large refactor
This commit is contained in:
+48
-469
@@ -9,119 +9,61 @@ import scipy as sp
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from custom_utils.custom_utils import load_model
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from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block
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from models.ae_body_model import AutoEncoder as BodyAE
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from models.ae_curv_model import AutoEncoder as CurvatureAE
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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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from models.base import Bottleneck, VariationalEncoder, Decoder
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from models.cond_encoder import AutoEncoder
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# from models.ae_body_model import AutoEncoder as BodyAE
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# from models.ae_curv_model import AutoEncoder as CurvatureAE
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# 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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def __init__(self, dim):
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super(Bottleneck, self).__init__()
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self.bottleneck = tf.keras.Sequential(
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[Block(dim=dim, drop_path=0,
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layer_scale_init_value=1e-6, prefix=f'bottleneck{j}') for j in range(5)]
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)
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def call(self, inputs, training=None):
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x = inputs
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x = self.bottleneck(x, training=training)
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return x
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class Encoder(tf.keras.Model):
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def __init__(self, latent_dim):
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super(Encoder, self).__init__()
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self.encoder = ConvNeXt(depths=[3, 3, 9, 3], dims=[32, 64, 128, 256], 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 Decoder(tf.keras.Model):
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def __init__(self):
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super(Decoder, self).__init__()
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self.dense = tf.keras.layers.Dense(16 * 8 * 80)
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self.reshape = tf.keras.layers.Reshape((16, 8, 80))
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self.decoder = ConvNeXtReverse(depths=list(reversed([3, 3, 9, 3])), dims=list(reversed([256, 128, 64, 16])))
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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 CVAE(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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def __init__(self, config):
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super(CVAE, self).__init__()
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self.latent_dim = latent_dim
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self.latent_dim = config['latent_dim']
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self.size = config['resolution']
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model : StaticAE = StaticAE()
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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 : AutoEncoder = AutoEncoder(config['cond_ae'])
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if config['static_model'] != '':
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load_model(model, config['static_model'])
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# self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
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self.static_enc = model.encoder
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model : BodyAE = BodyAE()
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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 : AutoEncoder = AutoEncoder(config['cond_ae'])
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if config['body_model'] != '':
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load_model(model, config['body_model'])
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self.body_enc = model.encoder
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model : CurvatureAE = CurvatureAE()
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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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model : AutoEncoder = AutoEncoder(config['cond_ae'])
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if config['curvature_model'] != '':
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load_model(model, config['curvature_model'])
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self.curv_enc = model.encoder
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if freeze_encoders:
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if config['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 = Encoder(latent_dim)
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self.decoder = Decoder()
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if avg_garment_path != '':
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self.avg_garment = np.load(avg_garment_path)
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self.encoder = VariationalEncoder(config['cvae']['latent_dim'], config['cvae']['encoder_depths'], config['cvae']['encoder_dims'], kernel_size=config['cvae']['kernel_size'])
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self.decoder = Decoder(config['cvae']['decoder_input'], config['cvae']['decoder_depths'], config['cvae']['decoder_dims'], config['cvae']['kernel_size'])
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if config['avg_garment_path'] != '':
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self.avg_garment = np.load(config['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.keras.layers.Dense(config['latent_dim']),])
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# self.shuffle = tf.keras.layers.Lambda(lambda x: tf.stack([tf.gather(x[i], tf.random.shuffle(tf.range(x[i].shape[0]))) for i in range(x.shape[0])]))
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@@ -212,30 +154,28 @@ class CVAE(tf.keras.Model):
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def call(self, inputs, training=None, use_mask=False):
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if use_mask:
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uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
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if self.size == 512:
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uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
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uv_template_high, uv_body_posed_high, uv_curv_high = uv_template, uv_body_posed, uv_curv
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else:
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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
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else:
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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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if self.size == 512:
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uv_unposed, uv_template, uv_body_posed, uv_curv = inputs
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uv_template_high, uv_body_posed_high, uv_curv_high = uv_template, uv_body_posed, uv_curv
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else:
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uv_unposed, uv_template, uv_body_posed, uv_curv, uv_template_high, uv_body_posed_high, uv_curv_high = inputs
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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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mean, logvar = self.encoder(x, training=training)
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z = self.reparameterize(mean, logvar)
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z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv)
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z_cond = self.encode_conds(uv_template_high, uv_body_posed_high, uv_curv_high)
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z = tf.concat([z, z_cond], axis=-1)
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x = self.decoder(z, training=training)
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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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if use_mask:
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@@ -243,364 +183,3 @@ class CVAE(tf.keras.Model):
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return x, mean, logvar, z
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class Encoder32(tf.keras.Model):
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def __init__(self, latent_dim):
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super(Encoder32, self).__init__()
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self.encoder = ConvNeXt(depths=[27, 27], dims=[256, 256], 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 Decoder32(tf.keras.Model):
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def __init__(self):
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super(Decoder32, self).__init__()
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self.dense = tf.keras.layers.Dense(4 * 2 * 256)
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self.reshape = tf.keras.layers.Reshape((4, 2, 256))
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self.decoder = ConvNeXtReverse(depths=list(reversed([27, 27])), dims=list(reversed([256, 256])))
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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 CVAE32(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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use_full_res_encoders=True):
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super(CVAE32, self).__init__()
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self.latent_dim = latent_dim
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if use_full_res_encoders:
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model : StaticAE = StaticAE()
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else:
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model : StaticAE32 = StaticAE32()
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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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if use_full_res_encoders:
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model : BodyAE = BodyAE()
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else:
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model : BodyAE32 = BodyAE32()
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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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if use_full_res_encoders:
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model : CurvatureAE = CurvatureAE()
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else:
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model : CurvatureAE32 = CurvatureAE32()
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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 = Encoder32(latent_dim)
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self.decoder = Decoder32()
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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, uv_template_high, uv_body_posed_high, uv_curv_high = 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_high)
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z_body = self.body_enc(uv_body_posed_high)
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z_curv = self.curv_enc(uv_curv_high)
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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):
|
||||
x = inputs
|
||||
|
||||
x = self.dense(x)
|
||||
x = self.reshape(x)
|
||||
x = self.decoder(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class CVAE128(tf.keras.Model):
|
||||
|
||||
def __init__(self, latent_dim=128,
|
||||
avg_garment_path='',
|
||||
static_model='',
|
||||
body_model='',
|
||||
curvature_model='',
|
||||
freeze_encoders=False,
|
||||
use_full_res_encoders=True):
|
||||
super(CVAE128, self).__init__()
|
||||
self.latent_dim = latent_dim
|
||||
|
||||
if use_full_res_encoders:
|
||||
model : StaticAE = StaticAE()
|
||||
else:
|
||||
model : StaticAE128 = StaticAE128()
|
||||
if static_model != '':
|
||||
load_model(model, static_model)
|
||||
self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
|
||||
|
||||
if use_full_res_encoders:
|
||||
model : BodyAE = BodyAE()
|
||||
else:
|
||||
model : BodyAE128 = BodyAE128()
|
||||
if body_model != '':
|
||||
load_model(model, body_model)
|
||||
self.body_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
|
||||
|
||||
if use_full_res_encoders:
|
||||
model : CurvatureAE = CurvatureAE()
|
||||
else:
|
||||
model : CurvatureAE128 = CurvatureAE128()
|
||||
if curvature_model != '':
|
||||
load_model(model, curvature_model)
|
||||
self.curv_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
|
||||
|
||||
if freeze_encoders:
|
||||
self.static_enc.trainable = False
|
||||
self.body_enc.trainable = False
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.encoder = Encoder128(latent_dim)
|
||||
self.decoder = Decoder128()
|
||||
|
||||
if avg_garment_path != '':
|
||||
self.avg_garment = np.load(avg_garment_path)
|
||||
else:
|
||||
self.avg_garment = None
|
||||
|
||||
self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
|
||||
tf.keras.layers.Dense(256, activation='relu'),
|
||||
tf.keras.layers.Dense(128, activation='relu'),
|
||||
tf.keras.layers.Dense(latent_dim),])
|
||||
|
||||
|
||||
# @tf.function
|
||||
def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask, nr_of_samples=4, fix_noise=False, zero_latent=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)
|
||||
|
||||
if zero_latent:
|
||||
eps = tf.zeros_like(eps)
|
||||
|
||||
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 reparameterize(self, mean, logvar):
|
||||
eps = tf.random.normal(shape=mean.shape)
|
||||
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 call(self, inputs, training=None, zero_latent=False):
|
||||
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 = 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])
|
||||
z_static = self.static_enc(uv_template_high)
|
||||
z_body = self.body_enc(uv_body_posed_high)
|
||||
z_curv = self.curv_enc(uv_curv_high)
|
||||
|
||||
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
|
||||
|
||||
mean, logvar = self.encoder(x)
|
||||
z = self.reparameterize(mean, logvar)
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
z_cond = self.cond_encoder(z_cond)
|
||||
|
||||
if zero_latent:
|
||||
z = tf.zeros_like(z)
|
||||
|
||||
z = tf.concat([z, z_cond], axis=-1)
|
||||
|
||||
# z = self.shuffle(z)
|
||||
|
||||
# z = tf.concat([z, z_curv], axis=-1)
|
||||
x = self.decoder(z)
|
||||
|
||||
|
||||
# x = tf.transpose(x, [0,3,1,2])
|
||||
# x = x * uv_mask[:,tf.newaxis,:,:]
|
||||
# x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
# x = x + tf.transpose(uv_shaped_body, [0,2,3,1])
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
# x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x, mean, logvar, z
|
||||
Reference in New Issue
Block a user