mirror of
https://github.com/igv/FSRCNN-TensorFlow.git
synced 2026-08-18 17:17:29 +08:00
72 lines
3.1 KiB
Python
72 lines
3.1 KiB
Python
import tensorflow as tf
|
|
|
|
class Model(object):
|
|
|
|
def __init__(self, config):
|
|
self.name = "FSRCNN"
|
|
# Different model layer counts and filter sizes for FSRCNN vs FSRCNN-s (fast), (d, s, m) in paper
|
|
model_params = [[56, 12, 4], [32, 8, 1]]
|
|
self.model_params = model_params[config.fast]
|
|
self.scale = config.scale
|
|
self.radius = config.radius
|
|
self.padding = config.padding
|
|
self.images = config.images
|
|
self.batch = config.batch
|
|
self.label_size = config.label_size
|
|
self.c_dim = config.c_dim
|
|
|
|
def model(self):
|
|
|
|
d, s, m = self.model_params
|
|
|
|
# Feature Extraction
|
|
size = self.padding + 1
|
|
weights = tf.get_variable('w1', shape=[size, size, 1, d], initializer=tf.variance_scaling_initializer(0.1))
|
|
biases = tf.get_variable('b1', initializer=tf.zeros([d]))
|
|
conv = tf.nn.conv2d(self.images, weights, strides=[1,1,1,1], padding='VALID', data_format='NHWC')
|
|
conv = self.prelu(tf.nn.bias_add(conv, biases, data_format='NHWC'), 1)
|
|
|
|
# Shrinking
|
|
if self.model_params[1] > 0:
|
|
weights = tf.get_variable('w2', shape=[1, 1, d, s], initializer=tf.variance_scaling_initializer(2))
|
|
biases = tf.get_variable('b2', initializer=tf.zeros([s]))
|
|
conv = tf.nn.conv2d(conv, weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
|
|
conv = self.prelu(tf.nn.bias_add(conv, biases, data_format='NHWC'), 2)
|
|
else:
|
|
s = d
|
|
|
|
# Mapping (# mapping layers = m)
|
|
for i in range(3, m + 3):
|
|
weights = tf.get_variable('w{}'.format(i), shape=[3, 3, s, s], initializer=tf.variance_scaling_initializer(2))
|
|
biases = tf.get_variable('b{}'.format(i), initializer=tf.zeros([s]))
|
|
conv = tf.nn.conv2d(conv, weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
|
|
conv = self.prelu(tf.nn.bias_add(conv, biases, data_format='NHWC'), i)
|
|
|
|
# Expanding
|
|
if self.model_params[1] > 0:
|
|
expand_weights = tf.get_variable('w{}'.format(m + 3), shape=[1, 1, s, d], initializer=tf.variance_scaling_initializer(2))
|
|
expand_biases = tf.get_variable('b{}'.format(m + 3), initializer=tf.zeros([d]))
|
|
conv = tf.nn.conv2d(conv, expand_weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
|
|
conv = self.prelu(tf.nn.bias_add(conv, expand_biases, data_format='NHWC'), m + 3)
|
|
|
|
# Deconvolution
|
|
deconv_size = self.radius * self.scale * 2 + 1
|
|
deconv_weights = tf.get_variable('w{}'.format(m + 4), shape=[deconv_size, deconv_size, 1, d], initializer=tf.variance_scaling_initializer(0.01))
|
|
deconv_biases = tf.get_variable('b{}'.format(m + 4), initializer=tf.zeros([1]))
|
|
deconv_output = [self.batch, self.label_size, self.label_size, self.c_dim]
|
|
deconv_stride = [1, self.scale, self.scale, 1]
|
|
deconv = tf.nn.conv2d_transpose(conv, deconv_weights, output_shape=deconv_output, strides=deconv_stride, padding='SAME', data_format='NHWC')
|
|
deconv = tf.nn.bias_add(deconv, deconv_biases, data_format='NHWC')
|
|
|
|
return deconv
|
|
|
|
def prelu(self, _x, i):
|
|
"""
|
|
PreLU tensorflow implementation
|
|
"""
|
|
alphas = tf.get_variable('alpha{}'.format(i), _x.get_shape()[-1], initializer=tf.constant_initializer(0.2), dtype=tf.float32)
|
|
|
|
return tf.nn.relu(_x) - alphas * tf.nn.relu(-_x)
|
|
|
|
|