116 lines
4.7 KiB
Python
116 lines
4.7 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 models.base import AEEncoder, VariationalEncoder, Decoder
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class CVAE(tf.keras.Model):
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def __init__(self, config):
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super(CVAE, self).__init__()
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self.latent_dim = config['cvae']['latent_dim']
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self.size = config['resolution']
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self.static_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'])
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if config['static_model'] != '':
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load_model(self.static_enc, config['static_model'] + '_enc')
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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'])
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if config['body_model'] != '':
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load_model(self.body_enc, config['body_model'] + '_enc')
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self.curv_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'])
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if config['curvature_model'] != '':
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load_model(self.curv_enc, config['curvature_model'] + '_enc')
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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.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(config['latent_dim']),])
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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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def sample(self, uv_template, uv_body_posed, uv_curv, uv_mask=None, noise=None):
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if noise is None:
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eps = tf.random.normal(shape=(uv_template.shape[0], self.latent_dim))
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else:
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eps = noise
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z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv)
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z = tf.concat([eps, z_cond], axis=-1)
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x = self.decoder(z)
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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 uv_mask is not None:
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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, fix_noise=False):
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if not fix_noise:
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eps = tf.random.normal(shape=mean.shape)
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else:
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eps = tf.random.normal(shape=(1, mean.shape[1]))
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eps = tf.tile(eps, [mean.shape[0],1])
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return eps * tf.exp(logvar * .5) + mean
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def encode_conds(self, uv_template, uv_body_posed, uv_curv):
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z_body = self.body_enc(uv_body_posed)
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z_static = self.static_enc(uv_template)
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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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return z_cond
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def call(self, inputs, training=None):
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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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x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
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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_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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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
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