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)
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sys.excepthook = utils.handle_exception
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logger = accelerate.logging.get_logger(__name__)
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logger.info(config)
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logger.info(accelerator.state, main_process_only=False)
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config.world_size = accelerator.num_processes
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config.global_rank = accelerator.process_index
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if config.batch_size % accelerator.num_processes != 0:
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config.batch_size -= config.batch_size % accelerator.num_processes != 0
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logger.info('turn batch size to', config.batch_size)
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# Set random seed.
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accelerate.utils.set_seed(config.seed, device_specific=True)
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# load dataset
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dataset = datasets.load_dataset('train', config.data_dir, config)
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test_dataset = datasets.load_dataset('test', config.data_dir, config)
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dataloader = torch.utils.data.DataLoader(np.arange(len(dataset)),
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# num_workers=8,
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shuffle=True,
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batch_size=1,
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collate_fn=dataset.collate_fn,
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# Set to False to enable switching
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# low-res. and high-res. data.
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persistent_workers=False,
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)
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test_dataloader = torch.utils.data.DataLoader(np.arange(len(test_dataset)),
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# num_workers=4,
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shuffle=False,
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batch_size=1,
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persistent_workers=False,
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collate_fn=test_dataset.collate_fn,
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)
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if config.rawnerf_mode:
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postprocess_fn = test_dataset.metadata['postprocess_fn']
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else:
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postprocess_fn = lambda z, _=None: z
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dataset_info_for_model = {
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'size': dataset.size,
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}
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# setup model and optimizer
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model = models.Model(config=config, dataset_info=dataset_info_for_model)
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optimizer, lr_fn = train_utils.create_optimizer(config, model)
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# use accelerate to prepare.
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model, dataloader, optimizer = accelerator.prepare(model, dataloader, optimizer)
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if config.resume_from_checkpoint:
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init_step = checkpoints.restore_checkpoint(config.checkpoint_dir, accelerator, logger, strict=True)
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else:
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init_step = 0
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module = accelerator.unwrap_model(model)
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dataiter = iter(dataloader)
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test_dataiter = iter(test_dataloader)
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num_params = train_utils.tree_len(list(model.parameters()))
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logger.info(f'Number of parameters being optimized: {num_params}')
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if (dataset.size > module.num_glo_embeddings and module.num_glo_features > 0):
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raise ValueError(f'Number of glo embeddings {module.num_glo_embeddings} '
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f'must be at least equal to number of train images '
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f'{dataset.size}')
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# metric handler
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metric_harness = image.MetricHarness()
|
# tensorboard
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if accelerator.is_main_process:
|
summary_writer = tensorboardX.SummaryWriter(config.exp_path)
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# function to convert image for tensorboard
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tb_process_fn = lambda x: x.transpose(2, 0, 1) if len(x.shape) == 3 else x[None]
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if config.rawnerf_mode:
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for name, data in zip(['train', 'test'], [dataset, test_dataset]):
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# Log shutter speed metadata in TensorBoard for debug purposes.
|
for key in ['exposure_idx', 'exposure_values', 'unique_shutters']:
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summary_writer.add_text(f'{name}_{key}', str(data.metadata[key]), 0)
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logger.info("Begin training...")
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total_time = 0
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total_steps = 0
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reset_stats = True
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if config.early_exit_steps is not None:
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num_steps = config.early_exit_steps
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else:
|
num_steps = config.max_steps
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with logging_redirect_tqdm():
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tbar = tqdm(range(init_step + 1, num_steps + 1),
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desc='Training', initial=init_step, total=num_steps,
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disable=not accelerator.is_main_process)
|
for step in tbar:
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try:
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batch = next(dataiter)
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except StopIteration:
|
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