eval.py

# Copyright 2021 Google LLC
#
# 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.

# Lint as: python3
"""Evaluation script for Nerf."""
import collections
import functools
import time
from typing import Any, Dict, Optional, Sequence

from absl import app
from absl import flags
from absl import logging
from flax import jax_utils
from flax import optim
from flax.metrics import tensorboard
from flax.training import checkpoints
import gin
import jax
from jax import numpy as jnp
from jax import random
import tensorflow as tf

from nerfies import configs
from nerfies import datasets
from nerfies import evaluation
from nerfies import gpath
from nerfies import image_utils
from nerfies import model_utils
from nerfies import models
from nerfies import types
from nerfies import utils
from nerfies import visualization as viz

flags.DEFINE_string('exp_dir', None, 'where to store ckpts and logs')
flags.DEFINE_string('data_dir', None, 'input data directory.')
flags.mark_flag_as_required('exp_dir')
flags.DEFINE_multi_string('gin_bindings', None, 'Gin parameter bindings.')
flags.DEFINE_multi_string('gin_configs', (), 'Gin config files.')
FLAGS = flags.FLAGS


def compute_multiscale_ssim(image1: jnp.ndarray, image2: jnp.ndarray):
  """Compute the multiscale SSIM metric."""
  image1 = tf.convert_to_tensor(image1)
  image2 = tf.convert_to_tensor(image2)
  return tf.image.ssim_multiscale(image1, image2, max_val=1.0)


def process_batch(*,
                  batch: Dict[str, jnp.ndarray],
                  rng: types.PRNGKey,
                  state: model_utils.TrainState,
                  tag: str,
                  item_id: str,
                  step: int,
                  summary_writer: tensorboard.SummaryWriter,
                  render_fn: Any,
                  save_dir: Optional[gpath.GPath],
                  datasource: datasets.DataSource):
  """Process and plot a single batch."""
  rgb, depth_exp, depth_med, acc = render_fn(state, batch, rng=rng)
  out = {}
  if jax.host_id() != 0:
    return out

  colorize_depth = functools.partial(viz.colorize,
                                     cmin=datasource.near,
                                     cmax=datasource.far,
                                     invert=True)

  depth_exp_viz = colorize_depth(depth_exp[..., 0])
  depth_med_viz = colorize_depth(depth_med[..., 0])
  if save_dir:
    save_dir.mkdir(parents=True, exist_ok=True)
    image_utils.save_image(save_dir / f'rgb_{item_id}.png',
                           image_utils.image_to_uint8(rgb))
    image_utils.save_image(save_dir / f'depth_expected_viz_{item_id}.png',
                           image_utils.image_to_uint8(depth_exp_viz))
    image_utils.save_depth(save_dir / f'depth_expected_{item_id}.png',
                           depth_med[..., 0])
    image_utils.save_image(save_dir / f'depth_median_viz_{item_id}.png',
                           image_utils.image_to_uint8(depth_med_viz))
    image_utils.save_depth(save_dir / f'depth_median_{item_id}.png',
                           depth_med[..., 0])

  summary_writer.image(f'rgb/{tag}/{item_id}', rgb, step)
  summary_writer.image(f'depth-expected/{tag}/{item_id}', depth_exp_viz, step)
  summary_writer.image(f'depth-median/{tag}/{item_id}', depth_med_viz, step)
  summary_writer.image(f'acc/{tag}/{item_id}', acc, step)

  if 'rgb' in batch:
    rgb_target = batch['rgb']
    mse = ((rgb - batch['rgb'])**2).mean()
    psnr = utils.compute_psnr(mse)
    ssim = compute_multiscale_ssim(rgb_target, rgb)
    out['mse'] = mse
    out['psnr'] = psnr
    out['ssim'] = ssim
    logging.info('\tMetrics: mse=%.04f, psnr=%.02f, ssim=%.02f',
                 mse, psnr, ssim)

    rgb_abs_error = viz.colorize(
        abs(rgb_target - rgb).sum(axis=-1), cmin=0, cmax=1)
    rgb_sq_error = viz.colorize(
        ((rgb_target - rgb)**2).sum(axis=-1), cmin=0, cmax=1)
    summary_writer.image(f'rgb-target/{tag}/{item_id}', rgb_target, step)
    summary_writer.image(f'rgb-abs-error/{tag}/{item_id}', rgb_abs_error, step)
    summary_writer.image(f'rgb-sq-error/{tag}/{item_id}', rgb_sq_error, step)

  if 'depth' in batch:
    depth_target = batch['depth']
    depth_target_viz = colorize_depth(depth_target[..., 0])
    out['depth_abs'] = jnp.nanmean(jnp.abs(depth_target - depth_med))
    summary_writer.image(
        f'depth-target/{tag}/{item_id}', depth_target_viz, step)
    depth_med_error = viz.colorize(
        abs(depth_target - depth_med).squeeze(axis=-1), cmin=0, cmax=1)
    summary_writer.image(
        f'depth-median-error/{tag}/{item_id}', depth_med_error, step)
    depth_exp_error = viz.colorize(
        abs(depth_target - depth_exp).squeeze(axis=-1), cmin=0, cmax=1)
    summary_writer.image(
        f'depth-expected-error/{tag}/{item_id}', depth_exp_error, step)
    rel_disp_pred = viz.colorize(1.0 / depth_exp[..., 0])
    summary_writer.image(
        f'relative-disparity/{tag}/{item_id}', rel_disp_pred, step)

  return out


def process_iterator(tag: str,
                     item_ids: Sequence[str],
                     iterator,
                     rng: types.PRNGKey,
                     state: model_utils.TrainState,
                     step: int,
                     render_fn: Any,
                     summary_writer: tensorboard.SummaryWriter,
                     save_dir: Optional[gpath.GPath],
                     datasource: datasets.DataSource):
  """Process a dataset iterator and compute metrics."""
  save_dir = save_dir / f'{step:08d}' / tag if save_dir else None
  meters = collections.defaultdict(utils.ValueMeter)
  for i, (item_id, batch) in enumerate(zip(item_ids, iterator)):
    logging.info('[%s:%d/%d] Processing %s ', tag, i+1, len(item_ids), item_id)
    if tag == 'test':
      test_rng = random.PRNGKey(step)
      shape = batch['origins'][..., :1].shape
      metadata = {}
      if datasource.use_appearance_id:
        appearance_id = random.choice(
            test_rng, jnp.asarray(datasource.appearance_ids))
        logging.info('\tUsing appearance_id = %d', appearance_id)
        metadata['appearance'] = jnp.full(shape, fill_value=appearance_id,
                                          dtype=jnp.uint32)
      if datasource.use_warp_id:
        warp_id = random.choice(test_rng, jnp.asarray(datasource.warp_ids))
        logging.info('\tUsing warp_id = %d', warp_id)
        metadata['warp'] = jnp.full(shape, fill_value=warp_id, dtype=jnp.uint32)
      if datasource.use_camera_id:
        camera_id = random.choice(test_rng, jnp.asarray(datasource.camera_ids))
        logging.info('\tUsing camera_id = %d', camera_id)
        metadata['camera'] = jnp.full(shape, fill_value=camera_id,
                                      dtype=jnp.uint32)
      batch['metadata'] = metadata

    stats = process_batch(batch=batch,
                          rng=rng,
                          state=state,
                          tag=tag,
                          item_id=item_id,
                          step=step,
                          render_fn=render_fn,
                          summary_writer=summary_writer,
                          save_dir=save_dir,
                          datasource=datasource)
    if jax.host_id() == 0:
      for k, v in stats.items():
        meters[k].update(v)

  if jax.host_id() == 0:
    for meter_name, meter in meters.items():
      summary_writer.scalar(tag=f'metrics-eval/{meter_name}/{tag}',
                            value=meter.reduce('mean'),
                            step=step)


def main(argv):
  del argv
  logging.info('*** Starting experiment')
  gin_configs = FLAGS.gin_configs

  logging.info('*** Loading Gin configs from: %s', str(gin_configs))
  gin.parse_config_files_and_bindings(
      config_files=gin_configs,
      bindings=FLAGS.gin_bindings,
      skip_unknown=True)

  # Load configurations.
  exp_config = configs.ExperimentConfig()
  model_config = configs.ModelConfig(use_stratified_sampling=False)
  train_config = configs.TrainConfig()
  eval_config = configs.EvalConfig()

  # Get directory information.
  exp_dir = gpath.GPath(FLAGS.exp_dir)
  if exp_config.subname:
    exp_dir = exp_dir / exp_config.subname
  logging.info('\texp_dir = %s', exp_dir)
  if not exp_dir.exists():
    exp_dir.mkdir(parents=True, exist_ok=True)

  summary_dir = exp_dir / 'summaries' / 'eval'
  logging.info('\tsummary_dir = %s', summary_dir)
  if not summary_dir.exists():
    summary_dir.mkdir(parents=True, exist_ok=True)

  renders_dir = exp_dir / 'renders'
  logging.info('\trenders_dir = %s', renders_dir)
  if not renders_dir.exists():
    renders_dir.mkdir(parents=True, exist_ok=True)

  checkpoint_dir = exp_dir / 'checkpoints'
  logging.info('\tcheckpoint_dir = %s', checkpoint_dir)

  rng = random.PRNGKey(20200823)

  devices_to_use = jax.devices()
  n_devices = len(
      devices_to_use) if devices_to_use else jax.local_device_count()

  datasource_spec = exp_config.datasource_spec
  if datasource_spec is None:
    datasource_spec = {
        'type': exp_config.datasource_type,
        'data_dir': FLAGS.data_dir,
    }
  logging.info('Creating datasource: %s', datasource_spec)
  datasource = datasets.from_config(
      datasource_spec,
      image_scale=exp_config.image_scale,
      use_appearance_id=model_config.use_appearance_metadata,
      use_camera_id=model_config.use_camera_metadata,
      use_warp_id=model_config.use_warp,
      random_seed=exp_config.random_seed)

  # Get training IDs to evaluate.
  train_eval_ids = utils.strided_subset(
      datasource.train_ids, eval_config.num_train_eval)
  train_eval_iter = datasource.create_iterator(train_eval_ids, batch_size=0)
  val_eval_ids = utils.strided_subset(
      datasource.val_ids, eval_config.num_val_eval)
  val_eval_iter = datasource.create_iterator(val_eval_ids, batch_size=0)

  test_cameras = datasource.load_test_cameras(count=eval_config.num_test_eval)
  if test_cameras:
    test_dataset = datasource.create_cameras_dataset(test_cameras)
    test_eval_ids = [f'{x:03d}' for x in range(len(test_cameras))]
    test_eval_iter = datasets.iterator_from_dataset(test_dataset, batch_size=0)
  else:
    test_eval_ids = None
    test_eval_iter = None

  rng, key = random.split(rng)
  params = {}
  model, params['model'] = models.nerf(
      key,
      model_config,
      batch_size=eval_config.chunk,
      num_appearance_embeddings=len(datasource.appearance_ids),
      num_camera_embeddings=len(datasource.camera_ids),
      num_warp_embeddings=len(datasource.warp_ids),
      near=datasource.near,
      far=datasource.far,
      use_warp_jacobian=False,
      use_weights=False)

  optimizer_def = optim.Adam(0.0)
  optimizer = optimizer_def.create(params)
  init_state = model_utils.TrainState(optimizer=optimizer, warp_alpha=0.0)
  del params

  def _model_fn(key_0, key_1, params, rays_dict, alpha):
    out = model.apply({'params': params},
                      rays_dict,
                      warp_alpha=alpha,
                      rngs={
                          'coarse': key_0,
                          'fine': key_1
                      },
                      mutable=False)
    return jax.lax.all_gather(out, axis_name='batch')

  pmodel_fn = jax.pmap(
      # Note rng_keys are useless in eval mode since there's no randomness.
      _model_fn,
      in_axes=(0, 0, 0, 0, None),  # Only distribute the data input.
      devices=devices_to_use,
      donate_argnums=(3,),  # Donate the 'rays' argument.
      axis_name='batch',
  )

  render_fn = functools.partial(evaluation.render_image,
                                model_fn=pmodel_fn,
                                device_count=n_devices,
                                chunk=eval_config.chunk)

  last_step = 0
  summary_writer = tensorboard.SummaryWriter(str(summary_dir))

  while True:
    if not checkpoint_dir.exists():
      logging.info('No checkpoints yet.')
      time.sleep(10)
      continue

    state = checkpoints.restore_checkpoint(checkpoint_dir, init_state)
    state = jax_utils.replicate(state, devices=devices_to_use)
    step = int(state.optimizer.state.step[0])
    if step <= last_step:
      logging.info('No new checkpoints (%d <= %d).', step, last_step)
      time.sleep(10)
      continue

    save_dir = renders_dir if eval_config.save_output else None
    process_iterator(tag='train',
                     item_ids=train_eval_ids,
                     iterator=train_eval_iter,
                     state=state,
                     rng=rng,
                     step=step,
                     render_fn=render_fn,
                     summary_writer=summary_writer,
                     save_dir=save_dir,
                     datasource=datasource)
    process_iterator(tag='val',
                     item_ids=val_eval_ids,
                     iterator=val_eval_iter,
                     state=state,
                     rng=rng,
                     step=step,
                     render_fn=render_fn,
                     summary_writer=summary_writer,
                     save_dir=save_dir,
                     datasource=datasource)
    if test_eval_iter:
      process_iterator(tag='test',
                       item_ids=test_eval_ids,
                       iterator=test_eval_iter,
                       state=state,
                       rng=rng,
                       step=step,
                       render_fn=render_fn,
                       summary_writer=summary_writer,
                       save_dir=save_dir,
                       datasource=datasource)

    if eval_config.eval_once:
      break
    if step >= train_config.max_steps:
      break
    last_step = step


if __name__ == '__main__':
  app.run(main)