From 9eb1183df43c4fd1321bb4350a6db821de561a77 Mon Sep 17 00:00:00 2001
From: Hunor Laczko <luni741@gmail.com>
Date: Tue, 21 Feb 2023 22:30:32 +0000
Subject: [PATCH] resnet 101 working

---
 configs/iccv/config_ae_static.py | 10 ++++------
 models/base.py                   | 10 +++++-----
 models/cond_encoder.py           |  4 ++--
 models/resnet.py                 |  8 ++++----
 4 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/configs/iccv/config_ae_static.py b/configs/iccv/config_ae_static.py
index 641ff1a..7d8fc68 100644
--- a/configs/iccv/config_ae_static.py
+++ b/configs/iccv/config_ae_static.py
@@ -1,7 +1,7 @@
 config = dict()
 
 
-config['batch_size'] = 4
+config['batch_size'] = 4 ##################################### CHANGE CHANGE CHANGE CHANGE CHANGE CHANGE CHANGE
 config['train_dir'] = '/home/workspace/local-data/train'
 config['valid_dir'] = '/home/workspace/local-data/valid'
 config['test_dir'] = '/home/workspace/data/dataset/test_ready'
@@ -23,11 +23,9 @@ config['cond_ae']['is_label'] = False
 config['cond_ae']['upscale_output'] = False
 config['cond_ae']['latent_dim'] = config['latent_dim']
 config['cond_ae']['kernel_size'] = 7
-config['cond_ae']['bottleneck_size'] = 8 * 4
-config['cond_ae']['encoder_depths'] = [3, 3, 3, 27, 3]
-config['cond_ae']['encoder_dims'] = [8, 64, 128, 128, config['cond_ae']['bottleneck_size']]
-config['cond_ae']['decoder_depths'] = config['cond_ae']['encoder_depths']
-config['cond_ae']['decoder_dims'] = config['cond_ae']['encoder_dims']
+config['cond_ae']['bottleneck_size'] = 4 # 8*4
+config['cond_ae']['layer_params'] = [3, 4, 5, 4, 3]
+# config['cond_ae']['layer_params'] = [3, 4, 23, 3, 3]
 config['cond_ae']['activation'] = 'linear'
 
 
diff --git a/models/base.py b/models/base.py
index e9b9c83..f27d464 100644
--- a/models/base.py
+++ b/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):
diff --git a/models/cond_encoder.py b/models/cond_encoder.py
index 076357b..6f91c2d 100644
--- a/models/cond_encoder.py
+++ b/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):
diff --git a/models/resnet.py b/models/resnet.py
index dc47cb9..b32e3a4 100644
--- a/models/resnet.py
+++ b/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():