File size: 5,925 Bytes
82567db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
#/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 22-02-09
# @Author : [email protected]
import tensorflow.compat.v1 as tf
import tf_slim as slim
def relu(x):
return tf.nn.relu(x)
def upsample_and_sum(x1, x2,output_channels,in_channels):
pool_size = 2
deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02))
deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1])
deconv_output = tf.add(deconv,x2)
return deconv_output
def upsample_and_concat(x1, x2, output_channels, in_channels):
pool_size = 2
deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02))
deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1])
deconv_output = tf.concat([deconv, x2], 3)
deconv_output.set_shape([None, None, None, output_channels * 2])
return deconv_output
def sc_net_1f(input):
# scratch capture single frame denoise network
# unet_2down_res_relu_64c5
with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(),
weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None):
conv1 = slim.conv2d(input, 64, [3, 3], rate=1, activation_fn=relu, scope='conv1_1')
res_conv1 = slim.conv2d(conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_1')
res_conv1 = slim.conv2d(res_conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_2')
res_block1 = conv1 + res_conv1
pool2 = slim.avg_pool2d(res_block1,[2,2],padding='SAME')
res_conv2 = slim.conv2d(pool2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_1')
res_conv2 = slim.conv2d(res_conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_2')
res_block2 = pool2 + res_conv2
pool3 = slim.avg_pool2d(res_block2,[2,2],padding='SAME')
res_conv3 = slim.conv2d(pool3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_1')
res_conv3 = slim.conv2d(res_conv3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_2')
res_block3 = pool3 + res_conv3
deconv1 = upsample_and_sum(res_block3, res_block2, 64, 64)
conv4 = slim.conv2d(deconv1, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv4_1')
res_conv4 = slim.conv2d(conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_1')
res_conv4 = slim.conv2d(res_conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_2')
res_block4 = conv4 + res_conv4
deconv2 = upsample_and_sum(res_block4, res_block1, 64, 64)
conv5 = slim.conv2d(deconv2, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv5_1')
res_conv5 = slim.conv2d(conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_1')
res_conv5 = slim.conv2d(res_conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_2')
res_block5 = conv5 + res_conv5
conv6 = slim.conv2d(res_block5, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv6_1')
conv7 = slim.conv2d(conv6, 4, [3, 3], rate=1, stride=1, activation_fn=None, scope='conv7_1')
out = conv7
return out
def sc_net(input):
with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(),
weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None):
conv1 = slim.conv2d(input, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv1_1')
conv1 = slim.conv2d(conv1, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv1_2')
pool1 = slim.max_pool2d(conv1, [2, 2], padding='SAME')
conv2 = slim.conv2d(pool1, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv2_1')
conv2 = slim.conv2d(conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv2_2')
pool2 = slim.max_pool2d(conv2, [2, 2], padding='SAME')
conv3 = slim.conv2d(pool2, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv3_1')
conv3 = slim.conv2d(conv3, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv3_2')
pool3 = slim.max_pool2d(conv3, [2, 2], padding='SAME')
conv4 = slim.conv2d(pool3, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv4_1')
conv4 = slim.conv2d(conv4, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv4_2')
pool4 = slim.max_pool2d(conv4, [2, 2], padding='SAME')
conv5 = slim.conv2d(pool4, 512, [3, 3], rate=1, activation_fn=relu, scope='g_conv5_1')
conv5 = slim.conv2d(conv5, 512, [3, 3], rate=1, activation_fn=relu, scope='g_conv5_2')
up6 = upsample_and_concat(conv5, conv4, 256, 512)
conv6 = slim.conv2d(up6, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv6_1')
conv6 = slim.conv2d(conv6, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv6_2')
up7 = upsample_and_concat(conv6, conv3, 128, 256)
conv7 = slim.conv2d(up7, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv7_1')
conv7 = slim.conv2d(conv7, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv7_2')
up8 = upsample_and_concat(conv7, conv2, 64, 128)
conv8 = slim.conv2d(up8, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv8_1')
conv8 = slim.conv2d(conv8, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv8_2')
up9 = upsample_and_concat(conv8, conv1, 32, 64)
conv9 = slim.conv2d(up9, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv9_1')
conv9 = slim.conv2d(conv9, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv9_2')
conv10 = slim.conv2d(conv9, 4, [1, 1], rate=1, activation_fn=relu, scope='g_conv10')
out = conv10
return out
|