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from collections import namedtuple
from utils import *
from ops import *
import time
from glob import glob
def gaussian_noise_layer(input_layer, std):
noise = tf.random.normal(
shape=tf.shape(input_layer), mean=0.0, stddev=std, dtype=tf.float32
)
return input_layer + noise
def generator_resnet(image, options, transfer=False, reuse=False, name="generator"):
with tf.compat.v1.variable_scope(name):
if reuse:
tf.compat.v1.get_variable_scope().reuse_variables()
else:
assert tf.compat.v1.get_variable_scope().reuse is False
def residule_block_dilated(x, dim, ks=3, s=1, name="res", down=False):
if down:
dim = dim * 2
y = instance_norm(
dilated_conv2d(x, dim, ks, s, padding="SAME", name=name + "_c1"),
name + "_bn1",
)
y = tf.nn.relu(y)
y = instance_norm(
dilated_conv2d(y, dim, ks, s, padding="SAME", name=name + "_c2"),
name + "_bn2",
)
out = y + x
if down:
out = tf.nn.relu(
instance_norm(
conv2d(out, dim // 2, 3, 1, name=name + "_down_c"),
name + "_in_down",
)
)
return out
def residual_block(x_init, dim, ks=3, s=1, name="resblock", down=False):
with tf.compat.v1.variable_scope(name):
if down:
dim = dim * 2
with tf.compat.v1.variable_scope("res1"):
x = instance_norm(
conv2d(x_init, dim, ks, s, padding="SAME", name=name + "_c1"),
name + "_in1",
)
x = tf.nn.relu(x)
with tf.compat.v1.variable_scope("res2"):
x = instance_norm(
conv2d(x, dim, ks, s, padding="SAME", name=name + "_c2"),
name + "_in2",
)
out = x + x_init
if down:
out = tf.nn.relu(
instance_norm(
conv2d(out, dim // 2, 3, 1, name=name + "_down_c"),
name + "_in_down",
)
)
return out
### Encoder architecture
c0 = tf.pad(image, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
c1 = tf.nn.relu(
instance_norm(
conv2d(c0, options.gf_dim, 7, 1, padding="VALID", name="g_e1_c"),
"g_e1_bn",
)
)
c2 = tf.nn.relu(
instance_norm(
conv2d(c1, options.gf_dim * 2, 3, 2, name="g_e2_c"), "g_e2_bn"
)
)
c3 = tf.nn.relu(
instance_norm(
conv2d(c2, options.gf_dim * 4, 3, 2, name="g_e3_c"), "g_e3_bn"
)
)
r1 = residule_block_dilated(c3, options.gf_dim * 4, name="g_r1")
r2 = residule_block_dilated(r1, options.gf_dim * 4, name="g_r2")
r3 = residule_block_dilated(r2, options.gf_dim * 4, name="g_r3")
r4 = residule_block_dilated(r3, options.gf_dim * 4, name="g_r4")
# r5 = residule_block_dilated(r4, options.gf_dim * 4, name='g_r5')
if transfer:
t1 = residual_block(r4, options.gf_dim * 4, name="g_t1")
t2 = residual_block(t1, options.gf_dim * 4, name="g_t2")
t3 = residual_block(t2, options.gf_dim * 4, name="g_t3")
t4 = residual_block(t3, options.gf_dim * 4, name="g_t4")
# feature = tf.concat([r4, t4], axis=3, name='g_concat')
# down = True
feature = t4
else:
feature = r4
t4 = None
down = False
### translation decoder architecture
r6 = residule_block_dilated(feature, options.gf_dim * 4, name="g_r6")
r7 = residule_block_dilated(r6, options.gf_dim * 4, name="g_r7")
r8 = residule_block_dilated(r7, options.gf_dim * 4, name="g_r8")
r9 = residule_block_dilated(r8, options.gf_dim * 4, name="g_r9")
d1 = deconv2d(r9, options.gf_dim * 2, 3, 2, name="g_d1_dc")
d1 = tf.nn.relu(instance_norm(d1, "g_d1_bn"))
d2 = deconv2d(d1, options.gf_dim, 3, 2, name="g_d2_dc")
d2 = tf.nn.relu(instance_norm(d2, "g_d2_bn"))
d2 = tf.pad(d2, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
pred = tf.nn.tanh(
conv2d(d2, options.output_c_dim, 7, 1, padding="VALID", name="g_pred_c")
)
### reconstruction decoder architecture
r5 = gaussian_noise_layer(r4, 0.02)
r6_rec = residule_block_dilated(r5, options.gf_dim * 4, name="g_r6_rec")
r6_rec = gaussian_noise_layer(r6_rec, 0.02)
r7_rec = residule_block_dilated(r6_rec, options.gf_dim * 4, name="g_r7_rec")
r8_rec = residule_block_dilated(r7_rec, options.gf_dim * 4, name="g_r8_rec")
r9_rec = residule_block_dilated(r8_rec, options.gf_dim * 4, name="g_r9_rec")
d1_rec = deconv2d(r9_rec, options.gf_dim * 2, 3, 2, name="g_d1_dc_rec")
d1_rec = tf.nn.relu(instance_norm(d1_rec, "g_d1_bn_rec"))
d2_rec = deconv2d(d1_rec, options.gf_dim, 3, 2, name="g_d2_dc_rec")
d2_rec = tf.nn.relu(instance_norm(d2_rec, "g_d2_bn_rec"))
d2_rec = tf.pad(d2_rec, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
pred_rec = tf.nn.tanh(
conv2d(
d2_rec, options.output_c_dim, 7, 1, padding="VALID", name="g_pred_c_rec"
)
)
## confidence prediction
if transfer:
d_conf = deconv2d(d1, options.gf_dim, 3, 2, name="g_d_dc_conf")
d_conf = tf.nn.relu(instance_norm(d_conf, "g_d_bn_conf"))
d_conf = tf.pad(d_conf, [[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT")
pred_conf = tf.nn.softplus(
conv2d(d_conf, 1, 7, 1, padding="VALID", name="g_pred_c_conf")
)
else:
pred_conf = None
return pred, pred_rec, r4, t4, pred_conf
def discriminator(image, options, n_scale=2, reuse=False, name="discriminator"):
images = []
for i in range(n_scale):
images.append(
tf.compat.v1.image.resize_bicubic(
image, [get_shape(image)[1] // (2**i), get_shape(image)[2] // (2**i)]
)
)
with tf.compat.v1.variable_scope(name):
if reuse:
tf.compat.v1.get_variable_scope().reuse_variables()
else:
assert tf.compat.v1.get_variable_scope().reuse is False
images = dis_down(images, 4, 2, n_scale, options.df_dim, "d_h0_conv_scale_")
images = dis_down(images, 4, 2, n_scale, options.df_dim * 2, "d_h1_conv_scale_")
images = dis_down(images, 4, 2, n_scale, options.df_dim * 4, "d_h2_conv_scale_")
images = dis_down(images, 4, 2, n_scale, options.df_dim * 8, "d_h3_conv_scale_")
images = final_conv(images, n_scale, "d_pred_scale_")
return images
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