def __init__(self, cfg):
super(LM, self).__init__()
self.cfg = cfg
# Dataset
self.train_dataset = get_dataset(
name=cfg.data.type,
data_dir=cfg.data.train_dir,
size=cfg.data.img_size,
lsun_categories=cfg.data.lsun_categories_train
)
# Number of labels
self.nlabels = min(len(self.train_dataset.classes), cfg.data.nlabels)
self.sample_nlabels = min(self.nlabels, cfg.train.sample_nlabels)
# Create models
self.generator = instantiate(cfg.generator)
self.discriminator = instantiate(cfg.discriminator)
self.gan_type = cfg.train.gan_type
self.reg_type = cfg.train.reg_type
self.reg_param = cfg.train.reg_param
# Distributions
self.ydist = get_ydist(self.nlabels)
self.zdist = get_zdist(cfg.z_dist.type, cfg.z_dist.dim)
# Save for tests
ntest = cfg.train.batch_size
x_real, self.ytest = utils.get_nsamples(self.train_dataloader(), ntest)
self.ytest.clamp_(None, self.nlabels-1)
self.ztest = self.zdist.sample((ntest,))
# Evaluator
self.evaluator = Evaluator(self.generator, self.zdist, self.ydist,
batch_size=cfg.train.batch_size)
# Create missing directories
if not path.exists(out_dir):
os.makedirs(out_dir)
if not path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
shutil.copyfile(sys.argv[0], out_dir + '/training_script.py')
# Logger
checkpoint_io = CheckpointIO(checkpoint_dir=checkpoint_dir)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Dataset
train_dataset, nlabels = get_dataset(
name=config['data']['type'],
data_dir=config['data']['train_dir'],
size=config['data']['img_size'],
lsun_categories=config['data']['lsun_categories_train'])
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=config['training']['nworkers'],
shuffle=True,
pin_memory=True,
sampler=None,
drop_last=True)
print('train_dataset=', train_dataset)
test_dataset, _ = get_dataset(
name=config['data']['type'],
data_dir=config['data']['test_dir'],
size=128,
def main():
pp = pprint.PrettyPrinter(indent=1)
pp.pprint({
'data': config['data'],
'generator': config['generator'],
'discriminator': config['discriminator'],
'clusterer': config['clusterer'],
'training': config['training']
})
is_cuda = torch.cuda.is_available()
# Short hands
batch_size = config['training']['batch_size']
log_every = config['training']['log_every']
inception_every = config['training']['inception_every']
backup_every = config['training']['backup_every']
sample_nlabels = config['training']['sample_nlabels']
nlabels = config['data']['nlabels']
sample_nlabels = min(nlabels, sample_nlabels)
checkpoint_dir = path.join(out_dir, 'chkpts')
nepochs = args.nepochs
# Create missing directories
if not path.exists(out_dir):
os.makedirs(out_dir)
if not path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# Logger
checkpoint_io = CheckpointIO(checkpoint_dir=checkpoint_dir)
device = torch.device("cuda:0" if is_cuda else "cpu")
train_dataset, _ = get_dataset(
name=config['data']['type'],
data_dir=config['data']['train_dir'],
size=config['data']['img_size'],
deterministic=config['data']['deterministic'])
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=config['training']['nworkers'],
shuffle=True,
pin_memory=True,
sampler=None,
drop_last=True)
# Create models
generator, discriminator = build_models(config)
# Put models on gpu if needed
generator = generator.to(device)
discriminator = discriminator.to(device)
for name, module in discriminator.named_modules():
if isinstance(module, nn.Sigmoid):
print('Found sigmoid layer in discriminator; not compatible with BCE with logits')
exit()
g_optimizer, d_optimizer = build_optimizers(generator, discriminator, config)
devices = [int(x) for x in args.devices]
generator = nn.DataParallel(generator, device_ids=devices)
discriminator = nn.DataParallel(discriminator, device_ids=devices)
# Register modules to checkpoint
checkpoint_io.register_modules(generator=generator,
discriminator=discriminator,
g_optimizer=g_optimizer,
d_optimizer=d_optimizer)
# Logger
logger = Logger(log_dir=path.join(out_dir, 'logs'),
img_dir=path.join(out_dir, 'imgs'),
monitoring=config['training']['monitoring'],
monitoring_dir=path.join(out_dir, 'monitoring'))
# Distributions
ydist = get_ydist(nlabels, device=device)
zdist = get_zdist(config['z_dist']['type'], config['z_dist']['dim'], device=device)
ntest = config['training']['ntest']
x_test, y_test = utils.get_nsamples(train_loader, ntest)
x_cluster, y_cluster = utils.get_nsamples(train_loader, config['clusterer']['nimgs'])
x_test, y_test = x_test.to(device), y_test.to(device)
z_test = zdist.sample((ntest, ))
utils.save_images(x_test, path.join(out_dir, 'real.png'))
logger.add_imgs(x_test, 'gt', 0)
# Test generator
if config['training']['take_model_average']:
print('Taking model average')
bad_modules = [nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]
for model in [generator, discriminator]:
for name, module in model.named_modules():
for bad_module in bad_modules:
if isinstance(module, bad_module):
print('Batch norm in discriminator not compatible with exponential moving average')
exit()
generator_test = copy.deepcopy(generator)
checkpoint_io.register_modules(generator_test=generator_test)
else:
generator_test = generator
clusterer = get_clusterer(config)(discriminator=discriminator,
x_cluster=x_cluster,
x_labels=y_cluster,
gt_nlabels=config['data']['nlabels'],
**config['clusterer']['kwargs'])
# Load checkpoint if it exists
it = utils.get_most_recent(checkpoint_dir, 'model') if args.model_it == -1 else args.model_it
it, epoch_idx, loaded_clusterer = checkpoint_io.load_models(it=it, load_samples='supervised' != config['clusterer']['name'])
if loaded_clusterer is None:
print('Initializing new clusterer. The first clustering can be quite slow.')
clusterer.recluster(discriminator=discriminator)
checkpoint_io.save_clusterer(clusterer, it=0)
np.savez(os.path.join(checkpoint_dir, 'cluster_samples.npz'), x=x_cluster)
else:
print('Using loaded clusterer')
clusterer = loaded_clusterer
# Evaluator
evaluator = Evaluator(
generator_test,
zdist,
ydist,
train_loader=train_loader,
clusterer=clusterer,
batch_size=batch_size,
device=device,
inception_nsamples=config['training']['inception_nsamples'])
# Trainer
trainer = Trainer(generator,
discriminator,
g_optimizer,
d_optimizer,
gan_type=config['training']['gan_type'],
reg_type=config['training']['reg_type'],
reg_param=config['training']['reg_param'])
# Training loop
print('Start training...')
while it < args.nepochs * len(train_loader):
epoch_idx += 1
for x_real, y in train_loader:
it += 1
x_real, y = x_real.to(device), y.to(device)
z = zdist.sample((batch_size, ))
y = clusterer.get_labels(x_real, y).to(device)
# Discriminator updates
dloss, reg = trainer.discriminator_trainstep(x_real, y, z)
logger.add('losses', 'discriminator', dloss, it=it)
logger.add('losses', 'regularizer', reg, it=it)
# Generators updates
gloss = trainer.generator_trainstep(y, z)
logger.add('losses', 'generator', gloss, it=it)
if config['training']['take_model_average']:
update_average(generator_test, generator, beta=config['training']['model_average_beta'])
# Print stats
if it % log_every == 0:
g_loss_last = logger.get_last('losses', 'generator')
d_loss_last = logger.get_last('losses', 'discriminator')
d_reg_last = logger.get_last('losses', 'regularizer')
print('[epoch %0d, it %4d] g_loss = %.4f, d_loss = %.4f, reg=%.4f'
% (epoch_idx, it, g_loss_last, d_loss_last, d_reg_last))
if it % config['training']['recluster_every'] == 0 and it > config['training']['burnin_time']:
# print cluster distribution for online methods
if it % 100 == 0 and config['training']['recluster_every'] <= 100:
print(f'[epoch {epoch_idx}, it {it}], distribution: {clusterer.get_label_distribution(x_real)}')
clusterer.recluster(discriminator=discriminator, x_batch=x_real)
# (i) Sample if necessary
if it % config['training']['sample_every'] == 0:
print('Creating samples...')
x = evaluator.create_samples(z_test, y_test)
x = evaluator.create_samples(z_test, clusterer.get_labels(x_test, y_test).to(device))
logger.add_imgs(x, 'all', it)
for y_inst in range(sample_nlabels):
x = evaluator.create_samples(z_test, y_inst)
logger.add_imgs(x, '%04d' % y_inst, it)
# (ii) Compute inception if necessary
if it % inception_every == 0 and it > 0:
print('PyTorch Inception score...')
inception_mean, inception_std = evaluator.compute_inception_score()
logger.add('metrics', 'pt_inception_mean', inception_mean, it=it)
logger.add('metrics', 'pt_inception_stddev', inception_std, it=it)
print(f'[epoch {epoch_idx}, it {it}] pt_inception_mean: {inception_mean}, pt_inception_stddev: {inception_std}')
# (iii) Backup if necessary
if it % backup_every == 0:
print('Saving backup...')
checkpoint_io.save('model_%08d.pt' % it, it=it)
checkpoint_io.save_clusterer(clusterer, int(it))
logger.save_stats('stats_%08d.p' % it)
if it > 0: checkpoint_io.save('model.pt', it=it)
os.makedirs(out_dir)
if not path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# Logger
checkpoint_io = CheckpointIO(
checkpoint_dir=checkpoint_dir
)
device = torch.device("cuda:0" if is_cuda else "cpu")
# Dataset
train_dataset, nlabels = get_dataset(
name=config['data']['type'],
data_dir=config['data']['train_dir'],
size=config['data']['img_size']
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=config['training']['nworkers'],
shuffle=True, pin_memory=True, sampler=None, drop_last=True
)
# Number of labels
nlabels = min(nlabels, config['data']['nlabels'])
# Create models
generator, discriminator = build_models(config)
def main():
checkpoint_dir = os.path.join(out_dir, 'chkpts')
batch_size = config['training']['batch_size']
if 'cifar' in config['data']['train_dir'].lower():
name = 'cifar10'
elif 'stacked_mnist' == config['data']['type']:
name = 'stacked_mnist'
else:
name = 'image'
if os.path.exists(os.path.join(out_dir, 'cluster_preds.npz')):
# if we've already computed assignments, load them and move on
with np.load(os.path.join(out_dir, 'cluster_preds.npz')) as f:
y_reals = f['y_reals']
y_preds = f['y_preds']
else:
train_dataset, _ = get_dataset(name=name,
data_dir=config['data']['train_dir'],
size=config['data']['img_size'])
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=config['training']['nworkers'],
shuffle=True,
pin_memory=True,
sampler=None,
drop_last=True)
checkpoint_io = CheckpointIO(checkpoint_dir=checkpoint_dir)
print('Loading clusterer:')
most_recent = utils.get_most_recent(
checkpoint_dir,
'model') if args.model_it is None else args.model_it
clusterer = checkpoint_io.load_clusterer(
most_recent, load_samples=False, pretrained=config['pretrained'])
if isinstance(clusterer.discriminator, nn.DataParallel):
clusterer.discriminator = clusterer.discriminator.module
y_preds = []
y_reals = []
for batch_num, (x_real, y_real) in enumerate(
tqdm(train_loader, total=len(train_loader))):
y_pred = clusterer.get_labels(x_real.cuda(), None)
y_preds.append(y_pred.detach().cpu())
y_reals.append(y_real)
y_reals = torch.cat(y_reals).numpy()
y_preds = torch.cat(y_preds).numpy()
np.savez(os.path.join(out_dir, 'cluster_preds.npz'),
y_reals=y_reals,
y_preds=y_preds)
if args.random:
y_preds = np.random.randint(0, 100, size=y_reals.shape)
nmi_score = nmi(y_preds, y_reals)
purity = purity_score(y_preds, y_reals)
print('nmi', nmi_score, 'purity', purity)
import numpy as np
import os
import sys
sys.path.append(os.path.join(os.getcwd(), '..'))
from gan_training.inputs import get_dataset
torch.manual_seed(0)
np.random.seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if not os.path.exists('cifar_real'):
os.makedirs('cifar_real')
train_dataset, nlabels = get_dataset(name='cifar10',
data_dir='../data',
size=32)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=10000,
num_workers=16,
shuffle=True,
pin_memory=True,
sampler=None,
drop_last=True)
for x_real, y in train_loader:
break
x_real = x_real / 2 + 0.5
def main():
checkpoint_dir = os.path.join(out_dir, 'chkpts')
most_recent = utils.get_most_recent(
checkpoint_dir, 'model') if args.model_it is None else args.model_it
cluster_path = os.path.join(out_dir, 'clusters')
print('Saving clusters/samples to', cluster_path)
os.makedirs(cluster_path, exist_ok=True)
shutil.copyfile('seeing/lightbox.html',
os.path.join(cluster_path, '+lightbox.html'))
checkpoint_io = CheckpointIO(checkpoint_dir=checkpoint_dir)
most_recent = utils.get_most_recent(
checkpoint_dir, 'model') if args.model_it is None else args.model_it
clusterer = checkpoint_io.load_clusterer(most_recent,
pretrained=config['pretrained'],
load_samples=False)
if isinstance(clusterer.discriminator, nn.DataParallel):
clusterer.discriminator = clusterer.discriminator.module
model_path = os.path.join(checkpoint_dir, 'model_%08d.pt' % most_recent)
sampler = SeededSampler(args.config,
model_path=model_path,
clusterer_path=os.path.join(
checkpoint_dir, f'clusterer{most_recent}.pkl'),
pretrained=config['pretrained'])
if args.show_clusters:
clusters = [[] for _ in range(config['generator']['nlabels'])]
train_dataset, _ = get_dataset(
name='webp' if 'cifar' not in config['data']['train_dir'].lower()
else 'cifar10',
data_dir=config['data']['train_dir'],
size=config['data']['img_size'])
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['training']['batch_size'],
num_workers=config['training']['nworkers'],
shuffle=True,
pin_memory=True,
sampler=None,
drop_last=True)
print('Generating clusters')
for batch_num, (x_real, y_gt) in enumerate(train_loader):
x_real = x_real.cuda()
y_pred = clusterer.get_labels(x_real, y_gt)
for i, yi in enumerate(y_pred):
clusters[yi].append(x_real[i].cpu())
# don't generate too many, we're only visualizing 20 per cluster
if batch_num * config['training']['batch_size'] >= 10000:
break
else:
clusters = [None] * config['generator']['nlabels']
nimgs = 20
nrows = 4
for i in range(len(clusters)):
if clusters[i] is None:
pass
elif len(clusters[i]) >= nimgs:
cluster = torch.stack(clusters[i])[:nimgs]
torchvision.utils.save_image(cluster * 0.5 + 0.5,
os.path.join(cluster_path,
f'{i}_real.png'),
nrow=nrows)
generated = []
for seed in range(nimgs):
img = sampler.conditional_sample(i, seed=seed)
generated.append(img.detach().cpu())
generated = torch.cat(generated)
torchvision.utils.save_image(generated * 0.5 + 0.5,
os.path.join(cluster_path,
f'{i}_gen.png'),
nrow=nrows)
print('Clusters/samples can be visualized under', cluster_path)
