def init_sampling(self, hwf):
# Unpack data props
H, W, _ = hwf
# Coordinates to sample from, list of H * W indices in form of (width, height), H * W * 2
self.coords = torch.stack(
meshgrid_xy(torch.arange(H), torch.arange(W)),
dim=-1,
).reshape((-1, 2))
def __init__(self,
data,
num_random_rays=None,
near=2,
far=6,
start=0,
stop=-1,
skip=None,
skip_every=None,
resolution=1.0,
scale=1.0):
"""
:param data: A loaded .sens file
:param num_random_rays: Number of rays that should be in a batch. If None, a batch consists of the full image.
:param near: Near plane for bounds.
:param far: Far plane for bounds.
:param start: Start index for images.
:param stop: Stop index for images.
:param skip: If any images should be skipped.
:param skip_every: The inverse of skip.
"""
self.scale = scale
self.num_random_rays = num_random_rays
self.data = data
self.skip = skip
self.skip_every = skip_every
self.resolution = resolution
if stop == -1:
self.stop = len(self.data.frames)
else:
self.stop = stop
if skip:
self.data_len = math.ceil(self.stop / skip)
skip_every = None
elif skip_every:
self.data_len = self.stop - math.ceil(self.stop / skip_every)
else:
self.data_len = len(data.frames)
self.H, self.W = int(self.data.color_height * resolution), int(
self.data.color_width * resolution)
self.dummy_rays = dummy_rays_simple_radial( # TODO: Implement other camera models
self.H, self.W, self.data.intrinsic_color, self.resolution)
if self.num_random_rays:
self.pixels = torch.stack(
meshgrid_xy(torch.arange(self.H), torch.arange(self.W)),
dim=-1,
).reshape((-1, 2))
self.ray_bounds = (torch.tensor(
[near, far],
dtype=torch.float32).view(1, 2).expand(self.num_random_rays,
2))
else:
self.ray_bounds = (torch.tensor(
[near, far], dtype=torch.float32).view(1, 2).expand(
self.dummy_rays.shape[0] * self.dummy_rays.shape[1], 2))
def dummy_rays_simple_radial(height: int, width: int, camera, resolution):
f, cx, cy, k = camera[0], camera[1], camera[2], camera[0]
f *= resolution
cx *= resolution
cy *= resolution
ii, jj = meshgrid_xy(
torch.arange(width, dtype=torch.float32),
torch.arange(height, dtype=torch.float32),
)
directions = torch.stack(
[
(ii - cx) / f,
(jj - cy) / f,
torch.ones_like(ii),
],
dim=-1,
)
# directions /= torch.norm(directions, dim=-1)[...,None]
return directions
def torch_normal_map(depthmap,
focal,
weights=None,
clean=True,
central_difference=False):
W, H = depthmap.shape
#normals = torch.zeros((H,W,3), device=depthmap.device)
cx = focal[2] * W
cy = focal[3] * H
fx = focal[0]
fy = focal[1]
ii, jj = meshgrid_xy(torch.arange(W, device=depthmap.device),
torch.arange(H, device=depthmap.device))
points = torch.stack([
((ii - cx) * depthmap) / fx,
-((jj - cy) * depthmap) / fy,
depthmap,
],
dim=-1)
difference = 2 if central_difference else 1
dx = (points[difference:, :, :] - points[:-difference, :, :])
dy = (points[:, difference:, :] - points[:, :-difference, :])
normals = torch.cross(dy[:-difference, :, :], dx[:, :-difference, :], 2)
normalize_factor = torch.sqrt(torch.sum(normals * normals, 2))
normals[:, :, 0] /= normalize_factor
normals[:, :, 1] /= normalize_factor
normals[:, :, 2] /= normalize_factor
normals = normals * 0.5 + 0.5
if clean and weights is not None: # Use volumetric rendering weights to clean up the normal map
mask = weights.repeat(3, 1, 1).permute(1, 2, 0)
mask = mask[:-difference, :-difference]
where = torch.where(mask > 0.22)
normals[where] = 1.0
normals = (1 - mask) * normals + (mask) * torch.ones_like(normals)
normals *= 255
#plt.imshow(normals.cpu().numpy().astype('uint8'))
#plt.show()
return normals
def cache_nerf_dataset(args):
images, poses, render_poses, hwf = (
None,
None,
None,
None,
)
i_train, i_val, i_test = None, None, None
if args.type == "blender":
images, poses, render_poses, hwf, i_split = load_blender_data(
args.datapath,
half_res=args.blender_half_res,
testskip=args.blender_stride)
i_train, i_val, i_test = i_split
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
elif args.type == "llff":
images, poses, bds, render_poses, i_test = load_llff_data(
args.datapath, factor=args.llff_downsample_factor)
hwf = poses[0, :3, -1]
poses = poses[:, :3, :4]
if not isinstance(i_test, list):
i_test = [i_test]
if args.llffhold > 0:
i_test = np.arange(images.shape[0])[::args.llffhold]
i_val = i_test
i_train = np.array([
i for i in np.arange(images.shape[0])
if (i not in i_test and i not in i_val)
])
H, W, focal = hwf
hwf = [int(H), int(W), focal]
images = torch.from_numpy(images)
poses = torch.from_numpy(poses)
# Device on which to run.
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
os.makedirs(os.path.join(args.savedir, "train"), exist_ok=True)
os.makedirs(os.path.join(args.savedir, "val"), exist_ok=True)
os.makedirs(os.path.join(args.savedir, "test"), exist_ok=True)
np.random.seed(args.randomseed)
for img_idx in tqdm(i_train):
for j in range(args.num_variations):
img_target = images[img_idx].to(device)
pose_target = poses[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(
H, W, focal, pose_target)
coords = torch.stack(
meshgrid_xy(
torch.arange(H).to(device),
torch.arange(W).to(device)),
dim=-1,
)
coords = coords.reshape((-1, 2))
target_s = None
save_path = None
if args.sample_all is False:
select_inds = np.random.choice(coords.shape[0],
size=(args.num_random_rays),
replace=False)
select_inds = coords[select_inds]
ray_origins = ray_origins[select_inds[:, 0], select_inds[:,
1], :]
ray_directions = ray_directions[select_inds[:, 0],
select_inds[:, 1], :]
target_s = img_target[select_inds[:, 0], select_inds[:, 1], :]
save_path = os.path.join(
args.savedir,
"train",
str(img_idx).zfill(4),
str(j).zfill(4),
".data",
)
else:
target_s = img_target
save_path = os.path.join(args.savedir, "train",
str(img_idx).zfill(4) + ".data")
batch_rays = torch.stack([ray_origins, ray_directions], dim=0)
cache_dict = {
"height": H,
"width": W,
"focal_length": focal,
"ray_bundle": batch_rays.detach().cpu(),
"target": target_s.detach().cpu(),
}
save_path = os.path.join(args.savedir, "train",
str(img_idx).zfill(4) + ".data")
torch.save(cache_dict, save_path)
if args.sample_all is True:
break
for img_idx in tqdm(i_val):
img_target = images[img_idx].to(device)
pose_target = poses[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(H, W, focal, pose_target)
cache_dict = {
"height": H,
"width": W,
"focal_length": focal,
"ray_origins": ray_origins.detach().cpu(),
"ray_directions": ray_directions.detach().cpu(),
"target": img_target.detach().cpu(),
}
save_path = os.path.join(args.savedir, "val",
str(img_idx).zfill(4) + ".data")
torch.save(cache_dict, save_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config",
type=str,
required=True,
help="Path to (.yml) config file.")
parser.add_argument(
"--load-checkpoint",
type=str,
default="",
help="Path to load saved checkpoint from.",
)
configargs = parser.parse_args()
# Read config file.
cfg = None
with open(configargs.config, "r") as f:
cfg_dict = yaml.load(f, Loader=yaml.FullLoader)
cfg = CfgNode(cfg_dict)
# # (Optional:) enable this to track autograd issues when debugging
# torch.autograd.set_detect_anomaly(True)
# If a pre-cached dataset is available, skip the dataloader.
USE_CACHED_DATASET = False
train_paths, validation_paths = None, None
images, poses, render_poses, hwf, i_split = None, None, None, None, None
H, W, focal, i_train, i_val, i_test = None, None, None, None, None, None
if hasattr(cfg.dataset, "cachedir") and os.path.exists(
cfg.dataset.cachedir):
train_paths = glob.glob(
os.path.join(cfg.dataset.cachedir, "train", "*.data"))
validation_paths = glob.glob(
os.path.join(cfg.dataset.cachedir, "val", "*.data"))
USE_CACHED_DATASET = True
else:
# Load dataset
images, poses, render_poses, hwf = None, None, None, None
if cfg.dataset.type.lower() == "blender":
images, poses, render_poses, hwf, i_split = load_blender_data(
cfg.dataset.basedir,
half_res=cfg.dataset.half_res,
testskip=cfg.dataset.testskip,
)
i_train, i_val, i_test = i_split
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
if cfg.nerf.train.white_background:
images = images[..., :3] * images[..., -1:] + (
1. - images[..., -1:])
elif cfg.dataset.type.lower() == "llff":
images, poses, bds, render_poses, i_test = load_llff_data(
cfg.dataset.basedir, factor=cfg.dataset.downsample_factor)
hwf = poses[0, :3, -1]
poses = poses[:, :3, :4]
if not isinstance(i_test, list):
i_test = [i_test]
if cfg.dataset.llffhold > 0:
i_test = np.arange(images.shape[0])[::cfg.dataset.llffhold]
i_val = i_test
i_train = np.array([
i for i in np.arange(images.shape[0])
if (i not in i_test and i not in i_val)
])
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
images = torch.from_numpy(images)
poses = torch.from_numpy(poses)
# Seed experiment for repeatability
seed = cfg.experiment.randomseed
np.random.seed(seed)
torch.manual_seed(seed)
# Device on which to run.
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
encode_position_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_xyz,
include_input=cfg.models.coarse.include_input_xyz,
log_sampling=cfg.models.coarse.log_sampling_xyz,
)
encode_direction_fn = None
if cfg.models.coarse.use_viewdirs:
encode_direction_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_dir,
include_input=cfg.models.coarse.include_input_dir,
log_sampling=cfg.models.coarse.log_sampling_dir,
)
# Initialize a coarse-resolution model.
model_coarse = getattr(models, cfg.models.coarse.type)(
num_encoding_fn_xyz=cfg.models.coarse.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.coarse.num_encoding_fn_dir,
include_input_xyz=cfg.models.coarse.include_input_xyz,
include_input_dir=cfg.models.coarse.include_input_dir,
use_viewdirs=cfg.models.coarse.use_viewdirs,
)
model_coarse.to(device)
# If a fine-resolution model is specified, initialize it.
model_fine = None
if hasattr(cfg.models, "fine"):
model_fine = getattr(models, cfg.models.fine.type)(
num_encoding_fn_xyz=cfg.models.fine.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.fine.num_encoding_fn_dir,
include_input_xyz=cfg.models.fine.include_input_xyz,
include_input_dir=cfg.models.fine.include_input_dir,
use_viewdirs=cfg.models.fine.use_viewdirs,
)
model_fine.to(device)
# Initialize optimizer.
trainable_parameters = list(model_coarse.parameters())
if model_fine is not None:
trainable_parameters += list(model_fine.parameters())
optimizer = getattr(torch.optim, cfg.optimizer.type)(trainable_parameters,
lr=cfg.optimizer.lr)
# Setup logging.
logdir = os.path.join(cfg.experiment.logdir, cfg.experiment.id)
os.makedirs(logdir, exist_ok=True)
writer = SummaryWriter(logdir)
# Write out config parameters.
with open(os.path.join(logdir, "config.yml"), "w") as f:
f.write(cfg.dump()) # cfg, f, default_flow_style=False)
# By default, start at iteration 0 (unless a checkpoint is specified).
start_iter = 0
# Load an existing checkpoint, if a path is specified.
if os.path.exists(configargs.load_checkpoint):
checkpoint = torch.load(configargs.load_checkpoint)
model_coarse.load_state_dict(checkpoint["model_coarse_state_dict"])
if checkpoint["model_fine_state_dict"]:
model_fine.load_state_dict(checkpoint["model_fine_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
start_iter = checkpoint["iter"]
# # TODO: Prepare raybatch tensor if batching random rays
for i in trange(start_iter, cfg.experiment.train_iters):
model_coarse.train()
if model_fine:
model_coarse.train()
rgb_coarse, rgb_fine = None, None
target_ray_values = None
if USE_CACHED_DATASET:
datafile = np.random.choice(train_paths)
cache_dict = torch.load(datafile)
ray_bundle = cache_dict["ray_bundle"].to(device)
ray_origins, ray_directions = (
ray_bundle[0].reshape((-1, 3)),
ray_bundle[1].reshape((-1, 3)),
)
target_ray_values = cache_dict["target"][..., :3].reshape((-1, 3))
select_inds = np.random.choice(
ray_origins.shape[0],
size=(cfg.nerf.train.num_random_rays),
replace=False,
)
ray_origins, ray_directions = (
ray_origins[select_inds],
ray_directions[select_inds],
)
target_ray_values = target_ray_values[select_inds].to(device)
# ray_bundle = torch.stack([ray_origins, ray_directions], dim=0).to(device)
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
cache_dict["height"],
cache_dict["width"],
cache_dict["focal_length"],
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="train",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
)
else:
img_idx = np.random.choice(i_train)
img_target = images[img_idx].to(device)
pose_target = poses[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(
H, W, focal, pose_target)
coords = torch.stack(
meshgrid_xy(
torch.arange(H).to(device),
torch.arange(W).to(device)),
dim=-1,
)
coords = coords.reshape((-1, 2))
select_inds = np.random.choice(
coords.shape[0],
size=(cfg.nerf.train.num_random_rays),
replace=False)
select_inds = coords[select_inds]
ray_origins = ray_origins[select_inds[:, 0], select_inds[:, 1], :]
ray_directions = ray_directions[select_inds[:, 0],
select_inds[:, 1], :]
# batch_rays = torch.stack([ray_origins, ray_directions], dim=0)
target_s = img_target[select_inds[:, 0], select_inds[:, 1], :]
then = time.time()
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
H,
W,
focal,
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="train",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
)
target_ray_values = target_s
coarse_loss = torch.nn.functional.mse_loss(rgb_coarse[..., :3],
target_ray_values[..., :3])
fine_loss = None
if rgb_fine is not None:
fine_loss = torch.nn.functional.mse_loss(
rgb_fine[..., :3], target_ray_values[..., :3])
# loss = torch.nn.functional.mse_loss(rgb_pred[..., :3], target_s[..., :3])
loss = coarse_loss + (fine_loss if fine_loss is not None else 0.0)
loss.backward()
psnr = mse2psnr(loss.item())
optimizer.step()
optimizer.zero_grad()
# Learning rate updates
num_decay_steps = cfg.scheduler.lr_decay * 1000
lr_new = cfg.optimizer.lr * (cfg.scheduler.lr_decay_factor
**(i / num_decay_steps))
for param_group in optimizer.param_groups:
param_group["lr"] = lr_new
if i % cfg.experiment.print_every == 0 or i == cfg.experiment.train_iters - 1:
tqdm.write("[TRAIN] Iter: " + str(i) + " Loss: " +
str(loss.item()) + " PSNR: " + str(psnr))
writer.add_scalar("train/loss", loss.item(), i)
writer.add_scalar("train/coarse_loss", coarse_loss.item(), i)
if rgb_fine is not None:
writer.add_scalar("train/fine_loss", fine_loss.item(), i)
writer.add_scalar("train/psnr", psnr, i)
# Validation
if (i % cfg.experiment.validate_every == 0
or i == cfg.experiment.train_iters - 1):
tqdm.write("[VAL] =======> Iter: " + str(i))
model_coarse.eval()
if model_fine:
model_coarse.eval()
start = time.time()
with torch.no_grad():
rgb_coarse, rgb_fine = None, None
target_ray_values = None
if USE_CACHED_DATASET:
datafile = np.random.choice(validation_paths)
cache_dict = torch.load(datafile)
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
cache_dict["height"],
cache_dict["width"],
cache_dict["focal_length"],
model_coarse,
model_fine,
cache_dict["ray_origins"].to(device),
cache_dict["ray_directions"].to(device),
cfg,
mode="validation",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
)
target_ray_values = cache_dict["target"].to(device)
else:
img_idx = np.random.choice(i_val)
img_target = images[img_idx].to(device)
pose_target = poses[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(
H, W, focal, pose_target)
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
H,
W,
focal,
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="validation",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
)
target_ray_values = img_target
coarse_loss = img2mse(rgb_coarse[..., :3],
target_ray_values[..., :3])
fine_loss = 0.0
if rgb_fine is not None:
fine_loss = img2mse(rgb_fine[..., :3],
target_ray_values[..., :3])
loss = coarse_loss + fine_loss
psnr = mse2psnr(loss.item())
writer.add_scalar("validation/loss", loss.item(), i)
writer.add_scalar("validation/coarse_loss", coarse_loss.item(),
i)
writer.add_scalar("validataion/psnr", psnr, i)
writer.add_image("validation/rgb_coarse",
cast_to_image(rgb_coarse[..., :3]), i)
if rgb_fine is not None:
writer.add_image("validation/rgb_fine",
cast_to_image(rgb_fine[..., :3]), i)
writer.add_scalar("validation/fine_loss", fine_loss.item(),
i)
writer.add_image(
"validation/img_target",
cast_to_image(target_ray_values[..., :3]),
i,
)
tqdm.write("Validation loss: " + str(loss.item()) +
" Validation PSNR: " + str(psnr) + " Time: " +
str(time.time() - start))
if i % cfg.experiment.save_every == 0 or i == cfg.experiment.train_iters - 1:
checkpoint_dict = {
"iter":
i,
"model_coarse_state_dict":
model_coarse.state_dict(),
"model_fine_state_dict":
None if not model_fine else model_fine.state_dict(),
"optimizer_state_dict":
optimizer.state_dict(),
"loss":
loss,
"psnr":
psnr,
}
torch.save(
checkpoint_dict,
os.path.join(logdir, "checkpoint" + str(i).zfill(5) + ".ckpt"),
)
tqdm.write("================== Saved Checkpoint =================")
print("Done!")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config",
type=str,
required=True,
help="Path to (.yml) config file.")
parser.add_argument(
"--load-checkpoint",
type=str,
default="",
help="Path to load saved checkpoint from.",
)
configargs = parser.parse_args()
# Read config file.
cfg = None
with open(configargs.config, "r") as f:
cfg_dict = yaml.load(f, Loader=yaml.FullLoader)
cfg = CfgNode(cfg_dict)
# # (Optional:) enable this to track autograd issues when debugging
# torch.autograd.set_detect_anomaly(True)
# If a pre-cached dataset is available, skip the dataloader.
USE_CACHED_DATASET = False
train_paths, validation_paths = None, None
images, poses, render_poses, hwf, i_split, expressions = None, None, None, None, None, None
H, W, focal, i_train, i_val, i_test = None, None, None, None, None, None
if hasattr(cfg.dataset, "cachedir") and os.path.exists(
cfg.dataset.cachedir):
train_paths = glob.glob(
os.path.join(cfg.dataset.cachedir, "train", "*.data"))
validation_paths = glob.glob(
os.path.join(cfg.dataset.cachedir, "val", "*.data"))
USE_CACHED_DATASET = True
else:
# Load dataset
images, poses, render_poses, hwf, expressions = None, None, None, None, None
if cfg.dataset.type.lower() == "blender":
images, poses, render_poses, hwf, i_split, expressions, _, bboxs = load_flame_data(
cfg.dataset.basedir,
half_res=cfg.dataset.half_res,
testskip=cfg.dataset.testskip,
)
i_train, i_val, i_test = i_split
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
if cfg.nerf.train.white_background:
images = images[..., :3] * images[..., -1:] + (
1.0 - images[..., -1:])
print("done loading data")
# Seed experiment for repeatability
seed = cfg.experiment.randomseed
np.random.seed(seed)
torch.manual_seed(seed)
# Device on which to run.
if torch.cuda.is_available():
device = "cuda" #+ ":" + str(cfg.experiment.device)
else:
device = "cpu"
encode_position_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_xyz,
include_input=cfg.models.coarse.include_input_xyz,
log_sampling=cfg.models.coarse.log_sampling_xyz,
)
encode_direction_fn = None
if cfg.models.coarse.use_viewdirs:
encode_direction_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_dir,
include_input=cfg.models.coarse.include_input_dir,
log_sampling=cfg.models.coarse.log_sampling_dir,
)
# Initialize a coarse-resolution model.
model_coarse = getattr(models, cfg.models.coarse.type)(
num_encoding_fn_xyz=cfg.models.coarse.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.coarse.num_encoding_fn_dir,
include_input_xyz=cfg.models.coarse.include_input_xyz,
include_input_dir=cfg.models.coarse.include_input_dir,
use_viewdirs=cfg.models.coarse.use_viewdirs,
num_layers=cfg.models.coarse.num_layers,
hidden_size=cfg.models.coarse.hidden_size,
include_expression=True)
model_coarse.to(device)
# If a fine-resolution model is specified, initialize it.
model_fine = None
if hasattr(cfg.models, "fine"):
model_fine = getattr(models, cfg.models.fine.type)(
num_encoding_fn_xyz=cfg.models.fine.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.fine.num_encoding_fn_dir,
include_input_xyz=cfg.models.fine.include_input_xyz,
include_input_dir=cfg.models.fine.include_input_dir,
use_viewdirs=cfg.models.fine.use_viewdirs,
num_layers=cfg.models.coarse.num_layers,
hidden_size=cfg.models.coarse.hidden_size,
include_expression=True)
model_fine.to(device)
###################################
###################################
train_background = False
supervised_train_background = False
blur_background = False
train_latent_codes = True
disable_expressions = False # True to disable expressions
disable_latent_codes = False # True to disable latent codes
fixed_background = True # Do False to disable BG
regularize_latent_codes = True # True to add latent code LOSS, false for most experiments
###################################
###################################
supervised_train_background = train_background and supervised_train_background
# Avg background
#images[i_train]
if train_background:
with torch.no_grad():
avg_img = torch.mean(images[i_train], axis=0)
# Blur Background:
if blur_background:
avg_img = avg_img.permute(2, 0, 1)
avg_img = avg_img.unsqueeze(0)
smoother = GaussianSmoothing(channels=3,
kernel_size=11,
sigma=11)
print("smoothed background initialization. shape ",
avg_img.shape)
avg_img = smoother(avg_img).squeeze(0).permute(1, 2, 0)
#avg_img = torch.zeros(H,W,3)
#avg_img = torch.rand(H,W,3)
#avg_img = 0.5*(torch.rand(H,W,3) + torch.mean(images[i_train],axis=0))
background = torch.tensor(avg_img, device=device)
background.requires_grad = True
if fixed_background: # load GT background
print("loading GT background to condition on")
from PIL import Image
background = Image.open(
os.path.join(cfg.dataset.basedir, 'bg', '00050.png'))
background.thumbnail((H, W))
background = torch.from_numpy(np.array(background).astype(
np.float32)).to(device)
background = background / 255
print("bg shape", background.shape)
print("should be ", images[i_train][0].shape)
assert background.shape == images[i_train][0].shape
else:
background = None
# Initialize optimizer.
trainable_parameters = list(model_coarse.parameters())
if model_fine is not None:
trainable_parameters += list(model_fine.parameters())
if train_background:
#background.requires_grad = True
#trainable_parameters.append(background) # add it later when init optimizer for different lr
print("background.is_leaf ", background.is_leaf, background.device)
if train_latent_codes:
latent_codes = torch.zeros(len(i_train), 32, device=device)
print("initialized latent codes with shape %d X %d" %
(latent_codes.shape[0], latent_codes.shape[1]))
if not disable_latent_codes:
trainable_parameters.append(latent_codes)
latent_codes.requires_grad = True
if train_background:
optimizer = getattr(torch.optim,
cfg.optimizer.type)([{
'params': trainable_parameters
}, {
'params': background,
'lr': cfg.optimizer.lr
}],
lr=cfg.optimizer.lr)
else:
optimizer = getattr(torch.optim, cfg.optimizer.type)(
[{
'params': trainable_parameters
}, {
'params': background,
'lr': cfg.optimizer.lr
}], # this is obsolete but need for continuing training
lr=cfg.optimizer.lr)
# Setup logging.
logdir = os.path.join(cfg.experiment.logdir, cfg.experiment.id)
os.makedirs(logdir, exist_ok=True)
writer = SummaryWriter(logdir)
# Write out config parameters.
with open(os.path.join(logdir, "config.yml"), "w") as f:
f.write(cfg.dump()) # cfg, f, default_flow_style=False)
# By default, start at iteration 0 (unless a checkpoint is specified).
start_iter = 0
# Load an existing checkpoint, if a path is specified.
if os.path.exists(configargs.load_checkpoint):
checkpoint = torch.load(configargs.load_checkpoint)
model_coarse.load_state_dict(checkpoint["model_coarse_state_dict"])
if checkpoint["model_fine_state_dict"]:
model_fine.load_state_dict(checkpoint["model_fine_state_dict"])
if checkpoint["background"] is not None:
print("loaded bg from checkpoint")
background = torch.nn.Parameter(
checkpoint['background'].to(device))
if checkpoint["latent_codes"] is not None:
print("loaded latent codes from checkpoint")
latent_codes = torch.nn.Parameter(
checkpoint['latent_codes'].to(device))
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
start_iter = checkpoint["iter"]
# # TODO: Prepare raybatch tensor if batching random rays
# Prepare importance sampling maps
ray_importance_sampling_maps = []
p = 0.9
print("computing boundix boxes probability maps")
for i in i_train:
bbox = bboxs[i]
probs = np.zeros((H, W))
probs.fill(1 - p)
probs[bbox[0]:bbox[1], bbox[2]:bbox[3]] = p
probs = (1 / probs.sum()) * probs
ray_importance_sampling_maps.append(probs.reshape(-1))
print("Starting loop")
for i in trange(start_iter, cfg.experiment.train_iters):
model_coarse.train()
if model_fine:
model_coarse.train()
rgb_coarse, rgb_fine = None, None
target_ray_values = None
background_ray_values = None
if USE_CACHED_DATASET:
datafile = np.random.choice(train_paths)
cache_dict = torch.load(datafile)
ray_bundle = cache_dict["ray_bundle"].to(device)
ray_origins, ray_directions = (
ray_bundle[0].reshape((-1, 3)),
ray_bundle[1].reshape((-1, 3)),
)
target_ray_values = cache_dict["target"][..., :3].reshape((-1, 3))
select_inds = np.random.choice(
ray_origins.shape[0],
size=(cfg.nerf.train.num_random_rays),
replace=False,
)
ray_origins, ray_directions = (
ray_origins[select_inds],
ray_directions[select_inds],
)
target_ray_values = target_ray_values[select_inds].to(device)
#target_ray_values = target_ray_values[select_inds].to(device)
# ray_bundle = torch.stack([ray_origins, ray_directions], dim=0).to(device)
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
cache_dict["height"],
cache_dict["width"],
cache_dict["focal_length"],
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="train",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
expressions=expressions)
else:
img_idx = np.random.choice(i_train)
img_target = images[img_idx].to(device)
pose_target = poses[img_idx, :3, :4].to(device)
if not disable_expressions:
expression_target = expressions[img_idx].to(device) # vector
else: # zero expr
expression_target = torch.zeros(76, device=device)
#bbox = bboxs[img_idx]
if not disable_latent_codes:
latent_code = latent_codes[img_idx].to(
device) if train_latent_codes else None
else:
latent_codes = torch.zeros(32, device=device)
#latent_code = torch.zeros(32).to(device)
ray_origins, ray_directions = get_ray_bundle(
H, W, focal, pose_target)
coords = torch.stack(
meshgrid_xy(
torch.arange(H).to(device),
torch.arange(W).to(device)),
dim=-1,
)
# Only randomly choose rays that are in the bounding box !
# coords = torch.stack(
# meshgrid_xy(torch.arange(bbox[0],bbox[1]).to(device), torch.arange(bbox[2],bbox[3]).to(device)),
# dim=-1,
# )
coords = coords.reshape((-1, 2))
# select_inds = np.random.choice(
# coords.shape[0], size=(cfg.nerf.train.num_random_rays), replace=False
# )
# Use importance sampling to sample mainly in the bbox with prob p
select_inds = np.random.choice(
coords.shape[0],
size=(cfg.nerf.train.num_random_rays),
replace=False,
p=ray_importance_sampling_maps[img_idx])
select_inds = coords[select_inds]
ray_origins = ray_origins[select_inds[:, 0], select_inds[:, 1], :]
ray_directions = ray_directions[select_inds[:, 0],
select_inds[:, 1], :]
#dump_rays(ray_origins, ray_directions)
# batch_rays = torch.stack([ray_origins, ray_directions], dim=0)
target_s = img_target[select_inds[:, 0], select_inds[:, 1], :]
background_ray_values = background[select_inds[:, 0],
select_inds[:, 1], :] if (
train_background or
fixed_background) else None
#if i<10000:
# background_ray_values = None
#background_ray_values = None
then = time.time()
rgb_coarse, _, _, rgb_fine, _, _, weights = run_one_iter_of_nerf(
H,
W,
focal,
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="train",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
expressions=expression_target,
background_prior=background_ray_values,
latent_code=latent_code if not disable_latent_codes else
torch.zeros(32, device=device))
target_ray_values = target_s
coarse_loss = torch.nn.functional.mse_loss(rgb_coarse[..., :3],
target_ray_values[..., :3])
fine_loss = None
if rgb_fine is not None:
fine_loss = torch.nn.functional.mse_loss(
rgb_fine[..., :3], target_ray_values[..., :3])
# loss = torch.nn.functional.mse_loss(rgb_pred[..., :3], target_s[..., :3])
loss = 0.0
# if fine_loss is not None:
# loss = fine_loss
# else:
# loss = coarse_loss
latent_code_loss = torch.zeros(1, device=device)
if train_latent_codes and not disable_latent_codes:
latent_code_loss = torch.norm(latent_code) * 0.0005
#latent_code_loss = torch.zeros(1)
background_loss = torch.zeros(1, device=device)
if supervised_train_background:
background_loss = torch.nn.functional.mse_loss(
background_ray_values[..., :3],
target_ray_values[..., :3],
reduction='none').sum(1)
background_loss = torch.mean(background_loss * weights) * 0.001
loss = coarse_loss + (fine_loss if fine_loss is not None else 0.0)
psnr = mse2psnr(loss.item())
#loss_total = loss #+ (latent_code_loss if latent_code_loss is not None else 0.0)
loss = loss + (latent_code_loss *
10 if regularize_latent_codes else 0.0)
loss_total = loss + (background_loss if supervised_train_background
is not None else 0.0)
#loss.backward()
loss_total.backward()
#psnr = mse2psnr(loss.item())
optimizer.step()
optimizer.zero_grad()
# Learning rate updates
num_decay_steps = cfg.scheduler.lr_decay * 1000
lr_new = cfg.optimizer.lr * (cfg.scheduler.lr_decay_factor
**(i / num_decay_steps))
for param_group in optimizer.param_groups:
param_group["lr"] = lr_new
if i % cfg.experiment.print_every == 0 or i == cfg.experiment.train_iters - 1:
tqdm.write("[TRAIN] Iter: " + str(i) + " Loss: " +
str(loss.item()) + " BG Loss: " +
str(background_loss.item()) + " PSNR: " + str(psnr) +
" LatentReg: " + str(latent_code_loss.item()))
#writer.add_scalar("train/loss", loss.item(), i)
if train_latent_codes:
writer.add_scalar("train/code_loss", latent_code_loss.item(), i)
if supervised_train_background:
writer.add_scalar("train/bg_loss", background_loss.item(), i)
writer.add_scalar("train/coarse_loss", coarse_loss.item(), i)
if rgb_fine is not None:
writer.add_scalar("train/fine_loss", fine_loss.item(), i)
writer.add_scalar("train/psnr", psnr, i)
# Validation
if (i % cfg.experiment.validate_every == 0
or i == cfg.experiment.train_iters - 1 and False):
#torch.cuda.empty_cache()
tqdm.write("[VAL] =======> Iter: " + str(i))
model_coarse.eval()
if model_fine:
model_coarse.eval()
start = time.time()
with torch.no_grad():
rgb_coarse, rgb_fine = None, None
target_ray_values = None
if USE_CACHED_DATASET:
datafile = np.random.choice(validation_paths)
cache_dict = torch.load(datafile)
rgb_coarse, _, _, rgb_fine, _, weights = run_one_iter_of_nerf(
cache_dict["height"],
cache_dict["width"],
cache_dict["focal_length"],
model_coarse,
model_fine,
cache_dict["ray_origins"].to(device),
cache_dict["ray_directions"].to(device),
cfg,
mode="validation",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
expressions=expression_target,
latent_code=torch.zeros(32, device=device))
target_ray_values = cache_dict["target"].to(device)
else:
# Do all validation set...
loss = 0
for img_idx in i_val[:2]:
img_target = images[img_idx].to(device)
#tqdm.set_description('val im %d' % img_idx)
#tqdm.refresh() # to show immediately the update
# # save val image for debug ### DEBUG ####
# #GT = target_ray_values[..., :3]
# import PIL.Image
# #img = GT.permute(2, 0, 1)
# # Conver to PIL Image and then np.array (output shape: (H, W, 3))
# #im_numpy = img_target.detach().cpu().numpy()
# #im_numpy = np.array(torchvision.transforms.ToPILImage()(img_target.detach().cpu()))
#
# # im = PIL.Image.fromarray(im_numpy)
# im = img_target
# im = im.permute(2, 0, 1)
# img = np.array(torchvision.transforms.ToPILImage()(im.detach().cpu()))
# im = PIL.Image.fromarray(img)
# im.save('val_im_target_debug.png')
# ### DEBUG #### END
pose_target = poses[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(
H, W, focal, pose_target)
rgb_coarse, _, _, rgb_fine, _, _, weights = run_one_iter_of_nerf(
H,
W,
focal,
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="validation",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
expressions=expression_target,
background_prior=background.view(-1, 3) if
(train_background or fixed_background) else None,
latent_code=torch.zeros(32).to(device)
if train_latent_codes or disable_latent_codes else
None,
)
#print("did one val")
target_ray_values = img_target
coarse_loss = img2mse(rgb_coarse[..., :3],
target_ray_values[..., :3])
curr_loss, curr_fine_loss = 0.0, 0.0
if rgb_fine is not None:
curr_fine_loss = img2mse(
rgb_fine[..., :3], target_ray_values[..., :3])
curr_loss = curr_fine_loss
else:
curr_loss = coarse_loss
loss += curr_loss + curr_fine_loss
loss /= len(i_val)
psnr = mse2psnr(loss.item())
writer.add_scalar("validation/loss", loss.item(), i)
writer.add_scalar("validation/coarse_loss", coarse_loss.item(),
i)
writer.add_scalar("validation/psnr", psnr, i)
writer.add_image("validation/rgb_coarse",
cast_to_image(rgb_coarse[..., :3]), i)
if rgb_fine is not None:
writer.add_image("validation/rgb_fine",
cast_to_image(rgb_fine[..., :3]), i)
writer.add_scalar("validation/fine_loss", fine_loss.item(),
i)
writer.add_image(
"validation/img_target",
cast_to_image(target_ray_values[..., :3]),
i,
)
if train_background or fixed_background:
writer.add_image("validation/background",
cast_to_image(background[..., :3]), i)
writer.add_image("validation/weights",
(weights.detach().cpu().numpy()),
i,
dataformats='HW')
tqdm.write("Validation loss: " + str(loss.item()) +
" Validation PSNR: " + str(psnr) + " Time: " +
str(time.time() - start))
#gpu_profile(frame=sys._getframe(), event='line', arg=None)
if i % cfg.experiment.save_every == 0 or i == cfg.experiment.train_iters - 1:
checkpoint_dict = {
"iter":
i,
"model_coarse_state_dict":
model_coarse.state_dict(),
"model_fine_state_dict":
None if not model_fine else model_fine.state_dict(),
"optimizer_state_dict":
optimizer.state_dict(),
"loss":
loss,
"psnr":
psnr,
"background":
None if not (train_background or fixed_background) else
background.data,
"latent_codes":
None if not train_latent_codes else latent_codes.data
}
torch.save(
checkpoint_dict,
os.path.join(logdir, "checkpoint" + str(i).zfill(5) + ".ckpt"),
)
tqdm.write("================== Saved Checkpoint =================")
print("Done!")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config",
type=str,
required=True,
help="Path to (.yml) config file.")
parser.add_argument(
"--load-checkpoint",
type=str,
default="",
help="Path to load saved checkpoint from.",
)
configargs = parser.parse_args()
# Read config file.
with open(configargs.config, "r") as f:
cfg_dict = yaml.load(f, Loader=yaml.FullLoader)
cfg = CfgNode(cfg_dict)
# # (Optional:) enable this to track autograd issues when debugging
# torch.autograd.set_detect_anomaly(True)
# Load dataset
if cfg.dataset.type.lower() == "llff":
images, poses, _, _, i_test = load_llff_data(
cfg.dataset.basedir, factor=cfg.dataset.downsample_factor)
hwf = poses[0, :3, -1]
poses = poses[:, :3, :4]
if not isinstance(i_test, list):
i_test = [i_test]
if cfg.dataset.llffhold > 0:
i_test = np.arange(images.shape[0])[::cfg.dataset.llffhold]
i_val = i_test
i_train = np.array([
i for i in np.arange(images.shape[0])
if (i not in i_test and i not in i_val)
])
H, W, focal = hwf
H, W = int(H), int(W)
images = torch.from_numpy(images)
poses = torch.from_numpy(poses)
USE_HR_LR = False
# Load LR images
if hasattr(cfg.dataset, "relative_lr_factor"):
assert hasattr(cfg.dataset, "hr_fps")
assert hasattr(cfg.dataset, "hr_frequency")
USE_HR_LR = True
images_lr, poses_lr, _, _, i_test = load_llff_data(
cfg.dataset.basedir,
factor=cfg.dataset.downsample_factor *
cfg.dataset.relative_lr_factor)
hwf = poses_lr[0, :3, -1]
poses_lr = poses_lr[:, :3, :4]
if not isinstance(i_test, list):
i_test = [i_test]
if cfg.dataset.llffhold > 0:
i_test = np.arange(images_lr.shape[0])[::cfg.dataset.llffhold]
i_train_lr = np.array([
i for i in np.arange(images_lr.shape[0]) if (i not in i_test)
])
H_lr, W_lr, focal_lr = hwf
H_lr, W_lr = int(H_lr), int(W_lr)
images_lr = torch.from_numpy(images_lr)
poses_lr = torch.from_numpy(poses_lr)
# Expose only some HR images
i_train = i_train[::cfg.dataset.hr_fps]
print(f'LR summary: N={i_train_lr.shape[0]}, '
f'resolution={H_lr}x{W_lr}, focal-length={focal_lr}')
print(f'HR summary: N={i_train.shape[0]}, '
f'resolution={H}x{W}, focal-length={focal}')
# Seed experiment for repeatability
seed = cfg.experiment.randomseed
np.random.seed(seed)
torch.manual_seed(seed)
# Device on which to run.
device = f"cuda:{cfg.experiment.gpu_id}"
encode_position_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_xyz,
include_input=cfg.models.coarse.include_input_xyz,
log_sampling=cfg.models.coarse.log_sampling_xyz,
)
encode_direction_fn = None
if cfg.models.coarse.use_viewdirs:
encode_direction_fn = get_embedding_function(
num_encoding_functions=cfg.models.coarse.num_encoding_fn_dir,
include_input=cfg.models.coarse.include_input_dir,
log_sampling=cfg.models.coarse.log_sampling_dir,
)
# Initialize a coarse and fine resolution model.
model_coarse = getattr(models, cfg.models.coarse.type)(
num_encoding_fn_xyz=cfg.models.coarse.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.coarse.num_encoding_fn_dir,
include_input_xyz=cfg.models.coarse.include_input_xyz,
include_input_dir=cfg.models.coarse.include_input_dir,
use_viewdirs=cfg.models.coarse.use_viewdirs,
)
model_coarse.to(device)
model_fine = getattr(models, cfg.models.fine.type)(
num_encoding_fn_xyz=cfg.models.fine.num_encoding_fn_xyz,
num_encoding_fn_dir=cfg.models.fine.num_encoding_fn_dir,
include_input_xyz=cfg.models.fine.include_input_xyz,
include_input_dir=cfg.models.fine.include_input_dir,
use_viewdirs=cfg.models.fine.use_viewdirs,
)
model_fine.to(device)
# Initialize optimizer.
trainable_parameters = list(model_coarse.parameters()) + list(
model_fine.parameters())
optimizer = getattr(torch.optim, cfg.optimizer.type)(trainable_parameters,
lr=cfg.optimizer.lr)
# Setup logging.
logdir = os.path.join(cfg.experiment.logdir, cfg.experiment.id)
os.makedirs(logdir, exist_ok=True)
writer = SummaryWriter(logdir)
# Write out config parameters.
with open(os.path.join(logdir, "config.yml"), "w") as f:
f.write(cfg.dump()) # cfg, f, default_flow_style=False)
# By default, start at iteration 0 (unless a checkpoint is specified).
start_iter = 0
# Load an existing checkpoint, if a path is specified.
if os.path.exists(configargs.load_checkpoint):
checkpoint = torch.load(configargs.load_checkpoint)
model_coarse.load_state_dict(checkpoint["model_coarse_state_dict"])
model_fine.load_state_dict(checkpoint["model_fine_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
start_iter = checkpoint["iter"]
# # TODO: Prepare raybatch tensor if batching random rays
for i in trange(start_iter, cfg.experiment.train_iters):
model_coarse.train()
model_fine.train()
if USE_HR_LR and i % cfg.dataset.hr_frequency != 0:
H_iter, W_iter, focal_iter = H_lr, W_lr, focal_lr
i_train_iter = i_train_lr
images_iter, poses_iter = images_lr, poses_lr
else:
H_iter, W_iter, focal_iter = H, W, focal
i_train_iter = i_train
images_iter, poses_iter = images, poses
img_idx = np.random.choice(i_train_iter)
img_target = images_iter[img_idx].to(device)
pose_target = poses_iter[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(H_iter, W_iter,
focal_iter, pose_target)
coords = torch.stack(
meshgrid_xy(
torch.arange(H_iter).to(device),
torch.arange(W_iter).to(device)),
dim=-1,
)
coords = coords.reshape((-1, 2))
select_inds = np.random.choice(coords.shape[0],
size=(cfg.nerf.train.num_random_rays),
replace=False)
select_inds = coords[select_inds]
ray_origins = ray_origins[select_inds[:, 0], select_inds[:, 1], :]
ray_directions = ray_directions[select_inds[:, 0], select_inds[:,
1], :]
target_s = img_target[select_inds[:, 0], select_inds[:, 1], :]
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
H_iter,
W_iter,
focal_iter,
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="train",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
)
target_ray_values = target_s
coarse_loss = torch.nn.functional.mse_loss(rgb_coarse[..., :3],
target_ray_values[..., :3])
fine_loss = torch.nn.functional.mse_loss(rgb_fine[..., :3],
target_ray_values[..., :3])
loss = coarse_loss + fine_loss
loss.backward()
psnr = mse2psnr(loss.item())
optimizer.step()
optimizer.zero_grad()
# Learning rate updates
num_decay_steps = cfg.scheduler.lr_decay * 1000
lr_new = cfg.optimizer.lr * (cfg.scheduler.lr_decay_factor
**(i / num_decay_steps))
for param_group in optimizer.param_groups:
param_group["lr"] = lr_new
if i % cfg.experiment.print_every == 0 or i == cfg.experiment.train_iters - 1:
tqdm.write("[TRAIN] Iter: " + str(i) + " Loss: " +
str(loss.item()) + " PSNR: " + str(psnr))
writer.add_scalar("train/loss", loss.item(), i)
writer.add_scalar("train/coarse_loss", coarse_loss.item(), i)
writer.add_scalar("train/fine_loss", fine_loss.item(), i)
writer.add_scalar("train/psnr", psnr, i)
# Validation
if (i % cfg.experiment.validate_every == 0
or i == cfg.experiment.train_iters - 1):
tqdm.write("[VAL] =======> Iter: " + str(i))
model_coarse.eval()
model_fine.eval()
start = time.time()
with torch.no_grad():
img_idx = np.random.choice(i_val)
img_target = images[img_idx].to(device)
pose_target = poses[img_idx, :3, :4].to(device)
ray_origins, ray_directions = get_ray_bundle(
H, W, focal, pose_target)
rgb_coarse, _, _, rgb_fine, _, _ = run_one_iter_of_nerf(
H,
W,
focal,
model_coarse,
model_fine,
ray_origins,
ray_directions,
cfg,
mode="validation",
encode_position_fn=encode_position_fn,
encode_direction_fn=encode_direction_fn,
)
target_ray_values = img_target
coarse_loss = img2mse(rgb_coarse[..., :3],
target_ray_values[..., :3])
fine_loss = img2mse(rgb_fine[..., :3],
target_ray_values[..., :3])
loss = coarse_loss + fine_loss
psnr = mse2psnr(loss.item())
writer.add_scalar("validation/loss", loss.item(), i)
writer.add_scalar("validation/coarse_loss", coarse_loss.item(),
i)
writer.add_scalar("validation/fine_loss", fine_loss.item(), i)
writer.add_scalar("validataion/psnr", psnr, i)
writer.add_image("validation/rgb_coarse",
cast_to_image(rgb_coarse[..., :3]), i)
writer.add_image("validation/rgb_fine",
cast_to_image(rgb_fine[..., :3]), i)
writer.add_image(
"validation/img_target",
cast_to_image(target_ray_values[..., :3]),
i,
)
tqdm.write("Validation loss: " + str(loss.item()) +
" Validation PSNR: " + str(psnr) + " Time: " +
str(time.time() - start))
if i % cfg.experiment.save_every == 0 or i == cfg.experiment.train_iters - 1:
checkpoint_dict = {
"iter": i,
"model_coarse_state_dict": model_coarse.state_dict(),
"model_fine_state_dict": model_fine.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"loss": loss,
"psnr": psnr,
}
torch.save(
checkpoint_dict,
os.path.join(logdir, "checkpoint" + str(i).zfill(5) + ".ckpt"),
)
tqdm.write("================== Saved Checkpoint =================")
print("Done!")