luni/cvpr
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

resnet condition encoders

Browse Source
This commit is contained in:
luni
2023-02-27 17:41:47 +00:00
parent bb7128f589
commit aa3defbf07
10 changed files with 151 additions and 111 deletions
Download Patch File Download Diff File Expand all files Collapse all files
models/base.py
+3 -3
View File
@@ -166,7 +166,7 @@ class AEEncoderNew(tf.keras.Model):
def __init__(self, layer_params, features, activation='tanh'):
super(AEEncoderNew, self).__init__()
self.encoder = resnet_18(layer_params, features)
self.encoder = resnet_101(layer_params, features)
self.act = tf.keras.layers.Activation(activation)
@@ -193,9 +193,9 @@ class DecoderNew(tf.keras.Model):
self.is_label = is_label
if not is_label:
self.decoder = resnet_18(layer_params, features, reversed=True, is_cvae=is_cvae)
self.decoder = resnet_101(layer_params, features, reversed=True, is_cvae=is_cvae)
else:
self.decoder = resnet_18(layer_params, 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/resnet.py
+10 -10
View File
@@ -133,7 +133,7 @@ class ResNetTypeI(tf.keras.Model):
self.labels = labels
if not reversed:
self.conv1 = tf.keras.layers.Conv2D(filters=64,
self.conv1 = tf.keras.layers.Conv2D(filters=32,
kernel_size=(7, 7),
strides=2,
padding="same")
@@ -149,19 +149,19 @@ class ResNetTypeI(tf.keras.Model):
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.UpSampling2D()
self.layer1 = make_basic_block_layer(filter_num=64,
self.layer1 = make_basic_block_layer(filter_num=32,
blocks=layer_params[0],
stride=1 if not reversed else 2,
upsample=reversed)
self.layer2 = make_basic_block_layer(filter_num=64,
self.layer2 = make_basic_block_layer(filter_num=32,
blocks=layer_params[1],
stride=2,
upsample=reversed)
self.layer3 = make_basic_block_layer(filter_num=64,
self.layer3 = make_basic_block_layer(filter_num=32,
blocks=layer_params[2],
stride=2,
upsample=reversed)
self.layer4 = make_basic_block_layer(filter_num=128,
self.layer4 = make_basic_block_layer(filter_num=64,
blocks=layer_params[3],
stride=2,
upsample=reversed)
@@ -207,7 +207,7 @@ class ResNetTypeII(tf.keras.Model):
self.labels = labels
if not reversed:
self.conv1 = tf.keras.layers.Conv2D(filters=64,
self.conv1 = tf.keras.layers.Conv2D(filters=32,
kernel_size=(7, 7),
strides=2,
padding="same")
@@ -223,19 +223,19 @@ class ResNetTypeII(tf.keras.Model):
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.UpSampling2D()
self.layer1 = make_bottleneck_layer(filter_num=64,
self.layer1 = make_bottleneck_layer(filter_num=32,
blocks=layer_params[0],
stride=1 if not reversed else 2,
upsample=reversed)
self.layer2 = make_bottleneck_layer(filter_num=128,
self.layer2 = make_bottleneck_layer(filter_num=64,
blocks=layer_params[1],
stride=2,
upsample=reversed)
self.layer3 = make_bottleneck_layer(filter_num=256,
self.layer3 = make_bottleneck_layer(filter_num=64,
blocks=layer_params[2],
stride=2,
upsample=reversed)
self.layer4 = make_bottleneck_layer(filter_num=512,
self.layer4 = make_bottleneck_layer(filter_num=128,
blocks=layer_params[3],
stride=2,
upsample=reversed)