EPOCHS = 50000
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Onehot',
out_shape=PRELOAD_LEN,
latent_dim=LATENT_DIM,
random_sampling=False
)
goptim_cfg = OptimConfig('Adam', lr=0.0002)
doptim_cfg = OptimConfig('Adam', lr=0.0002)
dataset_cfg = DatasetConfig('CelebA', dataset_len=DATASET_LEN, image_size=IMAGE_SIZE, preload_len=PRELOAD_LEN)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig(
'EDCG', input_size=LATENT_DIM,
hidden_size=128, output_size=3,
data_num=PRELOAD_LEN, out_64=IMAGE_SIZE == 64
)
dis_cfg = ModelConfig('DCD', input_size=3, hidden_size=64, output_size=1, out_64=IMAGE_SIZE == 64)
gan_cfg = GanConfig(
name='CelebAGAN', gen_cfg=gen_cfg, dis_cfg=dis_cfg,
gen_step=1, dis_step=1, gan_epoch=EPOCHS, loader_cfg=loader_cfg,
dataset_cfg=dataset_cfg, gloss_cfg=gloss_cfg, dloss_cfg=dloss_cfg,
goptim_cfg=goptim_cfg, doptim_cfg=doptim_cfg, label_smooth=False,
sampler_cfg=sampler_cfg, dist_loss=False, device=device
)
EXP_NAME = 'refocus_final_' + str(label_len)
# logger = SummaryWriter('./log/' + EXP_NAME)
LR = 2e-4
gloss_cfg = LossConfig('MSE')
dloss_cfg = LossConfig('MSE')
goptim_cfg = OptimConfig('Adam', lr=LR, beta=(0.5, 0.999))
doptim_cfg = OptimConfig('Adam', lr=LR, beta=(0.5, 0.999))
dataset_cfg = DatasetConfig('FlowerFull',
size=IMG_SIZE,
num=label_len,
train=False)
loader_cfg = LoaderConfig('naive', batch_size=1, shuffle=False)
# gen_cfg = ModelConfig('ResNetGen', input_nc=3, output_nc=3, ngf=64, n_blocks=6)
gen_cfg = ModelConfig('UNetGen',
input_nc=3,
output_nc=3,
num_downs=6,
ngf=64,
use_resizeconv=True,
ex_label=True,
label_len=label_len)
dis_cfg = ModelConfig('PatchDis', input_nc=6)
gan_cfg = GanConfig(name='Pix2Pix',
gen_cfg=gen_cfg,
dis_cfg=dis_cfg,
DATASET_LEN = 20000
EPOCHS = 30000
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Onehot',
out_shape=DATASET_LEN,
latent_dim=LATENT_DIM
)
goptim_cfg = OptimConfig('Adam', lr=1e-3)
doptim_cfg = OptimConfig('Adam', lr=1e-3)
dataset_cfg = DatasetConfig('MNIST', train=True, stack=False, along_width=False, size=32)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig('EDCG', input_size=LATENT_DIM, hidden_size=128, output_size=1, data_num=DATASET_LEN)
dis_cfg = ModelConfig('DCD', input_size=1, hidden_size=128, output_size=1)
train_data_cfg = DatasetConfig('MNIST', stack=False, train=True, along_width=False)
test_data_cfg = DatasetConfig('MNIST', stack=False, train=False, along_width=False)
train_loader = LoaderConfig('naive', batch_size=128, shuffle=True)
test_loader = LoaderConfig('naive', batch_size=128, shuffle=True)
util_cfg = UtilityModelConfig('NaiveClassifier', False, '/home/bourgan/gan_dev/checkpoints/mnist_naive.pth.tar',
15, train_loader, test_loader, train_data_cfg, test_data_cfg, 32, 'cuda')
gan_cfg = GanConfig(
name='StackMNISTLPG', gen_cfg=gen_cfg, dis_cfg=dis_cfg,
gen_step=1, dis_step=1, gan_epoch=EPOCHS, loader_cfg=loader_cfg,
BATCH_SIZE = 12
label_len = int(args.label_len)
EXP_NAME = 'refocus_final_' + str(label_len)
logger = SummaryWriter('./log/' + EXP_NAME)
LR = 2e-4
gloss_cfg = LossConfig('MSE')
dloss_cfg = LossConfig('MSE')
goptim_cfg = OptimConfig('Adam', lr=LR, beta=(0.5, 0.999))
doptim_cfg = OptimConfig('Adam', lr=LR, beta=(0.5, 0.999))
dataset_cfg = DatasetConfig('FlowerFull', size=IMG_SIZE, num=label_len)
loader_cfg = LoaderConfig('naive', batch_size=BATCH_SIZE, shuffle=True)
# gen_cfg = ModelConfig('ResNetGen', input_nc=3, output_nc=3, ngf=64, n_blocks=6)
gen_cfg = ModelConfig('UNetGen', input_nc=3, output_nc=3, num_downs=6, ngf=64,
use_resizeconv=True, ex_label=True, label_len=label_len)
dis_cfg = ModelConfig('PatchDis', input_nc=6)
gan_cfg = GanConfig(
name='Pix2Pix', gen_cfg=gen_cfg, dis_cfg=dis_cfg,
gen_step=1, dis_step=1, gan_epoch=GAN_EPOCHS, loader_cfg=loader_cfg,
dataset_cfg=dataset_cfg, gloss_cfg=gloss_cfg, dloss_cfg=dloss_cfg,
goptim_cfg=goptim_cfg, doptim_cfg=doptim_cfg, device=device
)
gan = gan_cfg.get()
gan.train(use_tqdm=True)
gan.save('p2p', EXP_NAME)