patches class for VIT encoder is corrected

src/eynollah/patch_encoder.py

@@ -1,52 +1,48 @@
 from keras import layers
 import tensorflow as tf
 
-projection_dim = 64
-patch_size = 1
-num_patches =21*21#14*14#28*28#14*14#28*28
+
+class Patches(layers.Layer):
+    def __init__(self, patch_size_x, patch_size_y):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
+        super(Patches, self).__init__()
+        self.patch_size_x = patch_size_x
+        self.patch_size_y = patch_size_y
+
+    def call(self, images):
+        #print(tf.shape(images)[1],'images')
+        #print(self.patch_size,'self.patch_size')
+        batch_size = tf.shape(images)[0]
+        patches = tf.image.extract_patches(
+            images=images,
+            sizes=[1, self.patch_size_y, self.patch_size_x, 1],
+            strides=[1, self.patch_size_y, self.patch_size_x, 1],
+            rates=[1, 1, 1, 1],
+            padding="VALID",
+        )
+        #patch_dims = patches.shape[-1]
+        patch_dims = tf.shape(patches)[-1]
+        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
+        return patches
 
 class PatchEncoder(layers.Layer):
-
-    def __init__(self):
-        super().__init__()
+    def __init__(self, num_patches, projection_dim):
+        super(PatchEncoder, self).__init__()
+        self.num_patches = num_patches
         self.projection = layers.Dense(units=projection_dim)
-        self.position_embedding = layers.Embedding(input_dim=num_patches, output_dim=projection_dim)
+        self.position_embedding = layers.Embedding(
+            input_dim=num_patches, output_dim=projection_dim
+        )
 
     def call(self, patch):
-        positions = tf.range(start=0, limit=num_patches, delta=1)
+        positions = tf.range(start=0, limit=self.num_patches, delta=1)
         encoded = self.projection(patch) + self.position_embedding(positions)
         return encoded
-
     def get_config(self):
+
         config = super().get_config().copy()
         config.update({
-            'num_patches': num_patches,
+            'num_patches': self.num_patches,
             'projection': self.projection,
             'position_embedding': self.position_embedding,
         })
         return config
-
-class Patches(layers.Layer):
-    def __init__(self, **kwargs):
-        super(Patches, self).__init__()
-        self.patch_size = patch_size
-
-    def call(self, images):
-        batch_size = tf.shape(images)[0]
-        patches = tf.image.extract_patches(
-            images=images,
-            sizes=[1, self.patch_size, self.patch_size, 1],
-            strides=[1, self.patch_size, self.patch_size, 1],
-            rates=[1, 1, 1, 1],
-            padding="VALID",
-        )
-        patch_dims = patches.shape[-1]
-        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
-        return patches
-    def get_config(self):
-
-        config = super().get_config().copy()
-        config.update({
-            'patch_size': self.patch_size,
-        })
-        return config