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making ready for skirts

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luni
2022-09-30 21:53:17 +00:00
parent 6a2f9ff4ff
commit dca5368704
22 changed files with 700 additions and 219 deletions
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models/cvae_model.py
+11 -64
View File
@@ -22,7 +22,7 @@ class CVAE(tf.keras.Model):
if config['static_model'] != '':
load_model(self.static_enc, config['static_model'] + '_enc')
self.body_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['latent_dim'], config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['bottleneck_size'], config['cond_ae_body']['kernel_size'])
if config['body_model'] != '':
load_model(self.body_enc, config['body_model'] + '_enc')
@@ -49,38 +49,6 @@ class CVAE(tf.keras.Model):
self.avg_garment = None
# @tf.function
def sample_single(self, uv_template, uv_body_posed, uv_curv, uv_mask, nr_of_samples=4, fix_noise=False):
if not fix_noise:
eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
else:
eps = tf.random.normal(shape=(1, self.latent_dim))
eps = tf.tile(eps, [nr_of_samples,1])
uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
if len(uv_curv.shape) == 3:
uv_curv = tf.tile(uv_curv[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
uv_mask = tf.tile(uv_mask[tf.newaxis,:,:], [nr_of_samples,1,1])
# uv_template, uv_body_posed = tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
z_static = self.static_enc(uv_template)
z_body = self.body_enc(uv_body_posed)
z_curv = self.curv_enc(uv_curv)
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
z_cond = self.cond_encoder(z_cond)
z = tf.concat([eps, z_cond], axis=-1)
x = self.decoder(z)
x = x + self.avg_garment[tf.newaxis,:,:,:]
x = x * uv_mask[:,:,:,tf.newaxis]
return x
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask=None, noise=None):
if noise is None:
eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
@@ -110,24 +78,9 @@ class CVAE(tf.keras.Model):
return eps * tf.exp(logvar * .5) + mean
def decode(self, z, apply_sigmoid=False):
logits = self.decoder(z)
if apply_sigmoid:
probs = tf.sigmoid(logits)
return probs
return logits
def encode_conds(self, uv_template, uv_body_posed, uv_curv):
if uv_template.shape[1] == 3:
uv_template = tf.transpose(uv_template, [0,2,3,1])
if uv_body_posed.shape[1] == 3:
uv_body_posed = tf.transpose(uv_body_posed, [0,2,3,1])
if uv_curv.shape[1] == 3:
uv_curv = tf.transpose(uv_curv, [0,2,3,1])
z_static = self.static_enc(uv_template)
z_body = self.body_enc(uv_body_posed)
z_static = self.static_enc(uv_template)
z_curv = self.curv_enc(uv_curv)
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
@@ -136,20 +89,14 @@ class CVAE(tf.keras.Model):
return z_cond
def call(self, inputs, training=None, use_mask=True):
def call(self, inputs, training=None):
if use_mask:
if self.size == 512:
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
uv_template_high, uv_body_posed_high, uv_curv_high = uv_template, uv_body_posed, uv_curv
else:
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_template_high, uv_body_posed_high, uv_curv_high, uv_mask_high = inputs
if self.size == 512:
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
uv_template_high, uv_body_posed_high, uv_curv_high = uv_template, uv_body_posed, uv_curv
else:
if self.size == 512:
uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
uv_template_high, uv_body_posed_high, uv_curv_high = uv_template, uv_body_posed, uv_curv
else:
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_template_high, uv_body_posed_high, uv_curv_high = inputs
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_template_high, uv_body_posed_high, uv_curv_high, uv_mask_high = inputs
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
mean, logvar = self.encoder(x, training=training)
@@ -161,8 +108,8 @@ class CVAE(tf.keras.Model):
if self.avg_garment is not None:
x = x + self.avg_garment[tf.newaxis,:,:,:]
if use_mask:
x = x * uv_mask[:,:,:,tf.newaxis]
x = x * uv_mask[:,:,:,tf.newaxis]
return x, mean, logvar, z
return x, mean, logvar
models/upscaler.py
-3
View File
@@ -75,9 +75,6 @@ class UpscalerBasic(tf.keras.Model):
def call(self, inputs, training=None):
img, mask = inputs
if img.shape[1] == 3:
img = tf.transpose(img, [0,2,3,1])
x = self.flatten(img)
x = self.seq(x, training=training)
x = self.reshape(x)