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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/cvae_model.py
+6 -6
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@@ -159,16 +159,16 @@ class CVAENew(tf.keras.Model):
self.size = config['resolution']
if not('only_curvatures' in config.keys() and config['only_curvatures'] == True):
self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'])
self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['bottleneck_size'], config['cond_ae_static']['activation'])
if config['static_model'] != '':
load_model(self.static_enc, config['static_model'] + '_enc')
self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['kernel_size'])
self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['bottleneck_size'])
if config['body_model'] != '':
load_model(self.body_enc, config['body_model'] + '_enc')
if config['use_curvature']:
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['bottleneck_size'], config['cond_ae_curv']['activation'])
if config['curvature_model'] != '':
load_model(self.curv_enc, config['curvature_model'] + '_enc')
@@ -179,10 +179,10 @@ class CVAENew(tf.keras.Model):
if config['use_curvature']:
self.curv_enc.trainable = False
self.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
self.encoder = VariationalEncoderNew(config['cvae']['encoder_dims'][-1])
self.decoder = DecoderNew(config['cvae']['decoder_dims'][-1], is_cvae=True)
self.reshape = tf.keras.layers.Reshape((8, 4, 64))
self.reshape = tf.keras.layers.Reshape((16, 8, config['cvae']['decoder_dims'][-1]))
if config['avg_garment_path'] != '':
self.avg_garment = np.load(config['avg_garment_path'])