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train.py
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train.py
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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)
generator = model.generator
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,
generator=generator,
)
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,
generator=generator,
)
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)