creating resnet101

models/base.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
-from models.resnet import resnet_18, make_basic_block_layer
+from models.resnet import resnet_18, make_basic_block_layer, resnet_34, resnet_50, resnet_101, resnet_152
 
 
 class Bottleneck(tf.keras.Model):
@@ -116,7 +116,7 @@ class VariationalEncoderNew(tf.keras.Model):
     def __init__(self, features):
         super(VariationalEncoderNew, self).__init__()
 
-        self.encoder = resnet_18(features)
+        self.encoder = resnet_101(features)
 
         self.mean_branch = tf.keras.Sequential([
             tf.keras.layers.Conv2D(filters=features,
@@ -166,7 +166,7 @@ class AEEncoderNew(tf.keras.Model):
     def __init__(self, features, activation='tanh'):
         super(AEEncoderNew, self).__init__()
 
-        self.encoder = resnet_18(features)
+        self.encoder = resnet_101(features)
         self.act = tf.keras.layers.Activation(activation)
 
 
@@ -187,17 +187,15 @@ class AEEncoderNew(tf.keras.Model):
 
 class DecoderNew(tf.keras.Model):
     
-    def __init__(self, features, is_label=False, is_cvae=False):
-    def __init__(self, depths, dims, kernel_size=7, is_label=False, learn_weights=False):
+    def __init__(self, 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_18(features, reversed=True, is_cvae=is_cvae)
-            self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size, learn_weights=learn_weights)
+            self.decoder = resnet_101(features, reversed=True, is_cvae=is_cvae)
         else:
-            self.decoder = resnet_18(features, reversed=True, labels=6, is_cvae=is_cvae)
+            self.decoder = resnet_101(features, reversed=True, labels=6, is_cvae=is_cvae)
             
 
     def call(self, inputs, training):