def load_model(instance, config, expname=None):
''' returns: nerf model and style code '''
parser = config_parser()
if expname is None:
args = parser.parse_args(['--config', config])
else:
args = parser.parse_args(['--config', config, '--expname', expname])
render_kwargs_train, render_kwargs_test, _, _, optimizer, styles = create_nerf(
args, return_styles=True)
if styles.shape[0] == 1:
basedir_args = parser.parse_args(['--config', config])
basedir_styles = create_nerf(basedir_args, return_styles=True)[-1]
styles = torch.cat([styles[:1], basedir_styles[1:]])
return render_kwargs_train, render_kwargs_test, optimizer, styles
def load_dataset(instance,
config,
N_instances,
num_canvases=9,
expname=None,
use_cached=True):
parser = config_parser()
if expname is None:
args = parser.parse_args(['--config', config])
else:
args = parser.parse_args(['--config', config, '--expname', expname])
N_instances = 1
poses, hwfs = get_poses_hwfs(args,
instance,
N_instances,
num_canvases=num_canvases)
if use_cached:
cache = {k: [] for k in ['alphas', 'features', 'weights']}
# You can choose to save the cache on disk.
# Saving the cache takes a while, and loading the cache takes a while the first time, but this is faster if you're editing the same instance frequently.
# cache_dir = os.path.join(args.expname, 'cache')
# if not os.path.exists(f'{cache_dir}/{instance}_{num_canvases-1}.pt'):
# alphas, features, weights = get_cache(config, instance, expname, save=True)
# cache['alphas'] = alphas[:num_canvases]
# cache['features'] = features[:num_canvases]
# cache['weights'] = weights[:num_canvases]
# else:
# for j in range(len(poses)):
# data = torch.load(f'{cache_dir}/{instance}_{j}.pt')
# cache['alphas'].append(data['alphas'].cuda())
# cache['features'].append(data['features'].cuda())
# cache['weights'].append(data['weights'].cuda())
# Computes and loads the cache. Computing the cache doesn't take noticably longer than rendering.
alphas, features, weights = get_cache(config, instance, expname,
num_canvases)
cache['alphas'] = alphas[:num_canvases]
cache['features'] = features[:num_canvases]
cache['weights'] = weights[:num_canvases]
else:
cache = None
return poses, hwfs, cache, args
def get_cache(config, instance, expname=None, num_canvases=9, save=False):
parser = config_parser()
if expname is None:
args = parser.parse_args(['--config', config])
N_instances = None
else:
args = parser.parse_args(['--config', config, '--expname', expname])
N_instances = 1
cachedir = os.path.join(args.expname, 'cache')
with torch.no_grad():
render_kwargs_train, render_kwargs_test, _, _, optimizer, styles = create_nerf(
args, return_styles=True)
if N_instances is None:
N_instances = styles.shape[0]
nfs = [[args.blender_near, args.blender_far] for _ in range(10)]
os.makedirs(cachedir, exist_ok=True)
poses, hwfs = get_poses_hwfs(args, instance, N_instances, num_canvases)
style = styles[instance].repeat((poses.shape[0], 1))
rgbs, disps, _, alphas, features, weights = render_path(
poses.cuda(),
style,
hwfs,
args.chunk,
render_kwargs_test,
nfs=nfs,
verbose=False,
get_cached='color')
if save:
for j, (a, f, w) in enumerate(zip(alphas, features, weights)):
torch.save({
'alphas': a,
'features': f,
'weights': w
}, f'{cachedir}/{instance}_{j}.pt')
utils.save_image(
torch.tensor(rgbs[:1]).permute(0, 3, 1, 2).cpu(),
os.path.join(args.expname, '{:03d}.png'.format(instance)))
return alphas, features, weights
def train():
parser = config_parser()
args = parser.parse_args()
# Create log dir and copy the config file
basedir = args.basedir
expname = args.savedir if args.savedir else args.expname
print('Experiment dir:', expname)
# Load data
images, poses, style, i_test, i_train, bds_dict, dataset, hwfs, near_fars, style_inds = load_data(
args)
_, poses_test, style_test, hwfs_test, nf_test = images[i_test], poses[
i_test], style[i_test], hwfs[i_test], near_fars[i_test]
_, poses_train, style_train, hwfs_train, nf_train = images[i_train], poses[
i_train], style[i_train], hwfs[i_train], near_fars[i_train]
os.makedirs(os.path.join(basedir, expname), exist_ok=True)
np.save(os.path.join(basedir, expname, 'poses.npy'), poses_train.cpu())
np.save(os.path.join(basedir, expname, 'hwfs.npy'), hwfs_train.cpu())
f = os.path.join(basedir, expname, 'args.txt')
with open(f, 'w') as file:
for arg in sorted(vars(args)):
attr = getattr(args, arg)
file.write('{} = {}\n'.format(arg, attr))
if args.config is not None:
f = os.path.join(basedir, expname, 'config.txt')
with open(f, 'w') as file:
file.write(open(args.config, 'r').read())
# Create nerf model
render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(
args)
print(render_kwargs_train['network_fine'])
old_coarse_network = copy.deepcopy(
render_kwargs_train['network_fn']).state_dict()
old_fine_network = copy.deepcopy(
render_kwargs_train['network_fine']).state_dict()
global_step = start
real_image_application = (args.real_image_dir is not None)
optimize_mlp = not real_image_application
render_kwargs_train.update(bds_dict)
render_kwargs_test.update(bds_dict)
loss = None
if start == 0:
# if we're starting from scratch, delete all the logs in that directory.
if os.path.exists(os.path.join(basedir, expname, 'log.txt')):
os.remove(os.path.join(basedir, expname, 'log.txt'))
start = start + 1
for i in range(start, args.n_iters + 1):
# Sample random ray batch
batch_rays, target_s, style, H, W, focal, near, far, viewdirs_reg = dataset.get_data_batch(
train_fn=render_kwargs_train, optimizer=optimizer, loss=loss)
render_kwargs_train.update({'near': near, 'far': far})
##### Core optimization loop #####
rgb, disp, acc, extras = render(H,
W,
focal,
style=style,
chunk=args.chunk,
rays=batch_rays,
viewdirs_reg=viewdirs_reg,
**render_kwargs_train)
optimizer.zero_grad()
img_loss = img2mse(rgb, target_s)
loss = img_loss
psnr = mse2psnr(img_loss)
if args.var_param > 0:
var = extras['var']
var0 = extras['var0']
var_loss = var.mean(dim=0)
var_loss_coarse = var0.mean(dim=0)
loss += args.var_param * var_loss
loss += args.var_param * var_loss_coarse
var_loss = var_loss.item()
var_loss_coarse = var_loss_coarse.item()
else:
var_loss = 0
var_loss_coarse = 0
if 'rgb0' in extras:
img_loss0 = img2mse(extras['rgb0'], target_s)
loss = loss + img_loss0
psnr0 = mse2psnr(img_loss0).item()
else:
psnr0 = -1
if args.weight_change_param >= 0:
weight_change_loss_coarse = 0.
for k, v in render_kwargs_train['network_fn'].named_parameters():
if 'weight' in k:
diff = (old_coarse_network[k] - v).pow(2).mean()
weight_change_loss_coarse += diff
weight_change_loss_fine = 0.
for k, v in render_kwargs_train['network_fine'].named_parameters():
if 'weight' in k:
diff = (old_fine_network[k] - v).pow(2).mean()
weight_change_loss_fine += diff
weight_change_loss = weight_change_loss_coarse + weight_change_loss_fine
loss = loss + args.weight_change_param * weight_change_loss
else:
weight_change_loss = torch.tensor(0.)
loss.backward()
if optimize_mlp:
optimizer.step()
# NOTE: IMPORTANT!
decay_rate = 0.1
decay_steps = args.lrate_decay * 1000
new_lrate = args.lrate * (decay_rate**(global_step / decay_steps))
for param_group in optimizer.param_groups:
param_group['lr'] = new_lrate
################################
##### end #####
if i % args.i_weights == 0:
path = os.path.join(basedir, expname, '{:06d}.tar'.format(i))
state_dict = {
'global_step':
global_step,
'network_fn_state_dict':
render_kwargs_train['network_fn'].state_dict(),
'optimizer_state_dict':
optimizer.state_dict(),
'styles':
dataset.style,
'style_optimizer':
dataset.style_optimizer.state_dict()
}
if args.N_importance > 0:
state_dict['network_fine_state_dict'] = render_kwargs_train[
'network_fine'].state_dict()
torch.save(state_dict, path)
print('Saved checkpoints at', path)
if i % args.i_testset == 0 and i > 0:
if real_image_application:
style_test = dataset.get_features().repeat(
(poses_test.shape[0], 1))
testsavedir = os.path.join(basedir, expname,
'testset_{:06d}'.format(i))
os.makedirs(testsavedir, exist_ok=True)
with torch.no_grad():
render_path(poses_test.to(device),
style_test,
hwfs_test,
args.chunk,
render_kwargs_test,
nfs=nf_test,
savedir=testsavedir,
maximum=100)
print('Saved test set')
if i % args.i_trainset == 0 and i > 0:
if real_image_application:
style_train = dataset.get_features().repeat(
(poses_train.shape[0], 1))
trainsavedir = os.path.join(basedir, expname,
'trainset_{:06d}'.format(i))
os.makedirs(trainsavedir, exist_ok=True)
with torch.no_grad():
render_path(poses_train.to(device),
style_train,
hwfs_train,
args.chunk,
render_kwargs_test,
nfs=nf_train,
savedir=trainsavedir,
maximum=100)
print('Saved train set')
if i % args.i_print == 0 or i == 1:
log_str = f"[TRAIN] Iter: {i} Loss: {loss.item()} PSNR: {psnr.item()} PSNR0: {psnr0} Var loss: {var_loss} Var loss coarse: {var_loss_coarse} Weight change loss: {weight_change_loss}"
with open(os.path.join(basedir, expname, 'log.txt'), 'a+') as f:
f.write(log_str + '\n')
print(log_str)
global_step += 1
if real_image_application and global_step - start == args.n_iters_real:
return
if real_image_application and global_step - start == args.n_iters_code_only:
optimize_mlp = True
dataset.optimizer_name = 'adam'
dataset.style_optimizer = torch.optim.Adam(dataset.params,
lr=dataset.lr)
print('Starting to jointly optimize weights with code')
log_str = f"[TRAIN] Iter: {i} Loss: {loss.item()} PSNR: {psnr.item()} PSNR0: {psnr0} Var loss: {var_loss} Var loss coarse: {var_loss_coarse} Weight change loss: {weight_change_loss}"
with open(os.path.join(basedir, expname, 'log.txt'), 'a+') as f:
f.write(log_str + '\n')
print(log_str)
global_step += 1
if real_image_application and global_step - start == args.n_iters_real:
return
if real_image_application and global_step - start == args.n_iters_code_only:
optimize_mlp = True
dataset.optimizer_name = 'adam'
dataset.style_optimizer = torch.optim.Adam(dataset.params,
lr=dataset.lr)
print('Starting to jointly optimize weights with code')
if __name__ == '__main__':
parser = config_parser()
args = parser.parse_args()
if args.instance != -1:
# Allows for scripting over single instance experiments.
exit_if_job_done(os.path.join(args.basedir, args.expname))
torch.set_default_tensor_type('torch.cuda.FloatTensor')
train()
mark_job_done(os.path.join(args.basedir, args.expname))
else:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
train()
def test():
parser = config_parser()
args = parser.parse_args()
images, poses, style, i_test, i_train, bds_dict, dataset, hwfs, near_fars, _ = load_data(
args)
images_test, poses_test, style_test, hwfs_test, nf_test = images[
i_test], poses[i_test], style[i_test], hwfs[i_test], near_fars[i_test]
images_train, poses_train, style_train, hwfs_train, nf_train = images[
i_train], poses[i_train], style[i_train], hwfs[i_train], near_fars[
i_train]
# Create log dir and copy the config file
basedir = args.basedir
expname = args.expname
render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(
args)
np.save(os.path.join(basedir, expname, 'poses.npy'), poses_train.cpu())
np.save(os.path.join(basedir, expname, 'hwfs.npy'), hwfs_train.cpu())
render_kwargs_train.update(bds_dict)
render_kwargs_test.update(bds_dict)
with torch.no_grad():
if args.render_test:
if args.shuffle_poses:
print('Shuffling test poses')
permutation = list(range(len(poses_test)))
random.shuffle(permutation)
poses_test = poses_test[permutation]
testsavedir = os.path.join(basedir, expname,
'test_imgs{:06d}'.format(start))
os.makedirs(testsavedir, exist_ok=True)
_, _, psnr = render_path(poses_test.to(device),
style_test,
hwfs_test,
args.chunk,
render_kwargs_test,
nfs=nf_test,
gt_imgs=images_test,
savedir=testsavedir)
print('Saved test set w/ psnr', psnr)
if args.render_train:
if args.shuffle_poses:
print('Shuffling train poses')
permutation = list(range(len(poses_train)))
random.shuffle(permutation)
poses_train = poses_train[permutation]
trainsavedir = os.path.join(basedir, expname,
'train_imgs{:06d}'.format(start))
os.makedirs(trainsavedir, exist_ok=True)
_, _, psnr = render_path(poses_train.to(device),
style_train,
hwfs_train,
args.chunk,
render_kwargs_test,
nfs=nf_train,
gt_imgs=images_train,
savedir=trainsavedir)
print('Saved train set w/ psnr', psnr)
def transfer_codes(config):
torch.set_default_tensor_type('torch.cuda.FloatTensor')
N_per_transfer = 8
N_transfer = 10
parser = config_parser()
args = parser.parse_args(['--config', config])
render_kwargs_train, render_kwargs_test, _, _, optimizer, styles = create_nerf(
args, return_styles=True)
nfs = [[args.blender_near, args.blender_far]
for _ in range(N_per_transfer)]
os.makedirs(f'{args.expname}/transfer_codes', exist_ok=True)
with torch.no_grad():
for i1 in range(N_transfer):
for i2 in tqdm(range(N_transfer)):
if i1 == i2:
continue
s1, s2 = styles[i1].unsqueeze(dim=0), styles[i2].unsqueeze(
dim=0)
poses_1, hwfs_1 = get_poses_hwfs(args,
i1,
styles.shape[0],
num_canvases=N_per_transfer)
poses_2, hwfs_2 = get_poses_hwfs(args,
i2,
styles.shape[0],
num_canvases=N_per_transfer)
rgb1 = render_path(poses_1,
s1,
hwfs_1,
4096,
render_kwargs_test,
nfs=nfs,
verbose=False)[0][0]
rgb2 = render_path(poses_2,
s2,
hwfs_2,
4096,
render_kwargs_test,
nfs=nfs,
verbose=False)[0][0]
utils.save_image(
torch.tensor(rgb1).permute(2, 0, 1),
f'{args.expname}/transfer_codes/{i1}.png')
utils.save_image(
torch.tensor(rgb2).permute(2, 0, 1),
f'{args.expname}/transfer_codes/{i2}.png')
take_color = torch.cat([s1[:, :32], s2[:, 32:]], dim=1)
take_shape = torch.cat([s2[:, :32], s1[:, 32:]], dim=1)
# i1 with shape from i2 is the same as i2 color from i1, so don't duplicate
color_from = render_path(poses_1,
take_color.repeat(
(N_per_transfer, 1)),
hwfs_1,
4096,
render_kwargs_test,
nfs=nfs,
verbose=False)[0]
shape_from = render_path(poses_2,
take_shape.repeat(
(N_per_transfer, 1)),
hwfs_2,
4096,
render_kwargs_test,
nfs=nfs,
verbose=False)[0]
utils.save_image(
torch.tensor(color_from).permute(0, 3, 1, 2),
f'{args.expname}/transfer_codes/{i1}_color_from_{i2}.png')
utils.save_image(
torch.tensor(shape_from).permute(0, 3, 1, 2),
f'{args.expname}/transfer_codes/{i1}_shape_from_{i2}.png')
def load_config(config):
parser = config_parser()
return parser.parse_args(['--config', config])
