fix a bunch of bugs with tf/keras version

README.md

@@ -63,15 +63,19 @@ from lambda_networks import λLayer
 <a href="https://github.com/shinel94">Shinel94</a> has added a Keras implementation! It won't be officially supported in this repository, so either copy / paste the code under `./lambda_networks/tfkeras.py` or make sure to install `tensorflow` and `keras` before running the following.
 
 ```python
+import tensorflow as tf
 from lambda_networks.tfkeras import LambdaLayer
 
 layer = LambdaLayer(
     dim_out = 32,
     r = 23,
     dim_k = 16,
     heads = 4,
-    dim_u = 4
+    dim_u = 1
 )
+
+x = tf.random.normal((1, 64, 64, 16))
+layer(x)
 ```
 
 ## Citations

lambda_networks/tfkeras.py

@@ -1,6 +1,6 @@
-from einops.layers.keras import Rearrange
-from keras.layers import Conv2D, BatchNormalization, Conv3D, ZeroPadding3D, Softmax, Lambda, Add, Layer
-from keras import initializers
+from einops.layers.tensorflow import Rearrange
+from tensorflow.keras.layers import Conv2D, BatchNormalization, Conv3D, ZeroPadding3D, Softmax, Lambda, Add, Layer
+from tensorflow.keras import initializers
 from tensorflow import einsum
 
 # helpers functions
@@ -12,7 +12,6 @@ def exists(val):
 def default(val, d):
     return val if exists(val) else d
 
-
 # lambda layer
 
 class LambdaLayer(Layer):
@@ -46,8 +45,7 @@ def __init__(
         self.local_contexts = exists(r)
         if exists(r):
             assert (r % 2) == 1, 'Receptive kernel size should be odd'
-            self.pos_padding = ZeroPadding3D(padding=(0, r//2, r//2))
-            self.pos_conv = Conv3D(dim_k, (1, r, r), padding='valid')
+            self.pos_conv = Conv3D(dim_k, (1, r, r), padding='same')
         else:
             assert exists(n), 'You must specify the total sequence length (h x w)'
             self.pos_emb = self.add_weight(name='pos_emb',
@@ -56,7 +54,7 @@ def __init__(
                                            trainable=True)
 
     def call(self, inputs, **kwargs):
-        b, c, hh, ww = inputs.get_shape().as_list()
+        b, hh, ww, c = inputs.get_shape().as_list()
         u, h = self.u, self.heads
         x = inputs
 
@@ -67,33 +65,32 @@ def call(self, inputs, **kwargs):
         q = self.norm_q(q)
         v = self.norm_v(v)
 
-        q = Rearrange('b (h k) hh ww -> b h k (hh ww)', h=h)(q)
-        k = Rearrange('b (u k) hh ww -> b u k (hh ww)', u=u)(k)
-        v = Rearrange('b (u v) hh ww -> b u v (hh ww)', u=u)(v)
+        q = Rearrange('b hh ww (h k) -> b h k (hh ww)', h=h)(q)
+        k = Rearrange('b hh ww (u k) -> b u k (hh ww)', u=u)(k)
+        v = Rearrange('b hh ww (u v) -> b u v (hh ww)', u=u)(v)
 
         k = Softmax()(k)
 
         Lc = Lambda(lambda x: einsum('b u k m, b u v m -> b k v', x[0], x[1]))([k, v])
         Yc = Lambda(lambda x: einsum('b h k n, b k v -> b n h v', x[0], x[1]))([q, Lc])
 
         if self.local_contexts:
-            v = Rearrange('b u v (hh ww) -> b u v hh ww', hh=hh, ww=ww)(v)
-            Lp = self.pos_padding(v)
-            Lp = self.pos_conv(Lp)
-            Lp = Rearrange('b c k h w -> b c k (h w)')(Lp)
-            Yp = Lambda(lambda x: einsum('b h k n, b k v n -> b n h v', x[0], x[1]))([q, Lp])
+            v = Rearrange('b u v (hh ww) -> b v hh ww u', hh=hh, ww=ww)(v)
+            Lp = self.pos_conv(v)
+            Lp = Rearrange('b v h w k -> b v k (h w)')(Lp)
+            Yp = Lambda(lambda x: einsum('b h k n, b v k n -> b n h v', x[0], x[1]))([q, Lp])
         else:
             Lp = Lambda(lambda x: einsum('n m k u, b u v m -> b n k v', x[0], x[1]))([self.pos_emb, v])
             Yp = Lambda(lambda x: einsum('b h k n, b n k v -> b n h v', x[0], x[1]))([q, Lp])
 
         Y = Add()([Yc, Yp])
-        out = Rearrange('b (hh ww) h v -> b (h v) hh ww', hh = hh, ww = ww)(Y)
+        out = Rearrange('b (hh ww) h v -> b hh ww (h v)', hh = hh, ww = ww)(Y)
         return out
 
     def compute_output_shape(self, input_shape):
-        return (input_shape[0], self.out_dim, input_shape[2], input_shape[3])
+        return (input_shape[0], input_shape[1], input_shape[2], self.out_dim)
 
     def get_config(self):
-        config = {'output_dim': (self.input_shape[0], self.out_dim, self.input_shape[2], self.input_shape[3])}
+        config = {'output_dim': (self.input_shape[0], self.input_shape[1], self.input_shape[2], self.out_dim)}
         base_config = super(LambdaLayer, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'lambda-networks',
   packages = find_packages(),
-  version = '0.3.0',
+  version = '0.3.1',
   license='MIT',
   description = 'Lambda Networks - Pytorch',
   author = 'Phil Wang',