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propagate training parameter

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luni
2022-10-12 21:05:49 +00:00
parent fb0fa16ed0
commit 4bf72f8b55
1 changed files with 11 additions and 11 deletions
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models/cvae_model.py
+11 -11
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@@ -49,22 +49,22 @@ class CVAE(tf.keras.Model):
self.avg_garment = None self.avg_garment = None
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask=None, noise=None): @tf.function
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, noise=None):
if noise is None: if noise is None:
eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim)) eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
else: else:
eps = noise eps = noise
z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv) z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv, training=False)
z = tf.concat([eps, z_cond], axis=-1) z = tf.concat([eps, z_cond], axis=-1)
x = self.decoder(z) x = self.decoder(z, training=False)
if self.avg_garment is not None: if self.avg_garment is not None:
x = x + self.avg_garment[tf.newaxis,:,:,:] x = x + self.avg_garment[tf.newaxis,:,:,:]
if uv_mask is not None: x = x * uv_mask[:,:,:,tf.newaxis]
x = x * uv_mask[:,:,:,tf.newaxis]
return x return x
@@ -78,13 +78,13 @@ class CVAE(tf.keras.Model):
return eps * tf.exp(logvar * .5) + mean return eps * tf.exp(logvar * .5) + mean
def encode_conds(self, uv_template, uv_body_posed, uv_curv): def encode_conds(self, uv_template, uv_body_posed, uv_curv, training=None):
z_body = self.body_enc(uv_body_posed) z_body = self.body_enc(uv_body_posed, training=training)
z_static = self.static_enc(uv_template) z_static = self.static_enc(uv_template, training=training)
z_curv = self.curv_enc(uv_curv) z_curv = self.curv_enc(uv_curv, training=training)
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1) z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
z_cond = self.cond_encoder(z_cond) z_cond = self.cond_encoder(z_cond, training=training)
return z_cond return z_cond
@@ -101,7 +101,7 @@ class CVAE(tf.keras.Model):
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1) x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
mean, logvar = self.encoder(x, training=training) mean, logvar = self.encoder(x, training=training)
z = self.reparameterize(mean, logvar) z = self.reparameterize(mean, logvar)
z_cond = self.encode_conds(uv_template_high, uv_body_posed_high, uv_curv_high) z_cond = self.encode_conds(uv_template_high, uv_body_posed_high, uv_curv_high, training=training)
z = tf.concat([z, z_cond], axis=-1) z = tf.concat([z, z_cond], axis=-1)
x = self.decoder(z, training=training) x = self.decoder(z, training=training)