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render.py
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# 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.
"""Render script for RegNeRF."""
import functools
from os import path
import time
from absl import app
import flax
from flax.training import checkpoints
from internal import configs, datasets, models, utils # pylint: disable=g-multiple-import
import jax
from jax import random
configs.define_common_flags()
jax.config.parse_flags_with_absl()
def main(unused_argv):
config = configs.load_config(save_config=False)
config.render_path = True
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
# Pre-define depth ranges for more across-settings consistent visualizations
if config.dataset_loader == 'llff':
eval_dict = {'fern': [0.059100067913532256, 0.8538959634304046],
'flower': [0.2099738734960556, 0.996519325375557],
'fortress': [0.3405687987804413, 0.8795422136783599],
'horns': [0.3501826047897339, 0.9596474349498749],
'leaves': [0.00022197533398866584, 0.9934533953666687],
'orchids': [0.23377860009670257, 0.9828365403413772],
'room': [0.4059941208362579, 0.9502887094020843],
'trex': [0.016071857213974, 0.9458529788255692]}
lo, hi = eval_dict[config.llff_scan] # pylint: disable=unused-variable
elif config.dataset_loader == 'dtu':
eval_dict = {'scan8': [0.9593777, 1.5342957],
'scan21': [0.98255014, 1.7484968],
'scan30': [1.1381109, 1.6074754],
'scan31': [1.0627427, 1.6069319],
'scan34': [1.1172018, 1.5005568],
'scan38': [1.0385504, 1.5373354],
'scan40': [0.8312144, 1.62111],
'scan41': [0.9469194, 1.5374442],
'scan45': [1.0098513, 1.5830635],
'scan55': [0.85020584, 1.513227],
'scan63': [1.1894969, 1.7325872],
'scan82': [1.0984676, 1.7162027],
'scan103': [1.0771852, 1.5858444],
'scan110': [0.96143025, 1.5147997],
'scan114': [0.96940583, 1.548706]}
lo, hi = eval_dict[config.dtu_scan]
# Rendering is forced to be deterministic even if training was randomized, as
# this eliminates 'speckle' artifacts.
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),
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',
)
path_fn = lambda x: path.join(out_dir, x)
# 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)
print(f'Rendering checkpoint at step {step}.')
out_name = 'path_renders' if config.render_path else 'test_preds'
out_name = f'{out_name}_step_{step}'
base_dir = config.render_dir
if base_dir is None:
base_dir = config.checkpoint_dir
out_dir = path.join(base_dir, out_name)
if not utils.isdir(out_dir):
utils.makedirs(out_dir)
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
utils.save_img_u8(rendering['rgb'], path_fn(f'color_{idx:03d}.png'))
time.sleep(3)
utils.save_img_u8(rendering['normals'] / 2. + 0.5,
path_fn(f'normals_{idx:03d}.png'))
time.sleep(3)
utils.save_img_f32(rendering['distance_mean'],
path_fn(f'distance_mean_{idx:03d}.tiff'))
time.sleep(3)
utils.save_img_f32(rendering['distance_median'],
path_fn(f'distance_median_{idx:03d}.tiff'))
time.sleep(3)
utils.save_img_f32(rendering['acc'], path_fn(f'acc_{idx:03d}.tiff'))
if __name__ == '__main__':
app.run(main)