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models.py
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models.py
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import tensorflow as tf
def resnet(input_image):
with tf.compat.v1.variable_scope("generator"):
W1 = weight_variable([9, 9, 3, 64], name="W1"); b1 = bias_variable([64], name="b1");
c1 = tf.nn.relu(conv2d(input_image, W1) + b1)
# residual 1
W2 = weight_variable([3, 3, 64, 64], name="W2"); b2 = bias_variable([64], name="b2");
c2 = tf.nn.relu(_instance_norm(conv2d(c1, W2) + b2))
W3 = weight_variable([3, 3, 64, 64], name="W3"); b3 = bias_variable([64], name="b3");
c3 = tf.nn.relu(_instance_norm(conv2d(c2, W3) + b3)) + c1
# residual 2
W4 = weight_variable([3, 3, 64, 64], name="W4"); b4 = bias_variable([64], name="b4");
c4 = tf.nn.relu(_instance_norm(conv2d(c3, W4) + b4))
W5 = weight_variable([3, 3, 64, 64], name="W5"); b5 = bias_variable([64], name="b5");
c5 = tf.nn.relu(_instance_norm(conv2d(c4, W5) + b5)) + c3
# residual 3
W6 = weight_variable([3, 3, 64, 64], name="W6"); b6 = bias_variable([64], name="b6");
c6 = tf.nn.relu(_instance_norm(conv2d(c5, W6) + b6))
W7 = weight_variable([3, 3, 64, 64], name="W7"); b7 = bias_variable([64], name="b7");
c7 = tf.nn.relu(_instance_norm(conv2d(c6, W7) + b7)) + c5
# residual 4
W8 = weight_variable([3, 3, 64, 64], name="W8"); b8 = bias_variable([64], name="b8");
c8 = tf.nn.relu(_instance_norm(conv2d(c7, W8) + b8))
W9 = weight_variable([3, 3, 64, 64], name="W9"); b9 = bias_variable([64], name="b9");
c9 = tf.nn.relu(_instance_norm(conv2d(c8, W9) + b9)) + c7
# Convolutional
W10 = weight_variable([3, 3, 64, 64], name="W10"); b10 = bias_variable([64], name="b10");
c10 = tf.nn.relu(conv2d(c9, W10) + b10)
W11 = weight_variable([3, 3, 64, 64], name="W11"); b11 = bias_variable([64], name="b11");
c11 = tf.nn.relu(conv2d(c10, W11) + b11)
# Final
W12 = weight_variable([9, 9, 64, 3], name="W12"); b12 = bias_variable([3], name="b12");
enhanced = tf.nn.tanh(conv2d(c11, W12) + b12) * 0.58 + 0.5
return enhanced
def adversarial(image_):
with tf.compat.v1.variable_scope("discriminator"):
conv1 = _conv_layer(image_, 48, 11, 4, batch_nn = False)
conv2 = _conv_layer(conv1, 128, 5, 2)
conv3 = _conv_layer(conv2, 192, 3, 1)
conv4 = _conv_layer(conv3, 192, 3, 1)
conv5 = _conv_layer(conv4, 128, 3, 2)
flat_size = 128 * 7 * 7
conv5_flat = tf.reshape(conv5, [-1, flat_size])
W_fc = tf.Variable(tf.compat.v1.truncated_normal([flat_size, 1024], stddev=0.01))
bias_fc = tf.Variable(tf.constant(0.01, shape=[1024]))
fc = leaky_relu(tf.matmul(conv5_flat, W_fc) + bias_fc)
W_out = tf.Variable(tf.compat.v1.truncated_normal([1024, 2], stddev=0.01))
bias_out = tf.Variable(tf.constant(0.01, shape=[2]))
adv_out = tf.nn.softmax(tf.matmul(fc, W_out) + bias_out)
return adv_out
def weight_variable(shape, name):
initial = tf.compat.v1.truncated_normal(shape, stddev=0.01)
return tf.Variable(initial, name=name)
def bias_variable(shape, name):
initial = tf.constant(0.01, shape=shape)
return tf.Variable(initial, name=name)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def leaky_relu(x, alpha = 0.2):
return tf.maximum(alpha * x, x)
def _conv_layer(net, num_filters, filter_size, strides, batch_nn=True):
weights_init = _conv_init_vars(net, num_filters, filter_size)
strides_shape = [1, strides, strides, 1]
bias = tf.Variable(tf.constant(0.01, shape=[num_filters]))
net = tf.nn.conv2d(net, weights_init, strides_shape, padding='SAME') + bias
net = leaky_relu(net)
if batch_nn:
net = _instance_norm(net)
return net
def _instance_norm(net):
batch, rows, cols, channels = [i.value for i in net.get_shape()]
var_shape = [channels]
mu, sigma_sq = tf.compat.v1.nn.moments(net, [1,2], keepdims=True)
shift = tf.Variable(tf.zeros(var_shape))
scale = tf.Variable(tf.ones(var_shape))
epsilon = 1e-3
normalized = (net-mu)/(sigma_sq + epsilon)**(.5)
return scale * normalized + shift
def _conv_init_vars(net, out_channels, filter_size, transpose=False):
_, rows, cols, in_channels = [i.value for i in net.get_shape()]
if not transpose:
weights_shape = [filter_size, filter_size, in_channels, out_channels]
else:
weights_shape = [filter_size, filter_size, out_channels, in_channels]
weights_init = tf.Variable(tf.compat.v1.truncated_normal(weights_shape, stddev=0.01, seed=1), dtype=tf.float32)
return weights_init