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white_bkgd=config.white_bkgd),
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axis_name='batch')
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render_eval_pfn = jax.pmap(
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render_eval_fn,
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in_axes=(None, None, 0), # Only distribute the data input.
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donate_argnums=(2,),
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axis_name='batch',
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)
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ssim_fn = jax.jit(functools.partial(math.compute_ssim, max_val=1.))
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if not utils.isdir(FLAGS.train_dir):
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utils.makedirs(FLAGS.train_dir)
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state = checkpoints.restore_checkpoint(FLAGS.train_dir, state)
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# Resume training a the step of the last checkpoint.
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init_step = state.optimizer.state.step + 1
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state = flax.jax_utils.replicate(state)
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if jax.host_id() == 0:
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summary_writer = tensorboard.SummaryWriter(FLAGS.train_dir)
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# Prefetch_buffer_size = 3 x batch_size
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pdataset = flax.jax_utils.prefetch_to_device(dataset, 3)
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rng = rng + jax.host_id() # Make random seed separate across hosts.
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keys = random.split(rng, jax.local_device_count()) # For pmapping RNG keys.
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gc.disable() # Disable automatic garbage collection for efficiency.
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stats_trace = []
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reset_timer = True
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for step, batch in zip(range(init_step, config.max_steps + 1), pdataset):
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if reset_timer:
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t_loop_start = time.time()
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reset_timer = False
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lr = learning_rate_fn(step)
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state, stats, keys = train_pstep(keys, state, batch, lr)
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if jax.host_id() == 0:
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stats_trace.append(stats)
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if step % config.gc_every == 0:
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gc.collect()
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# Log training summaries. This is put behind a host_id check because in
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# multi-host evaluation, all hosts need to run inference even though we
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# only use host 0 to record results.
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if jax.host_id() == 0:
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if step % config.print_every == 0:
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summary_writer.scalar('num_params', num_params, step)
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summary_writer.scalar('train_loss', stats.loss[0], step)
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summary_writer.scalar('train_psnr', stats.psnr[0], step)
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for i, l in enumerate(stats.losses[0]):
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summary_writer.scalar(f'train_losses_{i}', l, step)
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for i, p in enumerate(stats.psnrs[0]):
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summary_writer.scalar(f'train_psnrs_{i}', p, step)
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summary_writer.scalar('weight_l2', stats.weight_l2[0], step)
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avg_loss = np.mean(np.concatenate([s.loss for s in stats_trace]))
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avg_psnr = np.mean(np.concatenate([s.psnr for s in stats_trace]))
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max_grad_norm = np.max(
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np.concatenate([s.grad_norm for s in stats_trace]))
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avg_grad_norm = np.mean(
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np.concatenate([s.grad_norm for s in stats_trace]))
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max_clipped_grad_norm = np.max(
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np.concatenate([s.grad_norm_clipped for s in stats_trace]))
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max_grad_max = np.max(
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np.concatenate([s.grad_abs_max for s in stats_trace]))
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stats_trace = []
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summary_writer.scalar('train_avg_loss', avg_loss, step)
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summary_writer.scalar('train_avg_psnr', avg_psnr, step)
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summary_writer.scalar('train_max_grad_norm', max_grad_norm, step)
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summary_writer.scalar('train_avg_grad_norm', avg_grad_norm, step)
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summary_writer.scalar('train_max_clipped_grad_norm',
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max_clipped_grad_norm, step)
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summary_writer.scalar('train_max_grad_max', max_grad_max, step)
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summary_writer.scalar('learning_rate', lr, step)
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steps_per_sec = config.print_every / (time.time() - t_loop_start)
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reset_timer = True
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rays_per_sec = config.batch_size * steps_per_sec
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summary_writer.scalar('train_steps_per_sec', steps_per_sec, step)
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summary_writer.scalar('train_rays_per_sec', rays_per_sec, step)
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precision = int(np.ceil(np.log10(config.max_steps))) + 1
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print(('{:' + '{:d}'.format(precision) + 'd}').format(step) +
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f'/{config.max_steps:d}: ' + f'i_loss={stats.loss[0]:0.4f}, ' +
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f'avg_loss={avg_loss:0.4f}, ' +
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f'weight_l2={stats.weight_l2[0]:0.2e}, ' + f'lr={lr:0.2e}, ' +
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f'{rays_per_sec:0.0f} rays/sec')
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if step % config.save_every == 0:
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state_to_save = jax.device_get(jax.tree_map(lambda x: x[0], state))
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checkpoints.save_checkpoint(
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FLAGS.train_dir, state_to_save, int(step), keep=100)
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# Test-set evaluation.
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if FLAGS.render_every > 0 and step % FLAGS.render_every == 0:
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# We reuse the same random number generator from the optimization step
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# here on purpose so that the visualization matches what happened in
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# training.
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t_eval_start = time.time()
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eval_variables = jax.device_get(jax.tree_map(lambda x: x[0],
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state)).optimizer.target
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test_case = next(test_dataset)
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pred_color, pred_distance, pred_acc = models.render_image(
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functools.partial(render_eval_pfn, eval_variables),
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test_case['rays'],
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