TensorFlow模型实现：UNet模型

TensorFlow模型实现：UNet模型

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1.UNet模型

# -*-coding: utf-8 -*-
"""
    @Project: triple_path_networks
    @File   : UNet.py
    @Author : panjq
    @E-mail : [email protected]
    @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())