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GAN-生成对抗网络(Pytorch)合集(2)--pixtopix-CycleGAN
pixtopix(像素到像素)
原文连接：https://arxiv.org/pdf/1611.07004.pdf
输入一个域的图片转换为另一个域的图片（白天照片转成黑夜）
如下图，输入标记图片，输出真实图片缺点就是训练集两个域的图片要一一对应，所以叫pixtopix，
网络结构有点复杂，用到了语义分割的UNET网络结构
数据集：
地址忘了，也是官方的，想起来补
代码：这里是建筑物labels to facade的例子import torch import torch.nn as nn import torch.nn.functional as F from torch.utils import data import torchvision from torchvision import transforms import numpy as np import matplotlib.pyplot as plt import os import glob from PIL import Image # jpg是原始图片 images_path = glob.glob(r'base\*.jpg') annos_path = glob.glob(r'base\*.png') # png是分割的图片 transform = transforms.Compose([ transforms.ToTensor(), transforms.Resize((256, 256)), transforms.Normalize(0.5, 0.5) ]) class CMP_dataset(data.Dataset): def __init__(self, imgs_path, annos_path): self.imgs_path = imgs_path self.annos_path = annos_path def __getitem__(self, item): img_path = self.imgs_path[item] anno_path = self.annos_path[item] pil_img = Image.open(img_path) pil_img = transform(pil_img) anno_img = Image.open(anno_path) anno_img = anno_img.convert('RGB') pil_anno = transform(anno_img) return pil_anno, pil_img def __len__(self): return len(self.imgs_path) dataset = CMP_dataset(images_path, annos_path) batchsize = 32 dataloader = data.DataLoader(dataset, batch_size=batchsize, shuffle=True) annos_batch, images_batch = next(iter(dataloader)) for i, (anno, img) in enumerate(zip(annos_batch[:3], images_batch[:3])): anno = (anno.permute(1, 2, 0).numpy()+1)/2 img = (img.permute(1, 2, 0).numpy()+1)/2 plt.subplot(3, 2, i*2+1) plt.title('input_img') plt.imshow(anno) plt.subplot(3, 2, i*2+2) plt.title('output_img') plt.imshow(img) plt.show() # 定义下采样模块 class Downsample(nn.Module): def __init__(self, in_channels, out_channels): super(Downsample, self).__init__() self.conv_relu = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, 2, 1), nn.LeakyReLU(inplace=True) ) self.bn = nn.BatchNorm2d(out_channels) def forward(self, x, is_bn=True): x = self.conv_relu(x) if is_bn: x = self.bn(x) return x # 定义上采样模块 class Upsample(nn.Module): def __init__(self, in_channels, out_channels): super(Upsample, self).__init__() self.upconv_relu = nn.Sequential( nn.ConvTranspose2d(in_channels, out_channels, 3, 2, 1, output_padding=1), nn.LeakyReLU(inplace=True) ) self.bn = nn.BatchNorm2d(out_channels) def forward(self, x, is_drop=False): x = self.upconv_relu(x) x = self.bn(x) if is_drop: x = F.dropout2d(x) return x # 定义生成器，包含6个下采样，5上采样，1输出 class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() self.down1 = Downsample(3, 64) # 64,128,128 self.down2 = Downsample(64, 128) # 128,64,64 self.down3 = Downsample(128, 256) # 256,32,32 self.down4 = Downsample(256, 512) # 512, 16,16 self.down5 = Downsample(512, 512) # 512,8,8 self.down6 = Downsample(512, 512) # 512, 4,4 self.up1 = Upsample(512, 512) # 512 ,8,8 self.up2 = Upsample(1024, 512) # 512, 16,16 self.up3 = Upsample(1024, 256) # 256, 32,32 self.up4 = Upsample(512, 128) # 128,64,64 self.up5 = Upsample(256, 64) # 64,128,128 self.last = nn.ConvTranspose2d(128, 3, kernel_size=3, stride=2, padding=1, output_padding=1) def forward(self,x): x1 = self.down1(x) x2 = self.down2(x1) x3 = self.down3(x2) x4 = self.down4(x3) x5 = self.down5(x4) x6 = self.down6(x5) x6 = self.up1(x6, is_drop=True) x6 = torch.cat([x6, x5], dim=1) x6 = self.up2(x6, is_drop=True) x6 = torch.cat([x6, x4], dim=1) x6 = self.up3(x6, is_drop=True) x6 = torch.cat([x6, x3], dim=1) x6 = self.up4(x6, is_drop=True) x6 = torch.cat([x6, x2], dim=1) x6 = self.up5(x6) x6 = torch.cat([x6, x1], dim=1) x6 = torch.tanh(self.last(x6)) return x6 # 定义判别器 输入anno + img class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.down1 = Downsample(6, 64) # 64*128*128 self.down2 = Downsample(64, 128) # 128*64*64 self.conv1 = nn.Conv2d(128, 256, 3) self.bn1 = nn.BatchNorm2d(256) self.conv2 = nn.Conv2d(256, 1, 3) def forward(self, anno, img): x = torch.cat([anno, img], axis=1) # batch*6*h*w x = self.down1(x, is_bn=False) x = self.down2(x) x = F.dropout2d(self.bn1(F.leaky_relu(self.conv1(x)))) x = torch.sigmoid(self.conv2(x)) # batch*1* 60*60 return x device = 'cuda' if torch.cuda.is_available() else 'cpu' if device == 'cuda': print('using cuda:', torch.cuda.get_device_name(0)) else: print(device) Gen = Generator().to(device) Dis = Discriminator().to(device) d_optimizer = torch.optim.Adam(Dis.parameters(), lr=1e-3, betas=(0.5, 0.999)) g_optimizer = torch.optim.Adam(Gen.parameters(), lr=1e-3, betas=(0.5, 0.999)) # loss # cgan损失 loss_fn = torch.nn.BCELoss() # L1-loss 后面计算，求差绝对值的求和 # 绘图 def generator_images(model, test_anno, test_real): prediction = model(test_anno).permute(0, 2, 3, 1).detach().cpu().numpy() test_anno = test_anno.permute(0, 2, 3, 1).detach().cpu().numpy() test_real = test_real.permute(0, 2, 3, 1).detach().cpu().numpy() plt.figure(figsize=(10, 10)) display_list = [test_anno[0], test_real[0], prediction[0]] title = ['input', 'ground truth', 'output'] for i in range(3): plt.subplot(1, 3, i+1) plt.title(title[i]) plt.imshow(display_list[i]) plt.axis('off') plt.show() # 加载extend为测试 test_imgs_path = glob.glob('extended/*.jpg') test_annos_path = glob.glob('extended/*.png') test_dataset = CMP_dataset(test_imgs_path, test_annos_path) test_daloader = torch.utils.data.DataLoader( test_dataset, batch_size=batchsize ) # 返回一个批次 annos_batch, images_batch = next(iter(dataloader)) plt.figure(figsize=(6, 10)) for i, (anno, img) in enumerate(zip(annos_batch[:3], images_batch[:3])): anno = (anno.permute(1, 2, 0).numpy()+1)/2 img = (img.permute(1, 2, 0).numpy()+1)/2 plt.subplot(3, 2, i*2+1) plt.title('input_img') plt.imshow(anno) plt.subplot(3, 2, i*2+2) plt.title('output_img') plt.imshow(img) plt.show() annos_batch, images_batch = annos_batch.to(device), images_batch.to(device) LAMBDA = 7 # L1损失权重 D_loss = [] G_loss = [] for epoch in range(300): D_epoch_loss = 0 G_epoch_loss = 0 count = len(dataloader) for step, (annos, imgs) in enumerate(dataloader): imgs = imgs.to(device) annos = annos.to(device) d_optimizer.zero_grad() disc_real_output = Dis(annos, imgs) # 输入真实成对图片 d_real_loss = loss_fn(disc_real_output, torch.ones_like(disc_real_output, device=device) ) d_real_loss.backward() gen_output = Gen(annos) dis_gen_output = Dis(annos, gen_output.detach()) d_fake_loss = loss_fn(dis_gen_output, torch.zeros_like(dis_gen_output, device=device) ) d_fake_loss.backward() disc_loss = d_real_loss + d_fake_loss d_optimizer.step() disc_gen_out = Dis(annos, gen_output) gen_loss_crossentropyloss = loss_fn(disc_gen_out, torch.ones_like(disc_gen_out, device=device) ) gen_l1_loss = torch.mean(torch.abs(gen_output - imgs)) gen_loss = LAMBDA * gen_l1_loss + gen_loss_crossentropyloss gen_loss.backward() g_optimizer.step() with torch.no_grad(): D_epoch_loss += disc_loss.item() G_epoch_loss += gen_loss.item() with torch.no_grad(): D_epoch_loss /= count G_epoch_loss /= count D_loss.append(D_epoch_loss) G_loss.append(G_epoch_loss) print('Epoch', epoch) generator_images(Gen, annos_batch, images_batch)- 1
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给动漫素描自动上色的（AI上色）移步我的kaggle
https://www.kaggle.com/code/jiyuanhai/pix2pix-test-pytorchCycleGAN
这个厉害👍，我愿称之为最强，克服了pixtopix需要数据集一一对应的缺点
论文地址：https://arxiv.org/pdf/1703.10593.pdf
【推荐同济子豪兄】或者看论文详解：https://www.bilibili.com/video/BV1Ya411a78P?spm_id_from=333.999.0.0&vd_source=66d85dad339b02807124d27ef76332c9
B站也有很多讲的不错的视频。
创新型的提出了循环一致性损失，具体技术不多赘述了，有些复杂。import torch import torch.nn as nn import torch.nn.functional as F from torch.utils import data import torchvision from torchvision import transforms import numpy as np import matplotlib.pyplot as plt import os import glob from PIL import Image import itertools apples_path = glob.glob(r'E:\深度之眼深度学习\GAN生成对抗网络实战(PyTorch版)\00、代码+课件+数据\cyclegan-pytorch参考代码—日月光华\data\trainA\*.jpg') # 画图显示 # plt.figure(figsize=(8, 8)) # for i, imh_path in enumerate(apples_path[:4]): # img = Image.open(imh_path) # np_image = np.array(img) # plt.subplot(2, 2, i+1) # plt.imshow(np_image) # plt.title(str(np_image.shape)) # plt.show() oranges_path = glob.glob(r'E:\深度之眼深度学习\GAN生成对抗网络实战(PyTorch版)\00、代码+课件+数据\cyclegan-pytorch参考代码—日月光华\data\trainB\*.jpg') # plt.figure(figsize=(8, 8)) # for i, imh_path in enumerate(oranges_path[:4]): # img = Image.open(imh_path) # np_image = np.array(img) # plt.subplot(2, 2, i+1) # plt.imshow(np_image) # plt.title(str(np_image.shape)) # plt.show() apples_test_path = glob.glob(r'E:\深度之眼深度学习\GAN生成对抗网络实战(PyTorch版)\00、代码+课件+数据\cyclegan-pytorch参考代码—日月光华\data\trainA\*.jpg') #数据集已经处理成了256，不用裁减 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(0.5, 0.5) ]) class AO_Dataset(data.Dataset): def __init__(self, img_path): # 初始化方法 self.img_path = img_path def __getitem__(self, index): imgpath = self.img_path[index] pil_img = Image.open(imgpath) pil_img = transform(pil_img) return pil_img def __len__(self): return len(self.img_path) apple_dataset = AO_Dataset(apples_path) orange_dataset = AO_Dataset(oranges_path) apple_test_dataset = AO_Dataset(apples_test_path) BATHSIZE = 2 NUMWORKERS = 10 apple_dataloader = data.DataLoader(apple_dataset, batch_size=BATHSIZE, shuffle=True, #num_workers=NUMWORKERS ) orange_dataloader = data.DataLoader(orange_dataset, batch_size=BATHSIZE, shuffle=True, #num_workers=NUMWORKERS ) apple_dl_test = data.DataLoader( apple_test_dataset, batch_size=BATHSIZE, shuffle=True ) # 创建模型 # 定义下采样模块 class Downsample(nn.Module): def __init__(self, in_channels, out_channels): super(Downsample, self).__init__() self.conv_relu = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, 2, 1), nn.LeakyReLU(inplace=True) ) self.bn = nn.InstanceNorm2d(out_channels) def forward(self, x, is_bn=True): x = self.conv_relu(x) if is_bn: x = self.bn(x) return x # 定义上采样模块 class Upsample(nn.Module): def __init__(self, in_channels, out_channels): super(Upsample, self).__init__() self.upconv_relu = nn.Sequential( nn.ConvTranspose2d(in_channels, out_channels, 3, 2, 1, output_padding=1), nn.LeakyReLU(inplace=True) ) self.bn = nn.InstanceNorm2d(out_channels) def forward(self, x, is_drop=False): x = self.upconv_relu(x) x = self.bn(x) if is_drop: x = F.dropout2d(x) return x # 定义生成器，包含6个下采样，5上采样，1输出 class Generator(nn.Module): def __init__(self): super(Generator, self).__init__() self.down1 = Downsample(3, 64) # 64,128,128 self.down2 = Downsample(64, 128) # 128,64,64 self.down3 = Downsample(128, 256) # 256,32,32 self.down4 = Downsample(256, 512) # 512, 16,16 self.down5 = Downsample(512, 512) # 512,8,8 self.down6 = Downsample(512, 512) # 512, 4,4 self.up1 = Upsample(512, 512) # 512 ,8,8 self.up2 = Upsample(1024, 512) # 512, 16,16 self.up3 = Upsample(1024, 256) # 256, 32,32 self.up4 = Upsample(512, 128) # 128,64,64 self.up5 = Upsample(256, 64) # 64,128,128 self.last = nn.ConvTranspose2d(128, 3, kernel_size=3, stride=2, padding=1, output_padding=1) def forward(self,x): x1 = self.down1(x) x2 = self.down2(x1) x3 = self.down3(x2) x4 = self.down4(x3) x5 = self.down5(x4) x6 = self.down6(x5) x6 = self.up1(x6, is_drop=True) x6 = torch.cat([x6, x5], dim=1) x6 = self.up2(x6, is_drop=True) x6 = torch.cat([x6, x4], dim=1) x6 = self.up3(x6, is_drop=True) x6 = torch.cat([x6, x3], dim=1) x6 = self.up4(x6, is_drop=True) x6 = torch.cat([x6, x2], dim=1) x6 = self.up5(x6) x6 = torch.cat([x6, x1], dim=1) x6 = torch.tanh(self.last(x6)) return x6 # 定义判别器 输入 class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.down1 = Downsample(3, 64) # 128 self.down2 = Downsample(64, 128) # 64 self.last = nn.Conv2d(128, 1, 3) def forward(self, img): x = self.down1(img) x = self.down2(x) x = torch.sigmoid(self.last(x)) # batch*1* 60*60 return x device = 'cuda' if torch.cuda.is_available() else 'cpu' if device == 'cuda': print('using cuda:', torch.cuda.get_device_name(0)) else: print(device) # 初始化两个生成器（A->B B->A）， gen_AB = Generator().to(device) gen_BA = Generator().to(device) # 两个判别器 dis_A = Discriminator().to(device) dis_B = Discriminator().to(device) # 损失函数 # 1，对抗loss BCE # 2，cycle consistance loss # 3，identit loss BECLoss = torch.nn.BCELoss() L1_loss = torch.nn.L1Loss() gen_optimizer = torch.optim.Adam( itertools.chain( gen_AB.parameters(), gen_BA.parameters() ), lr=2e-4, betas=(0.5, 0.999) ) dis_A_optimizer = torch.optim.Adam( dis_A.parameters(), lr=2e-4, betas=(0.5, 0.999) ) dis_B_optimizer = torch.optim.Adam( dis_B.parameters(), lr=2e-4, betas=(0.5, 0.999) ) def generate_images(model, test_input): prediction = model(test_input).permute(0, 2, 3, 1).detach().cpu().numpy() test_input = test_input.permute(0, 2, 3, 1).cpu().numpy() plt.figure(figsize=(10, 6)) display_list = [test_input[0], prediction[0]] title = ['Input Image', 'Genrated Image'] for i in range(2): plt.subplot(1, 2, i+1) plt.title(title[i]) plt.imshow(display_list[i] * 0.5 + 0.5) plt.axis('off') plt.show() test_batch = next(iter(apple_dl_test)) test_input = torch.unsqueeze(test_batch[0], 0).to(device) D_loss = [] G_loss = [] epoches = 50 for epoch in range(epoches): d_epoch_loss = 0 g_epoch_loss = 0 for step, (real_A, real_B) in enumerate(zip(apple_dataloader, orange_dataloader)): real_A = real_A.to(device) real_B = real_B.to(device) # 训练生成器 gen_optimizer.zero_grad() # identity loss same_B = gen_AB(real_B) identity_B_loss = L1_loss(same_B, real_B) same_A = gen_BA(real_A) identity_A_loss = L1_loss(same_A, real_A) # 对抗损失 gan loss fake_B = gen_AB(real_A) D_pre_fake_B = dis_B(fake_B) gen_loss_AB = BECLoss(D_pre_fake_B, torch.ones_like(D_pre_fake_B, device=device)) fake_A = gen_BA(real_B) D_pre_fake_A = dis_A(fake_A) gen_loss_BA = BECLoss(D_pre_fake_A, torch.ones_like(D_pre_fake_A, device=device)) # 循环一致性损失 recovered_A = gen_BA(fake_B) cycle_loss_ABA = L1_loss(recovered_A, real_A) recovered_B = gen_AB(fake_A) cycle_loss_BAB = L1_loss(recovered_B, real_B) g_loss = identity_A_loss +identity_B_loss +gen_loss_AB +\ gen_loss_BA+cycle_loss_ABA+cycle_loss_BAB g_loss.backward() gen_optimizer.step() # dis_A train dis_A_optimizer.zero_grad() dis_A_real_output = dis_A(real_A) dis_A_real_loss = BECLoss(dis_A_real_output, torch.ones_like(dis_A_real_output, device=device)) dis_A_fake_output = dis_A(fake_A.detach()) dis_A_fake_loss = BECLoss(dis_A_fake_output, torch.zeros_like(dis_A_fake_output, device=device)) dis_A_loss = dis_A_real_loss + dis_A_fake_loss dis_A_loss.backward() dis_A_optimizer.step() # dis_B train dis_B_optimizer.zero_grad() dis_B_real_output = dis_B(real_B) dis_B_real_loss = BECLoss(dis_B_real_output, torch.ones_like(dis_B_real_output, device=device)) dis_B_fake_output = dis_B(fake_B.detach()) dis_B_fake_loss = BECLoss(dis_B_fake_output, torch.zeros_like(dis_B_fake_output, device=device)) dis_B_loss = dis_B_fake_loss + dis_B_real_loss dis_B_loss.backward() dis_B_optimizer.step() with torch.no_grad(): g_epoch_loss += g_loss.item() d_epoch_loss += (dis_A_loss + dis_B_loss).item() with torch.no_grad(): g_epoch_loss /= (step+1) d_epoch_loss /= (step+1) D_loss.append(d_epoch_loss) G_loss.append(g_epoch_loss) print('Epoch:', epoch+1) print('g_epoch_loss:', g_epoch_loss) print('d_epoch_loss:', d_epoch_loss) generate_images(gen_AB, test_input) # test_input is apple torch.save(gen_AB, 'Gen_AB.pth', _use_new_zipfile_serialization=False) torch.save(gen_BA, 'Gen_BA.pth', _use_new_zipfile_serialization=False) torch.save(dis_B, 'Dis_B.pth', _use_new_zipfile_serialization=False) torch.save(dis_A, 'Dis_A.pth', _use_new_zipfile_serialization=False)- 1
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- 原文地址：https://blog.csdn.net/qq_45882032/article/details/125620988
