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HeralDCGAN.py
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HeralDCGAN.py
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import torch.nn as nn
import torch
from torchvision.datasets import ImageFolder
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.optim as optim
import torchvision.utils as vutils
import torch.nn.init as init
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
IMAGE_X = 64
IMAGE_Y = 64
class MinibatchDiscrimination(nn.Module):
def __init__(self, in_features, out_features, kernel_dims, mean=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.kernel_dims = kernel_dims
self.mean = mean
self.T = nn.Parameter(torch.Tensor(in_features, out_features, kernel_dims))
init.normal_(self.T, 0, 1)
def forward(self, x):
# x is NxA
# T is AxBxC
matrices = x.mm(self.T.view(self.in_features, -1))
matrices = matrices.view(-1, self.out_features, self.kernel_dims)
M = matrices.unsqueeze(0) # 1xNxBxC
M_T = M.permute(1, 0, 2, 3) # Nx1xBxC
norm = torch.abs(M - M_T).sum(3) # NxNxB
expnorm = torch.exp(-norm)
o_b = (expnorm.sum(0) - 1) # NxB, subtract self distance
if self.mean:
o_b /= x.size(0) - 1
x = torch.cat([x, o_b], 1) # Nx(A+B)
return x
transform_comp = transforms.Compose([
transforms.Resize([IMAGE_X, IMAGE_Y], interpolation=transforms.InterpolationMode.BILINEAR),
transforms.ColorJitter(brightness=.3, hue=.1),
transforms.ToTensor(),
])
class ShapePrinter(nn.Module):
def __init__(self, name="ShapePrinter", once=True):
super(ShapePrinter, self).__init__()
if once:
self.done = False
self.once = once
self.name = name
def forward(self, x):
if not self.done:
print(f"Shape printer '{self.name}' -> {list(x.shape)}")
if self.once:
self.done = True
return x
# Discriminator Network
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.nc = 3
self.ndf = 16
self.main = nn.Sequential(
# input nc * 64 * 64
nn.Conv2d(self.nc, self.ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# second ndf * 32 * 32
nn.Conv2d(self.ndf, self.ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# third 2ndf * 16 * 16
nn.Conv2d(self.ndf * 2, self.ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# fourth 4ndf * 8 * 8
nn.Conv2d(self.ndf * 4, self.ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# output 8ndf * 4 * 4
nn.Conv2d(self.ndf * 8, self.ndf * 16, 4, 2, 1, bias=False),
ShapePrinter(name="Before Flatten"),
# Batch discrimination 16ndf * 2 * 2
nn.Flatten(),
MinibatchDiscrimination(16 * 2 * 2 * self.ndf, self.ndf * 2 * 2, self.ndf * 2 * 2, mean=True),
ShapePrinter(name="After Batch Discrim"),
nn.BatchNorm1d(1088),
nn.Linear(1088, 64),
nn.Sigmoid(),
nn.Linear(64, 1),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input)
# Generator Network
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.nz = 1000
self.ngf = 10
self.nc = 3
self.main = nn.Sequential(
# input Z; First
nn.ConvTranspose2d(self.nz, self.ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(self.ngf * 8),
nn.ReLU(True),
# second
nn.ConvTranspose2d(self.ngf * 8, self.ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ngf * 4),
nn.ReLU(True),
# third
nn.ConvTranspose2d(self.ngf * 4, self.ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ngf * 2),
nn.ReLU(True),
# fourth
nn.ConvTranspose2d(self.ngf * 2, self.ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(self.ngf),
nn.ReLU(True),
# output nc * 64 * 64
nn.ConvTranspose2d(self.ngf, self.nc, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
out = self.main(input)
# scale to (+1, -1) ish
out = 2 * out - 1
# add some noise
out = out + 0.05 * torch.randn_like(out)
return out
if __name__ == '__main__':
plt.ion()
has_cuda = True if torch.cuda.is_available() else False
dev = "cuda:0" if has_cuda else "cpu"
print("Using device:", dev)
data_dir = r"C:\Users\Tom\Documents\GitHub\HeraldryData\data\data"
batch_size = 128
num_epochs = 3000
data = ImageFolder(data_dir, transform_comp)
data_loader = DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4, pin_memory=True)
netG = Generator().to(dev)
netD = Discriminator().to(dev)
G_losses = []
D_losses = []
img_list = []
real_label = 0.95
fake_label = 0.00
nz = 1000
fixed_noise = torch.randn(64, nz, 1, 1, device=dev)
criterion = nn.BCELoss()
optimizerG = optim.Adam(netG.parameters(), lr=0.0001)
optimizerD = optim.Adam(netD.parameters(), lr=0.0001)
for e in range(num_epochs):
for i, (x, _) in enumerate(data_loader):
netD.zero_grad()
# train with real data
real_data = x.to(dev)
# scale to (+1, -1)
real_data = 2 * real_data - 1
# add some noise
real_data = real_data + 0.05 * torch.randn_like(real_data)
# make labels
batch_size = real_data.size(0)
labels = torch.full((batch_size,), real_label, device=dev)
# forward pass real data through D
real_outputD = netD(real_data).view(-1)
# calc error on real data
errD_real = criterion(real_outputD, labels)
# calc grad
errD_real.backward()
D_x = real_outputD.mean().item()
# train with fake data
noise = torch.randn(batch_size, nz, 1, 1, device=dev)
fake_data = netG(noise)
labels.fill_(fake_label)
# classify fake
fake_outputD = netD(fake_data.detach()).view(-1)
# calc error on fake data
errD_fake = criterion(fake_outputD, labels)
# calc grad
errD_fake.backward()
D_G_z1 = fake_outputD.mean().item()
# add all grad and update D
errD = errD_real + errD_fake
optimizerD.step()
########################################
########## Training Generator ##########
netG.zero_grad()
# since aim is fooling the netD, labels should be flipped
labels.fill_(real_label)
# forward pass with updated netD
fake_outputD = netD(fake_data).view(-1)
# calc error
errG = criterion(fake_outputD, labels)
# calc grad
errG.backward()
D_G_z2 = fake_outputD.mean().item()
# update G
optimizerG.step()
########################################
# output training stats
if (i + 1) % 500 == 0:
print(f'[{e+1}][{i+1}] Loss_D:{errD.item():.4f} Loss_G:{errG.item():.4f} D(x):{D_x:.4f} D(G(z)):{D_G_z1:.4f}/{D_G_z2:.4f}')
# for later plot
G_losses.append(errG.item())
D_losses.append(errD.item())
# generate fake image on fixed noise for comparison
if (i % 500 == 0):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
img_list.append(vutils.make_grid(fake, padding=2, normalize=True))
plt.imshow(img_list[-1].permute(1, 2, 0))
plt.imsave("outputs\\oup_{e}_{i}.png", img_list[-1].permute(1, 2, 0).numpy())
plt.draw()
torch.save(netD.state_dict(), "netD.pt")
torch.save(netG.state_dict(), "netG.pt")
plt.pause(0.001)