--- a
+++ b/utils/eval_utils.py
@@ -0,0 +1,422 @@
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from models.model_mil import MIL_fc, MIL_fc_mc
+from models.model_clam import CLAM
+from models.model_attention_mil import MIL_Attention_fc
+import pdb
+import os
+import pandas as pd
+from utils.utils import *
+from utils.core_utils import EarlyStopping,  Accuracy_Logger
+from utils.file_utils import save_pkl, load_pkl
+from sklearn.metrics import roc_auc_score, roc_curve, auc
+import h5py
+from models.resnet_custom import resnet50_baseline
+import math
+from sklearn.preprocessing import label_binarize
+
+def initiate_model(args, ckpt_path=None):
+    print('Init Model')    
+    model_dict = {"dropout": args.drop_out, 'n_classes': args.n_classes}
+    
+    if args.model_size is not None and args.model_type in ['clam', 'attention_mil', 'clam_new']:
+        model_dict.update({"size_arg": args.model_size})
+    
+    if args.model_type =='clam':
+        model = CLAM(**model_dict)
+    elif args.model_type == 'attention_mil':
+        model = MIL_Attention_fc(**model_dict)    
+    else: # args.model_type == 'mil'
+        if args.n_classes > 2:
+            model = MIL_fc_mc(**model_dict)
+        else:
+            model = MIL_fc(**model_dict)
+
+    model.relocate()
+    #print_network(model)
+
+    if ckpt_path is not None:
+        ckpt = torch.load(ckpt_path)
+        model.load_state_dict(ckpt, strict=False)
+
+    model.eval()
+    return model
+
+def eval(dataset, args, ckpt_path):
+    model = initiate_model(args, ckpt_path)
+    
+    print('Init Loaders')
+    loader = get_simple_loader(dataset)
+    patient_results, test_error, auc, aucs, df, _ = summary(model, loader, args)
+    print('test_error: ', test_error)
+    print('auc: ', auc)
+    for cls_idx in range(len(aucs)):
+        print('class {} auc: {}'.format(cls_idx, aucs[cls_idx]))
+    return model, patient_results, test_error, auc, aucs, df
+
+def infer(dataset, args, ckpt_path, class_labels):
+    model = initiate_model(args, ckpt_path)
+    df = infer_dataset(model, dataset, args, class_labels)
+    return model, df
+
+# Code taken from pytorch/examples for evaluating topk classification on on ImageNet
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(1.0 / batch_size))
+        return res
+
+def summary(model, loader, args):
+    acc_logger = Accuracy_Logger(n_classes=args.n_classes)
+    model.eval()
+    test_loss = 0.
+    test_error = 0.
+
+    all_probs = np.zeros((len(loader), args.n_classes))
+    all_labels = np.zeros(len(loader))
+    all_preds = np.zeros(len(loader))
+
+    slide_ids = loader.dataset.slide_data['slide_id']
+    patient_results = {}
+    for batch_idx, (data, label) in enumerate(loader):
+        data, label = data.to(device), label.to(device)
+        slide_id = slide_ids.iloc[batch_idx]
+        with torch.no_grad():
+            logits, Y_prob, Y_hat, _, results_dict = model(data)
+        
+        acc_logger.log(Y_hat, label)
+        
+        probs = Y_prob.cpu().numpy()
+
+        all_probs[batch_idx] = probs
+        all_labels[batch_idx] = label.item()
+        all_preds[batch_idx] = Y_hat.item()
+        
+        patient_results.update({slide_id: {'slide_id': np.array(slide_id), 'prob': probs, 'label': label.item()}})
+        
+        error = calculate_error(Y_hat, label)
+        test_error += error
+
+    del data
+    test_error /= len(loader)
+    if args.n_classes > 2:
+        acc1, acc3 = accuracy(torch.from_numpy(all_probs), torch.from_numpy(all_labels), topk=(1, 3))
+        print('top1 acc: {:.3f}, top3 acc: {:.3f}'.format(acc1.item(), acc3.item()))
+        
+    if len(np.unique(all_labels)) == 1:
+        auc_score = -1
+    else:
+        if args.n_classes == 2:
+            auc_score = roc_auc_score(all_labels, all_probs[:, 1])
+            aucs = []
+        else:
+            aucs = []
+            binary_labels = label_binarize(all_labels, classes=[i for i in range(args.n_classes)])
+            for class_idx in range(args.n_classes):
+                if class_idx in all_labels:
+                    fpr, tpr, _ = roc_curve(binary_labels[:, class_idx], all_probs[:, class_idx])
+                    aucs.append(auc(fpr, tpr))
+                else:
+                    aucs.append(float('nan'))
+            if args.micro_average:
+                binary_labels = label_binarize(all_labels, classes=[i for i in range(args.n_classes)])
+                fpr, tpr, _ = roc_curve(binary_labels.ravel(), all_probs.ravel())
+                auc_score = auc(fpr, tpr)
+            else:
+                auc_score = np.nanmean(np.array(aucs))
+
+    results_dict = {'slide_id': slide_ids, 'Y': all_labels, 'Y_hat': all_preds}
+    for c in range(args.n_classes):
+        results_dict.update({'p_{}'.format(c): all_probs[:,c]})
+    df = pd.DataFrame(results_dict)
+    return patient_results, test_error, auc_score, aucs, df, acc_logger
+
+def infer_dataset(model, dataset, args, class_labels, k=3):
+    model.eval()
+    all_probs = np.zeros((len(dataset), k))
+    all_preds = np.zeros((len(dataset), k))
+    all_preds_str = np.full((len(dataset), k), ' ', dtype=object)
+    slide_ids = dataset.slide_data
+    for batch_idx, data in enumerate(dataset):
+        data = data.to(device)
+        with torch.no_grad():
+            logits, Y_prob, Y_hat, _, results_dict = model(data)
+        
+        probs, ids = torch.topk(Y_prob, k)
+        probs = probs.cpu().numpy()
+        ids = ids.cpu().numpy()
+        all_probs[batch_idx] = probs
+        all_preds[batch_idx] = ids
+        all_preds_str[batch_idx] = np.array(class_labels)[ids]
+    del data
+    results_dict = {'slide_id': slide_ids}
+    for c in range(k):
+        results_dict.update({'Pred_{}'.format(c): all_preds_str[:, c]})
+        results_dict.update({'p_{}'.format(c): all_probs[:, c]})
+    df = pd.DataFrame(results_dict)
+    return df
+
+# def infer_dataset(model, dataset, args, class_labels, k=3):
+#     model.eval()
+
+#     all_probs = np.zeros((len(dataset), args.n_classes))
+#     all_preds = np.zeros(len(dataset))
+#     all_str_preds = np.full(len(dataset), ' ', dtype=object)
+
+#     slide_ids = dataset.slide_data
+#     for batch_idx, data in enumerate(dataset):
+#         data = data.to(device)
+#         with torch.no_grad():
+#             logits, Y_prob, Y_hat, _, results_dict = model(data)
+        
+#         probs = Y_prob.cpu().numpy()
+#         all_probs[batch_idx] = probs
+#         all_preds[batch_idx] = Y_hat.item()
+#         all_str_preds[batch_idx] = class_labels[Y_hat.item()]
+#     del data
+
+#     results_dict = {'slide_id': slide_ids, 'Prediction': all_str_preds, 'Y_hat': all_preds}
+#     for c in range(args.n_classes):
+#         results_dict.update({'p_{}_{}'.format(c, class_labels[c]): all_probs[:,c]})
+#     df = pd.DataFrame(results_dict)
+#     return df
+
+def compute_features(dataset, args, ckpt_path, save_dir, model=None, feature_dim=512):
+    if model is None:
+        model = initiate_model(args, ckpt_path)
+
+    names = dataset.get_list(np.arange(len(dataset))).values
+    file_path = os.path.join(save_dir, 'features.h5')
+
+    initialize_features_hdf5_file(file_path, len(dataset), feature_dim=feature_dim, names=names)
+    for i in range(len(dataset)):
+        print("Progress: {}/{}".format(i, len(dataset)))
+        save_features(dataset, i, model, args, file_path)
+
+def save_features(dataset, idx, model, args, save_file_path):
+    name = dataset.get_list(idx)
+    print(name)
+    features, label = dataset[idx]
+    features = features.to(device)
+    with torch.no_grad():
+        if type(model) == CLAM:
+            _, Y_prob, Y_hat, _, results_dict = model(features, instance_eval=False, return_features=True)
+            bag_feat = results_dict['features'][Y_hat.item()]
+        else:
+            _, Y_prob, Y_hat, _, results_dict = model(features, return_features=True)
+            bag_feat = results_dict['features']
+    del features
+    Y_hat = Y_hat.item()
+    Y_prob = Y_prob.view(-1).cpu().numpy()
+    bag_feat = bag_feat.view(1, -1).cpu().numpy()
+
+    with h5py.File(save_file_path, 'r+') as file:
+        print('label', label)
+        file['features'][idx, :] = bag_feat
+        file['label'][idx] = label
+        file['Y_hat'][idx] = Y_hat
+        file['Y_prob'][idx] = Y_prob[Y_hat]
+
+def initialize_features_hdf5_file(file_path, length, feature_dim=512, names = None):
+    
+    file = h5py.File(file_path, "w")
+
+    dset = file.create_dataset('features', 
+                                shape=(length, feature_dim), chunks=(1, feature_dim), dtype=np.float32)
+
+    # if names is not None:
+    #     names = np.array(names, dtype='S')
+    #     dset.attrs['names'] = names
+    if names is not None:
+        dt = h5py.string_dtype()
+        label_dset = file.create_dataset('names', 
+                                        shape=(length, ), chunks=(1, ), dtype=dt)
+    
+    label_dset = file.create_dataset('label', 
+                                        shape=(length, ), chunks=(1, ), dtype=np.int32)
+
+    pred_dset = file.create_dataset('Y_hat', 
+                                        shape=(length, ), chunks=(1, ), dtype=np.int32)
+
+    prob_dset = file.create_dataset('Y_prob', 
+                                        shape=(length, ), chunks=(1, ), dtype=np.float32)
+
+    file.close()
+    return file_path
+
+
+
+def eval2(datasets: tuple, cur: int, args: Namespace):
+    """   
+        train for a single fold
+    """
+    print('\nTraining Fold {}!'.format(cur))
+    writer_dir = os.path.join(args.results_dir, str(cur))
+    if not os.path.isdir(writer_dir):
+        os.mkdir(writer_dir)
+
+    if args.log_data:
+        from tensorboardX import SummaryWriter
+        writer = SummaryWriter(writer_dir, flush_secs=15)
+
+    else:
+        writer = None
+
+    if args.pretrain_VAE:
+        print("Initializing VAE")
+        VAE = GenomicVAE(input_dim=args.omic_input_dim, hidden=[1024, 256, 128])
+        ckpt = torch.load('./VAE/logs/tcga_base/000-all/%d/%d/%d_best.ckpt' % (cur, cur, cur))
+        state_dict = ckpt['state_dict']
+        state_dict = OrderedDict((k[6:], v) for k, v in state_dict.items())
+        VAE.load_state_dict(state_dict)
+        args.omic_input_dim = 128
+        VAE.relocate()
+        dfs_freeze(VAE)
+        VAE.eval()
+    else:
+        VAE = None
+
+    print('\nInit train/val/test splits...', end=' ')
+    train_split, val_split, test_split = datasets
+    save_splits(datasets, ['train', 'val', 'test'], os.path.join(args.results_dir, 'splits_{}.csv'.format(cur)))
+    print('Done!')
+    print("Training on {} samples".format(len(train_split)))
+    print("Validating on {} samples".format(len(val_split)))
+    print("Testing on {} samples".format(len(test_split)))
+
+    print('\nInit loss function...', end=' ')
+    if args.task_type == 'survival':
+        if args.bag_loss == 'ce_surv':
+            loss_fn = CrossEntropySurvLoss(alpha=args.alpha_surv)
+        elif args.bag_loss == 'nll_surv':
+            loss_fn = NLLSurvLoss(alpha=args.alpha_surv)
+        elif args.bag_loss == 'cox_surv':
+            loss_fn = CoxSurvLoss()
+        else:
+            raise NotImplementedError
+    else:
+        if args.bag_loss == 'svm':
+            from topk import SmoothTop1SVM
+            loss_fn = SmoothTop1SVM(n_classes = args.n_classes)
+            if device.type == 'cuda':
+                loss_fn = loss_fn.cuda()
+        elif args.bag_loss == 'ce':
+            loss_fn = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError
+
+    if args.reg_type == 'omic':
+        reg_fn = l1_reg_all
+    elif args.reg_type == 'pathomic':
+        reg_fn = l1_reg_modules
+    else:
+        reg_fn = None
+
+    print('Done!')
+    
+    print('\nInit Model...', end=' ')
+    model_dict = {"dropout": args.drop_out, 'n_classes': args.n_classes}
+    if args.model_type in ['clam', 'clam_simple'] and args.subtyping:
+        model_dict.update({'subtyping': True})
+    
+        if args.model_size is not None:
+            model_dict.update({"size_arg": args.model_size})
+    
+    if args.model_type in ['clam', 'clam_simple']:
+        if args.task_type == 'survival':
+            raise NotImplementedError
+        else:
+            if args.inst_loss == 'svm':
+                from topk import SmoothTop1SVM
+                instance_loss_fn = SmoothTop1SVM(n_classes = 2)
+                if device.type == 'cuda':
+                    instance_loss_fn = instance_loss_fn.cuda()
+            else:
+                instance_loss_fn = nn.CrossEntropyLoss()
+            
+            if args.model_type =='clam':
+                model = CLAM(**model_dict, instance_loss_fn=instance_loss_fn)
+            else:
+                model = CLAM_Simple(**model_dict, instance_loss_fn=instance_loss_fn)
+
+    elif args.model_type =='attention_mil':
+        if args.task_type == 'survival':
+            model = MIL_Attention_fc_surv(**model_dict)
+            # model.alpha.requires_grad = False
+        else:
+            model = MIL_Attention_fc(**model_dict)
+
+    elif args.model_type =='mm_attention_mil':
+        model_dict.update({'input_dim': args.omic_input_dim, 'meta_dim': args.meta_dim, 
+            'fusion': args.fusion, 'model_size_wsi':args.model_size_wsi, 'model_size_omic':args.model_size_omic,
+            'gate_path': args.gate_path, 'gate_omic': args.gate_omic, 'n_classes': args.n_classes, 
+            'pretrain': args.pretrain, 'tcga_proj': '_'.join(args.task.split('_')[:2]), 'split_idx': cur})
+
+        if args.task_type == 'survival':
+            model = MM_MIL_Attention_fc_surv(**model_dict)
+            # model.alpha.requires_grad = False
+        else:
+            model = MM_MIL_Attention_fc(**model_dict)
+
+    elif args.model_type =='max_net':
+        model_dict = {'input_dim': args.omic_input_dim, 'meta_dim': args.meta_dim, 'model_size_omic': args.model_size_omic, 'n_classes': args.n_classes}
+        if args.task_type == 'survival':
+            model = MaxNet(**model_dict)
+            # model.alpha.requires_grad = False
+        else:
+            raise NotImplementedError
+
+    else: # args.model_type == 'mil'
+        if args.task_type == 'survival':
+            raise NotImplementedError
+        else:
+            if args.n_classes > 2:
+                model = MIL_fc_mc(**model_dict)
+            else:
+                model = MIL_fc(**model_dict)
+
+    model.relocate()
+    print('Done!')
+    print_network(model)
+    ckpt = torch.load(os.path.join(args.results_dir, "s_{}_checkpoint.pt".format(cur)))
+    model.load_state_dict(ckpt, strict=False)
+    model.eval()
+
+    
+    print('\nInit Loaders...', end=' ')
+    train_loader = get_split_loader(train_split, training=True, testing = args.testing, 
+                                    weighted = args.weighted_sample, task_type=args.task_type, batch_size=args.batch_size)
+    val_loader = get_split_loader(val_split,  testing = args.testing, task_type=args.task_type, batch_size=args.batch_size)
+    test_loader = get_split_loader(test_split, testing = args.testing, task_type=args.task_type, batch_size=args.batch_size)
+    print('Done!')
+
+
+    if args.task_type == 'survival':
+        results_val_dict, val_c_index = summary_survival(model, val_loader, args.n_classes, VAE)
+        print('Val c-index: {:.4f}'.format(val_c_index))
+
+        results_test_dict, test_c_index = summary_survival(model, test_loader, args.n_classes, VAE)
+        print('Test c-index: {:.4f}'.format(test_c_index))
+
+        if writer:
+            writer.add_scalar('final/val_c_index', val_c_index, 0)
+            writer.add_scalar('final/test_c_index', test_c_index, 0)
+        
+        writer.close()
+        return results_val_dict, results_test_dict, val_c_index, test_c_index
+
+    elif args.task_type == 'classification':
+        pass