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fixed feature sizes, cvae working

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luni
2023-01-25 21:00:47 +00:00
parent cba9d8ce90
commit 46e37e22eb
12 changed files with 39 additions and 43 deletions
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models/resnet.py
+4 -4
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@@ -116,7 +116,7 @@ def make_bottleneck_layer(filter_num, blocks, stride=1):
class ResNetTypeI(tf.keras.Model):
def __init__(self, layer_params, reversed=False, labels=None):
def __init__(self, layer_params, features, reversed=False, labels=None, is_cvae=False):
super(ResNetTypeI, self).__init__()
self.reversed = reversed
self.labels = labels
@@ -153,7 +153,7 @@ class ResNetTypeI(tf.keras.Model):
blocks=layer_params[2],
stride=2,
upsample=reversed)
self.layer4 = make_basic_block_layer(filter_num=512,
self.layer4 = make_basic_block_layer(filter_num=features if not is_cvae else features*2,
blocks=layer_params[3],
stride=2 if not reversed else 1,
upsample=reversed)
@@ -228,8 +228,8 @@ class ResNetTypeII(tf.keras.Model):
return output
def resnet_18(reversed=False, labels=None):
return ResNetTypeI(layer_params=[2, 2, 2, 2], reversed=reversed, labels=labels)
def resnet_18(features, reversed=False, labels=None, is_cvae=False):
return ResNetTypeI(layer_params=[2, 2, 2, 2], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
def resnet_34():