Consistency Loss - Question on the implementation

[pbonazzi]

Assuming

          args.N_importance = 0
          args.consistency_model_type.startswith('clip_vit') = True

Is it right to say that the consistency loss will compare the first pixel row of the first image of the training batch with the first pixel row of a random image in the target batch ?

            with torch.no_grad():
                  targets_resize_model = F.interpolate(targets, (args.consistency_size, args.consistency_size), ....)
                  target_embeddings = embed(targets_resize_model)  # [N_images , Width , Height]
            target_emb = target_embeddings[:, 0]  # from all images take the first rows
            target_i = np.random.randint(target_emb.shape[0]) 
            target_emb = target_emb[target_i] # sample a random image


            rgbs_resize_c = F.interpolate(rgbs, size=(args.consistency_size, args.consistency_size), mode=args.pixel_interp_mode)
            rendered_embeddings = embed(rgbs_resize_c) # [N_images , Width , Height]
            rendered_embedding = rendered_embeddings[0]  # get the first image
            rendered_emb = rendered_embedding[0] # get the first row of pixels

            consistency_loss = -torch.cosine_similarity(target_emb, rendered_emb, dim=-1)