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render.py
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render.py
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import functools
import os
import jax
import numpy as np
import optax
import tensorflow as tf
from absl import app, flags, logging
from flax.training import checkpoints, train_state
from jax import numpy as jnp, lax
from ml_collections import config_flags
from tqdm import tqdm
from datasets.input_pipeline import get_dataset
from model import NeRF
from utils import (
disp_post,
eval_step,
gen_video,
prepare_render_data,
save_test_imgs,
to_np,
)
psnr_fn = lambda x: -10.0 * np.log(x) / np.log(10.0)
tf.config.experimental.set_visible_devices([], "GPU")
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file(
"config", None, "File path to the hyperparameter configuration."
)
flags.DEFINE_integer("seed", default=0, help="Initialization seed.")
flags.DEFINE_string("data_dir", default=None, help="Directory containing data files.")
flags.DEFINE_string("model_dir", default=None, help="Directory to save model data.")
flags.DEFINE_string("save_dir", default=None, help="Directory to save outputs.")
flags.DEFINE_string(
"render_video_set",
default="render",
help="Subset of data to use to render the video.",
)
flags.DEFINE_bool("render_video", default=True, help="Whether to render video.")
flags.DEFINE_bool("render_testset", default=False, help="Whether to render testset.")
def initialized(key, pts_shape, viewdirs_shape, model_config):
model = NeRF(
**model_config,
**FLAGS.config.emb,
use_viewdirs=FLAGS.config.use_viewdirs,
dtype=FLAGS.config.dtype,
)
initial_params = jax.jit(model.init)(
{"params": key},
jnp.ones(pts_shape, model.dtype),
jnp.ones(viewdirs_shape, model.dtype),
)
return model, initial_params["params"]
def main(_):
assert FLAGS.config.down_factor > 0 and FLAGS.config.render_factor > 0
save_dir = FLAGS.model_dir if FLAGS.save_dir is None else FLAGS.save_dir
logging.info("JAX host: %d / %d", jax.process_index(), jax.host_count())
logging.info("JAX local devices: %r", jax.local_devices())
rng = jax.random.PRNGKey(FLAGS.seed)
rng, rng_coarse, rng_fine = jax.random.split(rng, 3)
### Load dataset and data values
datasets, counts, optics, render_datasets = get_dataset(
FLAGS.data_dir, FLAGS.config, num_poses=FLAGS.config.num_poses
)
train_ds, val_ds, test_ds = datasets
train_items, val_items, test_items = counts
hwf, r_hwf, near, far = optics
render_ds, render_vdirs_ds, num_poses = render_datasets
logging.info("Num poses: %d", num_poses)
logging.info("Splits: train - %d, val - %d, test - %d", *counts)
logging.info("Images: height %d, width %d, focal %.5f", *hwf)
logging.info("Render: height %d, width %d, focal %.5f", *r_hwf)
### Init model parameters and optimizer
initialized_ = functools.partial(initialized, model_config=FLAGS.config.model)
pts_shape = (FLAGS.config.num_rand, FLAGS.config.num_samples, 3)
views_shape = (FLAGS.config.num_rand, 3)
model_coarse, params_coarse = initialized_(rng_coarse, pts_shape, views_shape)
schedule_fn = optax.exponential_decay(
init_value=FLAGS.config.learning_rate,
transition_steps=FLAGS.config.lr_decay * 1000,
decay_rate=FLAGS.config.decay_factor,
)
tx = optax.adam(learning_rate=schedule_fn)
state = train_state.TrainState.create(
apply_fn=(model_coarse.apply, None), params={"coarse": params_coarse}, tx=tx
)
if FLAGS.config.num_importance > 0:
pts_shape = (
FLAGS.config.num_rand,
FLAGS.config.num_importance + FLAGS.config.num_samples,
3,
)
model_fine, params_fine = initialized_(rng_fine, pts_shape, views_shape)
state = train_state.TrainState.create(
apply_fn=(model_coarse.apply, model_fine.apply),
params={"coarse": params_coarse, "fine": params_fine},
tx=tx,
)
state = checkpoints.restore_checkpoint(FLAGS.model_dir, state)
step = int(state.step)
state = jax.device_put_replicated(state, jax.local_devices())
# TODO: TPU Colab breaks without message if this is a list
# a list is preferred bc tqdm can show an ETA
render_dict = {
"train": zip(range(train_items), train_ds),
"val": zip(range(val_items), val_ds),
"test": zip(range(test_items), test_ds),
"poses": zip(range(num_poses), render_ds),
}
render_poses = render_dict[FLAGS.render_video_set]
def render_fn(state, rays):
step_fn = functools.partial(eval_step, FLAGS.config, near, far, state)
return lax.map(step_fn, rays)
p_eval_step = jax.pmap(
render_fn,
axis_name="batch",
# in_axes=(0, 0, None),
# donate_argnums=(0, 1))
)
if FLAGS.render_video:
rgb_list = []
disp_list = []
losses = []
for _, inputs in tqdm(render_poses, desc="Rays render"):
rays, padding = prepare_render_data(inputs["rays"].numpy())
preds, *_ = p_eval_step(state, rays)
preds = jax.tree_map(lambda x: to_np(x, r_hwf, padding), preds)
rgb_list.append(preds["rgb"])
disp_list.append(preds["disp"])
if FLAGS.config.render_factor == 1 and FLAGS.render_video_set != "render":
loss = np.mean((preds["rgb"] - inputs["image"]) ** 2.0)
losses.append(loss)
if FLAGS.config.render_factor == 1 and FLAGS.render_video_set != "render":
loss = np.mean(losses)
logging.info("Loss %.5f", loss)
logging.info("PSNR %.5f", psnr_fn(loss))
gen_video(save_dir, np.stack(rgb_list), "rgb", r_hwf, step)
disp = np.stack(disp_list)
gen_video(save_dir, disp_post(disp, FLAGS.config), "disp", r_hwf, step, ch=1)
if FLAGS.render_testset:
test_losses = []
for idx, inputs in tqdm(zip(range(test_items), test_ds), desc="Test render"):
rays, padding = prepare_render_data(inputs["rays"].numpy())
preds, *_ = p_eval_step(state, rays)
preds = jax.tree_map(lambda x: to_np(x, r_hwf, padding), preds)
save_test_imgs(save_dir, preds["rgb"], r_hwf, step, idx)
if FLAGS.config.render_factor == 1:
loss = np.mean((preds["rgb"] - inputs["image"]) ** 2.0)
test_losses.append(loss)
if FLAGS.config.render_factor == 1:
loss = np.mean(test_losses)
logging.info("Loss %.5f", loss)
logging.info("PSNR %.5f", psnr_fn(loss))
if __name__ == "__main__":
flags.mark_flags_as_required(["data_dir", "config", "model_dir"])
logging.set_verbosity(logging.INFO)
app.run(main)