eval.py

# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# 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.

"""Evaluation script for RegNeRF."""
import functools
from os import path
import time

from absl import app
import flax
from flax.metrics import tensorboard
from flax.training import checkpoints
from internal import configs, datasets, math, models, utils, vis  # pylint: disable=g-multiple-import
import jax
from jax import random
import numpy as np
from skimage.metrics import structural_similarity
import tensorflow as tf

######################################################
## ----- This file is modified significantly ------ ##
######################################################

## ---- import neccessary pkgs ---- ##
from lpips import LPIPS
import torch

lpips_vgg = LPIPS(net="vgg").cuda()
## --------------------------------- ##

CENSUS_EPSILON = 1 / 256  # Guard against ground-truth quantization.

configs.define_common_flags()
jax.config.parse_flags_with_absl()


def main(unused_argv):

  tf.config.experimental.set_visible_devices([], 'GPU')
  tf.config.experimental.set_visible_devices([], 'TPU')

  config = configs.load_config(save_config=False)
  if config.use_wandb:
    import wandb
    wandb.init(project=config.project, entity=config.entity, sync_tensorboard=True)
    wandb.run.name = config.expname
    wandb.run.save()
    wandb.config.update(config)
    
  dataset = datasets.load_dataset('test', config.data_dir, config)
  model, init_variables = models.construct_mipnerf(
      random.PRNGKey(20200823),
      dataset.peek()['rays'],
      config)
  optimizer = flax.optim.Adam(config.lr_init).create(init_variables)
  state = utils.TrainState(optimizer=optimizer)
  del optimizer, init_variables

  def ssim_fn(x, y):
    ## ---- fix the SSIM issue default in regnerf's code ---- ##
    return structural_similarity(x, y, multichannel=True, data_range=1.0)
    ## ------------------------------------------------------ ##
  def lpips_fn(x, y):
    score = lpips_vgg(torch.from_numpy(np.array(x)).cuda().permute(2, 0, 1).unsqueeze(0), 
                      torch.from_numpy(np.array(y)).cuda().permute(2, 0, 1).unsqueeze(0))
    return score.item()
  census_fn = jax.jit(
      functools.partial(math.compute_census_err, epsilon=CENSUS_EPSILON))

  ## ----- comment out as we use lpips instead ----- ##
  # print('WARNING: LPIPS calculation not supported. NaN values used instead.')
  # if config.eval_disable_lpips:
  #   lpips_fn = lambda x, y: np.nan
  # else:
  #   lpips_fn = lambda x, y: np.nan
  ## ------------------------------------------------ ##

  last_step = 0
  out_dir = path.join(config.checkpoint_dir,
                      'path_renders' if config.render_path else 'test_preds')
  path_fn = lambda x: path.join(out_dir, x)

  summary_writer = tensorboard.SummaryWriter(
        path.join(config.checkpoint_dir, 'eval'))
  # Fix for loading pre-trained models.
  try:
    state = checkpoints.restore_checkpoint(config.checkpoint_dir, state)
  except:  # pylint: disable=bare-except
    print('Using pre-trained model.')
    state_dict = checkpoints.restore_checkpoint(config.checkpoint_dir, None)
    for i in [9, 17]:
      del state_dict['optimizer']['target']['params']['MLP_0'][f'Dense_{i}']
    state_dict['optimizer']['target']['params']['MLP_0'][
        'Dense_9'] = state_dict['optimizer']['target']['params']['MLP_0'][
            'Dense_18']
    state_dict['optimizer']['target']['params']['MLP_0'][
        'Dense_10'] = state_dict['optimizer']['target']['params']['MLP_0'][
            'Dense_19']
    state_dict['optimizer']['target']['params']['MLP_0'][
        'Dense_11'] = state_dict['optimizer']['target']['params']['MLP_0'][
            'Dense_20']
    del state_dict['optimizerd']
    state = flax.serialization.from_state_dict(state, state_dict)

  step = int(state.optimizer.state.step)

  if config.freq_reg:
    # Rendering is forced to be deterministic even if training was randomized, as
    # this eliminates 'speckle' artifacts.
    freq_reg_mask = (
      math.get_freq_reg_mask(99, step, config.freq_reg_end, config.max_vis_freq_ratio),
      math.get_freq_reg_mask(27, step, config.freq_reg_end, config.max_vis_freq_ratio))
    def render_eval_fn(variables, _, rays):
      return jax.lax.all_gather(
          model.apply(
              variables,
              None,  # Deterministic.
              rays,
              resample_padding=config.resample_padding_final,
              compute_extras=True,
              freq_reg_mask=freq_reg_mask)[0], axis_name='batch')
  else:
    def render_eval_fn(variables, _, rays):
      return jax.lax.all_gather(
          model.apply(
              variables,
              None,  # Deterministic.
              rays,
              resample_padding=config.resample_padding_final,
              compute_extras=True)[0], axis_name='batch')
  # pmap over only the data input.
  render_eval_pfn = jax.pmap(
      render_eval_fn,
      in_axes=(None, None, 0),
      donate_argnums=2,
      axis_name='batch',
  )
  
  if step <= last_step:
    print(f'Checkpoint step {step} <= last step {last_step}, exit.')
    exit()
  print(f'Evaluating checkpoint at step {step}.')
  if config.eval_save_output and (not utils.isdir(out_dir)):
    utils.makedirs(out_dir)

  key = random.PRNGKey(0 if config.deterministic_showcase else step)
  perm = random.permutation(key, dataset.size)
  showcase_indices = np.sort(perm[:config.num_showcase_images])

  metrics = []
  showcases = []
  for idx in range(dataset.size):
    print(f'Evaluating image {idx+1}/{dataset.size}')
    eval_start_time = time.time()
    batch = next(dataset)
    rendering = models.render_image(
        functools.partial(render_eval_pfn, state.optimizer.target),
        batch['rays'],
        None,
        config)
    print(f'Rendered in {(time.time() - eval_start_time):0.3f}s')

    if jax.host_id() != 0:  # Only record via host 0.
      continue
    showcases.append((idx, rendering, batch))
    if not config.render_path:
      metric = {}
      metric['psnr'] = float(
          math.mse_to_psnr(((rendering['rgb'] - batch['rgb'])**2).mean()))
      metric['ssim'] = float(ssim_fn(rendering['rgb'], batch['rgb']))
      metric['lpips'] = float(lpips_fn(rendering['rgb'], batch['rgb']))
      metric['avg_err'] = float(
          math.compute_avg_error(
              psnr=metric['psnr'],
              ssim=metric['ssim'],
              lpips=metric['lpips'],
          ))
      metric['census_err'] = float(census_fn(rendering['rgb'], batch['rgb']))

      if config.compute_disp_metrics:
        disp = 1 / (1 + rendering['distance_mean'])
        metric['disp_mse'] = float(((disp - batch['disps'])**2).mean())

      if config.compute_normal_metrics:
        one_eps = 1 - np.finfo(np.float32).eps
        metric['normal_mae'] = float(
            np.arccos(
                np.clip(
                    np.sum(batch['normals'] * rendering['normals'], axis=-1),
                    -one_eps, one_eps)).mean())

      if config.dataset_loader == 'dtu':
        rgb = batch['rgb']
        rgb_hat = rendering['rgb']
        mask = batch['mask']
        mask_bin = (mask == 1.)

        rgb_fg = rgb * mask + (1 - mask)
        rgb_hat_fg = rgb_hat * mask + (1 - mask)

        metric['psnr_masked'] = float(
            math.mse_to_psnr(((rgb - rgb_hat)[mask_bin]**2).mean()))
        metric['ssim_masked'] = float(ssim_fn(rgb_hat_fg, rgb_fg))
        metric['lpips_masked'] = float(lpips_fn(rgb_hat_fg, rgb_fg))
        metric['avg_err_masked'] = float(
            math.compute_avg_error(
                psnr=metric['psnr_masked'],
                ssim=metric['ssim_masked'],
                lpips=metric['lpips_masked'],
            ))

      for m, v in metric.items():
        print(f'{m:10s} = {v:.4f}')
      metrics.append(metric)

    if config.eval_save_output and (config.eval_render_interval > 0):
      if (idx % config.eval_render_interval) == 0:
        utils.save_img_u8(rendering['rgb'], path_fn(f'color_{idx:03d}.png'))
        utils.save_img_u8(rendering['normals'] / 2. + 0.5,
                          path_fn(f'normals_{idx:03d}.png'))
        utils.save_img_f32(rendering['distance_mean'],
                            path_fn(f'distance_mean_{idx:03d}.tiff'))
        utils.save_img_f32(rendering['distance_median'],
                            path_fn(f'distance_median_{idx:03d}.tiff'))
        utils.save_img_f32(rendering['acc'], path_fn(f'acc_{idx:03d}.tiff'))

  if jax.host_id() == 0:
    for name in list(metrics[0].keys()):
      summary_writer.scalar(name, np.mean([m[name] for m in metrics]), step)
    if config.use_wandb and config.log_img_to_wandb:
      for i, r, b in showcases:
        for k, v in vis.visualize_suite(r, b['rays'], config).items():
          summary_writer.image(f'pred_{k}_{i}', v, step)
        if not config.render_path:
          summary_writer.image(f'target_{i}', b['rgb'], step)
  if (config.eval_save_output and (not config.render_path) and
      (jax.host_id() == 0)):
    print('#####################')
    for name in list(metrics[0].keys()):
      with utils.open_file(path_fn(f'metric_{name}_{step}.txt'), 'w') as f:
        f.write(' '.join([str(m[name]) for m in metrics]))
      print(f'{name}:', np.mean([m[name] for m in metrics]))
    print('evaluated exp:', config.expname)

  if config.use_wandb:
    wandb.finish()


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