updating train.py nontransformer backend

models.py

@@ -30,8 +30,8 @@ class Patches(layers.Layer):
         self.patch_size = patch_size
 
     def call(self, images):
-        print(tf.shape(images)[1],'images')
-        print(self.patch_size,'self.patch_size')
+        #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,
@@ -41,7 +41,7 @@ class Patches(layers.Layer):
             padding="VALID",
         )
         patch_dims = patches.shape[-1]
-        print(patches.shape,patch_dims,'patch_dims')
+        #print(patches.shape,patch_dims,'patch_dims')
         patches = tf.reshape(patches, [batch_size, -1, patch_dims])
         return patches
     def get_config(self):
@@ -51,6 +51,7 @@ class Patches(layers.Layer):
             'patch_size': self.patch_size,
         })
         return config
+
     
 class PatchEncoder(layers.Layer):
     def __init__(self, num_patches, projection_dim):
@@ -408,7 +409,11 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
     if pretraining:
         model = Model(inputs, x).load_weights(resnet50_Weights_path)
 
-    num_patches = x.shape[1]*x.shape[2]
+    #num_patches = x.shape[1]*x.shape[2]
+    
+    #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)
     # Encode patches.
     encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)

train.py

@@ -97,8 +97,6 @@ def run(_config, n_classes, n_epochs, input_height,
         pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
     
     if task == "segmentation" or task == "enhancement":
-        
-        num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
         if data_is_provided:
             dir_train_flowing = os.path.join(dir_output, 'train')
             dir_eval_flowing = os.path.join(dir_output, 'eval')
@@ -213,7 +211,15 @@ def run(_config, n_classes, n_epochs, input_height,
             index_start = 0
             if backbone_type=='nontransformer':
                 model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
-            elif backbone_type=='nontransformer':
+            elif backbone_type=='transformer':
+                num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
+                
+                if not (num_patches == (input_width / 32) * (input_height / 32)):
+                    print("Error: transformer num patches error. Parameter transformer_num_patches_xy should be set to (input_width/32) = {} and (input_height/32) =  {}".format(int(input_width / 32), int(input_height / 32)) )
+                    sys.exit(1)
+                if not (transformer_patchsize == 1):
+                    print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
+                    sys.exit(1)
                 model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width, task, weight_decay, pretraining)
         
         #if you want to see the model structure just uncomment model summary.