mask_s, mask_s, comet_exp, True, None, None) else: trainer.gen_update(images_as, images_bs, config, mask_s, mask_s, comet_exp, True, sem_a, sem_b) if trainer.use_classifier_sr and ( iterations + 1 ) % config["adaptation"]["classif_frequency"] == 0: trainer.domain_classifier_sr_update( images_as, images_bs, True, config["adaptation"]["dfeat_lambda"], iterations + 1, comet_exp) if trainer.train_seg: trainer.segmentation_head_update( images_as, images_bs, sem_a, sem_b, config['adaptation']['sem_seg_lambda'], comet_exp) # Write images if (iterations + 1) % config["image_save_iter"] == 0: with torch.no_grad(): test_image_outputs = trainer.sample( test_display_images_a, test_display_images_b) train_image_outputs = trainer.sample( train_display_images_a, train_display_images_b) write_2images( test_image_outputs, display_size, image_directory, "test_%08d" % (iterations + 1), comet_exp,
== 0): trainer.domain_classifier_sr_update( images_as, images_bs, mask_s, True, config["adaptation"]["dfeat_lambda"], iterations + 1, comet_exp, ) if trainer.train_seg: trainer.segmentation_head_update( images_as, images_bs, mask_s, sem_a, sem_b, config["adaptation"]["sem_seg_lambda"], comet_exp, ) if (iterations + 1) % config["image_save_iter"] == 0: with torch.no_grad(): test_image_outputs = trainer.sample( test_display_images_a, test_display_images_b, test_display_masks_a, test_display_masks_b, ) train_image_outputs = trainer.sample( train_display_images_a,