changed 3 years ago  
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Face image generation

tags: Deep Learning for Computer Vision

In this Task, I applied DC-GAN to implement Face image generation.

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DC-GAN

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DCGAN

Generator

# https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

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

Discriminator

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            # input is (nc) x 64 x 64
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf) x 32 x 32
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*2) x 16 x 16
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*4) x 8 x 8
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. (ndf*8) x 4 x 4
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

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

Hyperparameters :

  • Batch size : 64
  • Number of epochs : 100
  • Image size : 64*64
  • Learning rate : 0.0002
  • latent vector : 100*1
  • Learning rate scheduler : 0.8 * lr every 10 epoch
  • Optimizer : Adam(betas=(0.5, 0.999))

Model Ensemble :

I selected ten models from the last 10 epochs and averaged all the parameters.

import torch as t

model1 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_100.pth')
model2 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_99.pth')
model3 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_98.pth')
model4 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_97.pth')
model5 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_96.pth')
model6 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_95.pth')
model7 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_94.pth')
model8 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_93.pth')
model9 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_92.pth')
model10 = t.load('/content/drive/MyDrive/HW2/model/dcgan/dcgan_91.pth')

for key, value in model1.items():
      model1[key] = (value + model2[key] + model3[key] + model4[key] + model5[key] + model6[key] + model7[key] + model8[key] + model9[key] + model10[key]) / 10

ensemble = Generator(ngpu).to(device)
ensemble.load_state_dict(model1)

t.save(ensemble.state_dict(),  '/content/drive/MyDrive/HW2/model/dcgan/dcgan_ensemble.pth')

Example Results

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Fréchet inception distance (FID)

# https://github.com/mseitzer/pytorch-fid

FID: 22.067

Inception score (IS)

# https://github.com/sbarratt/inception-score-pytorch

IS: 2.045

Remark

When I use GAN to generate images, we must consider to the quality of the input data. For example, if the image is rotated by 45 degrees during data augmentation, the generated photos will also be rotated by 45 degrees.

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