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Update main.py #9

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50 changes: 21 additions & 29 deletions scripts/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
import pdb
import csv

#Constants
# Constants
DATE_FORMAT_STR = "%Y-%m-%d:%H-%M-%S"

if __name__ == '__main__':
Expand All @@ -28,23 +28,23 @@
repo = git.Repo(search_parent_directories=True)
commit = repo.head.object
args.commit = commit.hexsha
print("OncoNet main running from commit: \n\n{}\n{}author: {}, date: {}".format(
commit.hexsha, commit.message, commit.author, commit.committed_date))
print(f"OncoNet main running from commit: {commit.hexsha}\n{commit.message}\nauthor: {commit.author}, date: {commit.committed_date}")

if args.get_dataset_stats:
print("\nComputing image mean and std...")
args.img_mean, args.img_std = get_dataset_stats(args)
print('Mean: {}'.format(args.img_mean))
print('Std: {}'.format(args.img_std))
print(f'Mean: {args.img_mean}\nStd: {args.img_std}')

print("\nLoading data-augmentation scheme...")
transformers = transformer_factory.get_transformers(
args.image_transformers, args.tensor_transformers, args)
test_transformers = transformer_factory.get_transformers(
args.test_image_transformers, args.test_tensor_transformers, args)

# Load dataset and add dataset specific information to args
print("\nLoading data...")
train_data, dev_data, test_data = dataset_factory.get_dataset(args, transformers, test_transformers)

# Load model and add model specific information to args
if args.snapshot is None:
model = model_factory.get_model(args)
Expand All @@ -55,11 +55,11 @@
model._model.pool = non_trained_model._model.pool
model._model.args = non_trained_model._model.args


print(model)

# Load run parameters if resuming that run.
args.model_path = state.get_model_path(args)
print('Trained model will be saved to [%s]' % args.model_path)
print(f'Trained model will be saved to [{args.model_path}]')
if args.resume:
try:
state_keeper = state.StateKeeper(args)
Expand All @@ -68,20 +68,15 @@
args.current_epoch = epoch
args.lr = lr
args.epoch_stats = epoch_stats
except:
args.optimizer_state = None
args.current_epoch = None
args.lr = None
args.epoch_stats = None
print("\n Error loading previous state. \n Starting run from scratch.")
except Exception as e:
print(f"\nError loading previous state: {e}\nStarting run from scratch.")
else:
print("\n Restarting run from scratch.")

print("\nRestarting run from scratch.")

print("\nParameters:")
for attr, value in sorted(args.__dict__.items()):
if attr not in ['optimizer_state', 'patient_to_partition_dict', 'path_to_hidden_dict', 'exam_to_year_dict', 'exam_to_device_dict']:
print("\t{}={}".format(attr.upper(), value))
print(f"\t{attr.upper()}={value}")

save_path = args.results_path
print()
Expand All @@ -91,54 +86,51 @@

if args.plot_losses:
visualize.viz_utils.plot_losses(epoch_stats)
print("Save train/dev results to {}".format(save_path))
print(f"Save train/dev results to {save_path}")
args_dict = vars(args)
pickle.dump(args_dict, open(save_path, 'wb'))

print()
if args.dev:
print("-------------\nDev")
args.dev_stats = train.compute_threshold_and_dev_stats(dev_data, model, args)
print("Save dev results to {}".format(save_path))
print(f"Save dev results to {save_path}")
args_dict = vars(args)
pickle.dump(args_dict, open(save_path, 'wb'))

if args.test:

print("-------------\nTest")
args.test_stats = train.eval_model(test_data, model, args)
print("Save test results to {}".format(save_path))
print(f"Save test results to {save_path}")
args_dict = vars(args)
pickle.dump(args_dict, open(save_path, 'wb'))

if (args.dev or args.test) and args.prediction_save_path is not None:
exams, probs = [], []
if args.dev:
exams.extend( args.dev_stats['exams'])
probs.extend( args.dev_stats['probs'])
exams.extend(args.dev_stats['exams'])
probs.extend(args.dev_stats['probs'])
if args.test:
exams.extend( args.test_stats['exams'])
probs.extend( args.test_stats['probs'])
exams.extend(args.test_stats['exams'])
probs.extend(args.test_stats['probs'])
legend = ['patient_exam_id']
if args.callibrator_snapshot is not None:
callibrator = pickle.load(open(args.callibrator_snapshot,'rb'))
for i in range(args.max_followup):
legend.append("{}_year_risk".format(i+1))
legend.append(f"{i+1}_year_risk")
export = {}
with open(args.prediction_save_path,'w') as out_file:
writer = csv.DictWriter(out_file, fieldnames=legend)
writer.writeheader()
for exam, arr in zip(exams, probs):
export['patient_exam_id'] = exam
for i in range(args.max_followup):
key = "{}_year_risk".format(i+1)
key = f"{i+1}_year_risk"
raw_val = arr[i]
if args.callibrator_snapshot is not None:
val = callibrator[i].predict_proba([[raw_val]])[0,1]
else:
val = raw_val
export[key] = val
writer.writerow(export)
print("Exported predictions to {}".format(args.prediction_save_path))


print(f"Exported predictions to {args.prediction_save_path}")