pyramid available with 128 res
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@@ -128,3 +128,54 @@ class AutoEncoder32(tf.keras.Model):
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# x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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class Decoder128(tf.keras.Model):
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def __init__(self, bottleneck_size, latent_dim):
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super(Decoder128, self).__init__()
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self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
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self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
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self.decoder = ConvNeXtReverseLabels(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])), nr_of_classes=7)
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def call(self, inputs, training=None):
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x = inputs
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x = self.dense(x)
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x = self.reshape(x)
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x = self.decoder(x)
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return x
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class AutoEncoder128(tf.keras.Model):
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def __init__(self):
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super(AutoEncoder128, self).__init__()
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bottleneck_size = 512
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latent_dim = 128
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self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
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self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
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self.decoder = Decoder128(bottleneck_size, latent_dim)
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def call(self, inputs, training=None):
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x, uv_mask = inputs
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# x = tf.transpose(x, [0,2,3,1])
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x = self.encoder(x, training=training)
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x = self.bottleneck(x, training=training)
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x = self.decoder(x, training=training)
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# x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,:, tf.newaxis]
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# x = x * uv_mask[:,tf.newaxis,:,:]
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# x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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@@ -121,3 +121,52 @@ class AutoEncoder32(tf.keras.Model):
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# x = x * uv_mask[:,tf.newaxis,:,:]
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return x
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class Decoder128(tf.keras.Model):
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def __init__(self, bottleneck_size, latent_dim):
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super(Decoder128, self).__init__()
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self.dense = tf.keras.layers.Dense(4 * 2 * bottleneck_size)
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self.reshape = tf.keras.layers.Reshape((4, 2, bottleneck_size))
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self.decoder = ConvNeXtReverse(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([bottleneck_size, 256, 128, 64])))
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def call(self, inputs, training=None):
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x = inputs
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x = self.dense(x)
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x = self.reshape(x)
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x = self.decoder(x)
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return x
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class AutoEncoder128(tf.keras.Model):
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def __init__(self):
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super(AutoEncoder128, self).__init__()
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bottleneck_size = 512
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latent_dim = 128
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self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[64, 128, 256, bottleneck_size], include_top=False)
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self.bottleneck = Bottleneck(bottleneck_size, latent_dim)
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self.decoder = Decoder128(bottleneck_size, latent_dim)
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def call(self, inputs, training=None):
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x = inputs
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x = tf.transpose(x, [0,2,3,1])
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x = self.encoder(x, training=training)
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x = self.bottleneck(x, training=training)
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x = self.decoder(x, training=training)
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x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,:, tf.newaxis]
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# x = x * uv_mask[:,tf.newaxis,:,:]
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return x
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@@ -15,6 +15,10 @@ from models.ae_static_model import AutoEncoder as StaticAE
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from models.ae_body_model import AutoEncoder32 as BodyAE32
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from models.ae_curv_model import AutoEncoder32 as CurvatureAE32
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from models.ae_static_model import AutoEncoder32 as StaticAE32
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from models.ae_body_model import AutoEncoder128 as BodyAE128
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from models.ae_curv_model import AutoEncoder128 as CurvatureAE128
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from models.ae_static_model import AutoEncoder128 as StaticAE128
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class Bottleneck(tf.keras.Model):
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@@ -339,6 +343,177 @@ class CVAE32(tf.keras.Model):
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return logits
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def call(self, inputs, training=None, zero_latent=False):
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uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
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uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
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z_static = self.static_enc(uv_template)
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z_body = self.body_enc(uv_body_posed)
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z_curv = self.curv_enc(uv_curv)
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x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
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mean, logvar = self.encoder(x)
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z = self.reparameterize(mean, logvar)
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z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
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z_cond = self.cond_encoder(z_cond)
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if zero_latent:
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z = tf.zeros_like(z)
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z = tf.concat([z, z_cond], axis=-1)
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# z = self.shuffle(z)
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# z = tf.concat([z, z_curv], axis=-1)
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x = self.decoder(z)
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# x = tf.transpose(x, [0,3,1,2])
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# x = x * uv_mask[:,tf.newaxis,:,:]
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# x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# x = x + tf.transpose(uv_shaped_body, [0,2,3,1])
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if self.avg_garment is not None:
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x = x + self.avg_garment[tf.newaxis,:,:,:]
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# x = x * uv_mask[:,:,:,tf.newaxis]
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return x, mean, logvar, z
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class Encoder128(tf.keras.Model):
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def __init__(self, latent_dim):
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super(Encoder128, self).__init__()
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self.encoder = ConvNeXt(depths=[16, 16, 9, 9], dims=[256, 256, 128, 64], include_top=False)
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self.flatten = tf.keras.layers.Flatten()
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self.mean = tf.keras.layers.Dense(latent_dim)
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self.log_var = tf.keras.layers.Dense(latent_dim)
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def call(self, inputs, training=None):
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x = inputs
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x = self.encoder(x)
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x = self.flatten(x)
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mean = self.mean(x)
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log_var = self.log_var(x)
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return mean, log_var
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class Decoder128(tf.keras.Model):
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def __init__(self):
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super(Decoder128, self).__init__()
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self.dense = tf.keras.layers.Dense(4 * 2 * 128)
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self.reshape = tf.keras.layers.Reshape((4, 2, 128))
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self.decoder = ConvNeXtReverse(depths=list(reversed([16, 16, 9, 9])), dims=list(reversed([256, 256, 128, 64])))
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def call(self, inputs, training=None):
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x = inputs
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x = self.dense(x)
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x = self.reshape(x)
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x = self.decoder(x)
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return x
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class CVAE128(tf.keras.Model):
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def __init__(self, latent_dim=128,
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avg_garment_path='',
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static_model='',
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body_model='',
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curvature_model='',
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freeze_encoders=False):
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super(CVAE128, self).__init__()
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self.latent_dim = latent_dim
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model : StaticAE128 = StaticAE128()
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if static_model != '':
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load_model(model, static_model)
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self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
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model : BodyAE128 = BodyAE128()
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if body_model != '':
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load_model(model, body_model)
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self.body_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
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model : CurvatureAE128 = CurvatureAE128()
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if curvature_model != '':
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load_model(model, curvature_model)
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self.curv_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
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if freeze_encoders:
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self.static_enc.trainable = False
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self.body_enc.trainable = False
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self.curv_enc.trainable = False
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self.encoder = Encoder128(latent_dim)
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self.decoder = Decoder128()
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if avg_garment_path != '':
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self.avg_garment = np.load(avg_garment_path)
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else:
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self.avg_garment = None
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self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
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tf.keras.layers.Dense(256, activation='relu'),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(latent_dim),])
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# @tf.function
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def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, nr_of_samples=4, fix_noise=False, zero_latent=False):
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if not fix_noise:
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eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
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else:
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eps = tf.random.normal(shape=(1, self.latent_dim))
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eps = tf.tile(eps, [nr_of_samples,1])
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uv_template = tf.tile(tf.transpose(uv_template, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
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uv_body_posed = tf.tile(tf.transpose(uv_body_posed, [1,2,0])[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
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if len(uv_curv.shape) == 3:
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uv_curv = tf.tile(uv_curv[tf.newaxis,:,:,:], [nr_of_samples,1,1,1])
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uv_mask = tf.tile(uv_mask[tf.newaxis,:,:], [nr_of_samples,1,1])
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# uv_template, uv_body_posed = tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
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z_static = self.static_enc(uv_template)
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z_body = self.body_enc(uv_body_posed)
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z_curv = self.curv_enc(uv_curv)
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z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
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z_cond = self.cond_encoder(z_cond)
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if zero_latent:
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eps = tf.zeros_like(eps)
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z = tf.concat([eps, z_cond], axis=-1)
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x = self.decoder(z)
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x = x + self.avg_garment[tf.newaxis,:,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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def reparameterize(self, mean, logvar):
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eps = tf.random.normal(shape=mean.shape)
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return eps * tf.exp(logvar * .5) + mean
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def decode(self, z, apply_sigmoid=False):
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logits = self.decoder(z)
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if apply_sigmoid:
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probs = tf.sigmoid(logits)
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return probs
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return logits
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def call(self, inputs, training=None, zero_latent=False):
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uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
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uv_unposed, uv_template, uv_body_posed = tf.transpose(uv_unposed, [0,2,3,1]), tf.transpose(uv_template, [0,2,3,1]), tf.transpose(uv_body_posed, [0,2,3,1])
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@@ -7,15 +7,18 @@ import numpy as np
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# import plotly.express as ex
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from custom_utils.custom_utils import load_model
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from models.cvae_model import CVAE, CVAE32
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from models.cvae_model import CVAE, CVAE128, CVAE32
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class Pyramid(tf.keras.Model):
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def __init__(self, latent_dim=128, avg_garment_path=''):
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def __init__(self, low_res=32, latent_dim=128, avg_garment_path=''):
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super(Pyramid, self).__init__()
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self.model_32 = CVAE32(latent_dim=latent_dim)
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if low_res == 32:
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self.model_low = CVAE32(latent_dim=latent_dim)
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if low_res == 128:
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self.model_low = CVAE128(latent_dim=latent_dim)
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self.model_full = CVAE(latent_dim=latent_dim)
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if avg_garment_path != '':
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@@ -24,16 +27,16 @@ class Pyramid(tf.keras.Model):
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self.avg_garment = None
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def call(self, inputs, training=None):
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uv_unposed, uv_static, uv_body_posed, uv_curv, uv_unposed_32, uv_static_32, uv_body_posed_32, uv_curv_32 = inputs
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uv_unposed, uv_static, uv_body_posed, uv_curv, uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low = inputs
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pred_32, mean_32, logvar_32, _ = self.model_32([uv_unposed_32, uv_static_32, uv_body_posed_32, uv_curv_32], training=training)
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pred_low, mean_low, logvar_low, _ = self.model_low([uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low], training=training)
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x = tf.image.resize(pred_32, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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pred_full, mean_full, logvar_full, _ = self.model_full([uv_unposed, uv_static, uv_body_posed, uv_curv], training=training)
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x = x + pred_full
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return x, mean_full, logvar_full, pred_32, mean_32, logvar_32,
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return x, mean_full, logvar_full, pred_low, mean_low, logvar_low,
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