snapshot...

modules/deeplearning/unet.py

@@ -376,11 +376,13 @@ class UNET:
         activation = tf.nn.leaky_relu
         momentum = 0.99
 
-        num_filters = self.n_chans * 4
+        num_filters = self.n_chans * 8
 
         input_2d = self.inputs[0]
-        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=None)(input_2d)
-        print('Contracting Branch')
+        print('input: ', input_2d.shape)
+        conv = tf.keras.layers.Conv2D(num_filters, kernel_size=7, strides=1, padding='VALID', activation=None)(input_2d)
+        conv = conv[:, 6:70, 6:70, :]
+        print('Contracting Branch -----------')
         print('input: ', conv.shape)
         skip = conv
 
@@ -451,7 +453,7 @@ class UNET:
         print('4d: ', conv.shape)
 
         # Expanding (Decoding) branch -------------------------------------------------------------------------------
-        print('expanding branch')
+        print('expanding branch --------------')
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
@@ -834,8 +836,8 @@ class UNET:
         # f.close()
 
     def build_model(self):
-        # self.build_unet()
-        self.build_upsample()
+        self.build_unet()
+        # self.build_upsample()
         self.model = tf.keras.Model(self.inputs, self.logits)
 
     def restore(self, ckpt_dir):