def discriminator(inputs, is_training, reuse_mode):
with tf.variable_scope ('discriminator', reuse=reuse_mode):
# b x 28 x 28 x 1 -> b x 14 x 14 x 128
layer1 = tf_util2.conv_2d ('l1', inputs, 128, is_training, reuse_mode,
activation_fun = tf.nn.leaky_relu,
padding = 'same',
kernel_size = [3,3],
stride_size = (2,2),
use_bn = False)
# b x 14 x 14 x 128 -> b x 7 x 7 x 256
layer2 = tf_util2.conv_2d ('l2', layer1, 256, is_training, reuse_mode,
activation_fun = tf.nn.leaky_relu,
padding = 'same',
kernel_size = [3,3],
stride_size = (2,2),
use_bn = True)
# b x 7 x 7 x 256 -> b x 4 x 4 x 512
layer3 = tf_util2.conv_2d ('l3', layer2, 512, is_training, reuse_mode,
activation_fun = tf.nn.leaky_relu,
padding = 'same',
kernel_size = [3,3],
stride_size = (2,2),
use_bn = True)
# b x 4 x 4 x 512 -> b x 4*4*512
flatten = tf.reshape(layer3, (-1, 4*4*512))
# b x 4*4*512 x 1 -> b x 1
logits = tf_util2.fully_connection ('flatten', flatten, 1, is_training, reuse_mode, use_bn = False)
outputs = tf.sigmoid(logits)
return logits, outputs
def pc_vae_decoder(latent,
is_training,
reuse_mode=False,
bn_mode=False,
pc_cat=1):
with tf.variable_scope('vae_decoder', reuse=reuse_mode):
#latent2: b*1*256, ones*latent2: b*2048*256, points: b*2048*3 -> inputs: b*2048*259
latent2 = tf.reshape(latent, [tf.shape(latent)[0], 1, 256])
ones = tf.ones(shape=[tf.shape(latent)[0], 2048, 1])
points = tf.random_normal(shape=[tf.shape(latent)[0], 2048, 3],
mean=0,
stddev=1,
dtype=tf.float32,
seed=pc_cat)
inputs0 = tf.concat([points, tf.matmul(ones, latent2)], axis=2)
# b*2048*259*1 -> b*2048*1*256 -> b*2048*1*256 -> b*2048*1*128 -> b*2048*1*128 -> b*2048*1*3 -> b*2048*3
inputs = tf.reshape(inputs0, [-1, 2048, 259, 1])
layer1 = tf_util2.conv_2d('g1',
inputs,
256,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 259], (1, 1),
use_bn=bn_mode)
layer2 = tf_util2.conv_2d('g2',
layer1,
256,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer3 = tf_util2.conv_2d('g3',
layer2,
128,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer4 = tf_util2.conv_2d('g4',
layer3,
128,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer5 = tf_util2.conv_2d('g5',
layer4,
3,
is_training,
reuse_mode,
None,
'valid', [1, 1], (1, 1),
use_bn=False)
recon = tf.squeeze(layer5)
return recon
def img_encoder(image,
conv_net,
fully_con,
is_training=True,
reuse_mode=False):
layer = tf.reshape(image, [-1, np.shape(image)[1], np.shape(image)[2], 1])
with tf.variable_scope(conv_net['name'], reuse=reuse_mode):
for i in range(len(conv_net['layers'])):
layer = tf_util2.conv_2d(conv_net['layers'][i],
layer,
conv_net['outs'][i],
is_training,
reuse_mode,
activation[conv_net['activation'][i]],
conv_net['padding'][i],
conv_net['kenel'][i],
conv_net['stride'][i],
0.001,
'xaiver',
use_bn=conv_net['bn'][i],
bn_momentum=0.9)
layer = tf.layers.Flatten()(layer)
with tf.variable_scope(fully_con['name'], reuse=reuse_mode):
for i in range(len(fully_con['layers'])):
layer = tf_util2.fully_connection(
fully_con['layers'][i],
layer,
fully_con['outs'][i],
is_training,
reuse_mode,
activation[fully_con['activation'][i]],
0.001,
'xaiver',
use_bn=fully_con['bn'][i],
bn_momentum=0.9)
return layer
def pc_vae_encoder(pc_in, is_training, reuse_mode=False, bn_mode=True):
with tf.variable_scope('vae_encoder', reuse=reuse_mode):
# b*2048*3*1 -> b*2048*1*64 -> b*2048*1*128 -> b*2048*1*128 -> b*2048*1*256 -> b*2048*1*256
inputs = tf.reshape(pc_in, [-1, 2048, 3, 1])
layer1 = tf_util2.conv_2d('e1',
inputs,
64,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 3], (1, 1),
use_bn=bn_mode)
layer2 = tf_util2.conv_2d('e2',
layer1,
128,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer3 = tf_util2.conv_2d('e3',
layer2,
128,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer4 = tf_util2.conv_2d('e4',
layer3,
256,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer5 = tf_util2.conv_2d('e5',
layer4,
256,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
# b*2048*1*256 -> b*1*1*256 -> b*256
layer6 = tf.layers.max_pooling2d(layer5, [2048, 1], [1, 1],
padding='valid')
layer7 = tf.squeeze(layer6)
# b*256 -> (z_sigma, z_mu)
z_sigma = tf_util2.fully_connection('es',
layer7,
256,
is_training,
reuse_mode,
None,
0.001,
use_bn=False)
z_mu = tf_util2.fully_connection('em',
layer7,
256,
is_training,
reuse_mode,
None,
0.001,
use_bn=False)
epsion = tf.random_normal(shape=tf.shape(z_sigma),
mean=0,
stddev=1,
dtype=tf.float32)
latent = z_mu + tf.sqrt(tf.exp(z_sigma)) * epsion
return latent, z_sigma, z_mu
def pc_gan_discriminator(pc_in, is_training, reuse_mode=False, bn_mode=False):
with tf.variable_scope('gan_discriminator', reuse=reuse_mode):
# b*2048*3*1 -> b*2048*1*64 -> b*2048*1*128 -> b*2048*1*256 -> b*2048*1*1024
inputs = tf.reshape(pc_in, [-1, 2048, 3, 1])
layer1 = tf_util2.conv_2d('d1',
inputs,
64,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 3], (1, 1),
use_bn=bn_mode)
layer2 = tf_util2.conv_2d('d2',
layer1,
128,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer3 = tf_util2.conv_2d('d3',
layer2,
256,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
layer4 = tf_util2.conv_2d('d4',
layer3,
1024,
is_training,
reuse_mode,
tf.nn.relu,
'valid', [1, 1], (1, 1),
use_bn=bn_mode)
# b*2048*1*1024 -> b*1*1*1024 -> b*1024
layer5 = tf.layers.max_pooling2d(layer4, [2048, 1], [1, 1],
padding='valid')
layer6 = tf.squeeze(layer5)
# b*1024 -> b*256 -> b*256 -> b*1
layer7 = tf_util2.fully_connection('d7',
layer6,
256,
is_training,
reuse_mode,
tf.nn.relu,
0.001,
use_bn=False)
layer8 = tf_util2.fully_connection('d8',
layer7,
256,
is_training,
reuse_mode,
tf.nn.relu,
0.001,
use_bn=False)
logits = tf_util2.fully_connection('logits',
layer8,
1,
is_training,
reuse_mode,
None,
0.001,
use_bn=False)
outputs = tf.sigmoid(logits)
return outputs, logits
def cnn_net(train_data, is_training, reuse_mode=False, bn_mode=False):
with tf.variable_scope('cnn', reuse=reuse_mode):
# b*38*22 -> b*38*22*32 -> b*19*11*64 -> b*10*6*64 -> b*5*3*64
inputs = tf.reshape(train_data, [-1, 38, 22])
conv1 = tf_util2.conv_2d('conv1',
inputs,
32,
is_training,
reuse_mode,
tf.nn.relu,
'same', [2, 2], (1, 1),
use_bn=bn_mode)
conv2 = tf_util2.conv_2d('conv2',
conv1,
64,
is_training,
reuse_mode,
tf.nn.relu,
'same', [2, 2], (2, 2),
use_bn=bn_mode)
conv3 = tf_util2.conv_2d('conv3',
conv2,
64,
is_training,
reuse_mode,
tf.nn.relu,
'same', [2, 2], (2, 2),
use_bn=bn_mode)
conv4 = tf_util2.conv_2d('conv4',
conv3,
64,
is_training,
reuse_mode,
tf.nn.relu,
'same', [2, 2], (2, 2),
use_bn=bn_mode)
flatten = tf.reshape(conv4, [-1, 5 * 3 * 64])
# b*960 -> b*512 -> b*512 -> b*40
layer1 = tf_util2.fully_connection('l1',
flatten,
512,
is_training,
reuse_mode,
tf.nn.relu,
0.001,
use_bn=bn_mode)
layer2 = tf_util2.fully_connection('l2',
layer1,
512,
is_training,
reuse_mode,
tf.nn.relu,
0.001,
use_bn=bn_mode)
layer3 = tf_util2.fully_connection('l3',
layer2,
34,
is_training,
reuse_mode,
None,
0.001,
use_bn=bn_mode)
return layer3