mirror of
https://github.com/igv/FSRCNN-TensorFlow.git
synced 2025-12-12 06:14:08 +08:00
e3015e62dc
- decrease stride by 1 to overlap patches - train only once on the same image per epoch - replace low quality image - default model is 8-0-4-1
90 lines
4.0 KiB
Python
90 lines
4.0 KiB
Python
import tensorflow.compat.v1 as tf
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from utils import gradient_sensitive_loss, tf_ms_ssim
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class Model(object):
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def __init__(self, config):
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self.name = "FSRCNN"
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# Different model layer counts and filter sizes for FSRCNN vs FSRCNN-s (fast), (d, s, m) in paper
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model_params = [8, 0, 4, 1]
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self.model_params = model_params
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self.scale = config.scale
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self.radius = config.radius
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self.padding = config.padding
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self.images = config.images
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self.batch = config.batch
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self.image_size = config.image_size - self.padding
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self.label_size = config.label_size
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def model(self):
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d, s, m, r = self.model_params
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# Feature Extraction
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size = self.padding + 1
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weights = tf.get_variable('w1', shape=[size, size, 1, d], initializer=tf.variance_scaling_initializer(0.1))
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biases = tf.get_variable('b1', initializer=tf.zeros([d]))
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features = tf.nn.conv2d(self.images, weights, strides=[1,1,1,1], padding='VALID', data_format='NHWC')
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features = tf.nn.bias_add(features, biases, data_format='NHWC')
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# Shrinking
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if self.model_params[1] > 0:
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features = self.prelu(features, 1)
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weights = tf.get_variable('w2', shape=[1, 1, d, s], initializer=tf.variance_scaling_initializer(2))
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biases = tf.get_variable('b2', initializer=tf.zeros([s]))
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features = tf.nn.conv2d(features, weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
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features = tf.nn.bias_add(features, biases, data_format='NHWC')
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else:
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s = d
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conv = features
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# Mapping (# mapping layers = m)
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with tf.variable_scope("mapping_block") as scope:
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for ri in range(r):
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for i in range(3, m + 3):
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weights = tf.get_variable('w{}'.format(i), shape=[3, 3, s, s], initializer=tf.variance_scaling_initializer(2))
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biases = tf.get_variable('b{}'.format(i), initializer=tf.zeros([s]))
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if i > 3:
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conv = self.prelu(conv, i)
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conv = tf.nn.conv2d(conv, weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
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conv = tf.nn.bias_add(conv, biases, data_format='NHWC')
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if i == m + 2:
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conv = self.prelu(conv, m + 3)
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weights = tf.get_variable('w{}'.format(m + 3), shape=[1, 1, s, s], initializer=tf.variance_scaling_initializer(2))
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biases = tf.get_variable('b{}'.format(m + 3), initializer=tf.zeros([s]))
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conv = tf.nn.conv2d(conv, weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
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conv = tf.nn.bias_add(conv, biases, data_format='NHWC')
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conv = tf.add(conv, features)
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scope.reuse_variables()
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conv = self.prelu(conv, 2)
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# Expanding
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if self.model_params[1] > 0:
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expand_weights = tf.get_variable('w{}'.format(m + 4), shape=[1, 1, s, d], initializer=tf.variance_scaling_initializer(2))
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expand_biases = tf.get_variable('b{}'.format(m + 4), initializer=tf.zeros([d]))
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conv = tf.nn.conv2d(conv, expand_weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
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conv = tf.nn.bias_add(conv, expand_biases, data_format='NHWC')
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conv = self.prelu(conv, m + 4)
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# Sub-pixel convolution
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size = self.radius * 2 + 1
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deconv_weights = tf.get_variable('deconv_w', shape=[size, size, d, self.scale**2], initializer=tf.variance_scaling_initializer(0.01))
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deconv_biases = tf.get_variable('deconv_b', initializer=tf.zeros([self.scale**2]))
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deconv = tf.nn.conv2d(conv, deconv_weights, strides=[1,1,1,1], padding='SAME', data_format='NHWC')
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deconv = tf.nn.bias_add(deconv, deconv_biases, data_format='NHWC')
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if self.scale > 1:
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deconv = tf.depth_to_space(deconv, self.scale, name='pixel_shuffle', data_format='NHWC')
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return deconv
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def prelu(self, _x, i):
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"""
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PreLU tensorflow implementation
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"""
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alphas = tf.get_variable('alpha{}'.format(i), _x.get_shape()[-1], initializer=tf.constant_initializer(0.2), dtype=tf.float32)
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return tf.nn.relu(_x) - alphas * tf.nn.relu(-_x)
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def loss(self, Y, X):
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return gradient_sensitive_loss(Y, X)
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