class Discriminator(nn.Module):
def __init__(self, channels=3):
super(Discriminator, self).__init__()
self.channels = channels
def convlayer(n_input, n_output, k_size = 4, stride = 2, padding = 0, bn = False):
block = [nn.Conv2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False)]
if bn:
block.append(nn.BatchNorm2d(n_output))
block.append(nn.LeakyReLU(0.2, inplace=True))
return block
self.model = nn.Sequential(
*convlayer(self.channels, 32, 4, 2, 1),
*convlayer(32, 64, 4, 2, 1),
*convlayer(64, 128, 4, 2, 1, bn = True),
*convlayer(128, 256, 4, 2, 1, bn = True),
nn.Conv2d(256, 1, 4, 1, 0, bias = False),
)
def forward(self, imgs):
out = self.model(imgs)
return out.view(-1, 1)Relativistic AnimeGAN
computervision
deeplearning
keras
python
tensorflow
Generating Anime Faces using Relativistic GAN
::: {#cell-1 .cell _cell_guid=‘b1076dfc-b9ad-4769-8c92-a6c4dae69d19’ _uuid=‘8f2839f25d086af736a60e9eeb907d3b93b6e0e5’ execution_count=1}
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch, os
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from tqdm import tqdm_notebook as tqdm:::
::: {#cell-2 .cell _cell_guid=‘79c7e3d0-c299-4dcb-8224-4455121ee9b0’ _uuid=‘d629ff2d2480ee46fbb7e2d37f6b5fab8052498a’ execution_count=2}
class Generator(nn.Module):
def __init__(self, nz=128, channels=3):
super(Generator, self).__init__()
self.nz = nz
self.channels = channels
def convlayer(n_input, n_output, k_size = 4, stride = 2, padding = 0):
block = [
nn.ConvTranspose2d(n_input, n_output, kernel_size=k_size, stride=stride, padding=padding, bias=False),
nn.BatchNorm2d(n_output),
nn.ReLU(inplace=True),
]
return block
self.model = nn.Sequential(
*convlayer(self.nz, 1024, 4, 1, 0),
*convlayer(1024, 512, 4, 2, 1),
*convlayer(512, 256, 4, 2, 1),
*convlayer(256, 128, 4, 2, 1),
*convlayer(128, 64, 4, 2, 1),
nn.ConvTranspose2d(64, self.channels, 3, 1, 1),
nn.Tanh()
)
def forward(self, z):
z = z.view(-1, self.nz, 1, 1)
img = self.model(z)
return img:::
batch_size = 32
lr = 0.001
beta1 = 0.5
epochs = 300
real_label = 0.5
fake_label = 0
nz = 128
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")class AnimeFacesDataset(Dataset):
def __init__(self, img_dir, transform1=None, transform2=None):
self.img_dir = img_dir
self.img_names = os.listdir(img_dir)
self.transform1 = transform1
self.transform2 = transform2
self.imgs = []
for img_name in self.img_names:
img = Image.open(os.path.join(img_dir, img_name))
if self.transform1 is not None:
img = self.transform1(img)
self.imgs.append(img)
def __getitem__(self, index):
img = self.imgs[index]
if self.transform2 is not None:
img = self.transform2(img)
return img
def __len__(self):
return len(self.imgs)transform1 = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64)
])
random_transforms = [transforms.RandomRotation(degrees = 5)]
transform2 = transforms.Compose([
transforms.RandomHorizontalFlip(p = 0.5),
transforms.RandomApply(random_transforms, p = 0.3),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)
)
])
train_dataset = AnimeFacesDataset(
img_dir = '../input/data/data/',
transform1 = transform1,
transform2 = transform2
)
train_loader = DataLoader(
dataset = train_dataset,
batch_size = batch_size,
shuffle = True,
num_workers = 4
)
imgs = next(iter(train_loader))
imgs = imgs.numpy().transpose(0, 2, 3, 1)fig = plt.figure(figsize = (25, 16))
for ii, img in enumerate(imgs):
ax = fig.add_subplot(4, 8, ii + 1, xticks = [], yticks = [])
plt.imshow((img + 1) / 2)
netG = Generator(nz).to(device)
netD = Discriminator().to(device)
criterion = nn.BCELoss()
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
fixed_noise = torch.randn(25, nz, 1, 1, device=device)def show_generated_img():
noise = torch.randn(1, nz, 1, 1, device=device)
gen_image = netG(noise).to("cpu").clone().detach().squeeze(0)
gen_image = gen_image.numpy().transpose(1, 2, 0)
plt.imshow((gen_image+1)/2)
plt.show()for epoch in range(epochs):
for ii, real_images in tqdm(enumerate(train_loader), total=len(train_loader)):
netD.zero_grad()
real_images = real_images.to(device)
batch_size = real_images.size(0)
labels = torch.full((batch_size, 1), real_label, device=device)
outputR = netD(real_images)
noise = torch.randn(batch_size, nz, 1, 1, device=device)
fake = netG(noise)
outputF = netD(fake.detach())
errD = (torch.mean((outputR - torch.mean(outputF) - labels) ** 2) +
torch.mean((outputF - torch.mean(outputR) + labels) ** 2))/2
errD.backward(retain_graph=True)
optimizerD.step()
netG.zero_grad()
outputF = netD(fake)
errG = (torch.mean((outputR - torch.mean(outputF) + labels) ** 2) +
torch.mean((outputF - torch.mean(outputR) - labels) ** 2))/2
errG.backward()
optimizerG.step()
if (ii+1) % (len(train_loader)//2) == 0:
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f'
% (epoch + 1, epochs, ii+1, len(train_loader),
errD.item(), errG.item()))
show_generated_img()[1/300][337/674] Loss_D: 0.2228 Loss_G: 1.2557
[1/300][674/674] Loss_D: 0.4220 Loss_G: 2.8054
[2/300][337/674] Loss_D: 0.1080 Loss_G: 1.9196
[2/300][674/674] Loss_D: 0.1211 Loss_G: 1.6818
[3/300][337/674] Loss_D: 0.1240 Loss_G: 1.4734
[3/300][674/674] Loss_D: 0.0977 Loss_G: 1.9045
[4/300][337/674] Loss_D: 0.0401 Loss_G: 1.3443
[4/300][674/674] Loss_D: 0.0962 Loss_G: 1.3753
[5/300][337/674] Loss_D: 0.0621 Loss_G: 1.3746
[5/300][674/674] Loss_D: 0.1293 Loss_G: 2.1275
[6/300][337/674] Loss_D: 0.0967 Loss_G: 1.2415
[6/300][674/674] Loss_D: 0.0916 Loss_G: 1.2541
[7/300][337/674] Loss_D: 0.0688 Loss_G: 1.3309
[7/300][674/674] Loss_D: 0.0830 Loss_G: 1.0978
[8/300][337/674] Loss_D: 0.1210 Loss_G: 1.5478
[8/300][674/674] Loss_D: 0.1955 Loss_G: 1.7606
[9/300][337/674] Loss_D: 0.1035 Loss_G: 1.9064
[9/300][674/674] Loss_D: 0.1157 Loss_G: 0.9881
[10/300][337/674] Loss_D: 0.1077 Loss_G: 1.5257
[10/300][674/674] Loss_D: 0.1450 Loss_G: 2.0090
[11/300][337/674] Loss_D: 0.1498 Loss_G: 1.5121
[11/300][674/674] Loss_D: 0.3383 Loss_G: 2.4605
[12/300][337/674] Loss_D: 0.0704 Loss_G: 1.3353
[12/300][674/674] Loss_D: 0.1374 Loss_G: 1.5307
[13/300][337/674] Loss_D: 0.0670 Loss_G: 1.2464
[13/300][674/674] Loss_D: 0.1050 Loss_G: 1.3630
[14/300][337/674] Loss_D: 0.0757 Loss_G: 1.1511
[14/300][674/674] Loss_D: 0.3176 Loss_G: 1.4190
[15/300][337/674] Loss_D: 0.1194 Loss_G: 1.6059
[15/300][674/674] Loss_D: 0.0716 Loss_G: 1.3875
[16/300][337/674] Loss_D: 0.0843 Loss_G: 1.7008
[16/300][674/674] Loss_D: 0.0603 Loss_G: 0.9027
[17/300][337/674] Loss_D: 0.0414 Loss_G: 0.9865
[17/300][674/674] Loss_D: 0.2900 Loss_G: 1.0561
[18/300][337/674] Loss_D: 0.0567 Loss_G: 1.0921
[18/300][674/674] Loss_D: 0.0596 Loss_G: 1.2612
[19/300][337/674] Loss_D: 0.0700 Loss_G: 1.2655
[19/300][674/674] Loss_D: 0.0848 Loss_G: 1.5331
[20/300][337/674] Loss_D: 0.0707 Loss_G: 1.1320
[20/300][674/674] Loss_D: 0.0767 Loss_G: 1.6901
[21/300][337/674] Loss_D: 0.1091 Loss_G: 1.2790
[21/300][674/674] Loss_D: 0.1408 Loss_G: 1.6403
[22/300][337/674] Loss_D: 0.0439 Loss_G: 1.0915
[22/300][674/674] Loss_D: 0.0587 Loss_G: 1.3391
[23/300][337/674] Loss_D: 0.0383 Loss_G: 1.0457
[23/300][674/674] Loss_D: 0.0360 Loss_G: 1.2573
[24/300][337/674] Loss_D: 0.0391 Loss_G: 1.2543
[24/300][674/674] Loss_D: 0.0529 Loss_G: 1.3485
[25/300][337/674] Loss_D: 0.0862 Loss_G: 1.1176gen_z = torch.randn(32, nz, 1, 1, device=device)
gen_images = (netG(gen_z).to("cpu").clone().detach() + 1)/2
gen_images = gen_images.numpy().transpose(0, 2, 3, 1)fig = plt.figure(figsize=(25, 16))
for ii, img in enumerate(gen_images):
ax = fig.add_subplot(4, 8, ii + 1, xticks=[], yticks=[])
plt.imshow(img)