def __init__(self,
num_instances,
latent_dim,
tracing_steps,
has_params=False,
fit_single_srn=True,
use_unet_renderer=False,
freeze_networks=False):
super().__init__()
self.latent_dim = latent_dim
self.has_params = has_params
self.num_hidden_units_phi = 256
self.phi_layers = 4 # includes the in and out layers
self.rendering_layers = 5 # includes the in and out layers
self.sphere_trace_steps = tracing_steps
self.freeze_networks = freeze_networks
self.fit_single_srn = fit_single_srn
if self.fit_single_srn: # Fit a single scene with a single SRN (no hypernetworks)
self.phi = pytorch_prototyping.FCBlock(
hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.phi_layers - 2,
in_features=6,
out_features=self.num_hidden_units_phi)
self.ray_marcher = custom_layers.Raymarcher2(
num_feature_channels=self.num_hidden_units_phi,
raymarch_steps=self.sphere_trace_steps)
self.phi_scene = PhiScene()
if use_unet_renderer:
self.pixel_generator = custom_layers.DeepvoxelsRenderer(
nf0=32,
in_channels=self.num_hidden_units_phi,
input_resolution=128,
img_sidelength=128)
else:
self.pixel_generator = pytorch_prototyping.FCBlock(
hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.rendering_layers - 1,
in_features=self.num_hidden_units_phi,
out_features=3,
outermost_linear=True)
if self.freeze_networks:
all_network_params = (list(self.pixel_generator.parameters()) +
list(self.ray_marcher.parameters()) +
list(self.hyper_phi.parameters()))
for param in all_network_params:
param.requires_grad = False
# Losses
self.l2_loss = nn.MSELoss(reduction="mean")
# List of logs
self.logs = list()
print(self)
print("Number of parameters:")
util.print_network(self)
def __init__(self,
latent_dim,
tracing_steps,
has_params=False,
fit_single_srn=True,
freeze_networks=False):
super().__init__()
self.latent_dim = latent_dim
self.has_params = has_params
self.num_hidden_units_phi = 256
self.phi_layers = 4 # includes the in and out layers
self.rendering_layers = 5 # includes the in and out layers
self.sphere_trace_steps = tracing_steps
self.freeze_networks = freeze_networks
self.fit_single_srn = fit_single_srn
self.phi2 = pytorch_prototyping.FCBlock(
hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.phi_layers - 2,
in_features=3,
out_features=self.num_hidden_units_phi)
self.ray_marcher = DepthCompute()
self.phi1 = PhiScene()
self.pixel_generator = pytorch_prototyping.FCBlock(
hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.rendering_layers - 1,
in_features=self.num_hidden_units_phi,
out_features=3,
outermost_linear=True)
# Losses
self.l2_loss = nn.MSELoss(reduction="mean")
# List of logs
self.logs = list()
print(self)
print("Number of parameters:")
util.print_network(self)
def __init__(self,
in_ch,
out_ch,
hyper_in_ch,
hyper_num_hidden_layers,
hyper_hidden_ch):
super().__init__()
self.in_ch = in_ch
self.out_ch = out_ch
self.hypo_params = pytorch_prototyping.FCBlock(in_features=hyper_in_ch,
hidden_ch=hyper_hidden_ch,
num_hidden_layers=hyper_num_hidden_layers,
out_features=(in_ch * out_ch) + out_ch,
outermost_linear=True)
self.hypo_params[-1].apply(last_hyper_layer_init)
def __init__(self,
num_instances,
latent_dim,
tracing_steps,
has_params=False,
fit_single_srn=False,
use_unet_renderer=False,
freeze_networks=False):
super().__init__()
self.latent_dim = latent_dim
self.has_params = has_params
self.num_hidden_units_phi = 256
self.phi_layers = 4 # includes the in and out layers
self.rendering_layers = 5 # includes the in and out layers
self.sphere_trace_steps = tracing_steps
self.freeze_networks = freeze_networks
self.fit_single_srn = fit_single_srn
if self.fit_single_srn: # Fit a single scene with a single SRN (no hypernetworks)
self.phi = pytorch_prototyping.FCBlock(hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.phi_layers - 2,
in_features=3,
out_features=self.num_hidden_units_phi)
else:
# Auto-decoder: each scene instance gets its own code vector z
self.latent_codes = nn.Embedding(num_instances, latent_dim).cuda()
nn.init.normal_(self.latent_codes.weight, mean=0, std=0.01)
self.hyper_phi = hyperlayers.HyperFC(hyper_in_ch=self.latent_dim,
hyper_num_hidden_layers=1,
hyper_hidden_ch=self.latent_dim,
hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.phi_layers - 2,
in_ch=3,
out_ch=self.num_hidden_units_phi)
self.ray_marcher = custom_layers.Raymarcher(num_feature_channels=self.num_hidden_units_phi,
raymarch_steps=self.sphere_trace_steps)
if use_unet_renderer:
self.pixel_generator = custom_layers.DeepvoxelsRenderer(nf0=32, in_channels=self.num_hidden_units_phi,
input_resolution=128, img_sidelength=128)
else:
self.pixel_generator = pytorch_prototyping.FCBlock(hidden_ch=self.num_hidden_units_phi,
num_hidden_layers=self.rendering_layers - 1,
in_features=self.num_hidden_units_phi,
out_features=3,
outermost_linear=True)
if self.freeze_networks:
all_network_params = (self.pixel_generator.parameters()
+ self.ray_marcher.parameters()
+ self.hyper_phi.parameters())
for param in all_network_params:
param.requires_grad = False
# Losses
self.l2_loss = nn.MSELoss(reduction="mean")
# List of logs
self.logs = list()
print(self)
print("Number of parameters:")
util.print_network(self)