--- a
+++ b/main_mtl_concat.py
@@ -0,0 +1,191 @@
+from __future__ import print_function
+
+import argparse
+import pdb
+import os
+import math
+
+# internal imports
+from utils.file_utils import save_pkl, load_pkl
+from utils.utils import *
+from utils.core_utils_mtl_concat import train
+from datasets.dataset_mtl_concat import Generic_WSI_MTL_Dataset, Generic_MIL_MTL_Dataset
+
+# pytorch imports
+import torch
+from torch.utils.data import DataLoader, sampler
+import torch.nn as nn
+import torch.nn.functional as F
+
+import pandas as pd
+import numpy as np
+
+def main(args):
+    # create results directory if necessary
+    if not os.path.isdir(args.results_dir):
+        os.mkdir(args.results_dir)
+
+    if args.k_start == -1:
+        start = 0
+    else:
+        start = args.k_start
+    if args.k_end == -1:
+        end = args.k
+    else:
+        end = args.k_end
+
+    all_cls_test_auc = []
+    all_cls_val_auc = []
+    all_cls_test_acc = []
+    all_cls_val_acc = []
+    
+    all_site_test_auc = []
+    all_site_val_auc = []
+    all_site_test_acc = []
+    all_site_val_acc = []
+    folds = np.arange(start, end)
+    for i in folds:
+        seed_torch(args.seed)
+        train_dataset, val_dataset, test_dataset = dataset.return_splits(from_id=False, 
+                csv_path='{}/splits_{}.csv'.format(args.split_dir, i))
+        
+        print('training: {}, validation: {}, testing: {}'.format(len(train_dataset), len(val_dataset), len(test_dataset)))
+        datasets = (train_dataset, val_dataset, test_dataset)
+        results, cls_test_auc, cls_val_auc, cls_test_acc, cls_val_acc, site_test_auc, site_val_auc, site_test_acc, site_val_acc  = train(datasets, i, args)
+        all_cls_test_auc.append(cls_test_auc)
+        all_cls_val_auc.append(cls_val_auc)
+        all_cls_test_acc.append(cls_test_acc)
+        all_cls_val_acc.append(cls_val_acc)
+        
+        all_site_test_auc.append(site_test_auc)
+        all_site_val_auc.append(site_val_auc)
+        all_site_test_acc.append(site_test_acc)
+        all_site_val_acc.append(site_val_acc)
+        #write results to pkl
+        filename = os.path.join(args.results_dir, 'split_{}_results.pkl'.format(i))
+        save_pkl(filename, results)
+
+    final_df = pd.DataFrame({'folds': folds, 'cls_test_auc': all_cls_test_auc, 
+        'cls_val_auc': all_cls_val_auc, 'cls_test_acc': all_cls_test_acc, 'cls_val_acc' : all_cls_val_acc, 
+        'site_test_auc': all_site_test_auc, 
+        'site_val_auc': all_site_val_auc, 'site_test_acc': all_site_test_acc, 'site_val_acc' : all_site_val_acc})
+
+
+    if len(folds) != args.k:
+        save_name = 'summary_partial_{}_{}.csv'.format(start, end)
+    else:
+        save_name = 'summary.csv'
+    final_df.to_csv(os.path.join(args.results_dir, save_name))
+
+# Training settings
+parser = argparse.ArgumentParser(description='Configurations for WSI Training')
+parser.add_argument('--data_root_dir', type=str, help='data directory')
+parser.add_argument('--max_epochs', type=int, default=200,
+                    help='maximum number of epochs to train (default: 200)')
+parser.add_argument('--lr', type=float, default=1e-4,
+                    help='learning rate (default: 0.0001)')
+parser.add_argument('--reg', type=float, default=1e-5,
+                    help='weight decay (default: 1e-5)')
+parser.add_argument('--seed', type=int, default=1, 
+                    help='random seed for reproducible experiment (default: 1)')
+parser.add_argument('--k', type=int, default=10, help='number of folds (default: 10)')
+parser.add_argument('--k_start', type=int, default=-1, help='start fold (default: -1, last fold)')
+parser.add_argument('--k_end', type=int, default=-1, help='end fold (default: -1, first fold)')
+parser.add_argument('--results_dir', default='./results', help='results directory (default: ./results)')
+parser.add_argument('--split_dir', type=str, default=None, 
+                    help='manually specify the set of splits to use, ' 
+                    +'instead of infering from the task and label_frac argument (default: None)')
+parser.add_argument('--log_data', action='store_true', default=False, help='log data using tensorboard')
+parser.add_argument('--testing', action='store_true', default=False, help='debugging tool')
+parser.add_argument('--early_stopping', action='store_true', default=False, help='enable early stopping')
+parser.add_argument('--opt', type=str, choices = ['adam', 'sgd'], default='adam')
+parser.add_argument('--drop_out', action='store_true', default=False, help='enabel dropout (p=0.25)')
+parser.add_argument('--exp_code', type=str, help='experiment code for saving results')
+parser.add_argument('--weighted_sample', action='store_true', default=False, help='enable weighted sampling')
+parser.add_argument('--task', type=str, choices=['dummy_mtl_concat'])
+args = parser.parse_args()
+device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def seed_torch(seed=7):
+    import random
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if device.type == 'cuda':
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = True
+
+seed_torch(args.seed)
+
+encoding_size = 1024
+settings = {'num_splits': args.k, 
+            'k_start': args.k_start,
+            'k_end': args.k_end,
+            'task': args.task,
+            'max_epochs': args.max_epochs, 
+            'results_dir': args.results_dir, 
+            'lr': args.lr,
+            'experiment': args.exp_code,
+            'reg': args.reg,
+            'seed': args.seed,
+            "use_drop_out": args.drop_out,
+            'weighted_sample': args.weighted_sample,
+            'opt': args.opt}
+
+print('\nLoad Dataset')
+
+if args.task == 'dummy_mtl_concat':
+    args.n_classes=18
+    dataset = Generic_MIL_MTL_Dataset(csv_path = 'dataset_csv/dummy_dataset.csv',
+                            data_dir= os.path.join(args.data_root_dir,'DUMMY_DATA_DIR'),
+                            shuffle = False, 
+                            seed = args.seed, 
+                            print_info = True,
+                            label_dicts = [{'Lung':0, 'Breast':1, 'Colorectal':2, 'Ovarian':3, 
+                                            'Pancreatobiliary':4, 'Adrenal':5, 
+                                             'Skin':6, 'Prostate':7, 'Renal':8, 'Bladder':9, 
+                                             'Esophagogastric':10,  'Thyroid':11,
+                                             'Head Neck':12,  'Glioma':13, 
+                                             'Germ Cell':14, 'Endometrial': 15, 
+                                             'Cervix': 16, 'Liver': 17},
+                                            {'Primary':0,  'Metastatic':1},
+                                            {'F':0, 'M':1}],
+                            label_cols = ['label', 'site', 'sex'],
+                            patient_strat= False)
+else:
+    raise NotImplementedError
+
+
+    
+if not os.path.isdir(args.results_dir):
+    os.mkdir(args.results_dir)
+
+args.results_dir = os.path.join(args.results_dir, str(args.exp_code) + '_s{}'.format(args.seed))
+if not os.path.isdir(args.results_dir):
+    os.mkdir(args.results_dir)
+
+if args.split_dir is None:
+    args.split_dir = os.path.join('splits', args.task+'_{}'.format(int(100)))
+else:
+    args.split_dir = os.path.join('splits', args.split_dir)
+assert os.path.isdir(args.split_dir)
+
+settings.update({'split_dir': args.split_dir})
+
+with open(args.results_dir + '/experiment_{}.txt'.format(args.exp_code), 'w') as f:
+    print(settings, file=f)
+f.close()
+
+print("################# Settings ###################")
+for key, val in settings.items():
+    print("{}:  {}".format(key, val))        
+
+if __name__ == "__main__":
+    results = main(args)
+    print("finished!")
+    print("end script")
+
+