卷积生成对抗网络判别器的output是如何工作的，可以有全连接层吗？

Question

我正在构建一个 DCGAN，我遇到了 output 的形状问题，当我尝试计算 BCELoss 时，它与标签的形状不匹配。

要生成鉴别器 output，我是否必须一直使用卷积，或者我可以在某个点添加一个线性层以匹配我想要的形状？

我的意思是，我是否必须通过添加更多卷积层来减少形状，或者我可以添加一个完全连接的层？ 我认为它应该有一个全连接层，但在每个教程中我检查了鉴别器没有全连接层。

import random
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as torch_dataset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML

seed = 1
print("Random Seed: ", seed)
random.seed(seed)
torch.manual_seed(seed)
images_folder_path = "./spectrograms/"

batch_size = 1
image_size = 256
n_channels = 1
z_vector = 100
n_features_generator = 32
n_features_discriminator = 32
num_epochs = 5
lr = 0.0002
beta1 = 0.5

dataset = torch_dataset.ImageFolder(
    root=images_folder_path, transform=transforms.Compose(
        [
            transforms.Grayscale(num_output_channels=1),
            transforms.Resize(image_size),
            transforms.CenterCrop(image_size),
            transforms.ToTensor(),
            transforms.Normalize(0.5, 0.5)
         ]
    )
)

dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=0)

device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")


def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(z_vector, n_features_generator * 8, 4, 1, bias=False),
            nn.BatchNorm2d(n_features_generator * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(n_features_generator * 8, n_features_generator * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(n_features_generator * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(n_features_generator * 4, n_features_generator * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(n_features_generator * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(n_features_generator * 2, n_features_generator, 4, 2, 1, bias=False),
            nn.BatchNorm2d(n_features_generator),
            nn.ReLU(True),
            nn.ConvTranspose2d(n_features_generator, n_channels, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, inputs):
        return self.main(inputs)

# Convolutional Layer Output Shape = [(W−K+2P)/S]+1
# W is the input volume
# K is the Kernel size
# P is the padding
# S is the stride
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(n_channels, n_features_discriminator, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(n_features_discriminator, n_features_discriminator * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(n_features_discriminator * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(n_features_discriminator * 2, n_features_discriminator * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(n_features_discriminator * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(n_features_discriminator * 4, n_features_discriminator * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(n_features_discriminator * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(n_features_discriminator * 8, 1, 4, 1, bias=False),
        )

    def forward(self, inputs):
        return self.main(inputs)


netG = Generator().to(device)
if device.type == 'cuda':
    netG = nn.DataParallel(netG)
netG.apply(weights_init)
print(netG)

netD = Discriminator().to(device)
if device.type == 'cuda':
    netD = nn.DataParallel(netD)
netD.apply(weights_init)
print(netD)

criterion = nn.BCEWithLogitsLoss()

fixed_noise = torch.randn(64, z_vector, 1, 1, device=device)

real_label = 1.
fake_label = 0.

optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))

img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")
for epoch in range(num_epochs):
    for i, data in enumerate(dataloader, 0):
        netD.zero_grad()
        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
        output = netD(real_cpu)
        print(output.shape)
        print(label.shape)
        output = output.view(-1)
        errD_real = criterion(output, label)
        errD_real.backward()
        D_x = output.mean().item()

        noise = torch.randn(b_size, z_vector, 1, 1, device=device)
        fake = netG(noise)
        label.fill_(fake_label)
        output = netD(fake.detach()).view(-1)
        errD_fake = criterion(output, label)
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        errD = errD_real + errD_fake
        optimizerD.step()

        netG.zero_grad()
        label.fill_(real_label)
        output = netD(fake).view(-1)
        errG = criterion(output, label)
        errG.backward()
        D_G_z2 = output.mean().item()
        optimizerG.step()

        if i % 50 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        G_losses.append(errG.item())
        D_losses.append(errD.item())

        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

        iters += 1

我得到的错误：

Traceback (most recent call last):
  File "G:/Pastas Estruturadas/Conhecimento/CEFET/IA/SpectroGAN/dcgan.py", line 140, in <module>
    errD_real = criterion(output, label)
  File "C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\modules\loss.py", line 631, in forward
    reduction=self.reduction)
  File "C:\Users\Ramon\anaconda3\envs\vision\lib\site-packages\torch\nn\functional.py", line 2538, in binary_cross_entropy_with_logits
    raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([1])) must be the same as input size (torch.Size([169]))

output 的形状： torch.Size([1, 1, 13, 13]) ，label 的形状： torch.Size([1]) 。

Answer 1

DCGAN 描述了一个具体的架构，其中 Conv 层用于对特征图进行下采样。 如果您仔细设计您的 Conv 层，您可以不使用线性层，但这并不意味着当您使用线性层进行下采样（尤其是作为最后一层）时它不起作用。 DCGAN 论文刚刚发现使用 Conv 层而不是 Linear 来进行下采样效果更好。

如果您想维护此架构，您可以更改 kernel 大小或填充或步幅，以在最后一层为您提供准确的单个值。 请参阅有关 Conv 层的 Pytorch 文档，以查看 output 的大小应该是多少，给定输入大小