--- a
+++ b/test_msk_seg.py
@@ -0,0 +1,114 @@
+# Authors:
+# Akshay Chaudhari and Zhongnan Fang
+# May 2018
+# akshaysc@stanford.edu
+
+from __future__ import print_function, division
+
+import numpy as np
+import h5py
+import time
+import os
+import tensorflow as tf
+from keras import backend as K
+
+from utils.generator_msk_seg import calc_generator_info, img_generator_oai
+from utils.models import unet_2d_model
+from utils.losses import dice_loss_test
+import utils.utils_msk_seg as segutils
+
+# Specify directories
+test_result_path = './results/'
+test_path  = '/bmrNAS/people/akshay/dl/oai_data/unet_2d/test'
+
+# Tissue Type
+tissue = np.arange(4,6)
+
+# Parameters for the model testing
+img_size = (288,288,1)
+file_types = ['im']
+test_batch_size = 1
+save_file = False
+tag = 'oai_aug'
+
+# Test with pre-trained weights
+model_weights = '/bmrNAS/people/akshay/dl/oai_data/unet_2d/weights/unet_2d_men_weights.012--0.7692.h5'
+
+def test_seg(test_result_path, test_path, tissue, img_size, 
+            file_types, test_batch_size, save_file, model_weights):
+
+    img_cnt = 0
+
+    # set image format to be (N, dim1, dim2, dim3, ch)
+    K.set_image_data_format('channels_last')
+
+    # create the unet model
+    model = unet_2d_model(img_size)
+    model.load_weights(model_weights);
+
+    # All of the testing currently assumes that there is a reference to test against.
+    # Comment out these lines if testing on reference-les data
+    dice_losses = np.array([])
+    cv_values = np.array([])
+    voe_values = np.array([])
+    vd_values = np.array([])
+
+    start = time.time()
+
+    # Read the files that will be segmented 
+    test_files,ntest = calc_generator_info(test_path, test_batch_size)
+    print('INFO: Test size: %d, Number of batches: %d' % (len(test_files), ntest))
+
+    # Iterature through the files to be segmented 
+    for x_test, y_test, fname in img_generator_oai(test_path, test_batch_size, 
+                                                            img_size, tissue, tag, 
+                                                            testing= True, shuffle_epoch=False):
+
+        # Perform the actual segmentation using pre-loaded model
+        recon = model.predict(x_test, batch_size = test_batch_size)
+
+        # Calculate real time metrics
+        dl = np.mean(segutils.calc_dice(recon,y_test))
+        dice_losses = np.append(dice_losses,dl)
+
+        cv = np.mean(segutils.calc_cv(recon,y_test))
+        cv_values = np.append(cv_values,cv)
+
+        voe = np.mean(segutils.calc_voe(y_test, recon))
+        voe_values = np.append(voe_values,voe)
+
+        vd = np.mean(segutils.calc_vd(y_test, recon))
+        vd_values = np.append(vd_values,vd)
+
+        # print('Image #%0.2d (%s). Dice = %0.3f CV = %2.1f VOE = %2.1f VD = %2.1f' % ( img_cnt, fname[0:11], dl, cv, voe, vd) )
+
+        # Write output file per batch
+        if save_file is True:
+            save_name = '%s/%s.pred' %(test_result_path,fname)
+            with h5py.File(save_name,'w') as h5f:
+                h5f.create_dataset('recon',data=recon)
+
+        img_cnt += 1
+        if img_cnt == ntest:
+            break
+
+    end = time.time()
+
+    # Print some summary statistics
+    print('--'*20)
+    print('Overall Summary:')
+    print('Dice Mean= %0.4f Std = %0.3f'    %  (np.mean(dice_losses) ,  np.std(dice_losses) ))
+    print('CV Mean= %0.4f Std = %0.3f'      %  (np.mean(cv_values)   ,  np.std(cv_values) ))
+    print('VOE Mean= %0.4f Std = %0.3f'     %  (np.mean(voe_values)  ,  np.std(voe_values) ))
+    print('VD Mean= %0.4f Std = %0.3f'      %  (np.mean(vd_values)   ,  np.std(vd_values) ))
+    print('Time required = %0.1f seconds.'  %  (end-start))  
+    print('--'*20)    
+
+if __name__ == '__main__':
+
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+    
+    test_seg(test_result_path, test_path, tissue, img_size, 
+            file_types, test_batch_size, save_file, model_weights)
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