# net = net.myVGG16().to(device)
# optimizer = optim.SGD(net.classifier.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
optimizer = optim.Adam(net.parameters())
# net = net.myVGG16().to(device)
# net = myvgg.VGG('VGG16').to(device)
print('finish loading NN')
# optimizer = optim.SGD(net.parameters(), lr=lr, momentum=momentum)
# optimizer = optim.Adam(net.parameters())
# optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
print('finish loading optimizer')
criterion = nn.CrossEntropyLoss()
print('finish loading criterion')
logger = logger.TrainLogger(out_dir, epochs, args.n)
print('finish loading logger')
r = run.NNRun(net, optimizer, criterion, logger, device, log_interval,
out_dir, prefix)
print('start epoch loop')
for epoch in range(1, epochs + 1):
r.train(trainloader, epoch)
if epoch % test_interval == 0:
r.test(testloader)
if logger.max_accuracy == logger.valid_accuracy[-1]:
r.checkpoint(epoch)
logger.save_loss()
def main():
args = easydict.EasyDict({
"dataroot": "/mnt/gold/users/s18150/mywork/pytorch/data/gan",
"save_dir": "./",
"prefix": "feature",
"workers": 8,
"batch_size": 128,
"image_size": 64,
"nc": 3,
"nz": 100,
"ngf": 64,
"ndf": 64,
"epochs": 50,
"lr": 0.0002,
"beta1": 0.5,
"gpu": 6,
"use_cuda": True,
"feature_matching": True,
"mini_batch": False
})
manualSeed = 999
random.seed(manualSeed)
torch.manual_seed(manualSeed)
device = torch.device(
'cuda:{}'.format(args.gpu) if args.use_cuda else 'cpu')
transform = transforms.Compose([
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = dset.ImageFolder(root=args.dataroot, transform=transform)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
# Generator用のモデルのインスタンス作成
netG = net.Generator(args.nz, args.ngf, args.nc).to(device)
# Generator用のモデルの初期値を設定
netG.apply(net.weights_init)
# Discriminator用のモデルのインスタンス作成
netD = net.Discriminator(args.nc, args.ndf, device, args.batch_size,
args.mini_batch).to(device)
# Discriminator用のモデルの初期値を設定
netD.apply(net.weights_init)
# BCE Loss classのインスタンスを作成
criterionD = nn.BCELoss()
if args.feature_matching is True:
criterionG = nn.MSELoss(reduction='elementwise_mean')
else:
criterionG = nn.BCELoss()
# Generatorに入力するノイズをバッチごとに作成 (バッチ数は64)
# これはGeneratorの結果を描画するために使用する
fixed_noise = torch.randn(64, args.nz, 1, 1, device=device)
# 最適化関数のインスタンスを作成
optimizerD = optim.Adam(netD.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
r = run.NNRun(netD, netG, optimizerD, optimizerG, criterionD, criterionG,
device, fixed_noise, args)
# 学習
r.train(dataloader)
def main():
args = easydict.EasyDict({
# "dataroot": "/mnt/gold/users/s18150/mywork/pytorch/data/gan",
"dataroot": "/mnt/gold/users/s18150/mywork/pytorch/data",
"save_dir": "./",
"prefix": "test",
"workers": 8,
"batch_size": 128,
"image_size": 32,
# "image_size": 28,
# "nc": 3,
"nc": 1,
"nz": 100,
"ngf": 32,
"ndf": 32,
# "ngf": 28,
# "ndf": 64,
"epochs": 1,
"lr": 0.0002,
"beta1": 0.5,
"gpu": 7,
"use_cuda": True,
"feature_matching": True,
"mini_batch": True,
"iters": 50000,
"label_batch_size": 100,
"unlabel_batch_size": 100,
"test_batch_size": 10,
"out_dir": './result',
"log_interval": 500,
"label_num": 20
})
manualSeed = 999
np.random.seed(manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
device = torch.device(
'cuda:{}'.format(args.gpu) if args.use_cuda else 'cpu')
# transform = transforms.Compose([
# transforms.Resize(args.image_size),
# transforms.CenterCrop(args.image_size),
# transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
# ])
#
# dataset = dset.ImageFolder(root=args.dataroot, transform=transform)
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
# shuffle=True, num_workers=args.workers)
data_iterators = dataset.get_iters(root_path=args.dataroot,
l_batch_size=args.label_batch_size,
ul_batch_size=args.unlabel_batch_size,
test_batch_size=args.test_batch_size,
workers=args.workers,
n_labeled=args.label_num)
trainloader_label = data_iterators['labeled']
trainloader_unlabel = data_iterators['unlabeled']
testloader = data_iterators['test']
# Generator用のモデルのインスタンス作成
netG = net.Generator(args.nz, args.ngf, args.nc).to(device)
# Generator用のモデルの初期値を設定
netG.apply(net.weights_init)
# Discriminator用のモデルのインスタンス作成
netD = net.Discriminator(args.nc, args.ndf, device, args.batch_size,
args.mini_batch).to(device)
# Discriminator用のモデルの初期値を設定
netD.apply(net.weights_init)
# BCE Loss classのインスタンスを作成
criterionD = nn.CrossEntropyLoss()
# criterionD = nn.BCELoss()
if args.feature_matching is True:
criterionG = nn.MSELoss(reduction='elementwise_mean')
else:
criterionG = nn.BCELoss()
# Generatorに入力するノイズをバッチごとに作成 (バッチ数は64)
# これはGeneratorの結果を描画するために使用する
fixed_noise = torch.randn(64, args.nz, 1, 1, device=device)
# 最適化関数のインスタンスを作成
optimizerD = optim.Adam(netD.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999))
logger = TrainLogger(args)
r = run.NNRun(netD, netG, optimizerD, optimizerG, criterionD, criterionG,
device, fixed_noise, logger, args)
# 学習
# r.train(dataloader)
r.train(trainloader_label, trainloader_unlabel, testloader)
