INNER CODE UNIT · Python
resnet
aiff22/DPED · models.py:4
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");