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import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
from easydict import EasyDict as edict
from .base import BaseModel
from .pc_encoder import *
from .flow.transforms import Transform
from .cVAE import CondVAE
class ACSetVAE(BaseModel):
def __init__(self, hps):
self.prior_net = LatentEncoder(hps, name='prior')
self.posterior_net = LatentEncoder(hps, name='posterior')
self.cvae = CondVAE(hps.vae_params, name="cvae")
if hps.use_peq_embed:
self.peq_embed = SetXformer(hps)
super(ACSetVAE, self).__init__(hps)
def build_net(self):
with tf.compat.v1.variable_scope('acset_vae', reuse=tf.compat.v1.AUTO_REUSE):
self.x = tf.compat.v1.placeholder(tf.float32, [None, self.hps.set_size, self.hps.dimension]) #[200, 1024]
self.b = tf.compat.v1.placeholder(tf.float32, [None, self.hps.set_size, self.hps.dimension])
self.m = tf.compat.v1.placeholder(tf.float32, [None, self.hps.set_size, self.hps.dimension])
# build transform
self.transform = Transform(edict(self.hps.trans_params))
# prior
prior_inputs = tf.concat([self.x*self.b, self.b], axis=-1) # [200, 2048]
prior = self.prior_net(prior_inputs) # [256]
prior_sample = prior.sample() # [256]
prior_sample, _ = self.transform.inverse(prior_sample) # [256]
# peq embedding
cm = peq_embed = None
if self.hps.use_peq_embed:
peq_embed = self.peq_embed(prior_inputs) # [200, 256]
C = peq_embed.get_shape().as_list()[-1] # 256
cm = tf.reshape(peq_embed, [-1,C]) # [256]
# posterior
posterior_inputs = tf.concat([self.x*self.m, self.m], axis=-1) # [200, 2048]
posterior = self.posterior_net(posterior_inputs) # [256]
posterior_sample = posterior.sample() # [256]
# kl term
z_sample, logdet = self.transform.forward(posterior_sample) # [256], [1]
logp = tf.reduce_sum(input_tensor=prior.log_prob(z_sample), axis=1) + logdet # [256] -> [1]
kl = tf.reduce_sum(input_tensor=posterior.entropy(), axis=1) + logp # [256] -> [1]
# generator
x = tf.reshape(self.x, [-1,self.hps.dimension]) # [1024]
b = tf.reshape(self.b, [-1,self.hps.dimension])
m = tf.reshape(self.m, [-1,self.hps.dimension])
cv = tf.reshape(tf.tile(tf.expand_dims(posterior_sample, axis=1), [1,self.hps.set_size,1]), [-1,self.hps.latent_dim]) # [256] -> [1, 256] -> [1, 200, 256] -> [256]
if not cm is None:
c = tf.concat([cv, cm], axis=-1) # [512]
else:
c = cv
# vector-wise posterior
vec_kl, vec_post_sample = self.cvae.enc(tf.concat([x, c], axis=-1), c) # [1], [256]
vec_kl = tf.reshape(vec_kl, [-1, self.hps.set_size]) # [200]
recon_dist = self.cvae.dec(tf.concat([vec_post_sample, c], axis=-1), c)
log_likel = recon_dist.log_prob(x) # [1]
log_likel = tf.reshape(log_likel, [-1,self.hps.set_size]) # [200]
self.set_metric = self.set_elbo = log_likel + tf.expand_dims(kl, axis=1) / self.hps.set_size # [200]
log_likel = tf.reduce_mean(input_tensor=log_likel, axis=1)
tf.compat.v1.summary.scalar('log_likel', tf.reduce_mean(input_tensor=log_likel))
# elbo
self.elbo = log_likel + kl / self.hps.set_size + tf.reduce_mean(input_tensor=vec_kl, axis=1)
self.metric = self.elbo
self.loss = tf.reduce_mean(input_tensor=-self.elbo)
tf.compat.v1.summary.scalar('loss', self.loss)
# sample
x = tf.reshape(self.x, [-1,self.hps.dimension])
b = tf.reshape(self.b, [-1,self.hps.dimension])
m = tf.reshape(self.m, [-1,self.hps.dimension])
cv = tf.reshape(tf.tile(tf.expand_dims(prior_sample, axis=1), [1,self.hps.set_size,1]), [-1,self.hps.latent_dim])
if not cm is None:
c = tf.concat([cv, cm], axis=-1)
else:
c = cv
vec_prior_sample = tf.random.normal(shape=tf.shape(input=vec_post_sample))
sample_dist = self.cvae.dec(tf.concat([vec_prior_sample, c], axis=-1), c)
log_likel = sample_dist.log_prob(x)
sample = sample_dist.sample()
self.sample = tf.reshape(sample, [-1, self.hps.set_size, self.hps.dimension])
# compress
self.log_likel = tf.reshape(log_likel, [-1,self.hps.set_size]) + vec_kl
# total_params = 0
# for variable in tf.trainable_variables():
# shape = variable.get_shape()
# params = 1
# for dim in shape:
# params *= dim
# total_params += params
# print("Total number of parameters: ", total_params) |