transformer patch size is dynamic now.

models.py

@@ -6,25 +6,49 @@ from tensorflow.keras import layers
 from tensorflow.keras.regularizers import l2
 
 mlp_head_units = [2048, 1024]
-projection_dim = 64
+#projection_dim = 64
 transformer_layers = 8
 num_heads = 4
 resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
 IMAGE_ORDERING = 'channels_last'
 MERGE_AXIS = -1
 
-transformer_units = [
-    projection_dim * 2,
-    projection_dim,
-]  # Size of the transformer layers
 def mlp(x, hidden_units, dropout_rate):
     for units in hidden_units:
         x = layers.Dense(units, activation=tf.nn.gelu)(x)
         x = layers.Dropout(dropout_rate)(x)
     return x
 
-
 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]
+        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
+        return patches
+    def get_config(self):
+
+        config = super().get_config().copy()
+        config.update({
+            'patch_size_x': self.patch_size_x,
+            'patch_size_y': self.patch_size_y,
+        })
+        return config
+
+class Patches_old(layers.Layer):
     def __init__(self, patch_size):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
         super(Patches, self).__init__()
         self.patch_size = patch_size
@@ -369,8 +393,13 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
     return model
 
 
-def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
+def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
     inputs = layers.Input(shape=(input_height, input_width, 3))
+    
+    transformer_units = [
+        projection_dim * 2,
+        projection_dim,
+    ]  # Size of the transformer layers
     IMAGE_ORDERING = 'channels_last'
     bn_axis=3
 
@@ -414,7 +443,7 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
     #patch_size_y = input_height / x.shape[1]
     #patch_size_x = input_width / x.shape[2]
     #patch_size = patch_size_x * patch_size_y
-    patches = Patches(patch_size)(x)
+    patches = Patches(patch_size_x, patch_size_y)(x)
     # Encode patches.
     encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)
     
@@ -434,7 +463,7 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
         # Skip connection 2.
         encoded_patches = layers.Add()([x3, x2])
     
-    encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2], 64])
+    encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2] , int( projection_dim / (patch_size_x * patch_size_y) )])
 
     v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(encoded_patches)
     v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)