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resnet 101 working

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luni
2023-02-21 22:30:32 +00:00
parent 8447b7c505
commit 9eb1183df4
4 changed files with 15 additions and 17 deletions
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configs/iccv/config_ae_static.py
+4 -6
View File
@@ -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'
models/base.py
+5 -5
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@@ -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
+2 -2
View File
@@ -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
+4 -4
View File
@@ -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():