用DCGAN训练并生成自己的图像集(含tensorflow代码)

其他 2018-11-13 10:10:45 阅读次数: 0

第一片代码model_CT.py用于G和D的构造


# -*- coding: utf-8 -*-
"""
Created on Tue Jul 24 20:33:14 2018
E-mail: [email protected]
@author: DidiLv
"""


import tensorflow as tf
import numpy as np


# pooling and convolution definition
def conv2d(x, W):
    return tf.nn.conv2d(input = x, filter = W, strides = [1,1,1,1], padding = 'SAME')

def avg_pool_2x2(x):
    return tf.nn.avg_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = 'SAME')

def xavier_init(size):
    in_dim = size[0]
    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
    return tf.random_normal(shape=size, stddev=xavier_stddev)
def sample_z(shape):
    return np.random.uniform(-1., 1., size=shape)

# discriminator
def discriminator(x_image, reuse=False):
    with tf.variable_scope('discriminator') as scope:
        if (reuse):
            tf.get_variable_scope().reuse_variables()
        #First Conv and Pool Layers
        W_conv1 = tf.get_variable('d_wconv1', shape = [5, 5, 1, 8], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_conv1 = tf.get_variable('d_bconv1', shape = [8], initializer=tf.constant_initializer(0))
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)        
        h_pool1 = avg_pool_2x2(h_conv1)

        #Second Conv and Pool Layers
        W_conv2 = tf.get_variable('d_wconv2', shape = [5, 5, 8, 16], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_conv2 = tf.get_variable('d_bconv2', shape = [16], initializer=tf.constant_initializer(0))
        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
        h_pool2 = avg_pool_2x2(h_conv2)
        ##----------------------------------------------------------------------------->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        #Third Conv and Pool Layers
        W_conv3 = tf.get_variable('d_wconv3', shape = [5, 5, 16, 32], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_conv3 = tf.get_variable('d_bconv3', shape = [32], initializer=tf.constant_initializer(0))
        h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
        h_pool3 = avg_pool_2x2(h_conv3)

        W_conv4 = tf.get_variable('d_wconv4', shape = [5, 5, 32, 64], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_conv4 = tf.get_variable('d_bconv4', shape = [64], initializer=tf.constant_initializer(0))
        h_conv4 = tf.nn.relu(conv2d(h_pool3, W_conv4) + b_conv4)
        h_pool4 = avg_pool_2x2(h_conv4)

        #First Fully Connected Layer
        W_fc1 = tf.get_variable('d_wfc1', [14 * 12 * 64, 320], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_fc1 = tf.get_variable('d_bfc1', [320], initializer=tf.constant_initializer(0))
        h_pool4_flat = tf.reshape(h_pool4, [-1, 14 * 12 * 64]) # reshape the tensor into vector form
        h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1)

        #Second Fully Connected Layer
        W_fc2 = tf.get_variable('d_wfc2', [320, 80], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_fc2 = tf.get_variable('d_bfc2', [80], initializer=tf.constant_initializer(0))
        h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)


        #Third Fully Connected Layer
        W_fc3 = tf.get_variable('d_wfc3', [80, 1], initializer=tf.truncated_normal_initializer(stddev=0.02))
        b_fc3 = tf.get_variable('d_bfc3', [1], initializer=tf.constant_initializer(0))

        #Final Layer
        y_conv=(tf.matmul(h_fc2, W_fc3) + b_fc3)
    return y_conv


# generator from DCGAN, take a d-dimensional vector as input and upsample it to become a 28*28 image
# the structure is from https://arxiv.org/pdf/1511.06434v2.pdf
def generator(z, batch_size, z_dim, reuse = False):
    with tf.variable_scope('generator') as scope:
        if (reuse):
            tf.get_variable_scope().reuse_variables()
        ## number of filters for the first layer of generator 
        g_dim = 64
        ## color dimension of output 
        c_dim = 1
        ## size of output image
        s_w = 221
        s_h = 181
        s_w2, s_w4, s_w8, s_w16, s_w32, s_w64 = int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16), int(s_w/32), int(s_w/64)
        s_h2, s_h4, s_h8, s_h16, s_h32, s_h64 = int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16), int(s_h/32), int(s_h/64)

        # h0 dimension is [batch_size, z_width, z_height, z_channel] 
        h0 = tf.reshape(z, [batch_size, s_w64+1, s_h64+1, g_dim]) # s_w64, s_h64 = [3,2] --> 4*3*64
        h0 = tf.nn.relu(h0)
        ##Dimensions of h0 = batch_size x 4 x 3 x 64 = batch_size*768

        # first decovolution layer (fractionally-strided convolution layer)

        ## useful link for convolution :
        ## https://blog.csdn.net/mao_xiao_feng/article/details/71713358
        output1_shape = [batch_size, s_w32+1, s_h32+1, c_dim*256] # s_w32, s_h32 = [6,5] --> 7*6*256
        ## W_conv1 shape = [filter_height, filter_width, out_channels, in_channels]
        W_conv1 = tf.get_variable('g_wconv1', shape = [5,5,output1_shape[-1],int(h0.get_shape()[-1])],
                                    initializer=tf.truncated_normal_initializer(stddev = 0.1)
                                    )
        b_conv1 = tf.get_variable('g_bconv1', shape = [output1_shape[-1]], initializer=tf.constant_initializer(.1))
        ## H_conv1: h0 * W_conv1.T
        H_conv1 = tf.nn.conv2d_transpose(h0, W_conv1, output_shape = output1_shape, strides = [1,2,2,1], 
                                         padding = 'SAME')
        H_conv1 = tf.add(H_conv1, b_conv1)
        H_conv1 = tf.contrib.layers.batch_norm(inputs = H_conv1, center=True, scale=True, is_training=True, scope="g_bn1")
        H_conv1 = tf.nn.relu(H_conv1)
        ##Dimensions of H_conv1 = batch_size x 7 x 6 x ?

        # second deconvolution layer
        output2_shape = [batch_size, s_w16+1, s_h16+1, c_dim*128] # s_w16, s_h16 = [13,11] --> 14*12*?
        W_conv2 = tf.get_variable('g_wconv2', shape = [5,5,output2_shape[-1], int(H_conv1.get_shape()[-1])],
                                  initializer=tf.truncated_normal_initializer(stddev = 0.1))
        b_conv2 = tf.get_variable('g_bconv2', shape = [output2_shape[-1]], initializer=tf.truncated_normal_initializer(0.1))
        H_conv2 = tf.nn.conv2d_transpose(H_conv1, W_conv2, output_shape = output2_shape, strides = [1,2,2,1],
                               padding = 'SAME')
        H_conv2 = tf.add(H_conv2, b_conv2)
        H_conv2 = tf.contrib.layers.batch_norm(inputs = H_conv2, center=True, scale=True, is_training=True, scope="g_bn2")    
        ##Dimensions of H_conv2 = batch_size x 14 x 12 x ?
        H_conv2 = tf.nn.relu(H_conv2)


        #third DeConv Layer
        output3_shape = [batch_size, s_w8+1, s_h8+1, c_dim*64] # s_w8, s_h8 = [27, 22] --> 28*23*?
        W_conv3 = tf.get_variable('g_wconv3', [5, 5, output3_shape[-1], int(H_conv2.get_shape()[-1])], 
                                  initializer=tf.truncated_normal_initializer(stddev=0.1))
        b_conv3 = tf.get_variable('g_bconv3', [output3_shape[-1]], initializer=tf.constant_initializer(.1))
        H_conv3 = tf.nn.conv2d_transpose(H_conv2, W_conv3, output_shape=output3_shape, strides=[1, 2, 2, 1], 
                                         padding='SAME')
        H_conv3 = tf.add(H_conv3, b_conv3)
        H_conv3 = tf.contrib.layers.batch_norm(inputs = H_conv3, center=True, scale=True, is_training=True, scope="g_bn3")
        H_conv3 = tf.nn.relu(H_conv3)
        #Dimensions of H_conv3 = batch_size x 28 x 23 x ?

        #forth DeConv Layer
        output4_shape = [batch_size, s_w4+1, s_h4+1, c_dim*32] # s_w4, s_h8 = [55, 45] --> 56*46*?
        W_conv4 = tf.get_variable('g_wconv4', [5, 5, output4_shape[-1], int(H_conv3.get_shape()[-1])], 
                                  initializer=tf.truncated_normal_initializer(stddev=0.1))
        b_conv4 = tf.get_variable('g_bconv4', [output4_shape[-1]], initializer=tf.constant_initializer(.1))
        H_conv4 = tf.nn.conv2d_transpose(H_conv3, W_conv4, output_shape=output4_shape, strides=[1, 2, 2, 1], 
                                         padding='SAME')
        H_conv4 = tf.add(H_conv4, b_conv4)
        H_conv4 = tf.contrib.layers.batch_norm(inputs = H_conv4, center=True, scale=True, is_training=True, scope="g_bn4")
        H_conv4 = tf.nn.relu(H_conv4)
        #Dimensions of H_conv3 = batch_size x 56 x 46 x ?

        #fifth DeConv Layer
        output5_shape = [batch_size, s_w2+1, s_h2+1, c_dim*16] # s_w4, s_h8 = [110, 90] --> 111*91*?
        W_conv5 = tf.get_variable('g_wconv5', [5, 5, output5_shape[-1], int(H_conv4.get_shape()[-1])], 
                                  initializer=tf.truncated_normal_initializer(stddev=0.1))
        b_conv5 = tf.get_variable('g_bconv5', [output5_shape[-1]], initializer=tf.constant_initializer(.1))
        H_conv5 = tf.nn.conv2d_transpose(H_conv4, W_conv5, output_shape=output5_shape, strides=[1, 2, 2, 1], 
                                         padding='SAME')
        H_conv5 = tf.add(H_conv5, b_conv5)
        H_conv5 = tf.contrib.layers.batch_norm(inputs = H_conv5, center=True, scale=True, is_training=True, scope="g_bn5")
        H_conv5 = tf.nn.relu(H_conv5)
        #Dimensions of H_conv3 = batch_size x 111 x 91 x ?

        #Sixth DeConv Layer
        output6_shape = [batch_size, s_w, s_h, c_dim]
        W_conv6 = tf.get_variable('g_wconv6', [5, 5, output6_shape[-1], int(H_conv5.get_shape()[-1])], 
                                  initializer=tf.truncated_normal_initializer(stddev=0.1))
        b_conv6 = tf.get_variable('g_bconv6', [output6_shape[-1]], initializer=tf.constant_initializer(.1))
        H_conv6 = tf.nn.conv2d_transpose(H_conv5, W_conv6, output_shape=output6_shape, strides=[1, 2, 2, 1], 
                                         padding='SAME')
        H_conv6 = tf.add(H_conv6, b_conv6)
        H_conv6 = tf.nn.tanh(H_conv6)
        #Dimensions of H_conv4 = batch_size x 28 x 28 x 1
        return H_conv6

第二片代码data_generate_CT.py设计pipeline用于读取batch数据：

# -*- coding: utf-8 -*-
"""
Created on Thu Jul 19 15:40:11 2018
E-mail: [email protected]
@author: DidiLv
"""



import tensorflow as tf
import numpy as np
import os
import matplotlib.pyplot as plt


def get_files(file_dir):
    lung_img = [];
    label_lung_img = [];
    for file in os.listdir(file_dir):
        lung_img.append( file_dir + file)
        label_lung_img.append(1)

    image_list = np.hstack((lung_img))

    label_list = np.hstack((label_lung_img))

    temp = np.array([lung_img, label_lung_img]).T
    #利用shuffle打乱数据
    np.random.shuffle(temp)
    image_list = list(temp[:,0])
    label_list = list(temp[:,1])
    label_list = [int(i) for i in label_list]
    return image_list, label_list
#       
#将上面生成的List传入get_batch() ，转换类型，产生一个输入队列queue，因为img和lab  
#是分开的，所以使用tf.train.slice_input_producer()，然后用tf.read_file()从队列中读取图像
def get_batch(image,label,batch_size):

    image_W, image_H = 221, 181 

    #将python.list类型转换成tf能够识别的格式
    image=tf.cast(image,tf.string)
    label=tf.cast(label,tf.int32)

    #产生一个输入队列queue
    epoch_num = 50 #防止无限循环
    input_queue=tf.train.slice_input_producer([image,label], num_epochs=epoch_num)

    label=input_queue[1]
    image_contents=tf.read_file(input_queue[0])
    #将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等。  
    image=tf.image.decode_png(image_contents,channels=1)

    #将数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮。
    image=tf.image.resize_image_with_crop_or_pad(image,image_W,image_H)
    image=tf.image.per_image_standardization(image)

    #生成batch
    min_after_dequeue=1000
    capacity=min_after_dequeue+300*batch_size
    image_batch,label_batch=tf.train.shuffle_batch([image,label],batch_size=batch_size,num_threads=1024,capacity=capacity,min_after_dequeue=min_after_dequeue)

    #重新排列标签，行数为[batch_size]
#    label_batch=tf.reshape(label_batch,[batch_size])
    image_batch = tf.reshape(image_batch,[batch_size,image_W,image_H,1])
    image_batch=tf.cast(image_batch,np.float32)

    return image_batch, label_batch

if __name__ == "__main__":
    #训练样本在本地磁盘中的地址
    file_dir='D:\\CT_data\\Data_preprocessing\\' # 这里是输入数据的地址   
    image_list, label_list = get_files(file_dir)
    image_batch, label_batch = get_batch(image_list, label_list, 28)
    with tf.Session() as sess:
        ## 初始化工作
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        j = 0
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)

        try:
            while not coord.should_stop() and j<5: # 加上i其实是强制终止线程，但是文件队列线程并没有结束，因为我们产生的文件队列结束为epoch_num个epoch
#                for i in range(5):
                img, label = sess.run([image_batch, label_batch])

                # just test one batch
#                for j in np.arange(64):
#                    print('label: %d' %label[j])
#                    plt.imshow(img[j,:,:,0])
#                    plt.show()
                plt.imshow(img[0,:,:,0])
                plt.show()
                j+=1
#                j = 1

        except tf.errors.OutOfRangeError:
            print('done!')
        finally:
            coord.request_stop()
            print('-----------')
        coord.join(threads)

第三片代码train_CT.py用于训练GAN

# -*- coding: utf-8 -*-
"""
Created on Fri Jul 27 14:57:23 2018

@author: DidiLv
"""

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 25 09:42:35 2018
E-mail: [email protected]
@author: DidiLv
"""
import model_CT

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#import random
import data_generate_CT


#训练样本在本地磁盘中的地址
file_dir='D:\\CT_data\\Data_preprocessing\\' # 这里是输入数据的地址   


tf.reset_default_graph()
batch_size = 10
image_W = 221
image_H = 181 
image_C = 1
z_dimensions = 4*3*64

image_list, label_list = data_generate_CT.get_files(file_dir)
image_batch, _ = data_generate_CT.get_batch(image_list, label_list, batch_size)
# reset the graph to reset all variables we test before


##---------------------------------------------------------------------------------------------
# discriminator for input
#x_placeholder = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28, 1])
#x_placeholder = tf.Variable(image_batch,dtype = tf.float32)# it's wrong if image_batch is already a tensor 
x_placeholder = image_batch
#z_placeholder = tf.placeholder(dtype = tf.float32, shape = [None,z_dimensions])
z_placeholder = tf.Variable(np.random.normal(-1, 1, size=[batch_size, z_dimensions]), dtype = tf.float32)
##--------------------------------------------------------------------------------------------

Dx = model_CT.discriminator(x_placeholder) # for real training data
Gz = model_CT.generator(z_placeholder, batch_size, z_dimensions)
Dg = model_CT.discriminator(Gz, reuse=True)


g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.ones_like(Dg)))
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dx, labels = tf.ones_like(Dx)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels = tf.zeros_like(Dx)))
d_loss = d_loss_real + d_loss_fake

tvars = tf.trainable_variables()
d_vars = [var for var in tvars if 'd_' in var.name]
g_vars = [var for var in tvars if 'g_' in var.name]

with tf.variable_scope(tf.get_variable_scope(), reuse = False):
    # var_list: tf.Variable to update to minimize loss
    trainerD = tf.train.AdadeltaOptimizer(learning_rate = 1e-3).minimize(d_loss, var_list = d_vars)
    trainerG = tf.train.AdadeltaOptimizer(learning_rate = 1e-3).minimize(g_loss, var_list = g_vars)



iterations = 3000
##------------------------------------------------------------------------------------------------------------------------------------------------
#for i in range(iterations):
#    _,dLoss = sess.run([trainerD, d_loss]) #Update the discriminator
#    _,gLoss = sess.run([trainerG, g_loss]) #Update the generator
#    print((dLoss+gLoss))
##----------------------------------------------------------------------------------------------------------------------------------------------
with tf.Session() as sess:
        ## 初始化工作
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        i = 0
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)

        try:
            while not coord.should_stop() and i<iterations: # 加上i其实是强制终止线程，但是文件队列线程并没有结束，因为我们产生的文件队列结束为epoch_num个epoch                
                print(i)               
                _,dLoss = sess.run([trainerD, d_loss]) #Update the discriminator
                _,gLoss = sess.run([trainerG, g_loss]) #Update the generator
                print((dLoss+gLoss))
                i+=1

        except tf.errors.OutOfRangeError:
            print('done!')
        finally:
            coord.request_stop()
            print('-----------')
        coord.join(threads)

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