TensorFlow模型实现：UNet模型

傷城~ 2022-04-23 05:42 503阅读 0赞

# TensorFlow模型实现：UNet模型 #

--------------------

## 1.UNet模型 ##

# -*-coding: utf-8 -*-
    """
        @Project: triple_path_networks
        @File   : UNet.py
        @Author : panjq
        @E-mail : pan_jinquan@163.com
        @Date   : 2019-01-24 11:18:15
    """
    import tensorflow as tf
    import tensorflow.contrib.slim as slim
    
    
    def lrelu(x):
        return tf.maximum(x * 0.2, x)
    
    activation_fn=lrelu
    
    def UNet(inputs, reg):  # Unet
        conv1 = slim.conv2d(inputs, 32, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv1_1', weights_regularizer=reg)
        conv1 = slim.conv2d(conv1, 32, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv1_2',weights_regularizer=reg)
        pool1 = slim.max_pool2d(conv1, [2, 2], padding='SAME')
    
        conv2 = slim.conv2d(pool1, 64, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv2_1',weights_regularizer=reg)
        conv2 = slim.conv2d(conv2, 64, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv2_2',weights_regularizer=reg)
        pool2 = slim.max_pool2d(conv2, [2, 2], padding='SAME')
    
        conv3 = slim.conv2d(pool2, 128, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv3_1',weights_regularizer=reg)
        conv3 = slim.conv2d(conv3, 128, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv3_2',weights_regularizer=reg)
        pool3 = slim.max_pool2d(conv3, [2, 2], padding='SAME')
    
        conv4 = slim.conv2d(pool3, 256, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv4_1',weights_regularizer=reg)
        conv4 = slim.conv2d(conv4, 256, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv4_2',weights_regularizer=reg)
        pool4 = slim.max_pool2d(conv4, [2, 2], padding='SAME')
    
        conv5 = slim.conv2d(pool4, 512, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv5_1',weights_regularizer=reg)
        conv5 = slim.conv2d(conv5, 512, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv5_2',weights_regularizer=reg)
    
        up6 = upsample_and_concat(conv5, conv4, 256, 512)
        conv6 = slim.conv2d(up6, 256, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv6_1',weights_regularizer=reg)
        conv6 = slim.conv2d(conv6, 256, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv6_2',weights_regularizer=reg)
    
        up7 = upsample_and_concat(conv6, conv3, 128, 256)
        conv7 = slim.conv2d(up7, 128, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv7_1',weights_regularizer=reg)
        conv7 = slim.conv2d(conv7, 128, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv7_2',weights_regularizer=reg)
    
        up8 = upsample_and_concat(conv7, conv2, 64, 128)
        conv8 = slim.conv2d(up8, 64, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv8_1',weights_regularizer=reg)
        conv8 = slim.conv2d(conv8, 64, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv8_2',weights_regularizer=reg)
    
        up9 = upsample_and_concat(conv8, conv1, 32, 64)
        conv9 = slim.conv2d(up9, 32, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv9_1', weights_regularizer=reg)
        conv9 = slim.conv2d(conv9, 32, [3, 3], rate=1, activation_fn=activation_fn, scope='g_conv9_2',weights_regularizer=reg)
        print("conv9.shape:{}".format(conv9.get_shape()))
    
        type='UNet_1X'
        with tf.variable_scope(name_or_scope="output"):
            if type=='UNet_3X':#UNet放大三倍
                conv10 = slim.conv2d(conv9, 27, [1, 1], rate=1, activation_fn=None, scope='g_conv10',weights_regularizer=reg)
                out = tf.depth_to_space(conv10, 3)
            if type=='UNet_1X':#输入输出维度相同
                out = slim.conv2d(conv9, 6, [1, 1], rate=1, activation_fn=None, scope='g_conv10',weights_regularizer=reg)
        return out
    
    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
    
    if __name__=="__main__":
        weight_decay=0.001
        reg = slim.l2_regularizer(scale=weight_decay)
        inputs = tf.ones(shape=[4, 100, 200, 3])
        out=UNet(inputs,reg)
        print("net1.shape:{}".format(inputs.get_shape()))
        print("out.shape:{}".format(out.get_shape()))
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())