def main(unused_argv):
# Hide the GPUs and TPUs from TF so it does not reserve memory on them for
# LPIPS computation or dataset loading.
tf.config.experimental.set_visible_devices([], "GPU")
tf.config.experimental.set_visible_devices([], "TPU")
rng = random.PRNGKey(20200823)
if FLAGS.config is not None:
utils.update_flags(FLAGS)
if FLAGS.train_dir is None:
raise ValueError("train_dir must be set. None set now.")
if FLAGS.data_dir is None:
raise ValueError("data_dir must be set. None set now.")
dataset = datasets.get_dataset("test", FLAGS)
rng, key = random.split(rng)
model, init_variables = models.get_model(key, dataset.peek(), FLAGS)
optimizer = flax.optim.Adam(FLAGS.lr_init).create(init_variables)
state = utils.TrainState(optimizer=optimizer)
del optimizer, init_variables
lpips_model = tf_hub.load(LPIPS_TFHUB_PATH)
# Rendering is forced to be deterministic even if training was randomized, as
# this eliminates "speckle" artifacts.
def render_fn(variables, key_0, key_1, rays):
return jax.lax.all_gather(
model.apply(variables, key_0, key_1, rays, False), axis_name="batch")
# pmap over only the data input.
render_pfn = jax.pmap(
render_fn,
in_axes=(None, None, None, 0),
donate_argnums=3,
axis_name="batch",
)
# Compiling to the CPU because it's faster and more accurate.
ssim_fn = jax.jit(
functools.partial(utils.compute_ssim, max_val=1.), backend="cpu")
last_step = 0
out_dir = path.join(FLAGS.train_dir,
"path_renders" if FLAGS.render_path else "test_preds")
if not FLAGS.eval_once:
summary_writer = tensorboard.SummaryWriter(
path.join(FLAGS.train_dir, "eval"))
while True:
state = checkpoints.restore_checkpoint(FLAGS.train_dir, state)
step = int(state.optimizer.state.step)
if step <= last_step:
continue
if FLAGS.save_output and (not utils.isdir(out_dir)):
utils.makedirs(out_dir)
psnr_values = []
ssim_values = []
lpips_values = []
if not FLAGS.eval_once:
showcase_index = np.random.randint(0, dataset.size)
for idx in range(dataset.size):
print(f"Evaluating {idx+1}/{dataset.size}")
batch = next(dataset)
pred_color, pred_disp, pred_acc = utils.render_image(
functools.partial(render_pfn, state.optimizer.target),
batch["rays"],
rng,
FLAGS.dataset == "llff",
chunk=FLAGS.chunk)
if jax.host_id() != 0: # Only record via host 0.
continue
if not FLAGS.eval_once and idx == showcase_index:
showcase_color = pred_color
showcase_disp = pred_disp
showcase_acc = pred_acc
if not FLAGS.render_path:
showcase_gt = batch["pixels"]
if not FLAGS.render_path:
psnr = utils.compute_psnr(((pred_color - batch["pixels"])**2).mean())
ssim = ssim_fn(pred_color, batch["pixels"])
lpips = compute_lpips(pred_color, batch["pixels"], lpips_model)
print(f"PSNR = {psnr:.4f}, SSIM = {ssim:.4f}")
psnr_values.append(float(psnr))
ssim_values.append(float(ssim))
lpips_values.append(float(lpips))
if FLAGS.save_output:
utils.save_img(pred_color, path.join(out_dir, "{:03d}.png".format(idx)))
utils.save_img(pred_disp[Ellipsis, 0],
path.join(out_dir, "disp_{:03d}.png".format(idx)))
if (not FLAGS.eval_once) and (jax.host_id() == 0):
summary_writer.image("pred_color", showcase_color, step)
summary_writer.image("pred_disp", showcase_disp, step)
summary_writer.image("pred_acc", showcase_acc, step)
if not FLAGS.render_path:
summary_writer.scalar("psnr", np.mean(np.array(psnr_values)), step)
summary_writer.scalar("ssim", np.mean(np.array(ssim_values)), step)
summary_writer.scalar("lpips", np.mean(np.array(lpips_values)), step)
summary_writer.image("target", showcase_gt, step)
if FLAGS.save_output and (not FLAGS.render_path) and (jax.host_id() == 0):
with utils.open_file(path.join(out_dir, f"psnrs_{step}.txt"), "w") as f:
f.write(" ".join([str(v) for v in psnr_values]))
with utils.open_file(path.join(out_dir, f"ssims_{step}.txt"), "w") as f:
f.write(" ".join([str(v) for v in ssim_values]))
with utils.open_file(path.join(out_dir, f"lpips_{step}.txt"), "w") as f:
f.write(" ".join([str(v) for v in lpips_values]))
with utils.open_file(path.join(out_dir, "psnr.txt"), "w") as f:
f.write("{}".format(np.mean(np.array(psnr_values))))
with utils.open_file(path.join(out_dir, "ssim.txt"), "w") as f:
f.write("{}".format(np.mean(np.array(ssim_values))))
with utils.open_file(path.join(out_dir, "lpips.txt"), "w") as f:
f.write("{}".format(np.mean(np.array(lpips_values))))
if FLAGS.eval_once:
break
if int(step) >= FLAGS.max_steps:
break
last_step = step
def main(unused_argv):
rng = random.PRNGKey(20200823)
if FLAGS.config is not None:
utils.update_flags(FLAGS)
if FLAGS.train_dir is None:
raise ValueError("train_dir must be set. None set now.")
if FLAGS.data_dir is None:
raise ValueError("data_dir must be set. None set now.")
dataset = datasets.get_dataset("test", FLAGS)
rng, key = random.split(rng)
model, init_variables = models.get_model(key, dataset.peek(), FLAGS)
optimizer = flax.optim.Adam(FLAGS.lr_init).create(init_variables)
state = utils.TrainState(optimizer=optimizer)
del optimizer, init_variables
# Rendering is forced to be deterministic even if training was randomized, as
# this eliminates "speckle" artifacts.
def render_fn(variables, key_0, key_1, rays):
return jax.lax.all_gather(model.apply(variables, key_0, key_1, *rays,
False),
axis_name="batch")
# pmap over only the data input.
render_pfn = jax.pmap(
render_fn,
in_axes=(None, None, None, 0),
donate_argnums=3,
axis_name="batch",
)
last_step = 0
out_dir = path.join(FLAGS.train_dir,
"path_renders" if FLAGS.render_path else "test_preds")
if not FLAGS.eval_once:
summary_writer = tensorboard.SummaryWriter(
path.join(FLAGS.train_dir, "eval"))
while True:
state = checkpoints.restore_checkpoint(FLAGS.train_dir, state)
step = int(state.optimizer.state.step)
if step <= last_step:
continue
if FLAGS.save_output and (not utils.isdir(out_dir)):
utils.makedirs(out_dir)
psnrs = []
if not FLAGS.eval_once:
showcase_index = np.random.randint(0, dataset.size)
for idx in range(dataset.size):
print(f"Evaluating {idx+1}/{dataset.size}")
batch = next(dataset)
pred_color, pred_disp, pred_acc = utils.render_image(
functools.partial(render_pfn, state.optimizer.target),
batch["rays"],
rng,
FLAGS.dataset == "llff",
chunk=FLAGS.chunk)
if jax.host_id() != 0: # Only record via host 0.
continue
if not FLAGS.eval_once and idx == showcase_index:
showcase_color = pred_color
showcase_disp = pred_disp
showcase_acc = pred_acc
if not FLAGS.render_path:
showcase_gt = batch["pixels"]
if not FLAGS.render_path:
psnr = utils.compute_psnr(
((pred_color - batch["pixels"])**2).mean())
print(f" PSNR = {psnr:.4f}")
psnrs.append(float(psnr))
if FLAGS.save_output:
utils.save_img(pred_color,
path.join(out_dir, "{:03d}.png".format(idx)))
utils.save_img(
pred_disp[Ellipsis, 0],
path.join(out_dir, "disp_{:03d}.png".format(idx)))
if (not FLAGS.eval_once) and (jax.host_id() == 0):
summary_writer.image("pred_color", showcase_color, step)
summary_writer.image("pred_disp", showcase_disp, step)
summary_writer.image("pred_acc", showcase_acc, step)
if not FLAGS.render_path:
summary_writer.scalar("psnr", np.mean(np.array(psnrs)), step)
summary_writer.image("target", showcase_gt, step)
if FLAGS.save_output and (not FLAGS.render_path) and (jax.host_id()
== 0):
with utils.open_file(path.join(out_dir, "psnr.txt"), "w") as pout:
pout.write("{}".format(np.mean(np.array(psnrs))))
if FLAGS.eval_once:
break
if int(step) >= FLAGS.max_steps:
break
last_step = step