141 lines
5.3 KiB
Python
141 lines
5.3 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['latent_dim']
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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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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.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(self.latent_dim, activation='tanh'),])
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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'], is_label=config['cvae']['is_label'])
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self.add_noise = config['cvae']['add_noise']
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self.noise_amount = config['cvae']['noise_amount']
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# @tf.function
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def sample_single(self, uv_template, uv_body_posed, uv_mask, eps=None, nr_of_samples=4):
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if eps is None:
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eps = tf.random.normal(shape=(nr_of_samples, self.latent_dim))
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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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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_cond = tf.concat([z_static, z_body], axis=-1)
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z_cond = self.cond_encoder(z_cond)
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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 * uv_mask[:,:,:,tf.newaxis]
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return x
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def encode_conds(self, uv_template, uv_body_posed, training):
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z_static = self.static_enc(uv_template, training=training)
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z_body = self.body_enc(uv_body_posed, training=training)
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z_cond = tf.concat([z_static, z_body], axis=-1)
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z_cond = self.cond_encoder(z_cond, training=training)
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return z_cond
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def sample(self, uv_template, uv_body_posed, uv_mask, 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, training=False)
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z = tf.concat([eps, z_cond], axis=-1)
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x = self.decoder(z, training=False)
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x = x * uv_mask[:,:,:,tf.newaxis]
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background = tf.ones((uv_template.shape[0],512,256,1)) * (1 - uv_mask[:,:,:,tf.newaxis])
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x = tf.concat((background, x), axis=-1)
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x = tf.math.softmax(x)
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return x
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# encoders have to be the same for curv_vae and general CVAE
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def sample_without_encoding(self, z_static, z_body, uv_mask, eps=None):
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eps = tf.random.normal(shape=(z_static.shape[0], self.latent_dim))
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z_cond = tf.concat([z_static, z_body], axis=-1)
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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 * 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 call(self, inputs, training):
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uv_template, uv_body_posed, uv_curv, uv_mask = inputs
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x = tf.concat([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 = self.encode_conds(uv_template, uv_body_posed, training=training)
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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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x = x * uv_mask[:,:,:,tf.newaxis]
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background = tf.ones((4,512,256,1)) * (1 - uv_mask[:,:,:,tf.newaxis])
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x = tf.concat((background, x), axis=-1)
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x = tf.math.softmax(x)
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return x, mean, logvar
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