snapshot...

modules/deeplearning/espcn.py

@@ -212,11 +212,11 @@ class ESPCN:
         self.n_chans = 1
 
         # self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
-        self.X_img = tf.keras.Input(shape=(30, 30, self.n_chans))
+        self.X_img = tf.keras.Input(shape=(36, 36, self.n_chans))
 
         self.inputs.append(self.X_img)
         # self.inputs.append(tf.keras.Input(shape=(None, None, self.n_chans)))
-        self.inputs.append(tf.keras.Input(shape=(30, 30, self.n_chans)))
+        self.inputs.append(tf.keras.Input(shape=(36, 36, self.n_chans)))
 
         self.DISK_CACHE = False
 
@@ -411,7 +411,7 @@ class ESPCN:
         input_2d = self.inputs[0]
         print('input: ', input_2d.shape)
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding='VALID', activation=None)(input_2d)
-        conv = conv[:, 4:20, 4:20, :]
+        # conv = conv[:, 4:20, 4:20, :]
         print('Contracting Branch')
         print('input: ', conv.shape)
         skip = conv