def __init__(
self,
device,
model,
n_labels,
n_channels,
target,
lr,
l_inf_bound,
alpha,
beta,
gamma,
kappa,
c,
n_steps_D,
n_steps_G,
):
self.device = device
self.n_labels = n_labels
self.model = model
self.target = target
self.lr = lr
self.l_inf_bound = l_inf_bound
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.kappa = kappa
self.c = c
self.n_steps_D = n_steps_D
self.n_steps_G = n_steps_G
self.G = models.Generator(n_channels, n_channels, target).to(device)
self.D = models.Discriminator(n_channels).to(device)
# initialize all weights
self.G.apply(init_weights)
self.D.apply(init_weights)
# initialize optimizers
self.optimizer_G = torch.optim.Adam(self.G.parameters(), lr=self.lr)
self.optimizer_D = torch.optim.Adam(self.D.parameters(), lr=self.lr)
if not os.path.exists(models_path):
os.makedirs(models_path)
if not os.path.exists('{}{}/'.format(losses_path, target)):
os.makedirs('{}{}/'.format(losses_path, target))
def _create_models(self) -> None:
cfg = self._config
device = self._device
self._generator = models.Generator(style_code_dim=cfg.style_code_dim, )
self._generator_ema = models.Generator(
style_code_dim=cfg.style_code_dim, )
self._generator_ema.load_state_dict(self._generator.state_dict())
self._mapping = models.Mapping(
latent_dim=cfg.mapper_latent_code_dim,
hidden_dim=cfg.mapper_hidden_dim,
out_dim=cfg.style_code_dim,
num_shared_layers=cfg.mapper_shared_layers,
num_heads=cfg.num_domains,
)
self._mapping_ema = models.Mapping(
latent_dim=cfg.mapper_latent_code_dim,
hidden_dim=cfg.mapper_hidden_dim,
out_dim=cfg.style_code_dim,
num_shared_layers=cfg.mapper_shared_layers,
num_heads=cfg.num_domains,
)
self._mapping_ema.load_state_dict(self._mapping.state_dict())
self._style_encoder = models.StyleEncoder(
style_code_dim=cfg.style_code_dim,
num_heads=cfg.num_domains,
)
self._style_encoder_ema = models.StyleEncoder(
style_code_dim=cfg.style_code_dim,
num_heads=cfg.num_domains,
)
self._style_encoder_ema.load_state_dict(
self._style_encoder.state_dict())
self._discriminator = models.Discriminator(num_heads=cfg.num_domains, )
self._generator.to(device)
self._generator_ema.eval().to(device)
self._mapping.to(device)
self._mapping_ema.eval().to(device)
self._style_encoder.to(device)
self._style_encoder_ema.eval().to(device)
self._discriminator.to(device)
def prepare_gan(self):
self.replay = replay.ReplayBuffer(self.args, self.input_shape)
self.replay_dequeue = \
self.replay_queue.dequeue_many(self.args.disc_batch_size)
self.replay_thread = rl_utils.ReplayThread(
self.replay, self.replay_dequeue)
self.local_disc = models.Discriminator(
self.args, self.input_shape, "local")
self.disc_sync = ut.tf.get_sync_op(
self.global_disc.var_list,
self.local_disc.var_list)
def set_network(depth, ctx, ngf):
# Pixel2pixel networks
#netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netEn = models.Encoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD = models.Discriminator(in_channels=3, n_layers =depth, ndf=ngf, isthreeway=True)
# Initialize parameters
models.network_init(netDe, ctx=ctx)
models.network_init(netEn, ctx=ctx)
models.network_init(netD, ctx=ctx)
return netEn, netDe, netD
def __init__(self, opt):
self.opt = opt
self.G = models.Generator(opt)
self.D = models.Discriminator()
self.Lsloss = torch.nn.MSELoss()
self.optimizerG = torch.optim.Adam(self.G.parameters(), 1e-4,
(0, 0.999))
self.optimizerD = torch.optim.Adam(self.D.parameters(), 4e-4,
(0, 0.999))
self.d_losses = []
self.g_losses = []
self.G.cuda()
self.D.cuda()
self.G.apply(self.weights_init)
self.D.apply(self.weights_init)
self.latent_ebdy_generator = Get_Latent_Y(opt)
def __init__(self, device):
super().__init__()
self.device = device
self.SZ = 128
self.C = 3
self.nMask = 2
self.latent = 32
nf = 64
self.EncM = models.EncM(sizex=self.SZ,
nIn=self.C,
nMasks=self.nMask,
nRes=3,
nf=nf,
temperature=1).to(device)
self.GenX = models.GenX(sizex=self.SZ,
nOut=self.C,
nc=self.latent,
nf=nf,
nMasks=self.nMask,
selfAtt=False).to(device)
self.RecZ = models.RecZ(sizex=self.SZ,
nIn=self.C,
nc=self.latent,
nf=nf,
nMasks=self.nMask).to(device)
self.D = models.Discriminator(nIn=self.C, nf=nf,
selfAtt=False).to(device)
self.rd = torch.distributions.Normal(0, 1)
self.optEncM = optim.Adam(self.EncM.parameters(),
lr=1e-5,
betas=(0, 0.9),
weight_decay=1e-4,
amsgrad=False)
self.optGenX = optim.Adam(self.GenX.parameters(),
lr=1e-4,
betas=(0, 0.9),
amsgrad=False)
self.optRecZ = optim.Adam(self.RecZ.parameters(),
lr=1e-4,
betas=(0, 0.9),
amsgrad=False)
self.optD = optim.Adam(self.D.parameters(),
lr=1e-4,
betas=(0, 0.9),
amsgrad=False)
def createModels(self):
"""
This function will create models and their optimizers
"""
self.gen = models.Generator().cuda()
self.disc = models.Discriminator().cuda()
self.gOptimizer = Adam(self.gen.parameters(),
lr=self.gLR,
betas=(0.0, 0.99))
self.dOptimizer = Adam(self.disc.parameters(),
lr=self.dLR,
betas=(0.0, 0.99))
print(
'Models Instantiated. # of trainable parameters Disc:%e; Gen:%e' %
(sum([np.prod([*p.size()]) for p in self.disc.parameters()]),
sum([np.prod([*p.size()]) for p in self.gen.parameters()])))
def __init__(self, opt, logger):
self.opt = opt
self.isTrain = opt.isTrain
self.lr = opt.lr
self.every = opt.every
self.epoch = 0
self.best_loss = float("inf")
self.idt_name = opt.idt_name
self.logdir = opt.logdir
self.logger = logger
# loss
self.criterionGAN = GANLoss(gan_mode='mse').cuda()
self.criterionL1 = nn.L1Loss()
# G
self.netG = models.APBNet()
self.netG.apply(weight_init)
if opt.resume:
checkpoint = torch.load('{}/{}.pth'.format(
self.logdir, opt.resume_epoch
if opt.resume_epoch else '{}_best'.format(self.idt_name)))
self.netG.load_state_dict(checkpoint['net_G'])
self.epoch = checkpoint['epoch']
self.netG.cuda()
# D
if self.isTrain: # define discriminators
self.netD = models.Discriminator()
self.netD.apply(weight_init)
if opt.resume:
self.netD.load_state_dict(checkpoint['net_D'])
self.netD.cuda()
self.netD_poseye = models.Discriminator_poseeye()
self.netD_poseye.apply(weight_init)
if opt.resume:
self.netD_poseye.load_state_dict(checkpoint['net_D_poseeye'])
self.netD_poseye.cuda()
self.optimizer_G = torch.optim.Adam(self.netG.parameters(),
lr=self.lr,
betas=(0.99, 0.999))
self.optimizer_D = torch.optim.Adam(self.netD.parameters(),
lr=self.lr,
betas=(0.99, 0.999))
self.optimizer_D_poseeye = torch.optim.Adam(
self.netD_poseye.parameters(), lr=self.lr, betas=(0.99, 0.999))
def __init__(self,opt):
self.opt = opt
self.G = models.Generator(opt)
self.D = models.Discriminator(opt)
self.Lsloss = torch.nn.MSELoss()
self.optimizerG = torch.optim.Adam(self.G.parameters(),opt.g_lr,opt.g_betas)
self.optimizerD = torch.optim.Adam(self.D.parameters(),opt.d_lr,opt.d_betas)
self.d_losses = []
self.wd_losses = []
self.g_losses = []
self.wg_losses = []
self.losses = {'d':[],'g':[],'wd':[],'wg':[]}
self.G.cuda()
self.D.cuda()
self.G.apply(self.weights_init)
self.D.apply(self.weights_init)
self.latent_ebdy_generator = Get_Latent_Y(opt)
self.generate_imgs_count = 0
if False:
self.G.share_memory()
self.processes = []
self.main_to_thread1 = mp.Queue(maxsize=1)
self.main_to_thread2 = mp.Queue(maxsize=1)
self.main_to_thread3 = mp.Queue(maxsize=1)
self.main_to_thread4 = mp.Queue(maxsize=1)
self.thread1_to_main = mp.Queue(maxsize=1)
self.thread2_to_main = mp.Queue(maxsize=1)
self.thread3_to_main = mp.Queue(maxsize=1)
self.thread4_to_main = mp.Queue(maxsize=1)
self.thread1_grad = mp.Queue(maxsize=1)
self.thread2_grad = mp.Queue(maxsize=1)
self.thread3_grad = mp.Queue(maxsize=1)
self.thread4_grad = mp.Queue(maxsize=1)
p1 = mp.Process(target=func,args=(self.G,self.main_to_thread1,self.thread1_to_main,self.thread1_grad,self.optimizerG))
p1.start()
self.processes.append(p1)
p2 = mp.Process(target=func,args=(self.G,self.main_to_thread2,self.thread2_to_main,self.thread2_grad,self.optimizerG))
p2.start()
self.processes.append(p2)
p3 = mp.Process(target=func,args=(self.G,self.main_to_thread3,self.thread3_to_main,self.thread3_grad,self.optimizerG))
p3.start()
self.processes.append(p3)
p4 = mp.Process(target=func,args=(self.G,self.main_to_thread4,self.thread4_to_main,self.thread4_grad,self.optimizerG))
p4.start()
self.processes.append(p4)
def set_network(depth, ctx, lr, beta1, ngf):
# Pixel2pixel networks
#netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netEn = models.Encoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD = models.Discriminator(in_channels=3, n_layers =depth, ndf=ngf, isthreeway=True)
# Initialize parameters
models.network_init(netDe, ctx=ctx)
models.network_init(netEn, ctx=ctx)
models.network_init(netD, ctx=ctx)
# trainer for the generator and the discriminator
trainerEn = gluon.Trainer(netEn.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerDe = gluon.Trainer(netDe.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
return netEn, netDe, netD, trainerEn, trainerDe, trainerD
def set_network():
# Pixel2pixel networks
netG = models.CEGenerator(
in_channels=3) # UnetGenerator(in_channels=3, num_downs=8) #
netD = models.Discriminator(in_channels=6)
# Initialize parameters
models.network_init(netG, ctx=ctx)
models.network_init(netD, ctx=ctx)
# trainer for the generator and the discriminator
trainerG = gluon.Trainer(netG.collect_params(), 'adam', {
'learning_rate': lr,
'beta1': beta1
})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {
'learning_rate': lr,
'beta1': beta1
})
return netG, netD, trainerG, trainerD
def __init__(self, device, model, model_num_labels, box_min, box_max):
self.device = device
self.model_num_labels = model_num_labels
self.model = model
self.box_min = box_min
self.box_max = box_max
self.netG = models.Generator().to(device)
self.netDisc = models.Discriminator().to(device)
# initialize all weights
self.netG.apply(weights_init)
self.netDisc.apply(weights_init)
# initialize optimizers
self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=0.001)
self.optimizer_D = torch.optim.Adam(self.netDisc.parameters(),
lr=0.001)
if not os.path.exists(models_path):
os.makedirs(models_path)
def inpaint(args):
dataset = datasets.RandomPatchDataset(args.test_data_dir,weighted_mask=True, window_size=args.window_size)
dataloader = DataLoader(dataset, batch_size=args.batch_size)
# Loading trained GAN model
saved_gan = torch.load(args.gan_path)
generator = models.Generator(args).cuda()
discriminator = models.Discriminator(args).cuda()
generator.load_state_dict(saved_gan["state_dict_G"])
discriminator.load_state_dict(saved_gan["state_dict_D"])
for i, (corrupted_images, original_images, masks, weighted_masks) in enumerate(dataloader):
corrupted_images, masks, weighted_masks = corrupted_images.cuda(), masks.cuda(), weighted_masks.cuda()
z_optimum = nn.Parameter(torch.FloatTensor(np.random.normal(0, 1, (corrupted_images.shape[0],args.latent_dim,))).cuda())
optimizer_inpaint = optim.Adam([z_optimum])
print("Starting backprop to input ...")
for epoch in range(args.optim_steps):
optimizer_inpaint.zero_grad()
generated_images = generator(z_optimum)
discriminator_opinion = discriminator(generated_images)
c_loss = context_loss(corrupted_images, generated_images, weighted_masks)
prior_loss = torch.sum(-torch.log(discriminator_opinion))
inpaint_loss = c_loss + args.prior_weight*prior_loss
inpaint_loss.backward()
optimizer_inpaint.step()
print("[Epoch: {}/{}] \t[Loss: \t[Context: {:.3f}] \t[Prior: {:.3f}] \t[Inpaint: {:.3f}]] \r".format(1+epoch, args.optim_steps, c_loss,
prior_loss, inpaint_loss),end="")
print("")
blended_images = posisson_blending(masks, generated_images.detach(), corrupted_images)
image_range = torch.min(corrupted_images), torch.max(corrupted_images)
save_image(corrupted_images, "../outputs/corrupted_{}.png".format(i), normalize=True, range=image_range, nrow=5)
save_image(generated_images, "../outputs/output_{}.png".format(i), normalize=True, range=image_range, nrow=5)
save_image(blended_images, "../outputs/blended_{}.png".format(i), normalize=True, range=image_range, nrow=5)
save_image(original_images, "../outputs/original_{}.png".format(i), normalize=True, range=image_range, nrow=5)
del z_optimum, optimizer_inpaint
def __init__(self, opt, num_classes, source_train_ds, source_test_ds,
target_train_ds, mean, std):
super().__init__(opt, num_classes, source_train_ds, source_test_ds)
self.target_train_ds = target_train_ds
self.mean = mean
self.std = std
# Defining networks and optimizers
self.generator = models.Generator(opt, num_classes)
self.discriminator = models.Discriminator(opt, num_classes)
# Weight initialization
self.generator.apply(utils.weights_init)
self.discriminator.apply(utils.weights_init)
# Defining loss criterions
self.criterion_c = nn.CrossEntropyLoss()
self.criterion_s = nn.BCELoss()
if opt.gpu >= 0:
self.discriminator.cuda()
self.generator.cuda()
self.criterion_c.cuda()
self.criterion_s.cuda()
# Defining optimizers
self.optimizer_discriminator = optim.Adam(
self.discriminator.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
self.optimizer_generator = optim.Adam(self.generator.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
# Other variables
self.real_label_val = 1
self.fake_label_val = 0
BATCH_SIZE = 1
DIGIT = 1
CHANCE_DELUDE = 0.5
LR1 = 0.001
LR2 = 0.001
# define transform and dataloader
transform = torchvision.transforms.Compose(
[torchvision.transforms.Normalize((0.1307, ), (0.3081, ))])
dataloader = torch.utils.data.DataLoader(ds.MNISTDiscriminatorDataset(
transform, digit=DIGIT),
batch_size=BATCH_SIZE,
shuffle=True)
# define model and optimizer
discriminator = m.Discriminator()
generator = m.Generator()
criterion = nn.CrossEntropyLoss()
optimizer1 = optim.SGD(discriminator.parameters(), lr=LR1)
optimizer2 = optim.SGD(generator.parameters(), lr=LR2)
snaps = os.listdir('./snaps')
snaps.remove('.gitkeep')
if len(snaps) > 0:
latest = max(snaps)
path = f'./snaps/{latest}'
torch_object = torch.load(path)
assert args.beta_2 > 0
assert args.n_epochs > 0
assert args.n_iters > 0
assert args.noise_std >= 0
assert args.shift_pct <= 1 and args.shift_pct >= 0
os.makedirs('run', exist_ok=True)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
G = models.Generator(args.hid_dim, args.z_dim, args.v_dim, args.image_channels)
D = models.Discriminator(args.hid_dim, args.v_dim, args.image_channels)
def initialize_params(m):
"""
initialize neural network parameters
"""
if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
m.weight.data.normal_(mean=0, std=args.init_std)
m.bias.data.zero_()
G.apply(initialize_params)
D.apply(initialize_params)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def main():
trn_ds = loaders.SatelliteDataset(TRAIN_IMAGES_ROOT,
HR_PATCH,
scale_factor=SCALE)
trn_dl = DataLoader(trn_ds,
TRAIN_BATCH_SIZE,
shuffle=True,
num_workers=WORKERS)
val_ds = loaders.SatelliteValDataset(VAL_IMAGES_ROOT,
HR_PATCH,
scale_factor=SCALE)
val_dl = DataLoader(val_ds,
VALID_BATCH_SIZE,
shuffle=False,
num_workers=WORKERS)
start_epoch = 1
best_val_loss = float('inf')
# Generator
G = models.GeneratorBNFirst(3, 3, upscale=SCALE)
opt_G = optim.Adam(G.parameters(), lr=LR_G)
sched_G = optim.lr_scheduler.StepLR(opt_G, LR_STEP, gamma=LR_DECAY)
# Discriminator
D = models.Discriminator(48, HR_PATCH[0], sigmoid=True)
opt_D = optim.Adam(D.parameters(), lr=LR_D)
sched_D = optim.lr_scheduler.StepLR(opt_D, LR_STEP, gamma=LR_DECAY)
if not os.path.exists(WEIGHTS_SAVE_PATH):
os.mkdir(WEIGHTS_SAVE_PATH)
if LOAD_CHECKPOINT is not None:
checkpoint = torch.load(LOAD_CHECKPOINT, pickle_module=dill)
start_epoch = checkpoint['epoch']
G.load_state_dict(checkpoint['G_state_dict'])
D.load_state_dict(checkpoint['D_state_dict'])
opt_G = checkpoint['optimizer_G']
opt_D = checkpoint['optimizer_D']
sched_G = checkpoint['lr_scheduler_G']
sched_D = checkpoint['lr_scheduler_D']
best_val_loss = checkpoint['best_metrics']
G.to(DEVICE)
D.to(DEVICE)
# Losses
content_loss = losses.ContentLoss(VGG_FEATURE_LAYER, 'l2')
content_loss.to(DEVICE)
adv_loss = losses.AdversarialLoss()
adv_loss.to(DEVICE)
MSE = nn.MSELoss()
MSE.to(DEVICE)
train_losses = BookKeeping(TENSORBOARD_LOGDIR, suffix='trn')
val_losses = BookKeeping(TENSORBOARD_LOGDIR, suffix='val')
for epoch in range(start_epoch, EPOCHS + 1):
# Training loop
train(G, D, trn_dl, epoch, EPOCHS, content_loss, MSE, adv_loss, opt_G,
opt_D, train_losses)
# Validation loop
best_val_loss = evaluate(G, D, val_dl, epoch, EPOCHS, content_loss,
MSE, adv_loss, val_losses, best_val_loss)
sched_G.step()
sched_D.step()
save_checkpoint(epoch, G, D, None, opt_G, sched_G, opt_D, sched_D)
train_losses.update_tensorboard(epoch)
val_losses.update_tensorboard(epoch)
# Reset all loss for a new epoch
train_losses.reset()
val_losses.reset()
# Save real vs fake samples for quality inspection
generator = iter(val_dl)
for j in range(BATCHES_TO_SAVE):
lrs, hrs = next(generator)
fakes = G(lrs.to(DEVICE))
# Save samples at the end
save_images(END_EPOCH_SAVE_SAMPLES_PATH,
lrs.detach().cpu(),
fakes.detach().cpu(), hrs, epoch, j)
import numpy as np import dataset import helpers import models from config import * assert (file_name == 'vae-gan') writer, save_dir = helpers.init(gpu, file_name, experiment_name) use_ganloss = True # TODO can I use that here? use_vaeloss = True use_instancenoise = False iter_decay = 1000. # iterations after which instance noise is at 1/e # models # TODO to one model? netD = models.Discriminator().cuda() netG = models.Generator().cuda() netE = models.Encoder().cuda() # weight initialization netD.apply(models.init_weights) # xavier init netE.apply(models.init_weights) # xavier init netG.apply(models.init_weights) # xavier init criterion = nn.BCELoss().cuda() optD = torch.optim.Adam(netD.parameters(), lr=lr) optG = torch.optim.Adam(netG.parameters(), lr=lr) optE = torch.optim.Adam(netE.parameters(), lr=lr) def sample(n_samples):
def __init__(self, config):
self.rank, self.world_size = 0, 1
if config['dist']:
self.rank = dist.get_rank()
self.world_size = dist.get_world_size()
self.config = config
self.mode = config['dgp_mode']
self.custom_mask = config['custom_mask']
self.update_G = config['update_G']
self.update_embed = config['update_embed']
self.iterations = config['iterations']
self.ftr_num = config['ftr_num']
self.ft_num = config['ft_num']
self.lr_ratio = config['lr_ratio']
self.G_lrs = config['G_lrs']
self.z_lrs = config['z_lrs']
self.use_in = config['use_in']
self.select_num = config['select_num']
self.factor = 4 # Downsample factor
self.mask_path = config['mask_path']
#Create selective masking
if self.custom_mask:
self.mask = torch.ones(1, 1, 256, 256).cuda()
x = Image.open(self.mask_path)
pil_to_tensor = transforms.ToTensor()(x).unsqueeze_(0)
t = Variable(torch.Tensor([0.9])) # threshold
final_mask = F.interpolate(pil_to_tensor,
size=(256, 256),
mode='bilinear')
self.mask = (final_mask > t).float() * 1
self.mask = self.mask[0][0].cuda()
self.regions = self.get_regions(self.mask)
#########################
# create model
self.G = models.Generator(**config).cuda()
self.D = models.Discriminator(
**config).cuda() if config['ftr_type'] == 'Discriminator' else None
self.G.optim = torch.optim.Adam(
[{
'params': self.G.get_params(i, self.update_embed)
} for i in range(len(self.G.blocks) + 1)],
lr=config['G_lr'],
betas=(config['G_B1'], config['G_B2']),
weight_decay=0,
eps=1e-8)
# load weights
if config['random_G']:
self.random_G()
else:
utils.load_weights(self.G if not (config['use_ema']) else None,
self.D,
config['weights_root'],
name_suffix=config['load_weights'],
G_ema=self.G if config['use_ema'] else None,
strict=False)
self.G.eval()
if self.D is not None:
self.D.eval()
self.G_weight = deepcopy(self.G.state_dict())
# prepare latent variable and optimizer
self._prepare_latent()
# prepare learning rate scheduler
self.G_scheduler = utils.LRScheduler(self.G.optim, config['warm_up'])
self.z_scheduler = utils.LRScheduler(self.z_optim, config['warm_up'])
# loss functions
self.mse = torch.nn.MSELoss()
if config['ftr_type'] == 'Discriminator':
self.ftr_net = self.D
self.criterion = utils.DiscriminatorLoss(
ftr_num=config['ftr_num'][0])
else:
vgg = torchvision.models.vgg16(pretrained=True).cuda().eval()
self.ftr_net = models.subsequence(vgg.features, last_layer='20')
self.criterion = utils.PerceptLoss()
# Downsampler for producing low-resolution image
self.downsampler = Downsampler(n_planes=3,
factor=self.factor,
kernel_type='lanczos2',
phase=0.5,
preserve_size=True).type(
torch.cuda.FloatTensor)
def __init__(self, hyperparameters):
super(Model, self).__init__()
self.device = hyperparameters['device']
self.auxiliary_data_source = hyperparameters['auxiliary_data_source']
self.all_data_sources = ['resnet_features', self.auxiliary_data_source]
self.DATASET = hyperparameters['dataset']
self.num_shots = hyperparameters['num_shots']
self.latent_size = hyperparameters['latent_size']
self.batch_size = hyperparameters['batch_size']
self.hidden_size_rule = hyperparameters['hidden_size_rule']
self.warmup = hyperparameters['model_specifics']['warmup']
self.generalized = hyperparameters['generalized']
self.classifier_batch_size = 32
self.img_seen_samples = hyperparameters['samples_per_class'][
self.DATASET][0]
self.att_seen_samples = hyperparameters['samples_per_class'][
self.DATASET][1]
self.att_unseen_samples = hyperparameters['samples_per_class'][
self.DATASET][2]
self.img_unseen_samples = hyperparameters['samples_per_class'][
self.DATASET][3]
self.reco_loss_function = hyperparameters['loss']
self.nepoch = hyperparameters['epochs']
self.lr_cls = hyperparameters['lr_cls']
self.cross_reconstruction = hyperparameters['model_specifics'][
'cross_reconstruction']
self.cls_train_epochs = hyperparameters['cls_train_steps']
self.dataset = dataloader(self.DATASET,
copy.deepcopy(self.auxiliary_data_source),
device=self.device)
self.writer = SummaryWriter()
self.num_gen_iter = hyperparameters['num_gen_iter']
self.num_dis_iter = hyperparameters['num_dis_iter']
self.pretrain = hyperparameters['pretrain']
if self.DATASET == 'CUB':
self.num_classes = 200
self.num_novel_classes = 50
elif self.DATASET == 'SUN':
self.num_classes = 717
self.num_novel_classes = 72
elif self.DATASET == 'AWA1' or self.DATASET == 'AWA2':
self.num_classes = 50
self.num_novel_classes = 10
feature_dimensions = [2048, self.dataset.aux_data.size(1)]
# Here, the encoders and decoders for all modalities are created and put into dict
self.encoder = {}
for datatype, dim in zip(self.all_data_sources, feature_dimensions):
self.encoder[datatype] = models.encoder_template(
dim, self.latent_size, self.hidden_size_rule[datatype],
self.device)
print(str(datatype) + ' ' + str(dim))
print('latent size ' + str(self.latent_size))
self.decoder = {}
for datatype, dim in zip(self.all_data_sources, feature_dimensions):
self.decoder[datatype] = models.decoder_template(
self.latent_size, dim, self.hidden_size_rule[datatype],
self.device)
# An optimizer for all encoders and decoders is defined here
parameters_to_optimize = list(self.parameters())
for datatype in self.all_data_sources:
parameters_to_optimize += list(self.encoder[datatype].parameters())
parameters_to_optimize += list(self.decoder[datatype].parameters())
# The discriminator network is defined here
self.net_D_Att = models.Discriminator(
self.dataset.aux_data.size(1) + 2048, self.device)
self.net_D_Img = models.Discriminator(
2048 + self.dataset.aux_data.size(1), self.device)
self.optimizer_G = optim.Adam(parameters_to_optimize,
lr=hyperparameters['lr_gen_model'],
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0.0005,
amsgrad=True)
self.optimizer_D = optim.Adam(itertools.chain(
self.net_D_Att.parameters(), self.net_D_Img.parameters()),
lr=hyperparameters['lr_gen_model'],
betas=(0.5, 0.999),
weight_decay=0.0005)
if self.reco_loss_function == 'l2':
self.reconstruction_criterion = nn.MSELoss(reduction='sum')
elif self.reco_loss_function == 'l1':
self.reconstruction_criterion = nn.L1Loss(reduction='sum')
self.MSE = nn.MSELoss(reduction='sum')
self.L1 = nn.L1Loss(reduction='sum')
self.att_fake_from_att_sample = utils.Sample_from_Pool()
self.att_fake_from_img_sample = utils.Sample_from_Pool()
self.img_fake_from_img_sample = utils.Sample_from_Pool()
self.img_fake_from_att_sample = utils.Sample_from_Pool()
if self.generalized:
print('mode: gzsl')
self.clf = LINEAR_LOGSOFTMAX(self.latent_size, self.num_classes)
else:
print('mode: zsl')
self.clf = LINEAR_LOGSOFTMAX(self.latent_size,
self.num_novel_classes)
def main():
# Preparing dataset and loader
_ = datasets.MNIST(root='data', train=True, download=True, transform=None)
train_data = MNISTLayoutDataset()
train_loader = DataLoader(train_data, batch_size=args.batchsize)
# Defining required variables
element_num = 128
feature_size = 3
feature_geo_size = 2
feature_cls_size = 1
beta1 = 0.99
beta2 = 0.95
# Setting device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
# Loading the models
gen = models.Generator(args.batchsize, element_num, feature_geo_size,
feature_cls_size).to(device)
dis = models.Discriminator(args.batchsize).to(device)
# Defining the optimizers
g_optimizer = optim.Adam(gen.parameters(), args.lrate, (beta1, beta2))
d_optimizer = optim.Adam(dis.parameters(), args.lrate / 10, (beta1, beta2))
# Set models to training mode
gen.train()
dis.train()
# Train for given number of epochs
for epoch in range(1, args.epochs + 1):
start_time = time.time()
for batch_idx, real_images in enumerate(train_loader):
real_images = real_images.to(device)
curr_time = time.time()
elp_time = curr_time - start_time
print(
"\rEpoch: {}/{} Batch: {}/{} Time on Epoch: {:.0f} sec".format(
epoch, args.epochs, batch_idx, len(train_loader),
elp_time),
end="")
batch_size = real_images.size(0)
# TRAINING DISCRIMINATOR
d_optimizer.zero_grad()
# Loss for real images
real_images = Variable(real_images)
D_real = dis(real_images)
d_real_loss = real_loss(D_real, device)
# Loss for fake images
z_cls = torch.FloatTensor(batch_size, element_num,
feature_geo_size).uniform_(0, 1)
z_geo = torch.FloatTensor(batch_size, element_num,
feature_cls_size).normal_(0.5, 0.5)
z = torch.cat((z_cls, z_geo), 2).to(device)
fake_images_d = gen(z)
D_fake = dis(fake_images_d)
d_fake_loss = fake_loss(D_fake, device)
# Total loss
d_loss = d_real_loss + d_fake_loss
d_loss.backward()
d_optimizer.step()
# TRAINING GENERATOR
g_optimizer.zero_grad()
z_cls = torch.FloatTensor(batch_size, element_num,
feature_cls_size).uniform_(0, 1)
z_geo = torch.FloatTensor(batch_size, element_num,
feature_geo_size).normal_(0.5, 0.5)
z = torch.cat((z_cls, z_geo), 2).to(device)
fake_images_g = gen(z)
D_out = dis(fake_images_g)
g_loss = real_loss(D_out, device)
g_loss.backward()
g_optimizer.step()
if batch_idx % 100 == 0:
test_samples = 9
result_path = 'result'
os.makedirs(result_path, exist_ok=True)
z_cls = torch.FloatTensor(test_samples, element_num,
feature_cls_size).uniform_(0, 1)
z_geo = torch.FloatTensor(test_samples, element_num,
feature_geo_size).normal_(0.5, 0.5)
z = torch.cat((z_cls, z_geo), 2).to(device)
generated_images = gen(z)
generated_images = points_to_image(generated_images).view(
-1, 1, 28, 28)
save_image(generated_images,
'{}/{}_{}.png'.format(result_path, epoch,
batch_idx),
nrow=3)
print(
"[G: Loss = {:.4f}] [D: Total Loss = {:.4f} Real Loss = {:.4f} Fake Loss {:.4f}]"
.format(g_loss, d_loss, d_real_loss, d_fake_loss))
if epoch % args.save_every == 0:
model_path = 'trained_models'
os.makedirs(model_path, exist_ok=True)
# Save Models
torch.save({'state_dict': dis.state_dict()},
'{}/dis_epoch_{}.pth'.format(model_path, epoch))
torch.save({'state_dict': gen.state_dict()},
'{}/gen_epoch_{}.pth'.format(model_path, epoch))
print("GPUは使えません。")
if cuda:
gpu_id = input('使用するGPUの番号を入れてください : ')
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id
device = torch.device('cuda:' + gpu_id if cuda else 'cpu')
# 推定モデルの決定
p = 7
os.makedirs("output-images/p{0}".format(p), exist_ok=True)
os.makedirs("parameters/p{0}".format(p), exist_ok=True)
torch.manual_seed(opt.generator_seed)
generator = models.LinearGenerator(p=p, input_dim=1, is_bias=False)
torch.manual_seed(opt.discriminator_seed)
discriminator = models.Discriminator(
q=0, discriminator_hidden_unit=opt.discriminator_hidden_unit)
torch.manual_seed(opt.predictor_seed)
predictor = models.LinearPredictNet(p=p, input_dim=1, is_bias=True)
# 訓練データを一つ用いて学習させる
dataSeed = opt.data_seed
# こいつをtrain:validation=900:100に分割する
Data = trainDataSets[dataSeed]
Data = torch.tensor(Data, dtype=torch.float)
Data = Data.view(1, -1)
trainData = Data[:, :900]
valData = Data[:, 900:]
# trainDataとvalDataを {𝑋𝑡}𝑡0𝑡=𝑡0−𝑝 ごとに取り出しやすいようにMatrixに変換する
trainMatrix = []
for i in range(trainData.shape[1] - (p)):
ans = trainData[:, i:i + p + 1].view(1, Data.shape[0], -1)
parser.add_argument('--img_size', dest='img_size', type=int, default=128)
parser.add_argument('--batch_size', dest='batch_size', type=int, default=1)
parser.add_argument('--experiment_name',
dest='experiment_name',
default='stgan_128')
args = parser.parse_args()
# model
atts = args.atts
n_att = len(atts)
img_size = args.img_size
batch_size = args.batch_size
experiment_name = args.experiment_name
Gen = models.Generator()
Dis = models.Discriminator(n_att)
Enc = models.Encoder()
Stu = models.Stu()
x = tf.ones(shape=[2, 128, 128, 3], dtype=tf.float32)
a = tf.ones(shape=[2, 13], dtype=tf.float32)
z = Enc(x)
z_stu = Stu(z, a)
x_fake = Gen(z_stu, a - a)
d, att = Dis(x)
lr = tf.Variable(initial_value=0., trainable=False)
g_opt = tf.optimizers.Adam(lr, beta_1=0., beta_2=0.99)
d_opt = tf.optimizers.Adam(lr, beta_1=0., beta_2=0.99)
params = tf.Variable(initial_value=[5, 0], trainable=False, dtype=tf.int64)
def train_model():
# Setup session
sess = tu.setup_training_session()
##########
# Innputs
##########
# Setup async input queue of real images
X_real = du.read_celebA()
# Noise
batch_size = tf.shape(X_real)[0]
z_noise_for_D = tf.random_uniform((
batch_size,
FLAGS.z_dim,
),
minval=-1,
maxval=1,
name="z_input_D")
z_noise_for_G = tf.random_uniform((
batch_size,
FLAGS.z_dim,
),
minval=-1,
maxval=1,
name="z_input_G")
# k factor
k_factor = tf.Variable(initial_value=0.,
trainable=False,
name='anneal_factor')
# learning rate
lr = tf.Variable(initial_value=FLAGS.learning_rate,
trainable=False,
name='learning_rate')
########################
# Instantiate models
########################
G = models.Generator(nb_filters=FLAGS.nb_filters_G)
D = models.Discriminator(h_dim=FLAGS.h_dim, nb_filters=FLAGS.nb_filters_D)
##########
# Outputs
##########
X_rec_real = D(X_real, output_name="X_rec_real")
X_fake_for_D = G(z_noise_for_D, output_name="X_fake_for_D")
X_rec_fake_for_D = D(X_fake_for_D,
reuse=True,
output_name="X_rec_fake_for_D")
X_fake_for_G = G(z_noise_for_G, reuse=True, output_name="X_fake_for_G")
X_rec_fake_for_G = D(X_fake_for_G,
reuse=True,
output_name="X_rec_fake_for_G")
# output images for plots
real_toplot = du.unnormalize_image(X_real, name="real_toplot")
generated_toplot = du.unnormalize_image(X_fake_for_G,
name="generated_toplot")
real_rec_toplot = du.unnormalize_image(X_rec_real, name="rec_toplot")
generated_rec_toplot = du.unnormalize_image(X_rec_fake_for_G,
name="generated_rec_toplot")
###########################
# Instantiate optimizers
###########################
opt = tf.train.AdamOptimizer(learning_rate=lr, name='opt')
###########################
# losses
###########################
loss_real = losses.mae(X_real, X_rec_real)
loss_fake_for_D = losses.mae(X_fake_for_D, X_rec_fake_for_D)
loss_fake_for_G = losses.mae(X_fake_for_G, X_rec_fake_for_G)
L_D = loss_real - k_factor * loss_fake_for_D
L_G = loss_fake_for_G
Convergence = loss_real + tf.abs(FLAGS.gamma * loss_real - loss_fake_for_G)
###########################
# Compute updates ops
###########################
dict_G_vars = G.get_trainable_variables()
G_vars = [dict_G_vars[k] for k in dict_G_vars.keys()]
dict_D_vars = D.get_trainable_variables()
D_vars = [dict_D_vars[k] for k in dict_D_vars.keys()]
G_gradvar = opt.compute_gradients(L_G, var_list=G_vars)
G_update = opt.apply_gradients(G_gradvar, name='G_loss_minimize')
D_gradvar = opt.compute_gradients(L_D, var_list=D_vars)
D_update = opt.apply_gradients(D_gradvar, name='D_loss_minimize')
update_k_factor = tf.assign(
k_factor,
k_factor + FLAGS.lambdak * (FLAGS.gamma * loss_real - loss_fake_for_G))
update_lr = tf.assign(lr, lr / 2)
##########################
# Summary ops
##########################
# Add summary for gradients
tu.add_gradient_summary(G_gradvar)
tu.add_gradient_summary(D_gradvar)
# Add scalar symmaries for G
tf.summary.scalar("G loss", L_G)
# Add scalar symmaries for D
tf.summary.scalar("D loss", L_D)
# Add scalar symmaries for D
tf.summary.scalar("k_factor", k_factor)
tf.summary.scalar("Convergence", Convergence)
tf.summary.scalar("learning rate", lr)
summary_op = tf.summary.merge_all()
############################
# Start training
############################
# Initialize session
saver = tu.initialize_session(sess)
# Start queues
coord, threads = du.manage_queues(sess)
# Summaries
writer = tu.manage_summaries(sess)
# Run checks on data dimensions
list_data = [z_noise_for_D, z_noise_for_G]
list_data += [
X_real, X_rec_real, X_fake_for_G, X_rec_fake_for_G, X_fake_for_D,
X_rec_fake_for_D
]
list_data += [generated_toplot, real_toplot]
output = sess.run(list_data)
tu.check_data(output, list_data)
for e in tqdm(range(FLAGS.nb_epoch), desc="Training progress"):
# Anneal learning rate every 5 epoch
if (e + 1) % 5 == 0:
sess.run([update_lr])
t = tqdm(range(FLAGS.nb_batch_per_epoch),
desc="Epoch %i" % e,
mininterval=0.5)
for batch_counter in t:
output = sess.run([G_update, D_update, update_k_factor])
if batch_counter % (FLAGS.nb_batch_per_epoch //
(int(0.5 * FLAGS.nb_batch_per_epoch))) == 0:
output = sess.run([summary_op])
writer.add_summary(
output[-1], e * FLAGS.nb_batch_per_epoch + batch_counter)
t.set_description('Epoch %s:' % e)
# Plot some generated images
Xf, Xr, Xrrec, Xfrec = sess.run([
generated_toplot, real_toplot, real_rec_toplot,
generated_rec_toplot
])
vu.save_image(Xf, Xr, title="current_batch", e=e)
vu.save_image(Xrrec, Xfrec, title="reconstruction", e=e)
# Save session
saver.save(sess, os.path.join(FLAGS.model_dir, "model"), global_step=e)
# Show data statistics
output = sess.run(list_data)
tu.check_data(output, list_data)
# Stop threads
coord.request_stop()
coord.join(threads)
print('Finished training!')
# pickle to avoid encoding errors with json
with open(os.path.join(args.output_dir, 'charmap.pickle'), 'wb') as f:
pickle.dump(charmap, f)
with open(os.path.join(args.output_dir, 'inv_charmap.pickle'), 'wb') as f:
pickle.dump(inv_charmap, f)
real_inputs_discrete = tf.placeholder(tf.int32,
shape=[args.batch_size, args.seq_length])
real_inputs = tf.one_hot(real_inputs_discrete, len(charmap))
fake_inputs = models.Generator(args.batch_size, args.seq_length,
args.layer_dim, len(charmap))
fake_inputs_discrete = tf.argmax(fake_inputs,
fake_inputs.get_shape().ndims - 1)
disc_real = models.Discriminator(real_inputs, args.seq_length, args.layer_dim,
len(charmap))
disc_fake = models.Discriminator(fake_inputs, args.seq_length, args.layer_dim,
len(charmap))
disc_cost = tf.reduce_mean(disc_fake) - tf.reduce_mean(disc_real)
gen_cost = -tf.reduce_mean(disc_fake)
# WGAN lipschitz-penalty
alpha = tf.random_uniform(shape=[args.batch_size, 1, 1], minval=0., maxval=1.)
differences = fake_inputs - real_inputs
interpolates = real_inputs + (alpha * differences)
gradients = tf.gradients(
models.Discriminator(interpolates, args.seq_length, args.layer_dim,
len(charmap)), [interpolates])[0]
slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
import nets
netG = nets.UNet11(num_classes=1, pretrained='vgg')
# # ngf = 32
# use_dropout = False
# norm_layer = utils.get_norm_layer(norm_type='batch')
# # netG = models.ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
# netG = models.LeakyResnetGenerator(input_nc, output_nc, ngf=6, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
# # netG = models.LeakyResnetGenerator(input_nc, output_nc, ngf=64, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
# ## Unet Generator
# # netG = models.UnetGenerator(input_nc, output_nc, 7, ngf=3, norm_layer=norm_layer, use_dropout=use_dropout)
# utils.init_net(netG, init_type='normal', init_gain=0.02)
summary(netG, input_size=(3, img_size, img_size))
## Building Discriminator
netD = models.Discriminator(input_nc=1, img_size=img_size)
utils.init_net(netD, init_type='normal', init_gain=0.02)
summary(netD, input_size=(1, img_size, img_size))
lr = 0.00002
G_optimizer = Adam(netG.parameters(), lr=lr, betas=(0.9, 0.999))
D_optimizer = Adam(netD.parameters(), lr=lr, betas=(0.9, 0.999))
G_model_path = root / 'G_model.pt'
D_model_path = root / 'D_model.pt'
if G_model_path.exists() and D_model_path.exists():
state = torch.load(str(G_model_path))
netG.load_state_dict(state['model'])
state = torch.load(str(D_model_path))
epoch = state['epoch']
utils.save_image(torchvision.utils.make_grid(utils.denorm(tmp), padding=1),
output_path + 'images/test_sirf_normal.png')
real_image_mask_test, _ = next(iter(real_image_mask))
utils.save_image(torchvision.utils.make_grid(real_image_mask_test, padding=1),
output_path + 'images/MASK_TEST.png')
tmp = next(iter(sirfs_shading_val))
tmp = utils.denorm(tmp)
tmp = applyMask(var(tmp).type(torch.DoubleTensor), real_image_mask_test)
tmp = tmp.data
utils.save_image(torchvision.utils.make_grid(tmp, padding=1),
output_path + 'images/Validation_SIRFS_SHADING.png')
# featureNet = ResNet(BasicBlock, [2, 2, 2, 2], 27)
featureNet = models.BaseSimpleFeatureNet()
lightingNet = models.LightingNet()
D = models.Discriminator()
# R = models.ResNet(models.BasicBlock, [2, 2, 2, 2], 27) #
R = models.BaseSimpleFeatureNet()
print(featureNet)
print(lightingNet)
featureNet = featureNet.cuda()
lightingNet = lightingNet.cuda()
D = D.cuda()
R = R.cuda()
dtype = torch.FloatTensor
dtype = torch.cuda.FloatTensor ## UNCOMMENT THIS LINE IF YOU'RE ON A GPU!
# Training
if TrainSyn:
syn_net_train(featureNet,
torchvision.transforms.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5)))
transforms = torchvision.transforms.Compose(transforms_list)
mnist = dataset_loader.MNIST(data_path=opt.data_path,
transforms=transforms)
mnist_train, mnist_test = mnist.get_MNIST()
mnist_train_loader = torch.utils.data.DataLoader(mnist_train,
batch_size=opt.batch_size,
shuffle=True)
mnist_test_loader = torch.utils.data.DataLoader(mnist_test,
batch_size=opt.batch_size,
shuffle=False)
discriminator = models.Discriminator(ndf=opt.ndf).cuda()
# discriminator.initialize()
generator = models.Generator(ngf=opt.ngf).cuda()
# generator.initialize()
discriminator.apply(models.weights_init)
generator.apply(models.weights_init)
optim_discriminator = torch.optim.Adam(discriminator.parameters(),
lr=opt.learning_rate,
betas=(0.5, 0.999))
optim_generator = torch.optim.Adam(generator.parameters(),
lr=opt.learning_rate,
betas=(0.5, 0.999))
crit_discriminator = torch.nn.BCELoss().cuda()
crit_generator = torch.nn.BCELoss().cuda()
def __init__(self, args, server, cluster, env, queue_shapes,
trajectory_queue_size, replay_queue_size):
self.env = env
self.args = args
self.task = args.task
self.queue_shapes = queue_shapes
self.trajectory_queue_size = trajectory_queue_size
self.replay_queue_size = replay_queue_size
self.action_sizes = env.action_sizes
self.input_shape = list(self.env.observation_shape)
# used for summary
self._disc_step = 0
self._policy_step = 0
##################################
# Queue pipelines (ps/task=0~)
##################################
with tf.device('/job:ps/task:0'):
# TODO: we may need more than 1 queue
#for i in range(cluster.num_tasks('ps')):
if args.task != 1 or args.loss == 'l2':
self.trajectory_queue = tf.FIFOQueue(
self.trajectory_queue_size,
[tf.float32] * len(self.queue_shapes),
shapes=[shape for _, shape in self.queue_shapes],
names=[name for name, _ in self.queue_shapes],
shared_name='queue')
self.trajectory_queue_size_op = self.trajectory_queue.size()
if args.loss == 'gan':
self.replay_queue = tf.FIFOQueue(
self.replay_queue_size,
tf.float32,
shapes=dict(self.queue_shapes)['states'][1:],
shared_name='replay')
self.replay_queue_size_op = self.replay_queue.size()
else:
self.replay_queue = None
self.replay_queue_size_op = None
###########################
# Master policy (task!=1)
###########################
device = 'gpu' if self.task == 0 else 'cpu'
master_gpu = "/job:worker/task:{}/{}:0".format(self.args.task, device)
master_gpu_replica = tf.train. \
replica_device_setter(1, worker_device=master_gpu)
with tf.device(master_gpu_replica):
with tf.variable_scope("global"):
self.policy_step = tf.get_variable(
"policy_step", [],
tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
self.disc_step = tf.get_variable(
"disc_step", [],
tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
#master_cpu = "/job:worker/task:{}/cpu:0".format(self.args.task, device)
#master_cpu_replica = tf.train. \
# replica_device_setter(1, worker_device=master_cpu)
#with tf.device(master_cpu_replica):
# master should initialize discriminator
if args.task < 2 and args.loss == 'gan':
self.global_disc = models.Discriminator(
self.args, self.disc_step, self.input_shape, self.env.norm,
"global")
if args.task != 1 or args.loss == 'l2':
logger.debug(master_gpu)
with tf.device(master_gpu_replica):
self.prepare_master_network()
###########################
# Master policy network
###########################
if self.args.task == 0:
policy_batch_size = self.args.policy_batch_size
# XXX: may need this if you are lack of GPU memory
#policy_batch_size = int(self.args.policy_batch_size \
# / self.env.episode_length)
worker_device = "/job:worker/task:{}/cpu:0".format(self.task)
logger.debug(worker_device)
with tf.device(worker_device):
with tf.variable_scope("global"):
self.trajectory_dequeue = self.trajectory_queue. \
dequeue_many(policy_batch_size)
###########################
# Discriminator (task=1)
###########################
elif self.args.task == 1 and self.args.loss == 'gan':
device = 'gpu' if args.num_gpu > 0 else 'cpu'
worker_device = "/job:worker/task:{}/{}:0".format(
self.task, device)
logger.debug(worker_device)
with tf.device(worker_device):
self.prepare_gan()
worker_device = "/job:worker/task:{}/cpu:0".format(self.task)
logger.debug(worker_device)
with tf.device(worker_device):
with tf.variable_scope("global"):
self.replay_dequeue = self.replay_queue. \
dequeue_many(self.args.disc_batch_size)
#####################################################
# Local policy network (task >= 2 (gan) or 1 (l2))
#####################################################
elif self.args.task >= 1:
worker_device = "/job:worker/task:{}/cpu:0".format(self.task)
logger.debug(worker_device)
with tf.device(worker_device):
self.prepare_local_network()
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
print('load data: ', args.dataset)
train_loader, test_loader = data_loader.getTargetDataSet(
args.dataset, args.batch_size, args.imageSize, args.dataroot)
print('Load model')
model = models.vgg13()
print(model)
print('load GAN')
nz = 100
netG = models.Generator(1, nz, 64, 3) # ngpu, nz, ngf, nc
netD = models.Discriminator(1, 3, 64) # ngpu, nc, ndf
# Initial setup for GAN
real_label = 1
fake_label = 0
criterion = nn.BCELoss()
fixed_noise = torch.FloatTensor(64, nz, 1, 1).normal_(0, 1)
if args.cuda:
model.cuda()
netD.cuda()
netG.cuda()
criterion.cuda()
fixed_noise = fixed_noise.cuda()
fixed_noise = Variable(fixed_noise)
print('Setup optimizer')
