fix formatting issues

nobrainer/models/attention_unet.py

@@ -2,7 +2,6 @@
 Adapted from https://github.com/nikhilroxtomar/Semantic-Segmentation-Architecture/blob/main/TensorFlow/attention-unet.py
 """
 
-import tensorflow as tf
 from tensorflow.keras import layers
 import tensorflow.keras.layers as L
 from tensorflow.keras.models import Model

nobrainer/models/attention_unet_with_inception.py

@@ -11,7 +11,8 @@
 
 def expend_as(tensor, rep):
     # Anonymous lambda function to expand the specified axis by a factor of argument, rep.
-    # If tensor has shape (512,512,N), lambda will return a tensor of shape (512,512,N*rep), if specified axis=2
+    # If tensor has shape (512,512,N), lambda will return a tensor of shape
+    # (512,512,N*rep), if specified axis=2
 
     my_repeat = layers.Lambda(
         lambda x, repnum: K.repeat_elements(x, repnum, axis=4),
@@ -29,7 +30,8 @@ def conv3d_block(
     dilation_rate=1,
     recurrent=1,
 ):
-    # A wrapper of the Keras Conv3D block to serve as a building block for downsampling layers
+    # A wrapper of the Keras Conv3D block to serve as a building block for
+    # downsampling layers
     # Includes options to use batch normalization, dilation and recurrence
 
     conv = layers.Conv3D(
@@ -123,7 +125,8 @@ def transpose_block(
     batchnorm=True,
     recurrent=1,
 ):
-    # A wrapper of the Keras Conv3DTranspose block to serve as a building block for upsampling layers
+    # A wrapper of the Keras Conv3DTranspose block to serve as a building block 
+    # for upsampling layers
 
     shape_x = K.int_shape(input_tensor)
     shape_xskip = K.int_shape(skip_tensor)
@@ -165,10 +168,12 @@ def inception_block(
     layers_list=[],
 ):
     # Inception-style convolutional block similar to InceptionNet
-    # The first convolution follows the function arguments, while subsequent inception convolutions follow the parameters in
+    # The first convolution follows the function arguments, while subsequent 
+    # inception convolutions follow the parameters in
     # argument, layers
 
-    # layers is a nested list containing the different secondary inceptions in the format of (kernel_size, dil_rate)
+    # layers is a nested list containing the different secondary inceptions in 
+    # the format of (kernel_size, dil_rate)
 
     # E.g => layers=[ [(3,1),(3,1)], [(5,1)], [(3,1),(3,2)] ]
     # This will implement 3 sets of secondary convolutions

nobrainer/models/unetr.py

@@ -1,6 +1,6 @@
 """UNETR implementation in Tensorflow 2.0.
 
-Adapted from https://github.com/nikhilroxtomar/Semantic-Segmentation-Architecture/blob/main/TensorFlow/unetr.py
+Adapted from https://www.kaggle.com/code/usharengaraju/tensorflow-unetr-w-b
 """
 import math
 
@@ -140,7 +140,7 @@ def call(self, inputs):
         return self.b(self.c(tf.nn.relu(self.a(inputs))))
 
 
-##embeddings, projection_dim=embed_dim
+# embeddings, projection_dim=embed_dim
 class PatchEmbedding(tf.keras.layers.Layer):
     def __init__(self, cube_size, patch_size, embed_dim):
         super(PatchEmbedding, self).__init__()
@@ -184,7 +184,7 @@ def call(self, inputs):
         return patches, positionalEmbedding
 
 
-##transformerblock
+# transformerblock
 class TransformerLayer(tf.keras.layers.Layer):
     def __init__(self, embed_dim, num_heads, dropout, cube_size, patch_size):
         super(TransformerLayer, self).__init__()