Coding-CGAN-条件生成对抗网络-基于高阶FCN数据

编程 评论

摘要

main.py包括高阶FCN的处理，生成对抗网络的训练
generate.py使用生成器生成样本

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import argparse
import os
import numpy as np
import math

import torchvision.transforms as transforms
from torchvision.utils import save_image

from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F
import torch


from scipy.io import loadmat
from torch.utils import data  # 导入PyTorch数据工具模块
import random
from scipy.stats import pearsonr


os.makedirs("train_images", exist_ok=True)
os.makedirs("test_images", exist_ok=True)
os.makedirs("save_model", exist_ok=True)

parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=4, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="噪声的维度")
parser.add_argument("--n_classes", type=int, default=2, help="类别数量")
parser.add_argument("--img_size", type=int, default=116, help="功能连接矩阵的维度")
parser.add_argument("--channels", type=int, default=1, help="矩阵通道")
parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
opt = parser.parse_args()
print(opt)

img_shape = (opt.channels, opt.img_size, opt.img_size)

cuda = True if torch.cuda.is_available() else False

class my_dataset(data.Dataset):
    def __init__(self, Hig_X,label_):
        self.Hig_X = np.expand_dims(Hig_X,1)    # 加上通道数
        self.label = label_

    def __getitem__(self, index):
        X = self.Hig_X[index]  # 获取高阶FCN
        label = self.label[index]
        return X,label
    def __len__(self):
        return self.Hig_X.shape[0]  # 返回数据集的长度


def calculate_similarity(matrix1, matrix2):
    correlation, _ = pearsonr(matrix1.flatten(), matrix2.flatten())
    return correlation


# 精度判断，即计算两个矩阵的相关系数
def calculate_accuracy(generator, dataloader, device, val_subjects=None):
    generator.eval()
    correct_predictions = 0

    for batch_idx, (batch_matrix) in enumerate(dataloader):
        matrix = batch_matrix.to(device)
        size = matrix.size(0)
        random_noise = torch.randn(size, 100).to(device=device)

        with torch.no_grad():
            gen_matrix = generator(random_noise)

        for i in range(size):
            similarity = calculate_similarity(gen_matrix[i].cpu().numpy(), matrix[i].cpu().numpy())

            # 阈值是一个经验值，根据实际情况调整
            if similarity > 0.6 and (val_subjects is None or batch_idx in val_subjects):
                correct_predictions += 1

    accuracy = correct_predictions / len(dataloader.dataset)
    return accuracy

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes)

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(opt.latent_dim + opt.n_classes, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, noise, labels):
        # Concatenate label embedding and image to produce input
        gen_input = torch.cat((self.label_emb(labels), noise), -1)
        img = self.model(gen_input)
        img = img.view(img.size(0), *img_shape)
        return img


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        self.label_embedding = nn.Embedding(opt.n_classes, opt.n_classes)

        self.model = nn.Sequential(
            nn.Linear(opt.n_classes + int(np.prod(img_shape)), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 512),
            nn.Dropout(0.4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 512),
            nn.Dropout(0.4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 1),
        )

    def forward(self, img, labels):
        # Concatenate label embedding and image to produce input
        d_in = torch.cat((img.view(img.size(0), -1), self.label_embedding(labels)), -1)
        validity = self.model(d_in)
        return validity


# Loss functions
adversarial_loss = torch.nn.MSELoss()

# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()

if cuda:
    generator.cuda()
    discriminator.cuda()
    adversarial_loss.cuda()

# Configure data loader
    
# 原始文件路径
fMRI_file_path = './/ROISignals_insomnia_aal116.mat'

# 加载数据
fMRI_data = loadmat(fMRI_file_path)['ROISignals']

# 正常人为1,病人为0
fMRI_label = torch.cat((torch.ones(32,1),torch.zeros(30,1)),dim=0).squeeze()

# 低阶矩阵计算
Low_X_ = []
for i in range(fMRI_data.shape[2]):  
    temp = np.corrcoef(fMRI_data[:,:,i],rowvar=False)
    Low_X_.append(temp)
Low_X = np.array(Low_X_) #(62,116,116)

# 高阶矩阵计算
Hig_X_ = []
for i in range(Low_X.shape[0]):  
    temp = np.corrcoef(Low_X[:,:,i],rowvar=False)
    Hig_X_.append(temp)
Hig_X = np.array(Hig_X_)  #(62,116,116)


random_index = np.random.permutation(len(fMRI_label))
Hig_X = Hig_X[random_index]
fMRI_label = fMRI_label[random_index]

train_data = Hig_X[:50]
train_label = fMRI_label[:50]
test_data = Hig_X[50:]
test_label = fMRI_label[:50]

train_dataset = my_dataset(train_data,train_label)
test_dataset = my_dataset(test_data,test_label)

train_loader =  data.DataLoader(train_dataset,batch_size=opt.batch_size,shuffle=True)
test_loader = data.DataLoader(test_dataset,batch_size=opt.batch_size,shuffle=False)

# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor

# ----------
#  Training
# ----------

for epoch in range(opt.n_epochs):
    for i, (imgs, labels) in enumerate(train_loader):

        batch_size = imgs.shape[0]

        # Adversarial ground truths
        valid = Variable(FloatTensor(batch_size, 1).fill_(1.0), requires_grad=False)
        fake = Variable(FloatTensor(batch_size, 1).fill_(0.0), requires_grad=False)

        # Configure input
        real_imgs = Variable(imgs.type(FloatTensor))
        labels = Variable(labels.type(LongTensor))

        # -----------------
        #  Train Generator
        # -----------------

        optimizer_G.zero_grad()

        # Sample noise and labels as generator input
        z = Variable(FloatTensor(np.random.normal(0, 1, (batch_size, opt.latent_dim))))
        gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, batch_size)))

        # Generate a batch of images
        gen_imgs = generator(z, gen_labels)

        # Loss measures generator's ability to fool the discriminator
        validity = discriminator(gen_imgs, gen_labels)
        g_loss = adversarial_loss(validity, valid)

        g_loss.backward()
        optimizer_G.step()

        # ---------------------
        #  Train Discriminator
        # ---------------------

        optimizer_D.zero_grad()

        # Loss for real images
        validity_real = discriminator(real_imgs, labels)
        d_real_loss = adversarial_loss(validity_real, valid)

        # Loss for fake images
        validity_fake = discriminator(gen_imgs.detach(), gen_labels)
        d_fake_loss = adversarial_loss(validity_fake, fake)

        # Total discriminator loss
        d_loss = (d_real_loss + d_fake_loss) / 2

        d_loss.backward()
        optimizer_D.step()

        print(
            "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
            % (epoch, opt.n_epochs, i, len(train_loader), d_loss.item(), g_loss.item())
        )

        batches_done = epoch * len(train_loader) + i

        # 存储训练过程的结果
        if batches_done % opt.sample_interval == 0:  #一个epoch中每隔多少间隔保存一次
            gen_imgs_normalized = (gen_imgs - gen_imgs.min()) / (gen_imgs.max() - gen_imgs.min()) 
            save_image(gen_imgs_normalized.data[:25], "train_images/%d.png" % batches_done, nrow=5, normalize=False)

    # 测试过程
     
    with torch.no_grad():
        best_acc = 0
        generator.eval()
        correct_predictions = 0
        for imgs, label in test_loader:
            z = Variable(FloatTensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))
            gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, imgs.shape[0])))
            gen_imgs = generator(z, gen_labels)

            for i in range(imgs.shape[0]):
                similarity = calculate_similarity(gen_imgs[i].cpu().numpy(), imgs[i].cpu().numpy())

                # 阈值是一个经验值，根据实际情况调整
                if similarity > 0.6:
                    correct_predictions += 1

        accuracy = correct_predictions / len(test_loader.dataset)
        # 保存epoch中精度最好的模型
        if best_acc < accuracy:
            best_acc = accuracy
            #保存模型
            torch.save(generator.state_dict(), "save_model/best_model.pth")

        print('epoch:'+str(epoch)+' 测试集acc:'+str(accuracy)+" best_acc:"+str(best_acc))
        gen_imgs_normalized = (gen_imgs - gen_imgs.min()) / (gen_imgs.max() - gen_imgs.min()) 
        save_image(gen_imgs_normalized.data[:25], "test_images/acc_{}epoch_{}.png".format(accuracy,epoch), nrow=5, normalize=False)

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import argparse
import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from torch.autograd import Variable
from torchvision.utils import save_image
import os

parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=4, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="噪声的维度")
parser.add_argument("--n_classes", type=int, default=2, help="类别数量")
parser.add_argument("--img_size", type=int, default=116, help="功能连接矩阵的维度")
parser.add_argument("--channels", type=int, default=1, help="矩阵通道")
parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
opt = parser.parse_args()
print(opt)
img_shape = (opt.channels, opt.img_size, opt.img_size)
cuda = True if torch.cuda.is_available() else False
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes)

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(opt.latent_dim + opt.n_classes, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, noise, labels):
        # Concatenate label embedding and image to produce input
        gen_input = torch.cat((self.label_emb(labels), noise), -1)
        img = self.model(gen_input)
        img = img.view(img.size(0), *img_shape)
        return img
    
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor

os.makedirs("Generated_samples", exist_ok=True)

generator = Generator()
generator.cuda()
# 指定保存的模型文件路径
model_path = 'save_model\\best_model.pth'

# 加载保存的模型状态字典
generator.load_state_dict(torch.load(model_path))

# 将模型设置为评估模式
generator.eval()
# 设置生成样本数
batch_size = 25

z = Variable(FloatTensor(np.random.normal(0, 1, (batch_size, opt.latent_dim))))
gen_labels = Variable(LongTensor(np.random.randint(0, opt.n_classes, batch_size)))
gen_imgs = generator(z , gen_labels)
# 标准化
gen_imgs_normalized = (gen_imgs - gen_imgs.min()) / (gen_imgs.max() - gen_imgs.min()) 
# 存储样本和标签
np.savez('Generated_samples/Generated_samples.npz', array1=gen_imgs.detach().cpu(), array2=gen_labels.detach().cpu())
#存储图片
save_image(gen_imgs_normalized.data[:25], "Generated_samples/acc_epoch_.png", nrow=25, normalize=False)

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