moduler

test/test_layers.py

@@ -1,5 +1,5 @@
-from base import TestCase
 import keras
+from base import TestCase
 from keras import ops
 
 from videoswin.layers import (

videoswin/backbone.py

@@ -133,7 +133,9 @@ def __init__(
                 attn_drop_rate=attn_drop_rate,
                 drop_path_rate=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
                 norm_layer=norm_layer,
-                downsampling_layer=(VideoSwinPatchMerging if (i < num_layers - 1) else None),
+                downsampling_layer=(
+                    VideoSwinPatchMerging if (i < num_layers - 1) else None
+                ),
                 name=f"videoswin_basic_layer_{i + 1}",
             )
             x = layer(x)

videoswin/blocks/basic.py

@@ -132,11 +132,11 @@ def call(self, x, training=None):
             x = self.downsample(x)
 
         return x
-    
+
     def compute_output_shape(self, input_shape):
         if self.downsampling_layer is not None:
             output_shape = self.downsample.compute_output_shape(input_shape)
-            return output_shape 
+            return output_shape
 
         return input_shape
 
@@ -157,4 +157,3 @@ def get_config(self):
             }
         )
         return config
-

videoswin/layers/patch_embed.py

@@ -60,7 +60,7 @@ def call(self, x):
             x = self.norm(x)
 
         return x
-    
+
     def get_config(self):
         config = super().get_config()
         config.update(

videoswin/layers/patch_merging.py

@@ -59,9 +59,7 @@ def call(self, x):
 
     def compute_output_shape(self, input_shape):
         batch_size, depth, height, width, _ = input_shape
-        return (
-            batch_size, depth, height // 2, width // 2, 2 * self.input_dim
-        )
+        return (batch_size, depth, height // 2, width // 2, 2 * self.input_dim)
 
     def get_config(self):
         config = super().get_config()