minor

modules/deeplearning/unet.py

@@ -533,7 +533,8 @@ class UNET:
 
         input_2d = self.inputs[0]
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=None)(input_2d)
-        print(conv.shape)
+        print('Contracting Branch')
+        print('input: ', conv.shape)
         skip = conv
 
         if NOISE_TRAINING:
@@ -545,7 +546,6 @@ class UNET:
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=5, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
 
         skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
         skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
@@ -553,7 +553,7 @@ class UNET:
 
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
-        print(conv.shape)
+        print('1d: ', conv.shape)
         # -----------------------------------------------------------------------------------------------------------
 
         conv_2 = conv
@@ -562,7 +562,6 @@ class UNET:
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
 
         skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
         skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
@@ -570,7 +569,7 @@ class UNET:
 
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
-        print(conv.shape)
+        print('2d: ', conv.shape)
         # ----------------------------------------------------------------------------------------------------------
 
         conv_3 = conv
@@ -579,7 +578,6 @@ class UNET:
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
 
         skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
         skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
@@ -587,6 +585,7 @@ class UNET:
 
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
+        print('3d: ', conv.shape)
         # -----------------------------------------------------------------------------------------------------------
 
         conv_4 = conv
@@ -595,7 +594,6 @@ class UNET:
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.MaxPool2D(padding=padding)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
 
         skip = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=None)(skip)
         skip = tf.keras.layers.MaxPool2D(padding=padding)(skip)
@@ -603,6 +601,7 @@ class UNET:
 
         conv = conv + skip
         conv = tf.keras.layers.LeakyReLU()(conv)
+        print('4d: ', conv.shape)
 
         # Expanding (Decoding) branch -------------------------------------------------------------------------------
         print('expanding branch')
@@ -612,26 +611,25 @@ class UNET:
         conv = tf.keras.layers.concatenate([conv, conv_4])
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
+        print('5: ', conv.shape)
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
         conv = tf.keras.layers.concatenate([conv, conv_3])
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
+        print('6: ', conv.shape)
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
         conv = tf.keras.layers.concatenate([conv, conv_2])
         conv = tf.keras.layers.Conv2D(num_filters, kernel_size=3, strides=1, padding=padding, activation=activation)(conv)
         conv = tf.keras.layers.BatchNormalization()(conv)
-        print(conv.shape)
+        print('7: ', conv.shape)
 
         num_filters /= 2
         conv = tf.keras.layers.Conv2DTranspose(num_filters, kernel_size=3, strides=2, padding=padding)(conv)
-        conv = tf.keras.layers.concatenate([conv, conv_1])
-        print(conv.shape)
+        print('8: ', conv.shape)
 
         if NumClasses == 2:
             activation = tf.nn.sigmoid  # For binary