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luni
2023-02-24 20:31:04 +00:00
parent 0ce28a2e18
commit 784fe3eb45
5 changed files with 54 additions and 44 deletions
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models/base.py
+3 -3
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@@ -111,18 +111,18 @@ class Decoder(tf.keras.Model):
class VariationalEncoderNew(tf.keras.Model):
def __init__(self, depths, dims, kernel_size=7):
def __init__(self, depths, dims, kernel_size=7, latent_dim=64):
super(VariationalEncoderNew, self).__init__()
self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
self.mean_branch = tf.keras.Sequential(
[Block(dim=64, drop_path=0, kernel_size=kernel_size,
[Block(dim=latent_dim, drop_path=0, kernel_size=kernel_size,
layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
)
self.var_branch = tf.keras.Sequential(
[Block(dim=64, drop_path=0, kernel_size=kernel_size,
[Block(dim=latent_dim, drop_path=0, kernel_size=kernel_size,
layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
)
models/cvae_model.py
+2 -2
View File
@@ -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.encoder = VariationalEncoderNew(config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'], latent_dim=config['cvae']['latent_dim'])
self.decoder = DecoderNew(config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
self.reshape = tf.keras.layers.Reshape((8, 4, 64))
self.reshape = tf.keras.layers.Reshape((8, 4, config['cvae']['latent_dim']))
if config['avg_garment_path'] != '':
self.avg_garment = np.load(config['avg_garment_path'])