def build_model(x, scale, training, reuse):
hidden_size = 128
bottleneck_size = 64
x = tf.layers.conv2d(x,
hidden_size,
1,
activation=None,
name='in',
reuse=reuse)
for i in range(6):
x = util.crop_by_pixel(
x, 1) + conv(x, hidden_size, bottleneck_size, training,
'lr_conv' + str(i), reuse)
if (scale == 4):
scale = 2
x = tf.image.resize_nearest_neighbor(
x,
tf.shape(x)[1:3] * scale) + tf.layers.conv2d_transpose(
util.lrelu(x),
hidden_size,
scale,
strides=scale,
activation=None,
name='up1',
reuse=reuse)
x = util.crop_by_pixel(x, 1) + conv(x, hidden_size, bottleneck_size,
training, 'up_conv', reuse)
x = tf.image.resize_nearest_neighbor(
x,
tf.shape(x)[1:3] * scale) + tf.layers.conv2d_transpose(
util.lrelu(x),
hidden_size,
scale,
strides=scale,
activation=None,
name='up2',
reuse=reuse)
else:
x = tf.image.resize_nearest_neighbor(
x,
tf.shape(x)[1:3] * scale) + tf.layers.conv2d_transpose(
util.lrelu(x),
hidden_size,
scale,
strides=scale,
activation=None,
name='up',
reuse=reuse)
for i in range(4):
x = util.crop_by_pixel(
x, 1) + conv(x, hidden_size, bottleneck_size, training,
'hr_conv' + str(i), reuse)
x = util.lrelu(x)
x = tf.layers.conv2d(x, 3, 1, activation=None, name='out', reuse=reuse)
return x
def make_patches(hr_image, lr_image, scale, resize):
hr_image = tf.stack(flip([hr_image]))
lr_image = tf.stack(flip([lr_image]))
hr_image = util.crop_by_pixel(hr_image, 12)
lr_image = util.crop_by_pixel(lr_image, 12 / scale)
hr_patches = util.image_to_patches(hr_image)
if resize:
lr_image = util.resize_func(lr_image, tf.shape(hr_image)[1:3])
lr_patches = util.image_to_patches(lr_image)
else:
lr_patches = util.image_to_patches(lr_image, scale)
return hr_patches, lr_patches
def build_model(x, scale, training, reuse):
hidden_size = 128
bottleneck_size = 64
x = tf.layers.conv2d(x,
hidden_size,
1,
activation=None,
name='in',
reuse=reuse)
for i in range(6):
x = util.crop_by_pixel(
x, 1) + conv(x, hidden_size, bottleneck_size, training,
'lr_conv' + str(i), reuse)
x = util.lrelu(x)
if scale == 4:
scale = 2
x = tf.layers.conv2d_transpose(x,
hidden_size,
scale,
strides=scale,
activation=None,
name='up1',
reuse=reuse)
x = util.crop_by_pixel(x, 1) + conv(x, hidden_size, bottleneck_size,
training, 'up_conv', reuse)
x = util.lrelu(x)
hidden_size = 64
x = tf.layers.conv2d_transpose(x,
hidden_size,
scale,
strides=scale,
activation=None,
name='up2',
reuse=reuse)
else:
hidden_size = 64
x = tf.layers.conv2d_transpose(x,
hidden_size,
scale,
strides=scale,
activation=None,
name='up',
reuse=reuse)
for i in range(4):
x = util.crop_by_pixel(
x, 1) + conv(x, hidden_size, bottleneck_size, training,
'hr_conv' + str(i), reuse)
x = util.lrelu(x)
x = tf.layers.conv2d(x, 3, 1, activation=None, name='out', reuse=reuse)
return x
def build_model(x, scale, training, reuse):
hidden_size = 128
bottleneck_size = 32
survival_rate = 0.5
survival_rate = tf.constant(survival_rate, name='survival_rate')
x = tf.layers.conv2d(x,
hidden_size,
1,
activation=None,
name='in',
reuse=reuse)
for i in range(5):
x = util.crop_by_pixel(
x, 1) + conv(x, hidden_size, bottleneck_size, training,
'lr_conv' + str(i), reuse)
x = tf.nn.relu(x)
x = tf.layers.conv2d_transpose(x,
hidden_size,
scale,
strides=scale,
activation=None,
name='up',
reuse=reuse)
print(x.get_shape().as_list())
for i in range(5):
shortcut = util.crop_by_pixel(x, 1)
# print shortcut.get_shape().as_list()
resblock = conv(x,
hidden_size,
bottleneck_size,
training,
'hr_conv_share',
reuse=None if i == 0 else True)
if training:
survival_roll = tf.random_uniform(shape=[],
minval=0.0,
maxval=1.0,
name='suvival' + str(i))
survive = tf.less(survival_roll, survival_rate)
dummy_zero = tf.zeros_like(resblock)
x = tf.cond(survive, lambda: tf.add(shortcut, resblock),
lambda: tf.add(dummy_zero, shortcut))
else:
x = tf.add(tf.mul(resblock, survival_rate), shortcut)
x = tf.nn.relu(x)
x = tf.layers.conv2d(x, 3, 1, activation=None, name='out', reuse=reuse)
return x
def build_model(x, scale, training, reuse):
hidden_size = 128
bottleneck_size = 32
x = tf.layers.conv2d(x,
hidden_size,
1,
activation=None,
name='in',
reuse=reuse)
for i in range(10):
x = util.crop_by_pixel(x, 1) + conv(x, hidden_size, bottleneck_size,
training, 'conv' + str(i), reuse)
x = tf.nn.relu(x)
x = tf.layers.conv2d(x, 3, 1, activation=None, name='out', reuse=reuse)
return x
lr_image_padded = util.pad_boundary(lr_image)
sr_image = model.build_model(lr_image_padded - 0.5,
FLAGS.scale,
training=False,
reuse=False)
sr_image = util.crop_center(sr_image, hr_image_shape)
if data.residual:
if data.resize:
sr_image += lr_image
else:
sr_image += util.resize_func(lr_image, hr_image_shape)
sr_image = sr_image * tf.uint8.max + 0.5
sr_image = tf.saturate_cast(sr_image, tf.uint8)
sr_image = tf.cast(sr_image, tf.float32)
sr_image = sr_image * (1.0 / tf.uint8.max)
sr_image = util.crop_by_pixel(sr_image, FLAGS.scale + 6)
hr_image = util.crop_by_pixel(hr_image, FLAGS.scale + 6)
error = tf.losses.mean_squared_error(hr_image, sr_image)
init = tf.global_variables_initializer()
init_local = tf.local_variables_initializer()
saver = tf.train.Saver()
error_acc = .0
psnr_acc = .0
acc = 0
with tf.Session() as sess:
sess.run(init_local)
if tf.gfile.Exists(
FLAGS.model_file) or tf.gfile.Exists(FLAGS.model_file +
'.index'):
saver.restore(sess, FLAGS.model_file)
