--- a
+++ b/inference.py
@@ -0,0 +1,166 @@
+# ==============================================================================
+# Copyright (C) 2020 Vladimir Juras, Ravinder Regatte and Cem M. Deniz
+#
+# This file is part of 2019_IWOAI_Challenge
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+# ==============================================================================
+import tensorflow as tf
+import tf_utilities as tfut
+import tf_layers as tflay
+import models
+import nibabel as nib
+import numpy as np
+import re
+import time
+import os
+from functools import partial
+from pathlib import Path
+import h5py
+from scipy import ndimage
+from scipy.spatial import distance
+
+import glob
+from sklearn.model_selection import StratifiedKFold
+from sys import platform
+from sklearn.preprocessing import label_binarize
+
+tf.app.flags.DEFINE_string('model_path', "./InferenceModel", 'Name of output folder.')
+tf.app.flags.DEFINE_string('data_folder', './data', 'Data Folder')
+tf.app.flags.DEFINE_integer('cv', -1, 'which fold to run')
+tf.app.flags.DEFINE_integer('feature', 16, 'which fold to run')
+tf.app.flags.DEFINE_string('model', '4atrous248', 'Model name.')
+tf.app.flags.DEFINE_integer('reso', 384, 'Image size.')
+tf.app.flags.DEFINE_integer('slices', 160, 'Number Of Slices')
+tf.app.flags.DEFINE_integer('seed', 1234, 'Graph-level random seed.')
+tf.app.flags.DEFINE_boolean('resnet', False, 'Whether to use resnet shortcut.')
+tf.app.flags.DEFINE_integer('noImages', -1, 'how many images to infer')
+FLAGS = tf.app.flags.FLAGS
+
+num_classes = 7
+num_CV =1
+num_channels = 1
+
+
+def main(argv=None):
+
+    print('OUT:: ',FLAGS.feature,FLAGS.seed, FLAGS.resnet,FLAGS.model,FLAGS.reso)
+
+    tf.set_random_seed(FLAGS.seed)
+    np.random.seed(FLAGS.seed)
+            
+    batch_x = tf.placeholder(tf.float32, shape=(None, FLAGS.reso, FLAGS.reso, FLAGS.slices, 1))
+    batch_y = tf.placeholder(tf.float32, shape=(None, FLAGS.reso, FLAGS.reso, FLAGS.slices, num_classes))
+
+    keep_prob = tf.placeholder(tf.float32, shape=[])
+    class_weights = tf.placeholder(tf.float32, shape=(num_classes))
+
+    # choose the model
+    inference_raw = {'4unet': partial(models.inference_unet4),# the original architecture and use 4 layers 
+                      # replace the convolution operations between down-convolution and up-convolution layers 
+                      # by atrous convolution
+                     '4atrous248': partial(models.inference_atrous4, n_class=num_classes, dilation_rates=[2,4,8])}[FLAGS.model]
+
+    inference = partial(inference_raw, resnet=FLAGS.resnet)
+
+    score = inference(batch_x, features_root=FLAGS.feature, keep_prob=keep_prob, n_class=num_classes)
+    logits = tf.nn.softmax(score)
+
+    # load dataset from folder
+    dataFolder = FLAGS.data_folder + '/test'
+    pathNifti = Path(dataFolder)
+
+    X = []  # create an empty list
+    for fileList in list(pathNifti.glob('**/*.im')):
+        X.append(fileList)
+    X = sorted(X)
+
+    if FLAGS.noImages ==-1:
+        noOfFiles = len(X)
+    else:
+        noOfFiles = FLAGS.noImages
+    list_X = list( X[i] for i in range(noOfFiles) )
+    n_samples = len(list_X)
+    X_test, train_info = tfut.loadData_list_h5_image(list_X,num_channels)
+
+
+    X_test = tfut.zeroMeanUnitVariance(X_test)
+    X_test = X_test[...,np.newaxis]
+    cv=1
+
+    sample_size = X_test.shape[0]
+    output_path = FLAGS.model_path
+    
+    # find the model to read
+    fdr = Path(output_path)
+    cpktFile = sorted(fdr.glob(('**/*.meta')))
+    read_file = str((cpktFile[-1]))
+
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        saver = tf.train.Saver(max_to_keep=0)
+
+        saver.restore(sess, read_file[:-5])
+        print('Model restored from file: %s' % read_file[:-5])
+        
+        start = time.clock()
+        y_out = np.zeros((FLAGS.reso, FLAGS.reso, FLAGS.slices, num_classes))
+        for xi in range(sample_size):
+            prob = sess.run(logits,
+                                feed_dict={batch_x: X_test[xi:xi+1],
+                                            keep_prob:1})
+            y_out=prob
+
+            winOut = np.zeros(y_out.shape)
+            winOut[y_out[...,1]>0.5,...,1] =1
+            winOut[y_out[...,2]>0.5,...,2] =2
+            winOut[y_out[...,3]>0.5,...,3] =3
+            winOut[y_out[...,4]>0.5,...,4] =4
+            winOut[y_out[...,5]>0.5,...,5] =5
+            winOut[y_out[...,6]>0.1,...,6] =6
+
+            # place to keep only largest connected volume    
+            if 1:
+                for iii in range(1,7):
+                    inn = winOut[...,iii]
+                    all_labels, num_features = ndimage.label(inn)
+                    print('Label #:',iii, num_features,'Number of Connected Volumes')
+                    if num_features > 1:
+                        volume = ndimage.sum(inn, all_labels, index=range(num_features+1))
+                        print("Volume:", volume)
+                        cem = all_labels == np.argmax(volume)
+                        winOut[...,iii] = winOut[...,iii] * cem
+
+            winOut =  np.sum(winOut,axis=4)
+            winOut.astype(int)
+            seg = np.pad(np.squeeze(winOut),((1,1),(1,1),(1,1)),'edge')
+
+            #For the classes dimensions, the order for 4 classes are as the following: 
+            #0 = femoral cartilage, 1 = tibial cartilage, 2 = patellar cartilage, and 3 = meniscus.
+            #save as numpy array
+            saveNumpy = np.zeros((384,384,160,4))
+            saveNumpy[seg==1,...,0] = 1
+            saveNumpy[seg==2,...,1] = 1
+            saveNumpy[seg==3,...,1] = 1
+            saveNumpy[seg==4,...,2] = 1
+            saveNumpy[seg==5,...,3] = 1
+            saveNumpy[seg==6,...,3] = 1
+            savename= str(X[xi])
+            fdr = Path('./InferenceResults/%s.npy' % (savename[-15:-3]))
+
+            np.save(fdr, saveNumpy.astype(int), allow_pickle = False)
+            
+
+if __name__ == '__main__':
+    tf.app.run()