snapshot...

modules/deeplearning/unet.py

@@ -538,6 +538,33 @@ class UNET:
         print('num test samples: ', tst_idxs.shape[0])
         print('setup_pipeline: Done')
 
+    def setup_pipeline_files(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
+
+        self.train_data_files = data_files[0:num_train_files]
+        self.train_label_files = label_files[0:num_train_files]
+        self.test_data_files = data_files[num_train_files:]
+        self.test_label_files = label_files[num_train_files:]
+
+        trn_idxs = np.arange(num_train_files)
+        np.random.shuffle(trn_idxs)
+        tst_idxs = np.arange(num_test_files)
+
+        self.get_train_dataset(trn_idxs)
+        self.get_test_dataset(tst_idxs)
+
+        self.num_data_samples = num_train_files * 30  # approximately
+
+        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_test_pipeline(self, filename_l1b, filename_l2, seed=None, shuffle=False):
 
         if filename_l1b is not None: