2025年【笔记】ACGAN：采用辅助分类器使GAN获取图像分类功能

科技前沿 • 2025-03-16 07:08 • 阅读 24

大家好，我是讯享网，很高兴认识大家。

class Discriminator(nn.Module): def __init__(self, num_classes): """ initialize :param image_size: tuple (3, h, w) """ super().__init__() self.num_classes = num_classes net = [] # 1:预先定义 channels_in = [3+self.num_classes, 64, 128, 256] channels_out = [64, 128, 256, 512] padding = [1, 1, 1, 0] for i in range(len(channels_in)): net.append(nn.Conv2d(in_channels=channels_in[i], out_channels=channels_out[i], kernel_size=4, stride=2, padding=padding[i], bias=False)) if i == 0: net.append(nn.LeakyReLU(0.2)) else: net.append(nn.BatchNorm2d(num_features=channels_out[i])) net.append(nn.LeakyReLU(0.2)) net.append(nn.Dropout(0.5)) self.classify = nn.Linear(in_features=3*3*512, out_features=num_classes) self.softmax = nn.Softmax(dim=1) self.disciminate = nn.Conv2d(in_channels=512, out_channels=1, kernel_size=3, stride=1, padding=0) self.sigmoid = nn.Sigmoid() self.discriminator = nn.Sequential(*net) def forward(self, x, label): label = label.unsqueeze(2).unsqueeze(3) label = label.repeat(1, 1, x.size(2), x.size(3)) data = torch.cat(tensors=(x, label), dim=1) out = self.discriminator(data) out_ = out.view(x.size(0), -1) classsify = self.softmax(self.classify(out_)) real_or_fake = self.sigmoid(self.disciminate(out)) return real_or_fake.view(x.size(0), -1), classsify

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讯享网d_out_real_dis, d_out_real_cls = discriminator(image, onehot_label) real_loss_dis = bce_loss(d_out_real_dis, real_label) real_loss_cls = nll_loss(d_out_real_cls, label)

2025年【笔记】ACGAN：采用辅助分类器使GAN获取图像分类功能

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