def Disc_l(x, training_nn, scope): nlayers = [16, 32, 64, 96, ] x = tf.concat([x,tf.image.rgb_to_yuv(x)], axis=3) # Block 1 x1 = Conv(x, nlayers[1], scope+'/conv1', training_nn) x1 = Downsample(x1, nlayers[1], scope+'/conv2', training_nn) # Block 2 x2 = Conv(x1, nlayers[2], scope+'/conv3', training_nn) x2 = Downsample(x2, nlayers[2], scope+'/conv4', training_nn) # Block 3 x3 = Conv(x2, nlayers[2], scope+'/conv5', training_nn) x3 = Downsample(x3, nlayers[3], scope+'/conv6', training_nn) # Block 4 x4 = Conv(x3, nlayers[3], scope+'/conv7', training_nn) x4l = Conv(x4, 1, scope+'/conv8', training_nn, act=False, norm=False) x4s = Conv(x4, 1, scope+'/conv9', training_nn, act=False, norm=False) return x4l, x4s
def Gen(x, training_nn, scope): nlayers = [16, 64, 96, 96, ] x = tf.concat([x,tf.image.rgb_to_yuv(x)], axis=3) x0 = Conv(x, nlayers[1], scope+'/conv0', training_nn) # Block 1 x1 = Conv(x0, nlayers[2], scope+'/conv1', training_nn) x1 = Conv(x1, nlayers[3], scope+'/conv2', training_nn) x1 = Downsample(x1, nlayers[2], scope+'/conv3', training_nn) # Block 2 x2 = Conv(x1, nlayers[2], scope+'/conv4', training_nn) x2 = Conv(x2, nlayers[3], scope+'/conv5', training_nn) x2 = Downsample(x2, nlayers[2], scope+'/conv6', training_nn) # Block 3 x3 = Conv(x2, nlayers[2], scope+'/conv7', training_nn) x3 = Conv(x3, nlayers[3], scope+'/conv8', training_nn) x3 = Downsample(x3, nlayers[2], scope+'/conv9', training_nn) # Decoder u1 = Upsample(x3, nlayers[1], scope+'/up1', training_nn) u2 = Upsample(tf.concat([u1, x2], 3), nlayers[1], scope+'/up2', training_nn) u3 = Upsample(tf.concat([u2, x1], 3), nlayers[1], scope+'/up3', training_nn) n1 = tf.nn.tanh(Conv(Conv(u1, nlayers[0], scope+'/n1', training_nn), 6, scope+'/nn1', training_nn, act=False, norm=False)) n2 = tf.nn.tanh(Conv(Conv(u2, nlayers[0], scope+'/n2', training_nn), 3, scope+'/nn2', training_nn, act=False, norm=False)) n3 = tf.nn.tanh(Conv(Conv(u3, nlayers[0], scope+'/n3', training_nn), 3, scope+'/nn3', training_nn, act=False, norm=False)) s = tf.reduce_mean(n1[:,:,:,3:6], axis=[1,2], keepdims=True) b = tf.reduce_mean(n1[:,:,:,:3], axis=[1,2], keepdims=True) C = tf.nn.avg_pool(n2, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME') T = n3 # ESR map1 = tf.image.resize_images(x1,[32,32]) map2 = tf.image.resize_images(x2,[32,32]) map3 = tf.image.resize_images(x3,[32,32]) maps = tf.concat([map1, map2, map3],3) x4 = Conv(maps, nlayers[2], scope+'/conv10', training_nn, apply_dropout=True) x4 = Conv(x4, nlayers[1], scope+'/conv11', training_nn, apply_dropout=True) x5 = Conv(x4, 1, scope+'/conv12', training_nn, act=False, norm=False) return x5, s, b, C, T