# some default params dataset/architecture related
print("Params:")
for k, v in model_params.items():
print("\t%s: %s" % (k, v))
print("Train params:")
for k, v in train_params.items():
print("\t%s: %s" % (k, v))
# ==========================================================================
# DATA PREPARATION
# ==========================================================================
train_params['test'] = args.test
train_params['train'] = args.train
if not args.train:
train_params['validation_set'] = False
data_provider = get_data_provider_by_path(args.dataset, train_params)
# ==========================================================================
# TRAINING & TESTING & EVALUATING
# ==========================================================================
print("Initialize the model..")
model_params['sequence_length'] = train_params['sequence_length']
model_params['crop_size'] = train_params['crop_size']
model_params['Inc_params'] = Inc_params
# print('model_params:', model_params['crop_size'])
# print('model_params[crop_size][0]:', model_params['crop_size'][0])
# print('model_params[crop_size][1]:', model_params['crop_size'][1])
model = I3D_Net(data_provider=data_provider, **model_params)
# data_provider.data_shape
if args.train:
print("Data provider train videos: ", data_provider.train.num_examples)
def main(argv=None): # pylint: disable=unused-argument
config = argparser(is_train=True)
all_train_dir, all_result_file_name = construct_train_dir(config)
if config.hdf5FileNametrain == 'train_MRIdata_3_AD_MCI_Normal.hdf5':
dataset_path = os.path.join(r"./datasets/mri/3_AD_MCI_Normal/")
elif config.hdf5FileNametrain == 'train_MRIdata_2_AD_MCI.hdf5':
dataset_path = os.path.join(r"./datasets/mri/2_AD_MCI/")
elif config.hdf5FileNametrain == 'train_MRIdata_2_AD_Normal.hdf5':
dataset_path = os.path.join(r"./datasets/mri/2_AD_Normal/")
else:
dataset_path = os.path.join(r"./datasets/mri/2_MCI_Normal/")
input_file_name = config.hdf5FileNametrain
name_list = input_file_name.split("_")
class_num = name_list[2]
dataset_train_unlabelled, dataset_test, all_hdf5_data_train, all_hdf5_data_test, dataset_train_labelled, dataset_val, all_hdf5_data_val = dataset.create_default_splits8020(
dataset_path, config.hdf5FileNametrain, config.testhdf5FileName,
config.valhdf5FileName, config.idFileNametrain, config.testidFileName,
config.validFileName, config.num_less_label_data, class_num)
data_provider = get_data_provider_by_path(config, dataset_train_unlabelled,
dataset_train_labelled,
dataset_test,
all_hdf5_data_train,
all_hdf5_data_test, dataset_val,
all_hdf5_data_val, 0)
model = TripleGAN3D(config, data_provider, all_train_dir, 0, is_train=True)
if tf.gfile.Exists(all_train_dir[0] + "/GANconfusionMatrixResults"):
log.infov(all_train_dir[0] + "/GANconfusionMatrixResults")
else:
os.makedirs(all_train_dir[0] + "/GANconfusionMatrixResults")
if config.train:
total_start_time = time.time()
print("Data provider train labelled images: ",
data_provider.train_labelled.num_examples)
print("Data provider train unlabelled images: ",
data_provider.train_unlabelled.num_examples)
best_epoch = model.train_all_epochs(config)
total_training_time = time.time() - total_start_time
print(
"\n Total training time for all epoches : %s and %s seconds" %
(str(timedelta(seconds=total_training_time)), total_training_time))
fxx = open(
all_train_dir[0] +
"/GANconfusionMatrixResults/train_timeReport.txt", 'w')
fxx.write(
"\n Total training time for all epoches : %s and %s seconds" %
(str(timedelta(seconds=total_training_time)), total_training_time))
fxx.write('\n')
fxx.close()
if config.test:
model.load_model(best_epoch)
print("Data provider test images: ", data_provider.test.num_examples)
print("Testing...")
total_start_time = time.time()
model.test_and_record(all_result_file_name[0],
0,
config,
all_train_dir[0],
data_provider.test,
batch_size=config.batch_size_label)
total_test_time = time.time() - total_start_time
print("\n Total test time for all epoches : %s and %s seconds" %
(str(timedelta(seconds=total_test_time)), total_test_time))
fxx = open(
all_train_dir[0] +
"/GANconfusionMatrixResults/test_timeReport.txt", 'w')
fxx.write("\n Total test time for all epoches : %s and %s seconds" %
(str(timedelta(seconds=total_test_time)), total_test_time))
fxx.write('\n')
fxx.write(
"\n test time for each record : %s and %s seconds" %
(str(
timedelta(seconds=(total_test_time /
float(data_provider.test.num_examples)))),
(total_test_time / float(data_provider.test.num_examples))))
fxx.write('\n')
fxx.close()
input_file_name = config.hdf5FileNametrain
class_labels = []
name_list = input_file_name.split("_")
if int(name_list[2]) == 3:
class_labels.append(name_list[3])
class_labels.append(name_list[4])
last_class = name_list[5].split(".")
class_labels.append(last_class[0])
else:
class_labels.append(name_list[3])
last_class = name_list[4].split(".")
class_labels.append(last_class[0])
accuracy_10folds_all = []
fold_write = 0
for each_result_file_name in all_result_file_name:
if fold_write < 1:
accuracy, cr, cm, auc = calculateConfusionMatrix(
each_result_file_name, class_labels, all_train_dir[0],
int(name_list[2]))
else:
accuracy, cr, cm, auc = calculateConfusionMatrix(
each_result_file_name, class_labels, './train_dir',
int(name_list[2]))
f = open(
all_train_dir[0] +
"/GANconfusionMatrixResults/ConfusionMatrix.txt", 'w')
log.info("Fold: {}".format(fold_write))
f.write(each_result_file_name)
f.write(
'{}\n\nClassification Report\n\n{}\n\nConfusion Matrix\n\n{}\n'.
format(config.hdf5FileNametrain, cr, cm))
f.write("accuracy: {}\n".format(accuracy))
log.info("accuracy: {}".format(accuracy))
if int(name_list[2]) == 3:
for each_auc in auc:
f.write("auc: {}\n".format(each_auc))
log.info("auc: {}".format(each_auc))
else:
f.write("auc: {}\n".format(auc))
log.info("auc: {}".format(auc))
f.close()