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train-res.py
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train-res.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision.utils as vutils
from torch.autograd import Variable
import torch.utils.data
import torch.backends.cudnn as cudnn
import random
import argparse
from models.snres_generator import SNResGenerator
from models.snres_discriminator import SNResDiscriminator
parser = argparse.ArgumentParser(description='train SNDCGAN model')
parser.add_argument('--dataPath', required=True, help='path to dataset')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--gpu_ids', default=[0,1,2,3], help='gpu ids: e.g. 0,1,2, 0,2.')
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--n_dis', type=int, default=1, help='discriminator critic iters')
parser.add_argument('--nz', type=int, default=128, help='dimention of lantent noise')
parser.add_argument('--batchsize', type=int, default=32, help='training batch size')
opt = parser.parse_args()
print(opt)
dataset = datasets.ImageFolder(root=opt.dataPath,
transform=transforms.Compose([
transforms.Scale(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
)
'''
dataset = datasets.CIFAR10(root='dataset', download=True,
transform=transforms.Compose([
transforms.Scale(32),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
'''
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchsize,
shuffle=True, num_workers=int(2))
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
torch.cuda.set_device(opt.gpu_ids[0])
cudnn.benchmark = True
def weight_filler(m):
classname = m.__class__.__name__
if classname.find('Conv' or 'SNConv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
n_dis = opt.n_dis
nz = opt.nz
G = SNResGenerator(64, nz, 4)
SND = SNResDiscriminator(64, 4)
print(G)
print(SND)
G.apply(weight_filler)
SND.apply(weight_filler)
input = torch.FloatTensor(opt.batchsize, 3, 64, 64)
noise = torch.FloatTensor(opt.batchsize, nz)
fixed_noise = torch.FloatTensor(opt.batchsize, nz).normal_(0, 1)
label = torch.FloatTensor(opt.batchsize)
real_label = 1
fake_label = 0
fixed_noise = Variable(fixed_noise)
criterion = nn.BCELoss()
if opt.cuda:
G.cuda()
SND.cuda()
criterion.cuda()
input, label = input.cuda(), label.cuda()
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
optimizerG = optim.Adam(G.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizerSND = optim.Adam(SND.parameters(), lr=0.0002, betas=(0.5, 0.999))
for epoch in range(200):
for i, data in enumerate(dataloader, 0):
step = epoch * len(dataloader) + i
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
# train with real
SND.zero_grad()
real_cpu, _ = data
batch_size = real_cpu.size(0)
#if opt.cuda:
# real_cpu = real_cpu.cuda()
input.resize_(real_cpu.size()).copy_(real_cpu)
label.resize_(batch_size).fill_(real_label)
inputv = Variable(input)
labelv = Variable(label)
output = SND(inputv)
#errD_real = torch.mean(F.softplus(-output))
errD_real = criterion(output, labelv)
errD_real.backward()
D_x = output.data.mean()
# train with fake
noise.resize_(batch_size, nz).normal_(0, 1)
noisev = Variable(noise)
fake = G(noisev)
labelv = Variable(label.fill_(fake_label))
output = SND(fake.detach())
#errD_fake = torch.mean(F.softplus(output))
errD_fake = criterion(output, labelv)
errD_fake.backward()
D_G_z1 = output.data.mean()
errD = errD_real + errD_fake
optimizerSND.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
if step % n_dis == 0:
G.zero_grad()
labelv = Variable(label.fill_(real_label)) # fake labels are real for generator cost
output = SND(fake)
#errG = torch.mean(F.softplus(-output))
errG = criterion(output, labelv)
errG.backward()
D_G_z2 = output.data.mean()
optimizerG.step()
if i % 20 == 0:
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
% (epoch, 200, i, len(dataloader),
errD.data[0], errG.data[0], D_x, D_G_z1, D_G_z2))
if i % 100 == 0:
vutils.save_image(real_cpu,
'%s/real_samples.png' % 'log',
normalize=True)
fake = G(fixed_noise)
vutils.save_image(fake.data,
'%s/fake_samples_epoch_%03d.png' % ('log', epoch),
normalize=True)
# do checkpointing
torch.save(G.state_dict(), '%s/netG_epoch_%d.pth' % ('log', epoch))
torch.save(SND.state_dict(), '%s/netD_epoch_%d.pth' % ('log', epoch))