######## PREDICTION ######## log_dir = "learn2reg_t1_paired_train_logs" log_dir_tr = "demos/paired_mrus_brain/learn2reg_t1_paired_train_logs" ckpt_path = os.path.join(log_dir_tr, "save", "weights-epoch800.ckpt") config_path = os.path.join(log_dir_tr, "config.yaml") gpu = "" gpu_allow_growth = False predict( gpu=gpu, gpu_allow_growth=gpu_allow_growth, config_path=config_path, ckpt_path=ckpt_path, mode="test", batch_size=1, log_dir=log_dir, sample_label="all", save_png=True, ) # the numerical metrics are saved in the logs directory specified ######## VISUALISATION ######## # Now lets load in a few samples from the predicitons and plot them # change the following line to the path to image0 label0 path_to_image0_label0 = r"logs/learn2reg_t1_paired_train_logs/test" path_to_pred_fixed_img = os.path.join(path_to_image0_label0,
"\n\n\n\n\n" "=========================================================\n" "The prediction can also be launched using the following command.\n" "deepreg_predict --gpu '' " f"--config_path demos/{name}/{name}_{method}.yaml " f"--ckpt_path demos/{name}/dataset/pretrained/{method}/weights-epoch{ckpt_index}.ckpt " f"--log_dir demos/{name} " f"--log_dir logs_predict/{method} " "--save_png --split test\n" "=========================================================\n" "\n\n\n\n\n" ) log_dir = f"demos/{name}" exp_name = f"logs_predict/{method}/" + datetime.now().strftime("%Y%m%d-%H%M%S") ckpt_path = f"{log_dir}/dataset/pretrained/{method}/weights-epoch{ckpt_index}.ckpt" config_path = [f"{log_dir}/{name}_{method}.yaml"] if args.test: config_path.append("config/test/demo_unpaired_grouped.yaml") predict( gpu="0", gpu_allow_growth=True, ckpt_path=ckpt_path, split="test", batch_size=1, log_dir=log_dir, exp_name=exp_name, config_path=config_path, )
print("\n\n\n\n\n" "=========================================================\n" "The prediction can also be launched using the following command.\n" "deepreg_predict --gpu '' " f"--config_path demos/{name}/{name}.yaml " f"--ckpt_path demos/{name}/dataset/pretrained/ckpt-5000 " f"--log_root demos/{name} " "--log_dir logs_predict " "--save_png --mode test\n" "=========================================================\n" "\n\n\n\n\n") log_root = f"demos/{name}" log_dir = "logs_predict/" + datetime.now().strftime("%Y%m%d-%H%M%S") ckpt_path = f"{log_root}/dataset/pretrained/ckpt-5000" config_path = [f"{log_root}/{name}.yaml"] if args.test: config_path.append("config/test/demo_unpaired_grouped.yaml") predict( gpu="0", gpu_allow_growth=True, ckpt_path=ckpt_path, mode="test", batch_size=1, log_root=log_root, log_dir=log_dir, sample_label="all", config_path=config_path, )