def quick_check(run_makelinks=False):
if run_makelinks:
makelinks()
path = "./config/examples/train_example.yaml"
with open(path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
train(config)
def load_config(config_path, run_makelinks=False):
"""Reads config file and calculates additional dcm attributes such as
slice volume. Returns a dictionary used for patient wide calculations
such as TKV.
Args:
config_path (str): config file path
run_makelinks (bool, optional): Creates symbolic links during the first run. Defaults to False.
Returns:
dataloader, model, device, binarize_func, save_dir (str), model_name (str), split (str)
"""
if run_makelinks:
makelinks()
with open(config_path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
model_config = config["_MODEL_CONFIG"]
loader_to_eval = config["_LOADER_TO_EVAL"]
split = config[loader_to_eval]["dataset"]["splitter_key"].lower()
dataloader_config = config[loader_to_eval]
saved_checkpoint = config["_MODEL_CHECKPOINT"]
checkpoint_format = config["_NEW_CKP_FORMAT"]
model = get_object_instance(model_config)()
if saved_checkpoint is not None:
load_model_data(saved_checkpoint, model, new_format=checkpoint_format)
dataloader = get_object_instance(dataloader_config)()
# TODO: support other metrics as needed
# binarize_func = SigmoidBinarize(thresholds=[0.5])
pred_process_config = config["_LOSSES_METRICS_CONFIG"]["criterions_dict"][
"dice_metric"]["pred_process"]
pred_process = get_object_instance(pred_process_config)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
model_name = Path(config_path).parts[-3]
save_dir = "./saved_inference"
return (
dataloader,
model,
device,
pred_process,
save_dir,
model_name,
split,
)
def calculate_TKVs(config_path, run_makelinks=False, output=None):
if run_makelinks:
makelinks()
with open(config_path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
# val or test
split = config["_LOADER_TO_EVAL"].split("_")[1].lower()
dcm2attrib = evaluate(config)
patient_MR_TKV = defaultdict(float)
TKV_data = OrderedDict()
for key, value in dcm2attrib.items():
patient_MR = value["patient"] + value["MR"]
patient_MR_TKV[(patient_MR, "GT")] += value["Vol_GT"]
patient_MR_TKV[(patient_MR, "Pred")] += value["Vol_Pred"]
for key, value in dcm2attrib.items():
patient_MR = value["patient"] + value["MR"]
if patient_MR not in TKV_data:
summary = {
"TKV_GT": patient_MR_TKV[(patient_MR, "GT")],
"TKV_Pred": patient_MR_TKV[(patient_MR, "Pred")],
"sequence": value["seq"],
"split": split,
}
TKV_data[patient_MR] = summary
df = pd.DataFrame(TKV_data).transpose()
if output is not None:
df.to_csv(output)
return TKV_data
def quick_check(config_path, run_makelinks=False):
if run_makelinks:
makelinks()
with open(config_path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
evaluate(config)
# %%
def quick_check(config_path, run_makelinks=False):
if run_makelinks:
makelinks()
with open(config_path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
evaluate(config)
# %%
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config",
help="YAML config path",
type=str,
required=True)
parser.add_argument("--makelinks",
help="Make data links",
action="store_true")
args = parser.parse_args()
with open(args.config, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
if args.makelinks:
makelinks()
evaluate(config)