######## 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,
)