipNeRF360 if config.interlevel_loss_mult > 0 and not module.single_mlp: losses['interlevel'] = train_utils.interlevel_loss(ray_history, config) # interlevel loss in ZipNeRF360 if config.anti_interlevel_loss_mult > 0 and not module.single_mlp: losses['anti_interlevel'] = train_utils.anti_interlevel_loss(ray_history, config) # distortion loss if config.distortion_loss_mult > 0: losses['distortion'] = train_utils.distortion_loss(ray_history, config) # opacity loss if config.opacity_loss_mult > 0: losses['opacity'] = train_utils.opacity_loss(renderings, config) # orientation loss in RefNeRF if (config.orientation_coarse_loss_mult > 0 or config.orientation_loss_mult > 0): losses['orientation'] = train_utils.orientation_loss(batch, module, ray_history, config) # hash grid l2 weight decay if config.hash_decay_mults > 0: losses['hash_decay'] = train_utils.hash_decay_loss(ray_history, config) # normal supervision loss in RefNeRF if (config.predicted_normal_coarse_loss_mult > 0 or config.predicted_normal_loss_mult > 0): losses['predicted_normals'] = train_utils.predicted_normal_loss( module, ray_history, config) loss = sum(losses.values()) stats['loss'] = loss.item() stats['losses'] = tree_map(lambda x: x.item(), losses) # accelerator automatically handle the scale accelerator.backward(loss) # clip gradient by max/norm/nan train_utils.clip_gradients(model, accelerator, config) optimizer.step() stats['psnrs'] = image.mse_to_psnr(stats['mses']) stats['psnr'] = stats['psnrs'][-1] # Log training summaries. This is put behind a host_id check because in # multi-host evaluation, all hosts need to run inference even though we # only use host 0 to record results. if accelerator.is_main_process: stats_buffer.append(stats) if step == init_step + 1 or step % config.print_every == 0: elapsed_time = time.time() - train_start_time steps_per_sec = config.print_every / elapsed_time rays_per_sec = config.batch_size * steps_per_sec # A robust approximation of total training time, in case of pre-emption. total_time += int(round(TIME_PRECISION * elapsed_time)) total_steps += config.print_every approx_total_time = int(round(step * total_time / total_steps)) # Transpose and stack stats_buffer along axis 0. fs = [utils.flatten_dict(s, sep='/') for s in stats_buffer] stats_stacked = {k: np.stack([f[k] for f in fs]) for k in fs[0].keys()} # Split every statistic that isn't a vector into a set of statistics. stats_split = {} for k, v in stats_stacked.items(): if v.ndim not in [1, 2] and v.shape[0] != len(stats_buffer): raise ValueError('statistics must be of size [n], or [n, k].') if v.ndim == 1: stats_split[k] = v elif v.ndim == 2: for i, vi in enumerate(tuple(v.T)): stats_split[f'{k}/{i}'] = vi # Summarize the entire histogram of each statistic. for k, v in stats_split.items(): summary_writer.add_histogram('train_' + k, v, step) # Take the mean and max of each statistic since the last summary. avg_stats = {k: np.mean(v) for k, v in stats_split.items()} max_stats = {k: np.max(v) for k, v in stats_split.items()} summ_fn = lambda s, v: summary_writer.add_scalar(s, v, step) # pylint:disable=cell-var-from-loop # Summarize the mean and max of each statistic. for k, v in avg_stats.items(): summ_fn(f'train_avg_{k}', v) for k, v in max_stats.items(): summ_fn(f'train_max_{k}', v) summ_fn('train_num_params', num_params) summ_fn('train_learning_rate', learning_rate) summ_fn('train_steps_per_sec', steps_per_sec) summ_fn('train_rays_per_sec', rays_per_sec) summary_writer.add_scalar('train_avg_psnr_timed', avg_stats['psnr'], total_time // TIME_PRECISION) summary_writer.add_scalar('train_avg_psnr_timed_approx', avg_stats['psnr'], approx_total_time // TIME_PRECISION) if dataset.metadata is not None and module.learned_exposure_scaling: scalings = module.exposure_scaling_offsets.weight num_shutter_speeds = dataset.metadata['unique_shutters'].shape[0] for i_s in range(num_shutter_speeds): for j_s, value in enumerate(scalings[i_s]): summary_name = f'exposure/scaling_{i_s}_{j_s}' summary_writer.add_scalar(summary_name, value, step) precision = int(np.ceil(np.log10(config.max_steps))) + 1 avg_loss = avg_stats['loss'] avg_psnr = avg_stats['psnr'] str_losses = { # Grab each "losses_{x}" field and print it as "x[:4]". k[7:11]: (f'{v:0.5f}' if 1e-4 <= v < 10 else f'{v:0.1e}') for k, v in avg_stats.items() if k.startswith('losses/') } logger.info(f'{step}' + f'/{config.max_steps:d}:' + f'loss={avg_loss:0.5f},' + f'psnr={avg_psnr:.3f},' + f'lr={learning_rate:0.2e} | ' + ','.join([f'{k}={s}' for k, s in str_losses.items()]) + f',{rays_per_sec:0.0f} r/s') # Reset everything we are tracking between summarizations. reset_stats = True if step > 0 and step % config.checkpoint_every == 0 and accelerator.is_main_process: checkpoints.save_checkpoint(config.checkpoint_dir, accelerator, step, config.checkpoints_total_limit) # Test-set evaluation. if config.train_render_every > 0 and step % config.train_render_every == 0: # We reuse the same random number generator from the optimization step # here on purpose so that the visualization matches what happened in # training. eval_start_time = time.time() try: test_batch = next(test_dataiter) except StopIteration: test_dataiter = iter(test_dataloader) test_batch = next(test_dataiter) test_batch = accelerate.utils.send_to_device(test_batch, accelerator.device) # render a single image with all distributed processes rendering = models.render_image(model, accelerator, test_batch, False, train_frac, config) # move to numpy rendering = tree_map(lambda x: x.detach().cpu().numpy(), rendering) test_batch = tree_map(lambda x: x.detach().cpu().numpy() if x is not None else None, test_batch) # Log eval summaries on host 0. if accelerator.is_main_process: eval_time = time.time() - eval_start_time num_rays = np.prod(test_batch['directions'].shape[:-1]) rays_per_sec = num_rays / eval_time summary_writer.add_scalar('test_rays_per_sec', rays_per_sec, step) metric_start_time = time.time() metric = metric_harness( postprocess_fn(rendering['rgb']), postprocess_fn(test_batch['rgb'])) logger.info(f'Eval {step}: {eval_time:0.3f}s, {rays_per_sec:0.0f} rays/sec') logger.info(f'Metrics computed in {(time.time() - metric_start_time):0.3f}s') for name, val in metric.items(): if not np.isnan(val): logger.info(f'{name} = {val:.4f}') summary_writer.add_scalar('train_metrics/' + name, val, step) if config.vis_decimate > 1: d = config.vis_decimate decimate_fn = lambda x, d=d: None if x is None else x[::d, ::d] else: decimate_fn = lambda x: x rendering = tree_map(decimate_fn, rendering) test_batch = tree_map(decimate_fn, test_batch) vis_start_time = time.time() vis_suite = vis.visualize_suite(rendering, test_batch) with tqdm.external_write_mode(): logger.info(f'Visualized in {(time.time() - vis_start_time):0.3f}s') if config.rawnerf_mode: # Unprocess raw output. vis_suite['color_raw'] = rendering['rgb'] # Autoexposed colors. vis_suite['color_auto'] = postprocess_fn(rendering['rgb'], None) summary_writer.add_image('test_true_auto', tb_process_fn(postprocess_fn(test_batch['rgb'], None)), step) # Exposure sweep colors. exposures = test_dataset.metadata['exposure_levels'] for p, x in list(exposures.items()): vis_suite[f'color/{p}'] = postprocess_fn(rendering['rgb'], x) summary_writer.add_image(f'test_true_color/{p}', tb_process_fn(postprocess_fn(test_batch['rgb'], x)), step) summary_writer.add_image('test_true_color', tb_process_fn(test_batch['rgb']), step) if config.compute_normal_metrics: summary_writer.add_image('test_true_normals', tb_process_fn(test_batch['normals']) / 2. + 0.5, step) for k, v in vis_suite.items(): summary_writer.add_image('test_output_' + k, tb_process_fn(v), step) if accelerator.is_main_process and config.max_steps > init_step: logger.info('Saving last checkpoint at step {} to {}'.format(step, config.checkpoint_dir)) checkpoints.save_checkpoint(config.checkpoint_dir, accelerator, step, config.checkpoints_total_limit) logger.info('Finish training.') if __name__ == '__main__': with gin.config_scope('train'): app.run(main)