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