203 lines
6.3 KiB
Python
203 lines
6.3 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 convnext.models.convnext_tf import ConvNeXt, Block, ConvNeXtReverseLabels
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from auto_encoder_body import AutoEncoder as BodyAE
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from auto_encoder_static import AutoEncoder as StaticAE
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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 = ConvNeXtReverseLabels(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, static_model='checkpoints/ae_static_3',
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body_model='checpoints/ae_body_6',):
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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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load_model(model, static_model)
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self.static_enc = tf.keras.Sequential([model.encoder, model.bottleneck])
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self.static_enc.trainable = False
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model : BodyAE = BodyAE()
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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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self.body_enc.trainable = False
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self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(256, 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.encoder = Encoder(latent_dim)
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self.decoder = Decoder()
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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):
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if uv_template.shape[1] == 3:
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uv_template = tf.transpose(uv_template, [0,2,3,1])
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if uv_body_posed.shape[1] == 3:
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uv_body_posed = 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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return z_cond
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def sample(self, uv_template, uv_body_posed, 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)
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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 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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# 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 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):
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uv_template, uv_body_posed, uv_curv, uv_mask = inputs
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if uv_template.shape[1] == 3:
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uv_template = tf.transpose(uv_template, [0,2,3,1])
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if uv_body_posed.shape[1] == 3:
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uv_body_posed = tf.transpose(uv_body_posed, [0,2,3,1])
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if uv_curv.shape[1] == 3:
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uv_curv = tf.transpose(uv_curv, [0,2,3,1])
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z_cond = self.encode_conds(uv_template, uv_body_posed)
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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 = tf.concat([z, 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, mean, logvar, z
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