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run.py
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import argparse
import os
import torch
import utils.evaluator as eu
from quicknat import QuickNat
from settings import Settings
from solver import Solver
from utils.data_utils import get_imdb_dataset
from utils.log_utils import LogWriter
import shutil
torch.set_default_tensor_type('torch.FloatTensor')
def load_data(data_params):
print("Loading dataset")
train_data, test_data = get_imdb_dataset(data_params)
print("Train size: %i" % len(train_data))
print("Test size: %i" % len(test_data))
return train_data, test_data
def train(train_params, common_params, data_params, net_params):
train_data, test_data = load_data(data_params)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=train_params['train_batch_size'], shuffle=True,
num_workers=4, pin_memory=True)
val_loader = torch.utils.data.DataLoader(test_data, batch_size=train_params['val_batch_size'], shuffle=False,
num_workers=4, pin_memory=True)
if train_params['use_pre_trained']:
quicknat_model = torch.load(train_params['pre_trained_path'])
else:
quicknat_model = QuickNat(net_params)
solver = Solver(quicknat_model,
device=common_params['device'],
num_class=net_params['num_class'],
optim_args={"lr": train_params['learning_rate'],
"betas": train_params['optim_betas'],
"eps": train_params['optim_eps'],
"weight_decay": train_params['optim_weight_decay']},
model_name=common_params['model_name'],
exp_name=train_params['exp_name'],
labels=data_params['labels'],
log_nth=train_params['log_nth'],
num_epochs=train_params['num_epochs'],
lr_scheduler_step_size=train_params['lr_scheduler_step_size'],
lr_scheduler_gamma=train_params['lr_scheduler_gamma'],
use_last_checkpoint=train_params['use_last_checkpoint'],
log_dir=common_params['log_dir'],
exp_dir=common_params['exp_dir'])
solver.train(train_loader, val_loader)
final_model_path = os.path.join(common_params['save_model_dir'], train_params['final_model_file'])
quicknat_model.save(final_model_path)
print("final model saved @ " + str(final_model_path))
def evaluate(eval_params, net_params, data_params, common_params, train_params):
eval_model_path = eval_params['eval_model_path']
num_classes = net_params['num_class']
labels = data_params['labels']
data_dir = eval_params['data_dir']
label_dir = eval_params['label_dir']
volumes_txt_file = eval_params['volumes_txt_file']
remap_config = eval_params['remap_config']
device = common_params['device']
log_dir = common_params['log_dir']
exp_dir = common_params['exp_dir']
exp_name = train_params['exp_name']
save_predictions_dir = eval_params['save_predictions_dir']
prediction_path = os.path.join(exp_dir, exp_name, save_predictions_dir)
orientation = eval_params['orientation']
data_id = eval_params['data_id']
logWriter = LogWriter(num_classes, log_dir, exp_name, labels=labels)
avg_dice_score, class_dist = eu.evaluate_dice_score(eval_model_path,
num_classes,
data_dir,
label_dir,
volumes_txt_file,
remap_config,
orientation,
prediction_path,
data_id,
device,
logWriter)
logWriter.close()
def evaluate_bulk(net_params, eval_bulk):
data_dir = eval_bulk['data_dir']
prediction_path = eval_bulk['save_predictions_dir']
volumes_txt_file = eval_bulk['volumes_txt_file']
device = eval_bulk['device']
label_names = eval_bulk['labels']
batch_size = eval_bulk['batch_size']
need_unc = eval_bulk['estimate_uncertainty']
mc_samples = eval_bulk['mc_samples']
dir_struct = eval_bulk['directory_struct']
if eval_bulk['view_agg'] == 'True':
coronal_model_path = eval_bulk['coronal_model_path']
axial_model_path = eval_bulk['axial_model_path']
eu.evaluate2view(coronal_model_path,
axial_model_path,
volumes_txt_file,
data_dir, device,
prediction_path,
batch_size,
label_names,
dir_struct,
net_params,
need_unc,
mc_samples)
else:
coronal_model_path = eval_bulk['coronal_model_path']
eu.evaluate(coronal_model_path,
volumes_txt_file,
data_dir,
device,
prediction_path,
batch_size,
"COR",
label_names,
dir_struct,
net_params,
need_unc,
mc_samples)
def delete_contents(folder):
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print(e)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mode', '-m', required=True, help='run mode, valid values are train and eval')
args = parser.parse_args()
settings = Settings('settings.ini')
common_params, data_params, net_params, train_params, eval_params = settings['COMMON'], settings['DATA'], \
settings[
'NETWORK'], settings['TRAINING'], \
settings['EVAL']
if args.mode == 'train':
train(train_params, common_params, data_params, net_params)
elif args.mode == 'eval':
evaluate(eval_params, net_params, data_params, common_params, train_params)
elif args.mode == 'eval_bulk':
settings_eval = Settings('settings_eval.ini')
evaluate_bulk(net_params, settings_eval['EVAL_BULK'])
elif args.mode == 'clear':
shutil.rmtree(os.path.join(common_params['exp_dir'], train_params['exp_name']))
print("Cleared current experiment directory successfully!!")
shutil.rmtree(os.path.join(common_params['log_dir'], train_params['exp_name']))
print("Cleared current log directory successfully!!")
elif args.mode == 'clear-all':
delete_contents(common_params['exp_dir'])
print("Cleared experiments directory successfully!!")
delete_contents(common_params['log_dir'])
print("Cleared logs directory successfully!!")
else:
raise ValueError('Invalid value for mode. only support values are train, eval and clear')