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DCGAN代码解析（一）

代码
2023-03-14 22:57:17 时间

DCGAN代码解析

今天我们将对GAN领域中经典的论文DCGAN做一个简单的解析。


论文地址：Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks-ReadPaper论文阅读平台


1 初始化

import argparse
import os
import numpy as np
import torch
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=64, 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="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=1, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=400, help="interval between image sampling")
opt = parser.parse_args(args=[])
print(opt)


Namespace(b1=0.5, b2=0.999, batch_size=64, channels=1, img_size=32, latent_dim=100, lr=0.0002, n_cpu=8, n_epochs=200, sample_interval=400)


2 数据加载

加载后的数据为 32 * 32 的灰度图

import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision import datasets
# Configure data loader
os.makedirs("../../data/mnist", exist_ok=True)
dataloader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "../../data/mnist",
        train=True,
        download=True,
        transform=transforms.Compose(
            [transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
        ),
    ),
    batch_size=opt.batch_size,
    shuffle=True,
)
from torch.autograd import Variable
import matplotlib.pyplot as plt
def show_img(img, trans=True):
    if trans:
        img = np.transpose(img.detach().cpu().numpy(), (1, 2, 0))  # 把channel维度放到最后
        plt.imshow(img[:, :, 0], cmap="gray")
    else:
        plt.imshow(img, cmap="gray")
    plt.show()
   
mnist = datasets.MNIST("../../data/mnist")
for i in range(3):
    sample = mnist[i][0]
    show_img(np.array(sample), trans=False)


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[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-eC4sWl8G-1664249499188)(test_files/test_6_2.png)]

trans_resize = transforms.Resize(opt.img_size)
trans_to_tensor = transforms.ToTensor()
trans_normalize = transforms.Normalize([0.5], [0.5]) # x_n = (x - 0.5) / 0.5
print("shape =", np.array(sample).shape, '
')
print("data =", np.array(sample), '
')
sample_resize = trans_resize(sample) 
print("(trans_resize) shape =", np.array(sample_resize).shape, '
')
sample_tensor = trans_to_tensor(sample_resize)
print("(trans_to_tensor) data =", sample_tensor, '
')
sample_normalize = trans_normalize(sample_tensor)
print("(trans_normalize) data =", sample_normalize, '
')
shape = (28, 28) 
data = [[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0  67 232  39   0   0   0   0   0]
 [  0   0   0   0  62  81   0   0   0   0   0   0   0   0   0   0   0   0
    0   0 120 180  39   0   0   0   0   0]
 [  0   0   0   0 126 163   0   0   0   0   0   0   0   0   0   0   0   0
    0   2 153 210  40   0   0   0   0   0]
 [  0   0   0   0 220 163   0   0   0   0   0   0   0   0   0   0   0   0
    0  27 254 162   0   0   0   0   0   0]
 [  0   0   0   0 222 163   0   0   0   0   0   0   0   0   0   0   0   0
    0 183 254 125   0   0   0   0   0   0]
 [  0   0   0  46 245 163   0   0   0   0   0   0   0   0   0   0   0   0
    0 198 254  56   0   0   0   0   0   0]
 [  0   0   0 120 254 163   0   0   0   0   0   0   0   0   0   0   0   0
   23 231 254  29   0   0   0   0   0   0]
 [  0   0   0 159 254 120   0   0   0   0   0   0   0   0   0   0   0   0
  163 254 216  16   0   0   0   0   0   0]
 [  0   0   0 159 254  67   0   0   0   0   0   0   0   0   0  14  86 178
  248 254  91   0   0   0   0   0   0   0]
 [  0   0   0 159 254  85   0   0   0  47  49 116 144 150 241 243 234 179
  241 252  40   0   0   0   0   0   0   0]
 [  0   0   0 150 253 237 207 207 207 253 254 250 240 198 143  91  28   5
  233 250   0   0   0   0   0   0   0   0]
 [  0   0   0   0 119 177 177 177 177 177  98  56   0   0   0   0   0 102
  254 220   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  254 137   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  254  57   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  254  57   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  255  94   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  254  96   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  254 153   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 169
  255 153   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  96
  254 153   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]
 [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
    0   0   0   0   0   0   0   0   0   0]] 
(trans_resize) shape = (32, 32) 
(trans_to_tensor) data = tensor([[[0., 0., 0.,  ..., 0., 0., 0.],
         [0., 0., 0.,  ..., 0., 0., 0.],
         [0., 0., 0.,  ..., 0., 0., 0.],
         ...,
         [0., 0., 0.,  ..., 0., 0., 0.],
         [0., 0., 0.,  ..., 0., 0., 0.],
         [0., 0., 0.,  ..., 0., 0., 0.]]]) 
(trans_normalize) data = tensor([[[-1., -1., -1.,  ..., -1., -1., -1.],
         [-1., -1., -1.,  ..., -1., -1., -1.],
         [-1., -1., -1.,  ..., -1., -1., -1.],
         ...,
         [-1., -1., -1.,  ..., -1., -1., -1.],
         [-1., -1., -1.,  ..., -1., -1., -1.],
         [-1., -1., -1.,  ..., -1., -1., -1.]]])


3 模型

3.1生成器

包含1个全连接层和3个卷积层，使用LeakyReLU和Tanh激活函数，使用了BatchNorm和Upsample

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import torch.nn as nn
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.init_size = opt.img_size // 4
        self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))
        self.conv_blocks = nn.Sequential(
            nn.BatchNorm2d(128),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(128, 128, 3, stride=1, padding=1),
            nn.BatchNorm2d(128, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(128, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),
            nn.Tanh(),
        )
    def forward(self, z):
        out = self.l1(z)
        out = out.view(out.shape[0], 128, self.init_size, self.init_size)
        img = self.conv_blocks(out)
        return img
   
generator = Generator()
print(generator)
Generator(
  (l1): Sequential(
    (0): Linear(in_features=100, out_features=8192, bias=True)
  )
  (conv_blocks): Sequential(
    (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (1): Upsample(scale_factor=2, mode=nearest)
    (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): BatchNorm2d(128, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
    (4): LeakyReLU(negative_slope=0.2, inplace)
    (5): Upsample(scale_factor=2, mode=nearest)
    (6): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): BatchNorm2d(64, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
    (8): LeakyReLU(negative_slope=0.2, inplace)
    (9): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (10): Tanh()
  )
)


3.2判别器

包含4个卷积层和1个全连接层，使用LeakyReLU和Sigmoid激活函数，使用了Dropout和BatchNorm，使用Strided Conv进行下采样

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class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        def discriminator_block(in_filters, out_filters, bn=True):
            block = [nn.Conv2d(in_filters, out_filters, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True), nn.Dropout2d(0.25)]
            if bn:
                block.append(nn.BatchNorm2d(out_filters, 0.8))
            return block
        self.model = nn.Sequential(
            *discriminator_block(opt.channels, 16, bn=False),
            *discriminator_block(16, 32),
            *discriminator_block(32, 64),
            *discriminator_block(64, 128),
        )
        # The height and width of downsampled image
        ds_size = opt.img_size // 2 ** 4
        self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())
    def forward(self, img):
        out = self.model(img)
        out = out.view(out.shape[0], -1)
        validity = self.adv_layer(out)
        return validity
   
discriminator = Discriminator()
print(discriminator)
Discriminator(
  (model): Sequential(
    (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(negative_slope=0.2, inplace)
    (2): Dropout2d(p=0.25)
    (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (4): LeakyReLU(negative_slope=0.2, inplace)
    (5): Dropout2d(p=0.25)
    (6): BatchNorm2d(32, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
    (7): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): LeakyReLU(negative_slope=0.2, inplace)
    (9): Dropout2d(p=0.25)
    (10): BatchNorm2d(64, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
    (11): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (12): LeakyReLU(negative_slope=0.2, inplace)
    (13): Dropout2d(p=0.25)
    (14): BatchNorm2d(128, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
  )
  (adv_layer): Sequential(
    (0): Linear(in_features=512, out_features=1, bias=True)
    (1): Sigmoid()
  )
)


3.3初始化

对卷积层和BatchNorm层进行参数初始化

def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
Discriminator(
  (model): Sequential(
    (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (1): LeakyReLU(negative_slope=0.2, inplace)
    (2): Dropout2d(p=0.25)
    (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (4): LeakyReLU(negative_slope=0.2, inplace)
    (5): Dropout2d(p=0.25)
    (6): BatchNorm2d(32, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
    (7): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): LeakyReLU(negative_slope=0.2, inplace)
    (9): Dropout2d(p=0.25)
    (10): BatchNorm2d(64, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
    (11): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (12): LeakyReLU(negative_slope=0.2, inplace)
    (13): Dropout2d(p=0.25)
    (14): BatchNorm2d(128, eps=0.8, momentum=0.1, affine=True, track_running_stats=True)
  )
  (adv_layer): Sequential(
    (0): Linear(in_features=512, out_features=1, bias=True)
    (1): Sigmoid()
  )
)


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