Method validation_step

zero123plus/model.py:236–251

(self, batch, batch_idx)

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        # get input
        cond_imgs, target_imgs = self.prepare_batch_data(batch)

        images_pil = [v2.functional.to_pil_image(cond_imgs[i]) for i in range(cond_imgs.shape[0])]

        outputs = []
        for cond_img in images_pil:
            latent = self.pipeline(cond_img, num_inference_steps=75, output_type='latent').images
            image = unscale_image(self.pipeline.vae.decode(latent / self.pipeline.vae.config.scaling_factor, return_dict=False)[0])   # [-1, 1]
            image = (image * 0.5 + 0.5).clamp(0, 1)
            outputs.append(image)
        outputs = torch.cat(outputs, dim=0).to(self.device)
        images = torch.cat([target_imgs, outputs], dim=-2)
        
        self.validation_step_outputs.append(images)
    
    @torch.no_grad()
    def on_validation_epoch_end(self):