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model.py
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model.py
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# coding: utf-8
import os, pickle
import time
import tensorflow.contrib.slim as slim
from utils import *
n_epoch_Vae = 10000
n_epoch_Metric_1 = 12000
n_epoch_Metric_2 = 20000
n_epoch_Gan = 10000
hidden_dim = 128
batchsize = 128
dataname_a = 'horse'
dataname_b = 'camel'
featurefile_a = './'+dataname_a+'.mat'
featurefile_b = './'+dataname_b+'.mat'
lightfeildmat = dataname_a+dataname_b+'lfd.mat'
speed = 0.001
resultmax = 0.95
resultmin = -0.95
test_vae = True
test_gan = True
useS = False
tb = False
lambda_2 = 10.0
vae_ablity = 0.0
layer = 2
sp = False
timecurrent = time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))
logfolder = './' + timecurrent + 'test'
# print(sp)
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
class convMESH():
VAE = 'VAE'
METRIC = 'METRIC'
GAN = 'GAN'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# config.log_device_placement = True
# config.allow_soft_placement = True
def __init__(self):
self.sess = None
if not os.path.isdir(logfolder):
os.mkdir(logfolder)
print(featurefile_a)
self.feature_a, self.neighbour1_a, self.degree1_a, self.logrmin_a, self.logrmax_a, self.smin_a, self.smax_a, self.modelnum_a, \
self.pointnum1_a, self.maxdegree1_a, self.L1_a, self.cotw1_a = load_data(featurefile_a, resultmin, resultmax, useS=useS)
self.feature_b, self.neighbour1_b, self.degree1_b, self.logrmin_b, self.logrmax_b, self.smin_b, self.smax_b, self.modelnum_b, \
self.pointnum1_b, self.maxdegree1_b, self.L1_b, self.cotw1_b = load_data(featurefile_b, resultmin, resultmax, useS=useS)
self.lf_matrix, self.lf_matrix_min, self.lf_matrix_max, self.metric_lz_a, self.matric_lz_b = load_data_old(
lightfeildmat, 2, 0.05)
if os.path.isfile("id.dat"):
id = pickle.load(open('id.dat', 'rb'))
id.show()
self.Ia = id.Ia
self.Ib = id.Ib
else:
Ia = np.arange(len(self.feature_a))
Ib = np.arange(len(self.feature_b))
self.Ia = random.sample(list(Ia), int(len(self.feature_a) * (1 - vcgan.vae_ablity)))
self.Ib = random.sample(list(Ib), int(len(self.feature_b) * (1 - vcgan.vae_ablity)))
id = Id(self.Ia, self.Ib)
# id.show()
f = open('id.dat', 'wb')
pickle.dump(id, f, 0)
f.close()
id = pickle.load(open('id.dat', 'rb'))
id.show()
self.Ia_C=list(set(np.arange(len(self.feature_a))).difference(set(self.Ia)))
self.Ib_C=list(set(np.arange(len(self.feature_b))).difference(set(self.Ib)))
print(self.lf_matrix[0][0])
print(self.lf_matrix[0][1])
print(np.shape(self.metric_lz_a))
# self.lf_matrix, self.lf_matrix_mean, self.lf_matrix_std = load_lfd(lightfeildmat)
self.start = 'VAE'
self.start_step_vae = 0
self.start_step_metric = 0
self.start_step_gan = 0
self.batch_size = batchsize
self.hidden_dim = hidden_dim
self.dataset_name_a = dataname_a
self.pointnum1_a = self.pointnum1_a
self.maxdegree1_a = self.maxdegree1_a
self.model_num_a = self.modelnum_a
self.lambda1_a = 10.0
self.lambda2_a = lambda_2
self.dataset_name_b = dataname_b
self.pointnum1_b = self.pointnum1_b
self.maxdegree1_b = self.maxdegree1_b
self.model_num_b = self.modelnum_b
self.lambda1_b = 10.0
self.lambda2_b = lambda_2
self.log_dir = logfolder
self.checkpoint_dir = logfolder
if not useS:
self.vertex_dim = 3
self.finaldim = 3
else:
self.vertex_dim = 9
self.finaldim = 9
# ---------------------------------------------------feed_dict
self.lf_dis = tf.placeholder(tf.float32, [None, 1], name='lf_dis')
self.inputs_a = tf.placeholder(tf.float32, [None, self.pointnum1_a, self.vertex_dim], name='a/input_mesh')
self.inputs_b = tf.placeholder(tf.float32, [None, self.pointnum1_b, self.vertex_dim], name='b/input_mesh')
self.random_a = tf.placeholder(tf.float32, [None, self.hidden_dim], name='a/random_samples')
self.random_b = tf.placeholder(tf.float32, [None, self.hidden_dim], name='b/random_samples')
# ---------------------------------------------------
# >>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>
# >>>>>>>>>>>>>>>>>>>
#
# ---------------------------------------------------a
self.nb1_a = tf.constant(self.neighbour1_a, dtype='int32', shape=[self.pointnum1_a, self.maxdegree1_a],
name='a/nb_relation1')
self.degrees1_a = tf.constant(self.degree1_a, dtype='float32', shape=[self.pointnum1_a, 1], name='a/degrees1')
self.cw1_a = tf.constant(self.cotw1_a, dtype='float32', shape=[self.pointnum1_a, self.maxdegree1_a, 1],
name='a/cw1')
self.Laplace1_a = tf.constant(self.L1_a, dtype='float32', shape=[self.pointnum1_a, self.pointnum1_a],
name='a/L1')
# self.embedding_inputs = tf.placeholder(tf.float32, [None, self.hidden_dim], name='embedding_inputs')
self.object_stddev_a = tf.constant(np.concatenate((np.array([1, 1]).astype('float32'), 1
* np.ones(self.hidden_dim - 2).astype('float32'))))
if layer >= 1:
self.vae_n1_a, self.vae_e1_a = get_conv_weights(self.vertex_dim, self.vertex_dim, name='encoder/a/convw1')
print("layer|%d" % (layer))
if layer >= 2:
self.vae_n2_a, self.vae_e2_a = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/a/convw2')
print("layer|%d" % (layer))
if layer >= 3:
self.vae_n3_a, self.vae_e3_a = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/a/convw3')
print("layer|%d" % (layer))
if layer >= 4:
self.vae_n4_a, self.vae_e4_a = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/a/convw4')
print("layer|%d" % (layer))
if layer >= 5:
self.vae_n5_a, self.vae_e5_a = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/a/convw5')
print("layer|%d" % (layer))
self.vae_meanpara_a = tf.get_variable("encoder/a/mean_weights",
[self.pointnum1_a * self.finaldim, self.hidden_dim],
tf.float32, tf.random_normal_initializer(stddev=0.02))
self.vae_stdpara_a = tf.get_variable("encoder/a/std_weights",
[self.pointnum1_a * self.finaldim, self.hidden_dim],
tf.float32, tf.random_normal_initializer(stddev=0.02))
# self.vae_mean_dpara_a = tf.get_variable("decoder/a/mean_weights",
# [self.pointnum2_a * self.finaldim, self.hidden_dim],
# tf.float32, tf.random_normal_initializer(stddev=0.02))
# -------------------------------------------
# self.gen_n1_a, self.gen_e1_a = self.get_conv_weights(self.vertex_dim, self.vertex_dim, name='gen/a/convw1')
# self.gen_n2_a, self.gen_e2_a = self.get_conv_weights(self.vertex_dim, self.vertex_dim, name='gen/a/convw2')
# self.gen_n3_a, self.gen_e3_a = self.get_conv_weights(self.vertex_dim, self.finaldim, name='gen/a/convw3')
#
# self.dis_n3_a, self.dis_e3_a = self.get_conv_weights(self.finaldim, self.vertex_dim, name='dis/a/convw1')
# self.dis_n2_a, self.dis_e2_a = self.get_conv_weights(self.vertex_dim, self.vertex_dim, name='dis/a/convw2')
# self.dis_n1_a, self.dis_e1_a = self.get_conv_weights(self.vertex_dim, self.vertex_dim, name='dis/a/convw3')
#
# self.gen_dense_a = tf.get_variable("gen/a/dense_weights", [self.hidden_dim, self.pointnum2_b * self.finaldim],
# tf.float32,
# tf.random_normal_initializer(stddev=0.02))
# self.dis_dense_a = tf.get_variable("dis/a/dense_weights", [self.pointnum2_b * self.finaldim, 1], tf.float32,
# tf.random_normal_initializer(stddev=0.02))
self.z_mean_a, self.z_stddev_a = self.encoder_a(self.inputs_a)
self.guessed_z_a = self.z_mean_a + self.z_stddev_a * self.random_a
self.z_mean_test_a, self.z_stddev_test_a = self.encoder_a(self.inputs_a, train=False)
self.guessed_z_test_a = self.z_mean_test_a + self.z_stddev_test_a * self.random_a
self.generated_mesh_train_a = self.decoder_a(self.guessed_z_a, train=True)
self.generated_mesh_train_a = tf.clip_by_value(self.generated_mesh_train_a, resultmin + 1e-8, resultmax - 1e-8)
self.generated_mesh_test_a = self.decoder_a(self.guessed_z_test_a, train=False)
self.test_mesh_a = self.decoder_a(self.random_a, train=False)
marginal_likelihood_a = tf.reduce_sum(tf.pow(self.inputs_a - self.generated_mesh_train_a, 2.0),
[1, 2]) # + tf.abs(self.z_mean)
KL_divergence_a = 0.5 * tf.reduce_sum(
tf.square(self.z_mean_a) + tf.square(self.z_stddev_a / self.object_stddev_a) - tf.log(
1e-8 + tf.square(self.z_stddev_a / self.object_stddev_a)) - 1, 1)
self.neg_loglikelihood_a = self.lambda2_a * tf.reduce_mean(marginal_likelihood_a)
self.KL_divergence_a = self.lambda1_a * tf.reduce_mean(KL_divergence_a)
ELBO_a = - self.neg_loglikelihood_a - self.KL_divergence_a
if layer >= 1:
self.r2_a = tf.nn.l2_loss(self.vae_e1_a) + tf.nn.l2_loss(self.vae_n1_a)
print("layer|%d" % (layer))
if layer >= 2:
self.r2_a = self.r2_a + tf.nn.l2_loss(self.vae_e2_a) + tf.nn.l2_loss(self.vae_n2_a)
print("layer|%d" % (layer))
if layer >= 3:
self.r2_a = self.r2_a + tf.nn.l2_loss(self.vae_e3_a) + tf.nn.l2_loss(self.vae_n3_a)
print("layer|%d" % (layer))
if layer >= 4:
self.r2_a = self.r2_a + tf.nn.l2_loss(self.vae_e4_a) + tf.nn.l2_loss(self.vae_n4_a)
print("layer|%d" % (layer))
if layer >= 5:
self.r2_a = self.r2_a + tf.nn.l2_loss(self.vae_e5_a) + tf.nn.l2_loss(self.vae_n5_a)
print("layer|%d" % (layer))
# self.r2_a = tf.nn.l2_loss(self.vae_e1_a) + tf.nn.l2_loss(self.vae_n1_a) + \
# tf.nn.l2_loss(self.vae_e2_a) + tf.nn.l2_loss(self.vae_n2_a) + \
# tf.nn.l2_loss(self.vae_e3_a) + tf.nn.l2_loss(self.vae_n3_a) + \
self.r2_a = self.r2_a + tf.nn.l2_loss(self.vae_stdpara_a) + tf.nn.l2_loss(
self.vae_meanpara_a) # + tf.nn.l2_loss(self.vae_mean_dpara_a)
# self.r2_a = tf.losses.get_regularization_loss(scope='encoder/a') + tf.losses.get_regularization_loss(scope='decoder/a')
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope='encoder/a') + tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES, scope='decoder/a')
self.r2_a += sum(reg_losses)
# ---------------------------------------------------bbbbbbbbb------------------------------
self.nb1_b = tf.constant(self.neighbour1_b, dtype='int32', shape=[self.pointnum1_b, self.maxdegree1_b],
name='b/nb_relation1')
self.degrees1_b = tf.constant(self.degree1_b, dtype='float32', shape=[self.pointnum1_b, 1], name='b/degrees1')
self.cw1_b = tf.constant(self.cotw1_b, dtype='float32', shape=[self.pointnum1_b, self.maxdegree1_b, 1],
name='b/cw1')
# self.embedding_inputs = tf.placeholder(tf.float32, [None, self.hidden_dim], name='embedding_inputs')
self.object_stddev_b = tf.constant(
np.concatenate((np.array([1, 1]).astype('float32'), 1 * np.ones(self.hidden_dim - 2).astype('float32'))))
# ----------------------------------------
# self.vae_n1_b, self.vae_e1_b = get_conv_weights(self.vertex_dim, self.vertex_dim, name='encoder/b/convw1')
# self.vae_n2_b, self.vae_e2_b = get_conv_weights(self.vertex_dim, self.vertex_dim, name='encoder/b/convw2')
# self.vae_n3_b, self.vae_e3_b = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/b/convw3')
if layer >= 1:
self.vae_n1_b, self.vae_e1_b = get_conv_weights(self.vertex_dim, self.vertex_dim, name='encoder/b/convw1')
print("layer|%d" % (layer))
if layer >= 2:
self.vae_n2_b, self.vae_e2_b = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/b/convw2')
print("layer|%d" % (layer))
if layer >= 3:
self.vae_n3_b, self.vae_e3_b = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/b/convw3')
print("layer|%d" % (layer))
if layer >= 4:
self.vae_n4_b, self.vae_e4_b = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/b/convw4')
print("layer|%d" % (layer))
if layer >= 5:
self.vae_n5_b, self.vae_e5_b = get_conv_weights(self.vertex_dim, self.finaldim, name='encoder/b/convw5')
print("layer|%d" % (layer))
self.vae_meanpara_b = tf.get_variable("encoder/b/mean_weights",
[self.pointnum1_b * self.finaldim, self.hidden_dim],
tf.float32,
tf.random_normal_initializer(stddev=0.02))
self.vae_stdpara_b = tf.get_variable("encoder/b/std_weights",
[self.pointnum1_b * self.finaldim, self.hidden_dim],
tf.float32,
tf.random_normal_initializer(stddev=0.02))
self.z_mean_b, self.z_stddev_b = self.encoder_b(self.inputs_b)
self.guessed_z_b = self.z_mean_b + self.z_stddev_b * self.random_b
self.z_mean_test_b, self.z_stddev_test_b = self.encoder_b(self.inputs_b, train=False)
self.guessed_z_test_b = self.z_mean_test_b + self.z_stddev_test_b * self.random_b
self.generated_mesh_train_b = self.decoder_b(self.guessed_z_b, train=True)
self.generated_mesh_train_b = tf.clip_by_value(self.generated_mesh_train_b, resultmin + 1e-8, resultmax - 1e-8)
self.generated_mesh_test_b = self.decoder_b(self.guessed_z_test_b, train=False)
self.test_mesh_b = self.decoder_b(self.random_b, train=False)
marginal_likelihood_b = tf.reduce_sum(tf.pow(self.inputs_b - self.generated_mesh_train_b, 2.0),
[1, 2]) # + tf.abs(self.z_mean)
KL_divergence_b = 0.5 * tf.reduce_sum(
tf.square(self.z_mean_b) + tf.square(self.z_stddev_b / self.object_stddev_b) - tf.log(
1e-8 + tf.square(self.z_stddev_b / self.object_stddev_b)) - 1, 1)
self.neg_loglikelihood_b = self.lambda2_b * tf.reduce_mean(marginal_likelihood_b)
self.KL_divergence_b = self.lambda1_b * tf.reduce_mean(KL_divergence_b)
ELBO_b = - self.neg_loglikelihood_b - self.KL_divergence_b
if layer >= 1:
self.r2_b = tf.nn.l2_loss(self.vae_e1_b) + tf.nn.l2_loss(self.vae_n1_b)
print("layer|%d" % (layer))
if layer >= 2:
self.r2_b = self.r2_b + tf.nn.l2_loss(self.vae_e2_b) + tf.nn.l2_loss(self.vae_n2_b)
print("layer|%d" % (layer))
if layer >= 3:
self.r2_b = self.r2_b + tf.nn.l2_loss(self.vae_e3_b) + tf.nn.l2_loss(self.vae_n3_b)
print("layer|%d" % (layer))
if layer >= 4:
self.r2_b = self.r2_b + tf.nn.l2_loss(self.vae_e4_b) + tf.nn.l2_loss(self.vae_n4_b)
print("layer|%d" % (layer))
if layer >= 5:
self.r2_b = self.r2_b + tf.nn.l2_loss(self.vae_e5_b) + tf.nn.l2_loss(self.vae_n5_b)
print("layer|%d" % (layer))
self.r2_b = self.r2_b + tf.nn.l2_loss(self.vae_stdpara_b) + tf.nn.l2_loss(
self.vae_meanpara_b) # + tf.nn.l2_loss(self.vae_mean_dpara_b)
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope='encoder/b') + tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES, scope='decoder/b')
self.r2_b += sum(reg_losses)
# <<<<<<<<<
# <<<<<<<<<
# <<<<<<<<<
# <<<<<<<<<
# >>>>>>>>>
# >>>>>>>>>
# >>>>>>>>>
# >>>>>>>>>
self.distance = self.metric_net(self.z_mean_test_a, self.z_mean_test_b)
self.loss_metric_l2 = sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope='metric'))
self.random_gen_latent_a = self.gen_a(self.random_a)
self.random_gen_latent_b = self.gen_b(self.random_b)
self.random_gen_feature_a = self.decoder_b(self.random_gen_latent_a, train=False)
self.random_gen_feature_b = self.decoder_a(self.random_gen_latent_b, train=False)
self.gen_latent_a = self.gen_a(self.z_mean_test_a, reuse=True)
self.gen_latent_b = self.gen_b(self.z_mean_test_b, reuse=True)
self.gen_feature_a = self.decoder_b(self.gen_latent_a, train=False)
self.gen_feature_b = self.decoder_a(self.gen_latent_b, train=False)
self.dis_real_a = self.dis_a(self.generated_mesh_test_b)
self.dis_real_b = self.dis_b(self.generated_mesh_test_a)
self.dis_fake_a = self.dis_a(self.random_gen_feature_a, reuse=True)
self.dis_fake_b = self.dis_b(self.random_gen_feature_b, reuse=True)
self.real_label = tf.multiply(tf.ones_like(self.dis_real_a), 1.00)
self.fake_label = tf.add(tf.zeros_like(self.dis_fake_a), 0.00)
self.cycle_z_a = self.gen_b(self.gen_latent_a, reuse=True)
self.cycle_z_b = self.gen_a(self.gen_latent_b, reuse=True)
# <<<<<<<<<
# <<<<<<<<<
# <<<<<<<<<
# <<<<<<<<<
# ---------------------------------------------------test
# ---------------------------------------------------Metric
self.distance_test = self.metric_net(self.random_a, self.random_b, reuse=True)
# ---------------------------------------------------test Cycle
# x<----->decoder x
# gx<------>decoder gx
# fgx<-------> decoder fgx
self.decoder_x = self.decoder_a(self.z_mean_test_a, train=False)
self.decoder_gx = self.decoder_b(self.gen_latent_a, train=False)
self.decoder_fgx = self.decoder_a(self.cycle_z_a, train=False)
self.decoder_y = self.decoder_b(self.z_mean_test_b, train=False)
self.decoder_fy = self.decoder_a(self.gen_latent_b, train=False)
self.decoder_gfy = self.decoder_b(self.cycle_z_b, train=False)
# ---------------------------------------------------loss
self.loss_mapping = tf.reduce_mean(self.metric_net(self.random_a, self.random_gen_latent_a, training=False) \
+ self.metric_net(self.random_gen_latent_b, self.random_b, training=False))
self.loss_mapping = self.loss_mapping * 20.0
self.loss_metric_l2 = self.loss_metric_l2 * 0.1
self.loss_metric = loss_mse(self.lf_dis, self.distance) * 1000 + self.loss_metric_l2
self.loss_cycle = loss_mse(self.z_mean_test_a, self.cycle_z_a) + loss_mse(self.z_mean_test_b, self.cycle_z_b)
self.loss_cycle = self.loss_cycle * 20.0
self.loss_d = loss_mse(self.dis_real_a, self.real_label) + loss_mse(self.dis_real_b, self.real_label) \
+ loss_mse(self.dis_fake_a, self.fake_label) + loss_mse(self.dis_fake_b, self.fake_label)
self.loss_d = self.loss_d * 10
self.loss_g = loss_mse(self.dis_fake_a, self.real_label) + loss_mse(self.dis_fake_b, self.real_label)
self.loss_g = self.loss_g * 20
self.loss_g_all = 1000.0 * self.loss_mapping + self.loss_g + self.loss_cycle
self.loss_d_all = self.loss_d
# self.loss_vae = -ELBO_a - ELBO_b + 0.1 * self.r2_a + 0.1 * self.r2_b
# -------------------------------------------------
self.loss_vae_a = -ELBO_a + 0.15 * self.r2_a
self.loss_vae_b = -ELBO_b + 0.15 * self.r2_b
# -----------------------------------------
# ----------------------------------------------------train opts
tf.summary.scalar('loss_metric', self.loss_metric)
tf.summary.scalar('loss_metric_l2', self.loss_metric_l2)
# tf.summary.scalar('loss_vae', self.loss_vae)
# ---------------------------------------------
tf.summary.scalar('loss_vae_a', self.loss_vae_a)
tf.summary.scalar('loss_vae_b', self.loss_vae_b)
# ------------------------------------------
tf.summary.scalar("nll_a", self.neg_loglikelihood_a)
tf.summary.scalar("kl_a", self.KL_divergence_a)
tf.summary.scalar("L2_loss_a", self.r2_a)
tf.summary.scalar("nll_b", self.neg_loglikelihood_b)
tf.summary.scalar("kl_b", self.KL_divergence_b)
tf.summary.scalar("L2_loss_b", self.r2_b)
# self.optimizer_vae = tf.train.AdamOptimizer(speed, beta1=0.5, name='encoder')
# ----------------------------------------------------
self.optimizer_vae_a = tf.train.AdamOptimizer(speed, name='encoder/a')
self.optimizer_vae_b = tf.train.AdamOptimizer(speed, name='encoder/b')
# ------------------------------------------
self.optimizer_g = tf.train.AdamOptimizer(speed, name='gen')
self.optimizer_d = tf.train.AdamOptimizer(speed, name='dis')
self.optimizer_metric_1 = tf.train.AdamOptimizer(speed, name='metric1')
self.optimizer_metric_2 = tf.train.AdamOptimizer(speed / 20.0, name='metric2')
# variables_encoder = slim.get_variables(scope="encoder")
# ------------------------------------------------------
variables_encoder_a = slim.get_variables(scope="encoder/a")
variables_encoder_b = slim.get_variables(scope="encoder/b")
variables_decoder_a = slim.get_variables(scope="decoder/a")
variables_decoder_b = slim.get_variables(scope="decoder/b")
# -----------------------------------------
# variables_decoder = slim.get_variables(scope="decoder")
variables_metric = slim.get_variables(scope="metric")
variables_g = slim.get_variables(scope="gen")
variables_d = slim.get_variables(scope="dis")
# train_variables_vae = []
# --------------------------------------------
train_variables_vae_a = []
train_variables_vae_b = []
train_variables_vae_all = []
variables_vae_all = []
variables_vae_a = []
variables_vae_b = []
# ------------------------------------------
train_variables_metric = []
train_variables_g = []
train_variables_d = []
varaibles_gan_all = []
# ---------------------------------------------test para
variables_vae_metric = []
# ----------------select trainable variables for every par
# for v in variables_encoder:
# if v in tf.trainable_variables():
# train_variables_vae.append(v)
# ------------------------------------------
for v in variables_encoder_a:
variables_vae_metric.append(v)
variables_vae_a.append(v)
variables_vae_all.append(v)
if v in tf.trainable_variables():
train_variables_vae_a.append(v)
train_variables_vae_all.append(v)
for v in variables_decoder_a:
variables_vae_metric.append(v)
variables_vae_a.append(v)
variables_vae_all.append(v)
if v in tf.trainable_variables():
train_variables_vae_a.append(v)
train_variables_vae_all.append(v)
for v in variables_encoder_b:
variables_vae_metric.append(v)
variables_vae_b.append(v)
variables_vae_all.append(v)
if v in tf.trainable_variables():
train_variables_vae_b.append(v)
train_variables_vae_all.append(v)
for v in variables_decoder_b:
variables_vae_metric.append(v)
variables_vae_b.append(v)
variables_vae_all.append(v)
if v in tf.trainable_variables():
train_variables_vae_b.append(v)
train_variables_vae_all.append(v)
# -----------------------------------------
for v in variables_g:
varaibles_gan_all.append(v)
if v in tf.trainable_variables():
train_variables_g.append(v)
for v in variables_d:
varaibles_gan_all.append(v)
if v in tf.trainable_variables():
train_variables_d.append(v)
for v in variables_metric:
variables_vae_metric.append(v)
if v in tf.trainable_variables():
train_variables_metric.append(v)
# -----------------------------------------------------
self.saver_vae_a = tf.train.Saver(variables_vae_a, max_to_keep=None)
self.saver_vae_b = tf.train.Saver(variables_vae_b, max_to_keep=None)
self.saver_vae_all = tf.train.Saver(variables_vae_all, max_to_keep=None)
# -----------------------------------------
#self.saver_gan = tf.train.Saver(variables_g, max_to_keep=None)
#self.saver_dis = tf.train.Saver(variables_d, max_to_keep=None)
self.saver_gan = tf.train.Saver(varaibles_gan_all, max_to_keep=None)
self.saver_metric = tf.train.Saver(variables_metric, max_to_keep=None)
#self.saver = tf.train.Saver(max_to_keep=None)
# -------------------------------------------
self.train_op_vae_a = tf.contrib.training.create_train_op(self.loss_vae_a, self.optimizer_vae_a,
variables_to_train=train_variables_vae_a,
summarize_gradients=True)
self.train_op_vae_b = tf.contrib.training.create_train_op(self.loss_vae_b, self.optimizer_vae_b,
variables_to_train=train_variables_vae_b,
summarize_gradients=True)
# --------------------------------------------
self.train_op_g = tf.contrib.training.create_train_op(self.loss_g_all, self.optimizer_g,
variables_to_train=train_variables_g,
summarize_gradients=True)
self.train_op_d = tf.contrib.training.create_train_op(self.loss_d_all, self.optimizer_d,
variables_to_train=train_variables_d,
summarize_gradients=True)
self.train_op_metric_1 = tf.contrib.training.create_train_op(self.loss_metric, self.optimizer_metric_1,
variables_to_train=train_variables_metric,
summarize_gradients=True)
self.train_op_metric_2 = tf.contrib.training.create_train_op(self.loss_metric, self.optimizer_metric_2,
variables_to_train=train_variables_metric,
summarize_gradients=True)
self.checkpoint_dir_vae_a = os.path.join(logfolder, self.model_dir(self.VAE, dataname_a))
self.checkpoint_dir_vae_b = os.path.join(logfolder, self.model_dir(self.VAE, dataname_b))
self.checkpoint_dir_vae_all = os.path.join(logfolder, self.model_dir(self.VAE, dataname_a + dataname_b))
self.checkpoint_dir_metric = os.path.join(logfolder, self.model_dir(self.METRIC, dataname_a + dataname_b))
self.checkpoint_dir_gan = os.path.join(logfolder, self.model_dir(self.GAN, dataname_a + dataname_b))
if os.path.exists(logfolder + '/log.txt'):
self.file = open(logfolder + '/log.txt', 'a')
else:
self.file = open(logfolder + '/log.txt', 'w')
if os.path.exists(logfolder + '/log.vae'):
self.file_vae = open(logfolder + '/log.vae' + timecurrent, 'w')
else:
self.file_vae = open(logfolder + '/log.vae', 'w')
if os.path.exists(logfolder + '/log.metric'):
self.file_metric = open(logfolder + '/log.metric' + timecurrent, 'w')
else:
self.file_metric = open(logfolder + '/log.metric', 'w')
if os.path.exists(logfolder + '/log.gan'):
self.file_gan = open(logfolder + '/log.gan' + timecurrent, 'w')
else:
self.file_gan = open(logfolder + '/log.gan', 'w')
self.merge_summary = tf.summary.merge_all()
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>metric>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def metric_net(self, A, B, training=True, reuse=False):
with tf.variable_scope("metric") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not training or reuse:
training = False
reuse = True
scope.reuse_variables()
h1, matrix = linear_l2(tf.concat([A, B], axis=1), self.hidden_dim * 2, 2048, 'h1')
# h1, matrix = linear_l2(tf.abs(tf.subtract(A, B)), self.hidden_dim, 1024, 'h1')
# self.loss_metric_l2 = tf.nn.l2_loss(matrix)
h1bn = batch_norm_wrapper(h1, name='h1bn', is_training=training, decay=0.9)
h1a = leaky_relu(h1bn)
h2, matrix = linear_l2(h1a, 2048, 1024, 'h2')
# self.loss_metric_l2 += tf.nn.l2_loss(matrix)
h2bn = batch_norm_wrapper(h2, name='h2bn', is_training=training, decay=0.9)
h2a = leaky_relu(h2bn)
h3, matrix = linear_l2(h2a, 1024, 512, 'h3')
# self.loss_metric_l2 += tf.nn.l2_loss(matrix)
h3bn = batch_norm_wrapper(h3, name='h3bn', is_training=training, decay=0.9)
h3a = leaky_relu(h3bn)
h4, matrix = linear_l2(h3a, 512, 256, 'h4')
# self.loss_metric_l2 += tf.nn.l2_loss(matrix)
h4bn = batch_norm_wrapper(h4, name='h4bn', is_training=training, decay=0.9)
h4a = leaky_relu(h4bn)
distance, matrix = linear_l2(h4a, 256, 1, 'distance')
# self.loss_metric_l2 += tf.nn.l2_loss(matrix)
print(tf.concat([A, B], axis=1).shape)
print(h1.shape)
return tf.square(distance)
# return tf.abs(distance)
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<metric<<<<<<<<<<<<<<<<<<<<<<<<<<
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> A >>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def encoder_a(self, input_feature, train=True, reuse=False):
with tf.variable_scope("encoder/a") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
bnbn = True
if layer >= 1:
print("layer|%d" % (layer))
if layer == 1:
bnbn = False
conv1 = newconvlayer_pooling(input_feature, self.vertex_dim, self.vertex_dim, self.vae_n1_a,
self.vae_e1_a, self.nb1_a,
self.cw1_a, name='conv1', training=train, bn=bnbn, special_activation=sp)
if layer >= 2:
print("layer|%d" % (layer))
if layer == 2:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n2_a, self.vae_e2_a,
self.nb1_a, self.cw1_a,
name='conv2', training=train, bn=bnbn, special_activation=sp)
print('layer')
if layer >= 3:
print("layer|%d" % (layer))
if layer == 3:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n3_a, self.vae_e3_a,
self.nb1_a, self.cw1_a,
name='conv3', training=train, bn=bnbn, special_activation=sp)
if layer >= 4:
print("layer|%d" % (layer))
if layer == 4:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n4_a, self.vae_e4_a,
self.nb1_a, self.cw1_a,
name='conv4', training=train, bn=bnbn, special_activation=sp)
if layer >= 5:
print("layer|%d" % (layer))
if layer == 5:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n5_a, self.vae_e5_a,
self.nb1_a, self.cw1_a,
name='conv5', training=train, bn=bnbn, special_activation=sp)
conv1 = conv1
# conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.finaldim, self.vae_n3_a, self.vae_e3_a,
# self.nb2_a, self.cw2_a,
# name='conv3', training=train, bn=False)
l0 = tf.reshape(conv1, [tf.shape(conv1)[0], self.pointnum1_a * self.finaldim])
mean = linear1(l0, self.vae_meanpara_a, self.hidden_dim, 'mean')
stddev = linear1(l0, self.vae_stdpara_a, self.hidden_dim, 'stddev')
stddev = tf.sqrt(2 * tf.nn.sigmoid(stddev))
return mean, stddev
def decoder_a(self, latent_tensor, train=True, reuse=False):
with tf.variable_scope("decoder/a") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
l1 = linear1(latent_tensor, tf.transpose(self.vae_meanpara_a), self.pointnum1_a * self.finaldim, 'mean')
conv1 = tf.reshape(l1, [tf.shape(l1)[0], self.pointnum1_a, self.finaldim])
# conv1 = newconvlayer_pooling(l2, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n3_a),
# tf.transpose(self.vae_e3_a),
# self.nb2_a, self.cw2_a, name='conv4', training=train)
if layer == 1:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n1_a),
tf.transpose(self.vae_e1_a),
self.nb1_a, self.cw1_a, name='conv2', training=train, bn=False,
special_activation=sp)
if layer == 2:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n2_a),
tf.transpose(self.vae_e2_a),
self.nb1_a, self.cw1_a, name='conv3', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n1_a),
tf.transpose(self.vae_e1_a),
self.nb1_a, self.cw1_a, name='conv4', training=train, bn=False,
special_activation=sp)
if layer == 3:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n3_a),
tf.transpose(self.vae_e3_a),
self.nb1_a, self.cw1_a, name='conv4', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n2_a),
tf.transpose(self.vae_e2_a),
self.nb1_a, self.cw1_a, name='conv5', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n1_a),
tf.transpose(self.vae_e1_a),
self.nb1_a, self.cw1_a, name='conv6', training=train, bn=False,
special_activation=sp)
if layer == 4:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n4_a),
tf.transpose(self.vae_e4_a),
self.nb1_a, self.cw1_a, name='conv5', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n3_a),
tf.transpose(self.vae_e3_a),
self.nb1_a, self.cw1_a, name='conv6', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n2_a),
tf.transpose(self.vae_e2_a),
self.nb1_a, self.cw1_a, name='conv7', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n1_a),
tf.transpose(self.vae_e1_a),
self.nb1_a, self.cw1_a, name='conv8', training=train, bn=False,
special_activation=sp)
if layer == 5:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n5_a),
tf.transpose(self.vae_e5_a),
self.nb1_a, self.cw1_a, name='conv6', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n4_a),
tf.transpose(self.vae_e4_a),
self.nb1_a, self.cw1_a, name='conv7', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n3_a),
tf.transpose(self.vae_e3_a),
self.nb1_a, self.cw1_a, name='conv8', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n2_a),
tf.transpose(self.vae_e2_a),
self.nb1_a, self.cw1_a, name='conv9', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n1_a),
tf.transpose(self.vae_e1_a),
self.nb1_a, self.cw1_a, name='conv10', training=train, bn=False,
special_activation=sp)
conv1 = conv1
return conv1
def gen_a(self, latent_tensor, train=True, reuse=False): # TODO: can shu gongxiang ?
with tf.variable_scope("gen/a") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
h1 = linear(latent_tensor, self.hidden_dim, 512, 'h1')
h1bn = batch_norm_wrapper(h1, name='h1bn', is_training=train, decay=0.9)
h1a = leaky_relu(h1bn)
h2 = linear(h1a, 512, 1024, 'h2')
h2bn = batch_norm_wrapper(h2, name='h2bn', is_training=train, decay=0.9)
h2a = leaky_relu(h2bn)
h3 = linear(h2a, 1024, 2048, 'h3')
h3bn = batch_norm_wrapper(h3, name='h3bn', is_training=train, decay=0.9)
h3a = leaky_relu(h3bn)
h4 = linear(h3a, 2048, 1024, 'h4')
h4bn = batch_norm_wrapper(h4, name='h4bn', is_training=train, decay=0.9)
h4a = leaky_relu(h4bn)
h5a=h4a
# h5 = linear(h4a, 512, 256, 'h5')
# h5bn = batch_norm_wrapper(h5, name='h5bn', is_training=train, decay=0.9)
# h5a = leaky_relu(h5bn)
out_mean = linear(h5a, 1024, self.hidden_dim, 'out_mean')
return tf.nn.tanh(out_mean) + latent_tensor
def dis_a(self, input_feature, train=True, reuse=False):
with tf.variable_scope("dis/a") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
conv1 = convlayer_pooling(input_feature, self.vertex_dim, self.vertex_dim, self.nb1_b, self.cw1_b, name='conv1', training=train, bn = False)
# conv1 = convlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.nb1_b, self.cw1_b, name='conv2', training=train)
conv1 = convlayer_pooling(conv1, self.vertex_dim, self.finaldim, self.nb1_b, self.cw1_b, name='conv3', training=train, bn=False)
l0 = tf.reshape(conv1, [tf.shape(conv1)[0], self.pointnum1_b * self.finaldim])
l0 = linear(l0, self.pointnum1_b * self.finaldim, 1, 'dense1')
#return linear(l0, 2048, 1, 'dense2')
return l0
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< A <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> B >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def encoder_b(self, input_feature, train=True, reuse=False):
global conv1
with tf.variable_scope("encoder/b") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
bnbn = True
if layer >= 1:
print("layer|%d" % (layer))
if layer == 1:
bnbn = False
conv1 = newconvlayer_pooling(input_feature, self.vertex_dim, self.vertex_dim, self.vae_n1_b,
self.vae_e1_b, self.nb1_b,
self.cw1_b, name='conv1', training=train, bn=bnbn, special_activation=sp)
if layer >= 2:
print("layer|%d" % (layer))
if layer == 2:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n2_b, self.vae_e2_b,
self.nb1_b, self.cw1_b,
name='conv2', training=train, bn=bnbn, special_activation=sp)
if layer >= 3:
print("layer|%d" % (layer))
if layer == 3:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n3_b, self.vae_e3_b,
self.nb1_b, self.cw1_b,
name='conv3', training=train, bn=bnbn, special_activation=sp)
if layer >= 4:
print("layer|%d" % (layer))
if layer == 4:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n4_b, self.vae_e4_b,
self.nb1_b, self.cw1_b,
name='conv4', training=train, bn=bnbn, special_activation=sp)
if layer >= 5:
print("layer|%d" % (layer))
if layer == 5:
bnbn = False
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.vae_n5_b, self.vae_e5_b,
self.nb1_b, self.cw1_b,
name='conv5', training=train, bn=bnbn, special_activation=sp)
conv1 = conv1
l0 = tf.reshape(conv1, [tf.shape(conv1)[0], self.pointnum1_b * self.finaldim])
mean = linear1(l0, self.vae_meanpara_b, self.hidden_dim, 'mean')
stddev = linear1(l0, self.vae_stdpara_b, self.hidden_dim, 'stddev')
stddev = tf.sqrt(2 * tf.nn.sigmoid(stddev))
return mean, stddev
def decoder_b(self, latent_tensor, train=True, reuse=False):
with tf.variable_scope("decoder/b") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
l1 = linear1(latent_tensor, tf.transpose(self.vae_meanpara_b), self.pointnum1_b * self.finaldim, 'mean')
conv1 = tf.reshape(l1, [tf.shape(l1)[0], self.pointnum1_b, self.finaldim])
if layer == 1:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n1_b),
tf.transpose(self.vae_e1_b),
self.nb1_b, self.cw1_b, name='conv2', training=train, bn=False,
special_activation=sp)
if layer == 2:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n2_b),
tf.transpose(self.vae_e2_b),
self.nb1_b, self.cw1_b, name='conv3', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n1_b),
tf.transpose(self.vae_e1_b),
self.nb1_b, self.cw1_b, name='conv4', training=train, bn=False,
special_activation=sp)
if layer == 3:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n3_b),
tf.transpose(self.vae_e3_b),
self.nb1_b, self.cw1_b, name='conv4', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n2_b),
tf.transpose(self.vae_e2_b),
self.nb1_b, self.cw1_b, name='conv5', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n1_b),
tf.transpose(self.vae_e1_b),
self.nb1_b, self.cw1_b, name='conv6', training=train, bn=False,
special_activation=sp)
if layer == 4:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n4_b),
tf.transpose(self.vae_e4_b),
self.nb1_b, self.cw1_b, name='conv5', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n3_b),
tf.transpose(self.vae_e3_b),
self.nb1_b, self.cw1_b, name='conv6', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n2_b),
tf.transpose(self.vae_e2_b),
self.nb1_b, self.cw1_b, name='conv7', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n1_b),
tf.transpose(self.vae_e1_b),
self.nb1_b, self.cw1_b, name='conv8', training=train, bn=False,
special_activation=sp)
if layer == 5:
print("layer|%d" % (layer))
conv1 = newconvlayer_pooling(conv1, self.finaldim, self.vertex_dim, tf.transpose(self.vae_n5_b),
tf.transpose(self.vae_e5_b),
self.nb1_b, self.cw1_b, name='conv6', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n4_b),
tf.transpose(self.vae_e4_b),
self.nb1_b, self.cw1_b, name='conv7', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n3_b),
tf.transpose(self.vae_e3_b),
self.nb1_b, self.cw1_b, name='conv8', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n2_b),
tf.transpose(self.vae_e2_b),
self.nb1_b, self.cw1_b, name='conv9', training=train,
special_activation=sp)
conv1 = newconvlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, tf.transpose(self.vae_n1_b),
tf.transpose(self.vae_e1_b),
self.nb1_b, self.cw1_b, name='conv10', training=train, bn=False,
special_activation=sp)
conv1 = conv1
return conv1
def gen_b(self, latent_tensor, train=True, reuse=False):
with tf.variable_scope("gen/b") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
h1 = linear(latent_tensor, self.hidden_dim, 512, 'h1')
h1bn = batch_norm_wrapper(h1, name='h1bn', is_training=train, decay=0.9)
h1a = leaky_relu(h1bn)
h2 = linear(h1a, 512, 1024, 'h2')
h2bn = batch_norm_wrapper(h2, name='h2bn', is_training=train, decay=0.9)
h2a = leaky_relu(h2bn)
h3 = linear(h2a, 1024, 2048, 'h3')
h3bn = batch_norm_wrapper(h3, name='h3bn', is_training=train, decay=0.9)
h3a = leaky_relu(h3bn)
h4 = linear(h3a, 2048, 1024, 'h4')
h4bn = batch_norm_wrapper(h4, name='h4bn', is_training=train, decay=0.9)
h4a = leaky_relu(h4bn)
h5a=h4a
# h5 = linear(h4a, 512, 256, 'h5')
# h5bn = batch_norm_wrapper(h5, name='h5bn', is_training=train, decay=0.9)
# h5a = leaky_relu(h5bn)
out_mean = linear(h5a, 1024, self.hidden_dim, 'out_mean')
return tf.nn.tanh(out_mean)
def dis_b(self, input_feature, train=True, reuse=False):
with tf.variable_scope("dis/b") as scope:
scope.set_regularizer(tf.contrib.layers.l2_regularizer(scale=1.0))
if not train or reuse:
train = False
reuse = True
scope.reuse_variables()
conv1 = convlayer_pooling(input_feature, self.vertex_dim, self.vertex_dim, self.nb1_a, self.cw1_a,
name='conv1', training=train)
# conv1 = convlayer_pooling(conv1, self.vertex_dim, self.vertex_dim, self.nb1_a, self.cw1_a,
# name='conv2', training=train)
conv1 = convlayer_pooling(conv1, self.vertex_dim, self.finaldim, self.nb1_a, self.cw1_a,
name='conv3', training=train, bn=False)
l0 = tf.reshape(conv1, [tf.shape(conv1)[0], self.pointnum1_a * self.finaldim])
l0 = linear(l0, self.pointnum1_a * self.finaldim, 1, 'dense1')
#return linear(l0, 2048, 1, 'dense2')
return l0
# <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< B <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
def train_pre(self):
tf.global_variables_initializer().run()
self.write = tf.summary.FileWriter(logfolder + '/logs/', self.sess.graph)
if not os.path.exists(self.checkpoint_dir_vae_all):
os.makedirs(self.checkpoint_dir_vae_all)
if not os.path.exists(self.checkpoint_dir_vae_a):
os.makedirs(self.checkpoint_dir_vae_a)
if not os.path.exists(self.checkpoint_dir_vae_b):
os.makedirs(self.checkpoint_dir_vae_b)