def consistentG(g):
with tf.variable_scope('Consistency'):
g_const = util.catConst(g)
conv = ml.convLrelu(g_const, 16, 3, 1, name='g_conv1')
conv = ml.convLrelu(conv, 32, 3, 1, name='g_conv2')
conv = ml.convLrelu(conv, 3, 3, 1, name='g_conv3')
return ml.tanh(conv)
def DverticalS(x, reuse=False):
init = tf.random_normal_initializer(stddev=0.02)
with tf.variable_scope('DiscriminatorVS', reuse=reuse):
d = ml.convLrelu(x, 32, 3, 2, 'same', init=init, name='d_enc1')
d = ml.convLrelu(d, 64, 3, 2, 'same', init=init, name='d_enc2')
d = ml.convLrelu(d, 64, 3, 2, 'same', init=init, name='d_enc3')
d = ml.convLrelu(d, 64, 3, 2, 'same', init=init, name='d_enc4')
d = ml.dropout(d, 0.5, name='drop1')
d = ml.fullConn(d, 1, init=init, name='d_fc1')
return d, tf.reduce_mean(ml.sigmoid(d))
def Dm(x, ksz=5, s=2, prob=0.5, reuse=False):
init = tf.random_normal_initializer(stddev=0.02)
with tf.variable_scope('DiscriminatorM', reuse=reuse):
d = ml.convLrelu(x, 64, ksz, s, 'same', init=init,
name='dm_enc1') #(32,128)
d = ml.convLrelu(d, 128, ksz, s, 'same', init=init,
name='dm_enc2') #(16,64)
d = ml.convLrelu(d, 128, ksz, s, 'same', init=init,
name='dm_enc3') #(8,32)
d = ml.convLrelu(d, 128, ksz, s, 'same', init=init,
name='dm_enc4') #(4,16)
#d = ml.flatten(d)
d = ml.dropout(d, prob, name='drop1')
d = ml.fullConn(d, 1, init=init, name='d_fc1')
return d, tf.reduce_mean(ml.sigmoid(d))
def Gs3(x, ksz=3, s=2, init='he_uniform', reuse=False):
with tf.variable_scope('Generator', reuse=reuse):
enc_h = ml.convLrelu(x, 64, ksz, s, 'same', name='g_enc1') #(64,256)
enc_m = ml.convLrelu(enc_h, 128, ksz, s, 'same',
name='g_enc2') #(32,128)
enc1 = lrelu(conv2d(enc_m, 256, ksz, s, 'same',
name='g_enc3')) #(16,64)
enc2 = lrelu(conv2d(enc1, 256, ksz, s, 'same', name='g_enc4')) #(8,32)
enc3 = lrelu(conv2d(enc2, 256, ksz, s, 'same', name='g_enc5')) #(4,16)
enc4 = lrelu(conv2d(enc3, 512, ksz, s, 'same', name='g_enc6')) #(2,8)
enc5 = lrelu(conv2d(enc4, 512, ksz, s, 'same', name='g_enc7')) #(1,4)
dec1 = lrelu(conv2d(enc5, 512, ksz, s, 'same', name='g_dec1')) #(2,8)
dec2 = lrelu(
conv2d(dstack(dec1, enc4), 512, ksz, s, 'same',
name='g_dec2')) #(4,16)
dec3 = lrelu(
conv2d(dstack(dec2, enc3), 256, ksz, s, 'same',
name='g_dec3')) #(8,32)
dec4 = lrelu(
conv2d(dstack(dec3, enc2), 256, ksz, s, 'same',
name='g_dec4')) #(16,64)
dec5 = lrelu(
conv2d(dstack(dec4, enc1), 128, ksz, s, 'same',
name='g_dec5')) #(32,128)
dec6 = lrelu(
conv2d(dstack(dec5, enc_m), 64, ksz, s, 'same',
name='g_dec6')) #(64,128)
# Output high
dec_h = tanh(
deconv2d(dstack(dec6, enc_h), 3, ksz, s, 'same',
name='im_h')) #(128,256)
# Output med #1x1 conv
dec_m = tanh(deconv2d(dec6, 3, 1, 1, 'same', name='im_m'))
# Output small #1x1 conv
dec_s = tanh(deconv2d(dec5, 3, 1, 1, 'same', name='im_s'))
return dec_s, dec_m, dec_h
