def create_nerf():
embed_fn, input_chn = model.get_embedder(config.multires)
embeddirs_fn, input_chn_views = model.get_embedder(config.multires_views)
skips = [4]
net = model.get_nerf_model(config.net_depth, config.net_width, skips,
input_chn, input_chn_views)
net_fine = model.get_nerf_model(config.net_depth_fine,
config.net_width_fine, skips, input_chn,
input_chn_views)
return embed_fn, embeddirs_fn, net, net_fine
def forward(self, images, is_training, reuse=False):
"""See base class."""
embedder_strategy = self._config.embedder_strategy
embedder = model_module.get_embedder(
embedder_strategy,
self._config,
images,
is_training=is_training, reuse=reuse)
embeddings = embedder.construct_embedding()
if is_training:
self.variables_to_train = embedder.get_trainable_variables()
self.pretrained_init_fn = embedder.init_fn
return embeddings
def forward(self, images, is_training, reuse=False):
"""See base class."""
embedder_strategy = self._config.embedder_strategy
embedder = model_module.get_embedder(embedder_strategy,
self._config,
images,
is_training=is_training,
reuse=reuse)
embeddings = embedder.construct_embedding()
if is_training:
self.variables_to_train = embedder.get_trainable_variables()
self.pretrained_init_fn = embedder.init_fn
return embeddings
def forward(self, images_concat, is_training, reuse=False):
"""See base class."""
embedder_strategy = self._config.embedder_strategy
loss_strategy = self._config.loss_strategy
l2_normalize_embedding = self._config[loss_strategy].embedding_l2
embedder = model_module.get_embedder(
embedder_strategy,
self._config,
images_concat,
is_training=is_training,
l2_normalize_embedding=l2_normalize_embedding,
reuse=reuse)
embeddings_concat = embedder.construct_embedding()
variables_to_train = embedder.get_trainable_variables()
self.variables_to_train = variables_to_train
self.pretrained_init_fn = embedder.init_fn
return embeddings_concat