570 lines
19 KiB
Python
570 lines
19 KiB
Python
import os
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import time
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import random
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import tensorflow as tf
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import numpy as np
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import scipy as sp
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# import plotly.graph_objects as go
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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 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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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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super(CVAE, self).__init__()
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self.latent_dim = latent_dim
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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 : 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 : 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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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 = 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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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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# 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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# @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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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 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 = 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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use_full_res_encoders=True):
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super(CVAE128, 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 : 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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if use_full_res_encoders:
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model : BodyAE = BodyAE()
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else:
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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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if use_full_res_encoders:
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model : CurvatureAE = CurvatureAE()
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else:
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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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|
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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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|
|
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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:
|
|
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])
|
|
|
|
# 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)
|
|
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 |