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conditional_dcgan.py
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conditional_dcgan.py
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""" Conditional DCGAN for MNIST images generations.
Author: Moustafa Alzantot ([email protected])
All rights reserved.
"""
import os
import argparse
import numpy as np
import torch
from torch import nn, optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torchvision
from torchvision.utils import save_image
from torchvision import datasets, transforms
class ModelD(nn.Module):
def __init__(self):
super(ModelD, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 5, 1, 2)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 64, 5, 1, 2)
self.bn2 = nn.BatchNorm2d(64)
self.fc1 = nn.Linear(64*28*28+1000, 1024)
self.fc2 = nn.Linear(1024, 1)
self.fc3 = nn.Linear(10, 1000)
def forward(self, x, labels):
batch_size = x.size(0)
x = x.view(batch_size, 1, 28,28)
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(x)
x = x.view(batch_size, 64*28*28)
y_ = self.fc3(labels)
y_ = F.relu(y_)
x = torch.cat([x, y_], 1)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
return F.sigmoid(x)
class ModelG(nn.Module):
def __init__(self, z_dim):
self.z_dim = z_dim
super(ModelG, self).__init__()
self.fc2 = nn.Linear(10, 1000)
self.fc = nn.Linear(self.z_dim+1000, 64*28*28)
self.bn1 = nn.BatchNorm2d(64)
self.deconv1 = nn.ConvTranspose2d(64, 32, 5, 1, 2)
self.bn2 = nn.BatchNorm2d(32)
self.deconv2 = nn.ConvTranspose2d(32, 1, 5, 1, 2)
def forward(self, x, labels):
batch_size = x.size(0)
y_ = self.fc2(labels)
y_ = F.relu(y_)
x = torch.cat([x, y_], 1)
x = self.fc(x)
x = x.view(batch_size, 64, 28, 28)
x = self.bn1(x)
x = F.relu(x)
x = self.deconv1(x)
x = self.bn2(x)
x = F.relu(x)
x = self.deconv2(x)
x = F.sigmoid(x)
return x
if __name__ == '__main__':
parser = argparse.ArgumentParser('Conditional DCGAN')
parser.add_argument('--batch_size', type=int, default=128,
help='Batch size (default=128)')
parser.add_argument('--lr', type=float, default=0.01,
help='Learning rate (default=0.01)')
parser.add_argument('--epochs', type=int, default=10,
help='Number of training epochs.')
parser.add_argument('--nz', type=int, default=100,
help='Number of dimensions for input noise.')
parser.add_argument('--cuda', action='store_true',
help='Enable cuda')
parser.add_argument('--save_every', type=int, default=1,
help='After how many epochs to save the model.')
parser.add_argument('--print_every', type=int, default=50,
help='After how many epochs to print loss and save output samples.')
parser.add_argument('--save_dir', type=str, default='models',
help='Path to save the trained models.')
parser.add_argument('--samples_dir', type=str, default='samples',
help='Path to save the output samples.')
args = parser.parse_args()
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
if not os.path.exists(args.samples_dir):
os.mkdir(args.samples_dir)
INPUT_SIZE = 784
SAMPLE_SIZE = 80
NUM_LABELS = 10
train_dataset = datasets.MNIST(root='data',
train=True,
download=True,
transform=transforms.ToTensor())
train_loader = DataLoader(train_dataset, shuffle=True,
batch_size=args.batch_size)
model_d = ModelD()
model_g = ModelG(args.nz)
criterion = nn.BCELoss()
input = torch.FloatTensor(args.batch_size, INPUT_SIZE)
noise = torch.FloatTensor(args.batch_size, (args.nz))
fixed_noise = torch.FloatTensor(SAMPLE_SIZE, args.nz).normal_(0,1)
fixed_labels = torch.zeros(SAMPLE_SIZE, NUM_LABELS)
for i in range(NUM_LABELS):
for j in range(SAMPLE_SIZE // NUM_LABELS):
fixed_labels[i*(SAMPLE_SIZE // NUM_LABELS) + j, i] = 1.0
label = torch.FloatTensor(args.batch_size)
one_hot_labels = torch.FloatTensor(args.batch_size, 10)
if args.cuda:
model_d.cuda()
model_g.cuda()
input, label = input.cuda(), label.cuda()
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
one_hot_labels = one_hot_labels.cuda()
fixed_labels = fixed_labels.cuda()
optim_d = optim.SGD(model_d.parameters(), lr=args.lr)
optim_g = optim.SGD(model_g.parameters(), lr=args.lr)
fixed_noise = Variable(fixed_noise)
fixed_labels = Variable(fixed_labels)
real_label = 1
fake_label = 0
for epoch_idx in range(args.epochs):
model_d.train()
model_g.train()
d_loss = 0.0
g_loss = 0.0
for batch_idx, (train_x, train_y) in enumerate(train_loader):
batch_size = train_x.size(0)
train_x = train_x.view(-1, INPUT_SIZE)
if args.cuda:
train_x = train_x.cuda()
train_y = train_y.cuda()
input.resize_as_(train_x).copy_(train_x)
label.resize_(batch_size).fill_(real_label)
one_hot_labels.resize_(batch_size, NUM_LABELS).zero_()
one_hot_labels.scatter_(1, train_y.view(batch_size,1), 1)
inputv = Variable(input)
labelv = Variable(label)
output = model_d(inputv, Variable(one_hot_labels))
optim_d.zero_grad()
errD_real = criterion(output, labelv)
errD_real.backward()
realD_mean = output.data.cpu().mean()
one_hot_labels.zero_()
rand_y = torch.from_numpy(
np.random.randint(0, NUM_LABELS, size=(batch_size,1))).cuda()
one_hot_labels.scatter_(1, rand_y.view(batch_size,1), 1)
noise.resize_(batch_size, args.nz).normal_(0,1)
label.resize_(batch_size).fill_(fake_label)
noisev = Variable(noise)
labelv = Variable(label)
onehotv = Variable(one_hot_labels)
g_out = model_g(noisev, onehotv)
output = model_d(g_out, onehotv)
errD_fake = criterion(output, labelv)
fakeD_mean = output.data.cpu().mean()
errD = errD_real + errD_fake
errD_fake.backward()
optim_d.step()
# train the G
noise.normal_(0,1)
one_hot_labels.zero_()
rand_y = torch.from_numpy(
np.random.randint(0, NUM_LABELS, size=(batch_size,1))).cuda()
one_hot_labels.scatter_(1, rand_y.view(batch_size,1), 1)
label.resize_(batch_size).fill_(real_label)
onehotv = Variable(one_hot_labels)
noisev = Variable(noise)
labelv = Variable(label)
g_out = model_g(noisev, onehotv)
output = model_d(g_out, onehotv)
errG = criterion(output, labelv)
optim_g.zero_grad()
errG.backward()
optim_g.step()
d_loss += errD.data[0]
g_loss += errG.data[0]
if batch_idx % args.print_every == 0:
print(
"\t{} ({} / {}) mean D(fake) = {:.4f}, mean D(real) = {:.4f}".
format(epoch_idx, batch_idx, len(train_loader), fakeD_mean,
realD_mean))
g_out = model_g(fixed_noise, fixed_labels).data.view(
SAMPLE_SIZE, 1, 28,28).cpu()
save_image(g_out,
'{}/{}_{}.png'.format(
args.samples_dir, epoch_idx, batch_idx))
print('Epoch {} - D loss = {:.4f}, G loss = {:.4f}'.format(epoch_idx,
d_loss, g_loss))
if epoch_idx % args.save_every == 0:
torch.save({'state_dict': model_d.state_dict()},
'{}/model_d_epoch_{}.pth'.format(
args.save_dir, epoch_idx))
torch.save({'state_dict': model_g.state_dict()},
'{}/model_g_epoch_{}.pth'.format(
args.save_dir, epoch_idx))