Python DNet.cuda 예제들

예제 #1

from model import UNet, DNet
import data_loader
from data_loader import *

##############################################################
# Initialise the generator and discriminator with the UNet and
# DNet architectures respectively.
generator = UNet(True)
discriminator = DNet()

##################################################################
# Utilize GPU for performing all the calculations performed in the
# forward and backward passes. Thus allocate all the generator and
# discriminator variables on the default GPU device.
generator.cuda()
discriminator.cuda()

###################################################################
# Create ADAM optimizer for the generator as well the discriminator.
# Create loss criterion for calculating the L1 and adversarial loss.
d_optimizer = optim.Adam(discriminator.parameters(), betas=(0.5, 0.999), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), betas=(0.5, 0.999), lr=0.0002)

d_criterion = nn.BCELoss()
g_criterion_1 = nn.BCELoss()
g_criterion_2 = nn.L1Loss()

train_()

def train_():
	"""

예제 #2

opt = parser.parse_args()

if torch.cuda.is_available() and not opt.cuda:
    print(
        "WARNING: You have a CUDA device, so you should probably run with --cuda"
    )

Gnet_AB = GNet(opt.G_init_filter, opt.G_depth, opt.G_width)
Gnet_BA = GNet(opt.G_init_filter, opt.G_depth, opt.G_width)
Dnet_A = DNet(opt.D_init_filter, opt.D_depth)
Dnet_B = DNet(opt.D_init_filter, opt.D_depth)

if opt.cuda:
    Gnet_AB.cuda()
    Gnet_BA.cuda()
    Dnet_A.cuda()
    Dnet_B.cuda()

# Weight Initialization from a Gaussian distribution N(0, 0:02)
Gnet_AB.apply(weights_init_normal)
Gnet_BA.apply(weights_init_normal)
Dnet_A.apply(weights_init_normal)
Dnet_B.apply(weights_init_normal)

# Lossess
L_GAN = nn.MSELoss()
L_cyc = nn.L1Loss()
L_identity = nn.L1Loss()

if opt.cuda:
    L_GAN.cuda()