resnet 101 working

models/base.py

@@ -163,10 +163,10 @@ class VariationalEncoderNew(tf.keras.Model):
 
 class AEEncoderNew(tf.keras.Model):
     
-    def __init__(self, features, activation='tanh'):
+    def __init__(self, layer_params, features, activation='tanh'):
         super(AEEncoderNew, self).__init__()
 
-        self.encoder = resnet_101(features)
+        self.encoder = resnet_101(layer_params, features)
         self.act = tf.keras.layers.Activation(activation)
 
 
@@ -187,15 +187,15 @@ class AEEncoderNew(tf.keras.Model):
 
 class DecoderNew(tf.keras.Model):
     
-    def __init__(self, features, is_label=False, is_cvae=False, learn_weights=False):
+    def __init__(self, layer_params, features, is_label=False, is_cvae=False, learn_weights=False):
         super(DecoderNew, self).__init__()
 
         self.is_label = is_label
 
         if not is_label:
-            self.decoder = resnet_101(features, reversed=True, is_cvae=is_cvae)
+            self.decoder = resnet_101(layer_params, features, reversed=True, is_cvae=is_cvae)
         else:
-            self.decoder = resnet_101(features, reversed=True, labels=6, is_cvae=is_cvae)
+            self.decoder = resnet_101(layer_params, features, reversed=True, labels=6, is_cvae=is_cvae)
             
 
     def call(self, inputs, training):

models/cond_encoder.py

@@ -25,8 +25,8 @@ class AutoEncoderNew(tf.keras.Model):
     def __init__(self, config):
         super(AutoEncoderNew, self).__init__()
 
-        self.encoder : AEEncoderNew = AEEncoderNew(config['bottleneck_size'], config['activation'])
-        self.decoder : DecoderNew = DecoderNew(config['bottleneck_size'], is_label=config['is_label'])
+        self.encoder : AEEncoderNew = AEEncoderNew(config['layer_params'], config['bottleneck_size'], config['activation'])
+        self.decoder : DecoderNew = DecoderNew(config['layer_params'], config['bottleneck_size'], is_label=config['is_label'])
 
 
     def call(self, inputs, training):

models/resnet.py

@@ -272,8 +272,8 @@ class ResNetTypeII(tf.keras.Model):
         return x
 
 
-def resnet_18(features, reversed=False, labels=None, is_cvae=False):
-    return ResNetTypeI(layer_params=[2, 2, 2, 2, 2], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
+def resnet_18(layer_params, features, reversed=False, labels=None, is_cvae=False):
+    return ResNetTypeI(layer_params=layer_params, features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
 
 
 def resnet_34():
@@ -284,8 +284,8 @@ def resnet_50():
     return ResNetTypeII(layer_params=[3, 4, 6, 3])
 
 
-def resnet_101(features, reversed=False, labels=None, is_cvae=False):
-    return ResNetTypeII(layer_params=[3, 4, 23, 3, 3], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
+def resnet_101(layer_params, features, reversed=False, labels=None, is_cvae=False):
+    return ResNetTypeII(layer_params=layer_params, features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
 
 
 def resnet_152():