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model.py
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model.py
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import numpy as np
from config import cfg
import tensorflow as tf
from util import *
def batchnormalize(X, eps=1e-5, g=None, b=None, batch_size=10):
if X.get_shape().ndims == 5:
if batch_size == 1:
mean = 0
std = 1 - eps
else:
mean = tf.reduce_mean(X, [0, 1, 2, 3])
std = tf.reduce_mean(tf.square(X - mean), [0, 1, 2, 3])
X = (X - mean) / tf.sqrt(std + eps)
if g is not None and b is not None:
g = tf.reshape(g, [1, 1, 1, 1, -1])
b = tf.reshape(b, [1, 1, 1, 1, -1])
X = X * g + b
# depth--start
elif X.get_shape().ndims == 4:
if batch_size == 1:
mean = 0
std = 1 - eps
else:
mean = tf.reduce_mean(X, [0, 1, 2])
std = tf.reduce_mean(tf.square(X - mean), [0, 1, 2])
X = (X - mean) / tf.sqrt(std + eps)
if g is not None and b is not None:
g = tf.reshape(g, [1, 1, 1, -1])
b = tf.reshape(b, [1, 1, 1, -1])
X = X * g + b
# depth--end
elif X.get_shape().ndims == 2:
if batch_size == 1:
mean = 0
std = 1 - eps
else:
mean = tf.reduce_mean(X, 0)
std = tf.reduce_mean(tf.square(X - mean), 0)
X = (X - mean) / tf.sqrt(std + eps) #std
if g is not None and b is not None:
g = tf.reshape(g, [1, -1])
b = tf.reshape(b, [1, -1])
X = X * g + b
else:
raise NotImplementedError
return X
def layernormalize(X, eps=1e-5, g=None, b=None):
if X.get_shape().ndims == 5:
mean, std = tf.nn.moments(X, [1, 2, 3, 4], keep_dims=True)
X = (X - mean) / tf.sqrt(std + eps)
if g is not None and b is not None:
X = X * g + b
elif X.get_shape().ndims == 2:
mean = tf.reduce_mean(X, 1)
std = tf.reduce_mean(tf.square(X - mean), 1)
X = (X - mean) / tf.sqrt(std + eps) #std
if g is not None and b is not None:
X = X * g + b
else:
raise NotImplementedError
return X
def lrelu(X, leak=0.2):
return tf.maximum(X, leak * X)
def softmax(X, batch_size, vox_shape):
c = tf.reduce_max(X, 4)
c = tf.reshape(c,
[batch_size, vox_shape[0], vox_shape[1], vox_shape[2], 1])
exp = tf.exp(tf.subtract(X, c))
expsum = tf.reduce_sum(exp, 4)
expsum = tf.reshape(
expsum, [batch_size, vox_shape[0], vox_shape[1], vox_shape[2], 1])
soft = tf.div(exp, expsum)
return soft
class FCR_aGAN():
def __init__(
self,
batch_size=20,
vox_shape=[80, 48, 80, 12],
dep_shape=[320, 240, 1],
dim_z=16,
dim=[512, 256, 128, 64, 12],
start_vox_size=[5, 3, 5],
kernel=[[5, 5, 5, 5, 5], [3, 3, 3, 3, 3], [5, 5, 5, 5, 5]],
stride=[1, 2, 2, 2, 1],
dim_code=750,
refine_ch=32,
refine_kernel=3,
):
self.batch_size = batch_size
self.vox_shape = vox_shape
# depth--start
self.dep_shape = dep_shape
# depth--end
self.n_class = vox_shape[3]
self.dim_z = dim_z
self.dim_W1 = dim[0]
self.dim_W2 = dim[1]
self.dim_W3 = dim[2]
self.dim_W4 = dim[3]
self.dim_W5 = dim[4]
self.start_vox_size = np.array(start_vox_size)
self.kernel = np.array(kernel)
self.kernel1 = self.kernel[:, 0]
self.kernel2 = self.kernel[:, 1]
self.kernel3 = self.kernel[:, 2]
self.kernel4 = self.kernel[:, 3]
self.kernel5 = self.kernel[:, 4]
self.stride = stride
# depth--start
self.stride_dep = [1, 2, 2, 1]
# depth--end
self.lamda_recons = cfg.LAMDA_RECONS
self.lamda_gamma = cfg.LAMDA_GAMMA
self.dim_code = dim_code
self.refine_ch = refine_ch
self.refine_kernel = refine_kernel
self.gen_W1 = tf.Variable(
tf.random_normal([
self.dim_z * self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2], self.dim_W1 * self.start_vox_size[0] *
self.start_vox_size[1] * self.start_vox_size[2]
],
stddev=0.02),
name='gen_W1')
self.gen_bn_g1 = tf.Variable(
tf.random_normal([
self.dim_W1 * self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2]
],
mean=1.0,
stddev=0.02),
name='gen_bn_g1')
self.gen_bn_b1 = tf.Variable(
tf.zeros([
self.dim_W1 * self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2]
]),
name='gen_bn_b1')
self.gen_W2 = tf.Variable(
tf.random_normal([
self.kernel2[0], self.kernel2[1], self.kernel2[2], self.dim_W2,
self.dim_W1
],
stddev=0.02),
name='gen_W2')
self.gen_bn_g2 = tf.Variable(
tf.random_normal([self.dim_W2], mean=1.0, stddev=0.02),
name='gen_bn_g2')
self.gen_bn_b2 = tf.Variable(tf.zeros([self.dim_W2]), name='gen_bn_b2')
self.gen_W3 = tf.Variable(
tf.random_normal([
self.kernel3[0], self.kernel3[1], self.kernel3[2], self.dim_W3,
self.dim_W2
],
stddev=0.02),
name='gen_W3')
self.gen_bn_g3 = tf.Variable(
tf.random_normal([self.dim_W3], mean=1.0, stddev=0.02),
name='gen_bn_g3')
self.gen_bn_b3 = tf.Variable(tf.zeros([self.dim_W3]), name='gen_bn_b3')
self.gen_W4 = tf.Variable(
tf.random_normal([
self.kernel4[0], self.kernel4[1], self.kernel4[2], self.dim_W4,
self.dim_W3
],
stddev=0.02),
name='gen_W4')
self.gen_bn_g4 = tf.Variable(
tf.random_normal([self.dim_W4], mean=1.0, stddev=0.02),
name='gen_bn_g4')
self.gen_bn_b4 = tf.Variable(tf.zeros([self.dim_W4]), name='gen_bn_b4')
self.gen_W5 = tf.Variable(
tf.random_normal([
self.kernel5[0], self.kernel5[1], self.kernel5[2], self.dim_W5,
self.dim_W4
],
stddev=0.02),
name='gen_W5')
self.gen_bn_g5 = tf.Variable(
tf.random_normal([self.dim_W5], mean=1.0, stddev=0.02),
name='gen_bn_g5')
self.gen_bn_b5 = tf.Variable(tf.zeros([self.dim_W5]), name='gen_bn_b5')
self.encode_W1 = tf.Variable(
tf.random_normal([
self.kernel5[0], self.kernel5[1], self.kernel5[2], 1,
self.dim_W4
],
stddev=0.02),
name='encode_W1')
self.encode_bn_g1 = tf.Variable(
tf.random_normal([self.dim_W4], mean=1.0, stddev=0.02),
name='encode_bn_g1')
self.encode_bn_b1 = tf.Variable(
tf.zeros([self.dim_W4]), name='encode_bn_b1')
self.encode_W2 = tf.Variable(
tf.random_normal([
self.kernel4[0], self.kernel4[1], self.kernel4[2], self.dim_W4,
self.dim_W3
],
stddev=0.02),
name='encode_W2')
self.encode_bn_g2 = tf.Variable(
tf.random_normal([self.dim_W3], mean=1.0, stddev=0.02),
name='encode_bn_g2')
self.encode_bn_b2 = tf.Variable(
tf.zeros([self.dim_W3]), name='encode_bn_b2')
self.encode_W3 = tf.Variable(
tf.random_normal([
self.kernel3[0], self.kernel3[1], self.kernel3[2], self.dim_W3,
self.dim_W2
],
stddev=0.02),
name='encode_W3')
self.encode_bn_g3 = tf.Variable(
tf.random_normal([self.dim_W2], mean=1.0, stddev=0.02),
name='encode_bn_g3')
self.encode_bn_b3 = tf.Variable(
tf.zeros([self.dim_W2]), name='encode_bn_b3')
self.encode_W4 = tf.Variable(
tf.random_normal([
self.kernel2[0], self.kernel2[1], self.kernel2[2], self.dim_W2,
self.dim_W1
],
stddev=0.02),
name='encode_W4')
self.encode_bn_g4 = tf.Variable(
tf.random_normal([self.dim_W1], mean=1.0, stddev=0.02),
name='encode_bn_g4')
self.encode_bn_b4 = tf.Variable(
tf.zeros([self.dim_W1]), name='encode_bn_b4')
self.encode_W5 = tf.Variable(
tf.random_normal([1, 1, 1, self.dim_W1, self.dim_z], stddev=0.02),
name='encode_W5')
self.encode_W5_sigma = tf.Variable(
tf.random_normal([1, 1, 1, self.dim_W1, self.dim_z], stddev=0.02),
name='encode_W5_sigma')
# depth--start
self.encode_dep_W1 = tf.Variable(
tf.random_normal(
[self.kernel5[0], self.kernel5[1], 1, self.dim_W4],
stddev=0.02),
name='depthproject_W1')
self.encode_dep_bn_g1 = tf.Variable(
tf.random_normal([self.dim_W4], mean=1.0, stddev=0.02),
name='depthproject_bn_g1')
self.encode_dep_bn_b1 = tf.Variable(
tf.zeros([self.dim_W4]), name='depthproject_bn_b1')
self.encode_dep_W2 = tf.Variable(
tf.random_normal(
[self.kernel4[0], self.kernel4[1], self.dim_W4, self.dim_W3],
stddev=0.02),
name='depthproject_W2')
self.encode_dep_bn_g2 = tf.Variable(
tf.random_normal([self.dim_W3], mean=1.0, stddev=0.02),
name='depthproject_bn_g2')
self.encode_dep_bn_b2 = tf.Variable(
tf.zeros([self.dim_W3]), name='depthproject_bn_b2')
self.encode_dep_W3 = tf.Variable(
tf.random_normal(
[self.kernel3[0], self.kernel3[1], self.dim_W3, self.dim_W2],
stddev=0.02),
name='depthproject_W3')
self.encode_dep_bn_g3 = tf.Variable(
tf.random_normal([self.dim_W2], mean=1.0, stddev=0.02),
name='depthproject_bn_g3')
self.encode_dep_bn_b3 = tf.Variable(
tf.zeros([self.dim_W2]), name='depthproject_bn_b3')
self.encode_dep_W4 = tf.Variable(
tf.random_normal(
[self.kernel2[0], self.kernel2[1], self.dim_W2, self.dim_W1],
stddev=0.02),
name='depthproject_W4')
self.encode_dep_bn_g4 = tf.Variable(
tf.random_normal([self.dim_W1], mean=1.0, stddev=0.02),
name='depthproject_bn_g4')
self.encode_dep_bn_b4 = tf.Variable(
tf.zeros([self.dim_W1]), name='depthproject_bn_b4')
self.encode_dep_W5 = tf.Variable(
tf.random_normal(
[self.kernel2[0], self.kernel2[1], self.dim_W1, self.dim_W1],
stddev=0.02),
name='depthproject_W5')
self.encode_dep_bn_g5 = tf.Variable(
tf.random_normal([self.dim_W1], mean=1.0, stddev=0.02),
name='depthproject_bn_g5')
self.encode_dep_bn_b5 = tf.Variable(
tf.zeros([self.dim_W1]), name='depthproject_bn_b5')
self.encode_dep_W6 = tf.Variable(
tf.random_normal(
[self.kernel2[0], self.kernel2[1], self.dim_W1, 256],
stddev=0.02),
name='depthproject_W6')
self.encode_dep_bn_g6 = tf.Variable(
tf.random_normal([256], mean=1.0, stddev=0.02),
name='depthproject_bn_g6')
self.encode_dep_bn_b6 = tf.Variable(
tf.zeros([256]), name='depthproject_bn_b6')
self.encode_dep_W7 = tf.Variable(
tf.random_normal([
5 * 4 * 256, self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2] * self.dim_W1
],
stddev=0.02),
name='depthproject_W7')
self.encode_dep_W8 = tf.Variable(
tf.random_normal([1, 1, 1, self.dim_W1, self.dim_z], stddev=0.02),
name='depthproject_W8')
self.encode_dep_W8_sigma = tf.Variable(
tf.random_normal([1, 1, 1, self.dim_W1, self.dim_z], stddev=0.02),
name='depthproject_W8_sigma')
# depth--end
self.discrim_W1 = tf.Variable(
tf.random_normal([
self.kernel5[0], self.kernel5[1], self.kernel5[2], self.dim_W5,
self.dim_W4
],
stddev=0.02),
name='discrim_vox_W1')
self.discrim_bn_g1 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_vox_bn_g1')
self.discrim_bn_b1 = tf.Variable(
tf.zeros([1]), name='discrim_vox_bn_b1')
self.discrim_W2 = tf.Variable(
tf.random_normal([
self.kernel4[0], self.kernel4[1], self.kernel4[2], self.dim_W4,
self.dim_W3
],
stddev=0.02),
name='discrim_vox_W2')
self.discrim_bn_g2 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_vox_bn_g2')
self.discrim_bn_b2 = tf.Variable(
tf.zeros([1]), name='discrim_vox_bn_b2')
self.discrim_W3 = tf.Variable(
tf.random_normal([
self.kernel3[0], self.kernel3[1], self.kernel3[2], self.dim_W3,
self.dim_W2
],
stddev=0.02),
name='discrim_vox_W3')
self.discrim_bn_g3 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_vox_bn_g3')
self.discrim_bn_b3 = tf.Variable(
tf.zeros([1]), name='discrim_vox_bn_b3')
self.discrim_W4 = tf.Variable(
tf.random_normal([
self.kernel2[0], self.kernel2[1], self.kernel2[2], self.dim_W2,
self.dim_W1
],
stddev=0.02),
name='discrim_vox_W4')
self.discrim_bn_g4 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_vox_bn_g4')
self.discrim_bn_b4 = tf.Variable(
tf.zeros([1]), name='discrim_vox_bn_b4')
self.discrim_W5 = tf.Variable(
tf.random_normal([
self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2] * self.dim_W1, 1
],
stddev=0.02),
name='discrim_vox_W5')
# depth--start
self.discrim_dep_W1 = tf.Variable(
tf.random_normal([
self.kernel5[0], self.kernel5[1], self.kernel5[2], self.dim_W5,
self.dim_W4
],
stddev=0.02),
name='discrim_dep_W1')
self.discrim_dep_bn_g1 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_dep_bn_g1')
self.discrim_dep_bn_b1 = tf.Variable(
tf.zeros([1]), name='discrim_dep_bn_b1')
self.discrim_dep_W2 = tf.Variable(
tf.random_normal([
self.kernel4[0], self.kernel4[1], self.kernel4[2], self.dim_W4,
self.dim_W3
],
stddev=0.02),
name='discrim_dep_W2')
self.discrim_dep_bn_g2 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_dep_bn_g2')
self.discrim_dep_bn_b2 = tf.Variable(
tf.zeros([1]), name='discrim_dep_bn_b2')
self.discrim_dep_W3 = tf.Variable(
tf.random_normal([
self.kernel3[0], self.kernel3[1], self.kernel3[2], self.dim_W3,
self.dim_W2
],
stddev=0.02),
name='discrim_dep_W3')
self.discrim_dep_bn_g3 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_dep_bn_g3')
self.discrim_dep_bn_b3 = tf.Variable(
tf.zeros([1]), name='discrim_dep_bn_b3')
self.discrim_dep_W4 = tf.Variable(
tf.random_normal([
self.kernel2[0], self.kernel2[1], self.kernel2[2], self.dim_W2,
self.dim_W1
],
stddev=0.02),
name='discrim_dep_W4')
self.discrim_dep_bn_g4 = tf.Variable(
tf.random_normal([1], mean=1.0, stddev=0.02),
name='discrim_dep_bn_g4')
self.discrim_dep_bn_b4 = tf.Variable(
tf.zeros([1]), name='discrim_dep_bn_b4')
self.discrim_dep_W5 = tf.Variable(
tf.random_normal([
self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2] * self.dim_W1, 1
],
stddev=0.02),
name='discrim_dep_W5')
# depth--end
self.cod_W1 = tf.Variable(
tf.random_normal([
self.dim_z * self.start_vox_size[0] * self.start_vox_size[1] *
self.start_vox_size[2], self.dim_code
],
stddev=0.02),
name='cod_W1')
self.cod_bn_g1 = tf.Variable(
tf.random_normal([dim_code], mean=1.0, stddev=0.02),
name='cod_bn_g1')
self.cod_bn_b1 = tf.Variable(tf.zeros([dim_code]), name='cod_bn_b1')
self.cod_W2 = tf.Variable(
tf.random_normal([dim_code, dim_code], stddev=0.02), name='cod_W2')
self.cod_bn_g2 = tf.Variable(
tf.random_normal([dim_code], mean=1.0, stddev=0.02),
name='cod_bn_g2')
self.cod_bn_b2 = tf.Variable(tf.zeros([dim_code]), name='cod_bn_b2')
self.cod_W3 = tf.Variable(
tf.random_normal([dim_code, 1], stddev=0.02), name='cod_W3')
self.refine_W1 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.dim_W5, self.refine_ch
],
stddev=0.02),
name='refine_W1')
self.refine_res1_W1 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res1_W1')
self.refine_res1_W2 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res1_W2')
self.refine_res2_W1 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res2_W1')
self.refine_res2_W2 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res2_W2')
self.refine_res3_W1 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res3_W1')
self.refine_res3_W2 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res3_W2')
self.refine_res4_W1 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res4_W1')
self.refine_res4_W2 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.refine_ch
],
stddev=0.02),
name='refine__res4_W2')
self.refine_W2 = tf.Variable(
tf.random_normal([
self.refine_kernel, self.refine_kernel, self.refine_kernel,
self.refine_ch, self.dim_W5
],
stddev=0.02),
name='refine_W2')
self.saver = tf.train.Saver()
def build_model(self):
vox_real_ = tf.placeholder(tf.int32, [
self.batch_size, self.vox_shape[0], self.vox_shape[1],
self.vox_shape[2]
])
vox_real = tf.one_hot(vox_real_, self.n_class)
vox_real = tf.cast(vox_real, tf.float32)
# depth--start
"""
dep_real = tf.placeholder(
tf.float32,
[self.batch_size, self.dep_shape[0], self.dep_shape[1], self.dep_shape[2]])
# depth--end
"""
# tsdf--start
tsdf_real_ = tf.placeholder(tf.int32, [
self.batch_size, self.vox_shape[0], self.vox_shape[1],
self.vox_shape[2]
])
tsdf_real = tf.one_hot(tsdf_real_, 1)
tsdf_real = tf.cast(tsdf_real, tf.float32)
# tsdf--end
Z = tf.placeholder(tf.float32, [
self.batch_size, self.start_vox_size[0], self.start_vox_size[1],
self.start_vox_size[2], self.dim_z
])
filter_bilateral = tf.placeholder(
tf.float32, [self.batch_size] +
[self.vox_shape[0], self.vox_shape[1], self.vox_shape[2], 4])
mean, sigma = self.encoder(tsdf_real)
Z_encode = mean
# depth--start
"""
mean_dep, sigma_dep = self.encoder_dep(dep_real)
Z_encode_dep = mean_dep
"""
# depth--end
#code_discriminator
p_code_encode, h_code_encode = self.code_discriminator(Z_encode)
p_code_real, h_code_real = self.code_discriminator(Z)
# depth--start
"""
p_code_encode_dep, h_code_encode_dep = self.code_discriminator(Z_encode_dep)
"""
# depth--start
code_encode_loss = tf.reduce_mean(
tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_code_encode, labels=tf.ones_like(h_code_encode)),
[1]))
code_discrim_loss = tf.reduce_mean(
tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_code_real, labels=tf.ones_like(h_code_real)),
[1])) + tf.reduce_mean(
tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_code_encode,
labels=tf.zeros_like(h_code_encode)), [1]))
# depth--start
"""
code_encode_dep_loss = tf.reduce_mean(
tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_code_encode_dep,
labels=tf.ones_like(h_code_encode_dep)),
[1]))
code_discrim_dep_loss = tf.reduce_mean(
tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_code_real,
labels=tf.ones_like(h_code_real)),
[1])) + tf.reduce_mean(
tf.reduce_sum(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_code_encode_dep,
labels=tf.zeros_like(h_code_encode_dep)),
[1]))
code_compare_loss = tf.reduce_mean(
tf.reduce_sum(
tf.squared_difference(
Z_encode_dep,
Z_encode),
[1,2,3,4]))
"""
# depth--end
#reconstruction
vox_gen_decode, _ = self.generate(Z_encode)
"""
vox_gen_decode_dep, _ = self.generate(Z_encode_dep)
"""
batch_mean_vox_real = tf.reduce_mean(vox_real, [0, 1, 2, 3])
# batch_mean_vox_real ranges from 0 to 1
ones = tf.ones_like(batch_mean_vox_real)
# inverse ranges from 0.5 to 1
inverse = tf.div(ones, tf.add(batch_mean_vox_real, ones))
# inverse ranges from 1/1.1 to 10
inverse = tf.div(ones, batch_mean_vox_real + 0.1)
weight = inverse * tf.div(1., tf.reduce_sum(inverse))
recons_loss = -tf.reduce_sum(
self.lamda_gamma * vox_real * tf.log(1e-6 + vox_gen_decode) +
(1 - self.lamda_gamma) *
(1 - vox_real) * tf.log(1e-6 + 1 - vox_gen_decode), [1, 2, 3])
recons_loss = tf.reduce_mean(tf.reduce_sum(recons_loss * weight, 1))
# Completion loss
vox_real_complete = tf.stack([
vox_real[:, :, :, :, 0],
tf.reduce_sum(vox_real[:, :, :, :, 1:], 4)
], 4)
vox_gen_complete = tf.stack([
vox_gen_decode[:, :, :, :, 0],
tf.reduce_sum(vox_gen_decode[:, :, :, :, 1:], 4)
], 4)
complete_loss = -tf.reduce_sum(
self.lamda_gamma * vox_real_complete *
tf.log(1e-6 + vox_gen_complete) + (1 - self.lamda_gamma) *
(1 - vox_real_complete) * tf.log(1e-6 + 1 - vox_gen_complete),
[1, 2, 3])
weight_complete = tf.stack([weight[0], tf.reduce_sum(weight[1:])])
recons_loss += tf.reduce_mean(
tf.reduce_sum(complete_loss * weight_complete, 1))
"""
recons_dep_loss = -tf.reduce_sum(
self.lamda_gamma *vox_real * tf.log(1e-6 + vox_gen_decode_dep) + (1- self.lamda_gamma) * (1-vox_real) * tf.log(1e-6 + 1-vox_gen_decode_dep),
[1,2,3])
recons_dep_loss = tf.reduce_mean(
tf.reduce_sum(
recons_dep_loss * weight, 1))
"""
#Refiner
vox_after_refine_dec = self.refine(vox_gen_decode)
recons_loss_refine = -tf.reduce_sum(
self.lamda_gamma * vox_real * tf.log(1e-6 + vox_after_refine_dec) +
(1 - self.lamda_gamma) *
(1 - vox_real) * tf.log(1e-6 + 1 - vox_after_refine_dec),
[1, 2, 3])
recons_loss_refine = tf.reduce_mean(
tf.reduce_sum(recons_loss_refine * weight, 1))
#GAN_generate
vox_gen, _ = self.generate(Z)
vox_after_refine_gen = self.refine(vox_gen)
p_real, h_real = self.discriminate(vox_real)
p_gen, h_gen = self.discriminate(vox_gen)
p_gen_dec, h_gen_dec = self.discriminate(vox_gen_decode)
# depth--start
"""
p_real_dep, h_real_dep = self.discriminate_dep(vox_real)
p_gen_dep, h_gen_dep = self.discriminate_dep(vox_gen)
p_gen_dec_dep, h_gen_dec_dep = self.discriminate_dep(vox_gen_decode_dep)
"""
# depth--end
p_gen_ref, h_gen_ref = self.discriminate(vox_after_refine_gen)
p_gen_dec_ref, h_gen_dec_ref = self.discriminate(vox_after_refine_dec)
#Standard_GAN_Loss
discrim_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_real, labels=tf.ones_like(h_real))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen,
labels=tf.zeros_like(h_gen))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dec,
labels=tf.zeros_like(h_gen_dec)))
gen_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen, labels=tf.ones_like(h_gen))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dec, labels=tf.ones_like(h_gen_dec)))
# depth--start
"""
discrim_dep_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_real_dep,
labels=tf.ones_like(h_real_dep))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dep,
labels=tf.zeros_like(h_gen_dep))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dec_dep,
labels=tf.zeros_like(h_gen_dec_dep)))
gen_dep_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dep,
labels=tf.ones_like(h_gen_dep))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dec_dep,
labels=tf.ones_like(h_gen_dec_dep)))
"""
# depth--end
#for refine
discrim_loss_refine = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_real, labels=tf.ones_like(h_real))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_ref,
labels=tf.zeros_like(h_gen_ref))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dec_ref,
labels=tf.zeros_like(h_gen_dec_ref)))
gen_loss_refine = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_ref,
labels=tf.ones_like(h_gen_ref))) + tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=h_gen_dec_ref,
labels=tf.ones_like(h_gen_dec_ref)))
"""
#LS_GAN_Loss
a=-1
b=1
c=0
discrim_loss = tf.reduce_mean(0.5*((h_real-b)**2) + 0.5*((h_gen-a)**2) + 0.5*((h_gen_dec-a)**2))
gen_loss = tf.reduce_mean(0.5*((h_gen-c)**2) + 0.5*((h_gen_dec-c)**2))
"""
#Cost
cost_enc = code_encode_loss + self.lamda_recons * recons_loss
cost_gen = self.lamda_recons * recons_loss + gen_loss
cost_discrim = discrim_loss
cost_code = code_discrim_loss
cost_gen_ref = self.lamda_recons * recons_loss_refine + gen_loss_refine
cost_discrim_ref = discrim_loss_refine
"""
cost_enc_dep = code_encode_dep_loss + self.lamda_recons*recons_dep_loss
cost_gen_dep = self.lamda_recons*recons_dep_loss + gen_dep_loss
cost_discrim_dep = discrim_dep_loss
cost_code_dep = code_discrim_dep_loss
"""
tf.summary.scalar("recons_loss", tf.reduce_mean(recons_loss))
tf.summary.scalar("gen_loss", tf.reduce_mean(gen_loss))
tf.summary.scalar("discrim_loss", tf.reduce_mean(discrim_loss))
tf.summary.scalar("code_encode_loss", tf.reduce_mean(code_encode_loss))
tf.summary.scalar("code_discrim_loss",
tf.reduce_mean(code_discrim_loss))
summary_op = tf.summary.merge_all()
return Z, Z_encode, vox_real_, vox_gen, vox_gen_decode, vox_after_refine_dec, vox_after_refine_gen,\
recons_loss, code_encode_loss, gen_loss, discrim_loss, recons_loss_refine, gen_loss_refine, discrim_loss_refine,\
cost_enc, cost_code, cost_gen, cost_discrim, cost_gen_ref, cost_discrim_ref, summary_op,\
tsdf_real
"""
Z_encode_dep, dep_real, vox_gen_decode_dep,\
recons_dep_loss, code_encode_dep_loss, gen_dep_loss, discrim_dep_loss,\
cost_enc_dep, cost_code_dep, cost_gen_dep, cost_discrim_dep, code_compare_loss,\
"""
def encoder(self, vox):
h1 = lrelu(
tf.nn.conv3d(
vox, self.encode_W1, strides=self.stride, padding='SAME'))
h2 = lrelu(
batchnormalize(
tf.nn.conv3d(
h1, self.encode_W2, strides=self.stride, padding='SAME'),
g=self.encode_bn_g2,
b=self.encode_bn_b2,
batch_size=self.batch_size))
h3 = lrelu(
batchnormalize(
tf.nn.conv3d(
h2, self.encode_W3, strides=self.stride, padding='SAME'),
g=self.encode_bn_g3,
b=self.encode_bn_b3,
batch_size=self.batch_size))
h4 = lrelu(
batchnormalize(
tf.nn.conv3d(
h3, self.encode_W4, strides=self.stride, padding='SAME'),
g=self.encode_bn_g4,
b=self.encode_bn_b4,
batch_size=self.batch_size))
h5 = tf.nn.conv3d(
h4, self.encode_W5, strides=[1, 1, 1, 1, 1], padding='SAME')
h5_sigma = tf.nn.conv3d(
h4, self.encode_W5_sigma, strides=[1, 1, 1, 1, 1], padding='SAME')
return h5, h5_sigma
def encoder_dep(self, dep):
h1 = lrelu(
tf.nn.conv2d(
dep,
self.encode_dep_W1,
strides=self.stride_dep,
padding='SAME'))
h2 = lrelu(
batchnormalize(
tf.nn.conv2d(
h1,
self.encode_dep_W2,
strides=self.stride_dep,
padding='SAME'),
g=self.encode_dep_bn_g2,
b=self.encode_dep_bn_b2,
batch_size=self.batch_size))
h3 = lrelu(
batchnormalize(
tf.nn.conv2d(
h2,
self.encode_dep_W3,
strides=self.stride_dep,
padding='SAME'),
g=self.encode_dep_bn_g3,
b=self.encode_dep_bn_b3,
batch_size=self.batch_size))
h4 = lrelu(
batchnormalize(
tf.nn.conv2d(
h3,
self.encode_dep_W4,
strides=self.stride_dep,
padding='SAME'),
g=self.encode_dep_bn_g4,
b=self.encode_dep_bn_b4,
batch_size=self.batch_size))
h5 = lrelu(
batchnormalize(
tf.nn.conv2d(
h4,
self.encode_dep_W5,
strides=self.stride_dep,
padding='SAME'),
g=self.encode_dep_bn_g5,
b=self.encode_dep_bn_b5,
batch_size=self.batch_size))
h6 = lrelu(
batchnormalize(
tf.nn.conv2d(
h5,
self.encode_dep_W6,
strides=self.stride_dep,
padding='SAME'),
g=self.encode_dep_bn_g6,
b=self.encode_dep_bn_b6,
batch_size=self.batch_size))
h6 = tf.reshape(h6, [self.batch_size, -1])
h7 = tf.matmul(h6, self.encode_dep_W7)
h7 = tf.reshape(h7, [
self.batch_size, self.start_vox_size[0], self.start_vox_size[1],
self.start_vox_size[2], self.dim_W1
])
h8 = tf.nn.conv3d(
h7, self.encode_dep_W8, strides=[1, 1, 1, 1, 1], padding='SAME')
h8_sigma = tf.nn.conv3d(
h7,
self.encode_dep_W8_sigma,
strides=[1, 1, 1, 1, 1],
padding='SAME')
return h8, h8_sigma
def discriminate(self, vox):
h1 = lrelu(
tf.nn.conv3d(
vox, self.discrim_W1, strides=self.stride, padding='SAME'))
h2 = lrelu(
layernormalize(
tf.nn.conv3d(
h1, self.discrim_W2, strides=self.stride, padding='SAME'),
g=self.discrim_bn_g2,
b=self.discrim_bn_b2))
h3 = lrelu(
layernormalize(
tf.nn.conv3d(
h2, self.discrim_W3, strides=self.stride, padding='SAME'),
g=self.discrim_bn_g3,
b=self.discrim_bn_b3))
h4 = lrelu(
layernormalize(
tf.nn.conv3d(
h3, self.discrim_W4, strides=self.stride, padding='SAME'),
g=self.discrim_bn_g4,
b=self.discrim_bn_b4))
h4 = tf.reshape(h4, [self.batch_size, -1])
h5 = tf.matmul(h4, self.discrim_W5)
y = tf.nn.sigmoid(h5)
return y, h5
def discriminate_dep(self, vox):
h1 = lrelu(
tf.nn.conv3d(
vox, self.discrim_dep_W1, strides=self.stride, padding='SAME'))
h2 = lrelu(
layernormalize(
tf.nn.conv3d(
h1,
self.discrim_dep_W2,
strides=self.stride,
padding='SAME'),
g=self.discrim_dep_bn_g2,
b=self.discrim_dep_bn_b2))
h3 = lrelu(
layernormalize(
tf.nn.conv3d(
h2,
self.discrim_dep_W3,
strides=self.stride,
padding='SAME'),
g=self.discrim_dep_bn_g3,
b=self.discrim_dep_bn_b3))
h4 = lrelu(