Method forward

visualization/visualize_results_fauna.py:69–74

(self, feat)

                               or isinstance(self.amb, int))

    def forward(self, feat):
        batch_size = feat.shape[0]
        if self.is_hacking:
            return torch.concat([self.light_dir, self.amb, self.diff], -1)
        else:
            return torch.concat([self.light_dir, torch.FloatTensor([self.amb, self.diff]).to(self.light_dir.device)], -1).expand(batch_size, -1)

    def shade(self, feat, kd, normal):
        light_params = self.forward(feat)