minor...

modules/deeplearning/unet.py

@@ -309,35 +309,35 @@ class UNET:
         dataset = dataset.map(self.data_function_evaluate, num_parallel_calls=8)
         self.eval_dataset = dataset
 
-    def setup_pipeline(self, data_nda, label_nda, perc=0.20):
-
-        num_samples = data_nda.shape[0]
-        num_test = int(num_samples * perc)
-        self.num_data_samples = num_samples - num_test
-        num_train = self.num_data_samples
-
-        self.train_data_nda = data_nda[0:num_train]
-        self.train_label_nda = label_nda[0:num_train]
-        self.test_data_nda = data_nda[num_train:]
-        self.test_label_nda = label_nda[num_train:]
-
-        trn_idxs = np.arange(self.train_data_nda.shape[0])
-        tst_idxs = np.arange(self.test_data_nda.shape[0])
-
-        np.random.shuffle(tst_idxs)
-
-        self.get_train_dataset(trn_idxs)
-        self.get_test_dataset(tst_idxs)
-
-        print('datetime: ', now)
-        print('training and test data: ')
-        print('---------------------------')
-        print('num train samples: ', self.num_data_samples)
-        print('BATCH SIZE: ', BATCH_SIZE)
-        print('num test samples: ', tst_idxs.shape[0])
-        print('setup_pipeline: Done')
-
-    def setup_pipeline_files(self, data_files, label_files, perc=0.20):
+    # def setup_pipeline(self, data_nda, label_nda, perc=0.20):
+    #
+    #     num_samples = data_nda.shape[0]
+    #     num_test = int(num_samples * perc)
+    #     self.num_data_samples = num_samples - num_test
+    #     num_train = self.num_data_samples
+    #
+    #     self.train_data_nda = data_nda[0:num_train]
+    #     self.train_label_nda = label_nda[0:num_train]
+    #     self.test_data_nda = data_nda[num_train:]
+    #     self.test_label_nda = label_nda[num_train:]
+    #
+    #     trn_idxs = np.arange(self.train_data_nda.shape[0])
+    #     tst_idxs = np.arange(self.test_data_nda.shape[0])
+    #
+    #     np.random.shuffle(tst_idxs)
+    #
+    #     self.get_train_dataset(trn_idxs)
+    #     self.get_test_dataset(tst_idxs)
+    #
+    #     print('datetime: ', now)
+    #     print('training and test data: ')
+    #     print('---------------------------')
+    #     print('num train samples: ', self.num_data_samples)
+    #     print('BATCH SIZE: ', BATCH_SIZE)
+    #     print('num test samples: ', tst_idxs.shape[0])
+    #     print('setup_pipeline: Done')
+
+    def setup_pipeline(self, data_files, label_files, perc=0.20):
         num_files = len(data_files)
         num_test_files = int(num_files * perc)
         num_train_files = num_files - num_test_files
@@ -859,7 +859,7 @@ class UNET:
     def run(self, directory):
         data_files = glob.glob(directory+'mod_res*.npy')
         label_files = [f.replace('mod', 'img') for f in data_files]
-        self.setup_pipeline_files(data_files, label_files)
+        self.setup_pipeline(data_files, label_files)
         self.build_model()
         self.build_training()
         self.build_evaluation()