def semixup_data_provider(model, alpha, n_classes, train_labeled_data, train_unlabeled_data, val_labeled_data,
transforms, parse_item, bs, num_workers, item_loaders=dict(), root="", augmentation=None,
data_rearrange=None):
"""
Default setting of data provider for Semixup
"""
# item_loaders["labeled_train"] = MixUpSampler(meta_data=train_labeled_data, name='l_mixup', alpha=alpha, model=model,
# transform=transforms['train'], parse_item_cb=parse_item, batch_size=bs,
# data_rearrange=data_rearrange,
# num_workers=num_workers, root=root, shuffle=True)
item_loaders["labeled_train"] = ItemLoader(meta_data=train_labeled_data, name='l_norm', transform=transforms['train'],
parse_item_cb=parse_item, batch_size=bs, num_workers=num_workers,
root=root, shuffle=True)
item_loaders["unlabeled_train"] = SemixupSampler(meta_data=train_unlabeled_data, name='u_mixup', alpha=alpha,
model=model, min_lambda=0.55,
transform=transforms['train'], parse_item_cb=parse_item, batch_size=bs,
data_rearrange=data_rearrange,
num_workers=num_workers, augmentation=augmentation, root=root,
shuffle=True)
item_loaders["labeled_eval"] = ItemLoader(meta_data=val_labeled_data, name='l_norm', transform=transforms['eval'],
parse_item_cb=parse_item, batch_size=bs, num_workers=num_workers,
root=root, shuffle=False)
return DataProvider(item_loaders)
def create_data_provider(args, config, parser, metadata, mean, std):
"""
Setup dataloader and augmentations
:param args: General arguments
:param config: Experiment parameters
:param parser: Function for loading images
:param metadata: Image paths and subject IDs
:param mean: Dataset mean
:param std: Dataset std
:return: The compiled dataloader
"""
# Compile ItemLoaders
item_loaders = dict()
for stage in ['train', 'val']:
item_loaders[f'bfpn_{stage}'] = ItemLoader(
meta_data=metadata[stage],
transform=train_test_transforms(
config,
mean,
std,
crop_size=tuple(config['training']['crop_size']))[stage],
parse_item_cb=parser,
batch_size=config['training']['bs'],
num_workers=args.num_threads,
shuffle=True if stage == "train" else False)
return DataProvider(item_loaders)
def main(cfg):
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
data_dir = os.path.join(os.environ['PWD'], cfg.data_dir)
train_ds, classes = get_cifar10(data_folder=data_dir, train=True)
eval_ds, _ = get_cifar10(data_folder=data_dir, train=False)
n_channels = 3
criterion = torch.nn.CrossEntropyLoss()
model = ResNet(in_channels=n_channels, n_features=64, drop_rate=0.3).to(device).half()
optimizer = torch.optim.SGD(params=model.parameters(), lr=cfg.lr, momentum=cfg.momentum, weight_decay=cfg.wd,
nesterov=True)
# Tensorboard visualization
log_dir = cfg.log_dir
comment = cfg.comment
summary_writer = SummaryWriter(log_dir=log_dir, comment=comment)
item_loaders = dict()
for stage, df in zip(['train', 'eval'], [train_ds, eval_ds]):
item_loaders[f'loader_{stage}'] = ItemLoader(meta_data=df,
transform=my_transforms()[stage],
parse_item_cb=parse_item,
batch_size=cfg.bs, num_workers=cfg.num_workers,
shuffle=True if stage == "train" else False)
data_provider = DataProvider(item_loaders)
train_cbs = (CosineAnnealingWarmRestartsWithWarmup(optimizer=optimizer, warmup_epochs=(0, 10, 20),
warmup_lrs=(0, 0.1, 0.01), T_O=5, T_mult=2, eta_min=0),
RunningAverageMeter(name="loss"),
AccuracyMeter(name="acc"))
val_cbs = (RunningAverageMeter(name="loss"),
AccuracyMeter(name="acc"),
ScalarMeterLogger(writer=summary_writer),
ModelSaver(metric_names='loss', save_dir=cfg.snapshots, conditions='min', model=model),
ModelSaver(metric_names='acc', save_dir=cfg.snapshots, conditions='max', model=model))
session = dict()
session['mymodel'] = Session(data_provider=data_provider,
train_loader_names=cfg.sampling.train.data_provider.mymodel.keys(),
val_loader_names=cfg.sampling.eval.data_provider.mymodel.keys(),
module=model, loss=criterion, optimizer=optimizer,
train_callbacks=train_cbs,
val_callbacks=val_cbs)
strategy = Strategy(data_provider=data_provider,
data_sampling_config=cfg.sampling,
strategy_config=cfg.strategy,
sessions=session,
n_epochs=cfg.n_epochs,
device=device)
strategy.run()
def ict_data_provider(model,
alpha,
n_classes,
train_labeled_data,
train_unlabeled_data,
val_labeled_data,
val_unlabeled_data,
transforms,
parse_item,
bs,
num_threads,
item_loaders=dict(),
root=""):
"""
Default setting of data provider for ICT
"""
item_loaders["labeled_train"] = ItemLoader(meta_data=train_labeled_data,
name='l_norm',
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
root=root,
shuffle=True)
item_loaders["unlabeled_train"] = MixUpSampler(
meta_data=train_unlabeled_data,
name='u_mixup',
alpha=alpha,
model=model,
transform=transforms[0],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
root=root,
shuffle=True)
item_loaders["labeled_eval"] = ItemLoader(meta_data=val_labeled_data,
name='l_norm',
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
root=root,
shuffle=False)
item_loaders["unlabeled_eval"] = MixUpSampler(meta_data=val_unlabeled_data,
name='u_mixup',
alpha=alpha,
model=model,
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
root=root,
shuffle=False)
return DataProvider(item_loaders)
def create_data_provider(args, config, parser, metadata, mean, std):
# Compile ItemLoaders
item_loaders = dict()
for stage in ['train', 'val']:
item_loaders[f'bfpn_{stage}'] = ItemLoader(meta_data=metadata[stage],
transform=train_test_transforms(config, mean, std)[stage],
parse_item_cb=parser,
batch_size=args.bs, num_workers=args.num_threads,
shuffle=True if stage == "train" else False)
return DataProvider(item_loaders)
def distributed_gan_data_provider(g_network, item_loaders, train_ds, classes,
latent_size, transforms, parse_item, args):
"""
Default setting of data provider for GAN
"""
bs = args.batch_size
num_threads = args.workers
gpu = args.gpu
distributed = args.distributed
if torch.cuda.is_available():
device = torch.device('cuda:{}'.format(gpu))
else:
device = torch.device('cpu')
if distributed:
item_loaders['real'] = DistributedItemLoader(meta_data=train_ds,
transform=transforms,
parse_item_cb=parse_item,
args=args)
item_loaders['fake'] = DistributedGANFakeSampler(
g_network=g_network,
batch_size=bs,
latent_size=latent_size,
gpu=gpu)
item_loaders['noise'] = GaussianNoiseSampler(batch_size=bs,
latent_size=latent_size,
device=gpu,
n_classes=len(classes))
else:
item_loaders['real'] = ItemLoader(meta_data=train_ds,
transform=transforms,
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=True)
item_loaders['fake'] = GANFakeSampler(g_network=g_network,
batch_size=bs,
latent_size=latent_size)
item_loaders['noise'] = GaussianNoiseSampler(batch_size=bs,
latent_size=latent_size,
device=device,
n_classes=len(classes))
return DataProvider(item_loaders)
def mixmatch_ema_data_provider(model,
augmentation,
labeled_meta_data,
unlabeled_meta_data,
val_labeled_data,
n_augmentations,
parse_item,
bs,
transforms,
root="",
num_threads=4):
itemloader_dict = {}
itemloader_dict['all_train'] = MixMatchSampler(
model=model,
name="train_mixmatch",
augmentation=augmentation,
labeled_meta_data=labeled_meta_data,
unlabeled_meta_data=unlabeled_meta_data,
n_augmentations=n_augmentations,
data_key='data',
target_key='target',
parse_item_cb=parse_item,
batch_size=bs,
transform=transforms[0],
num_workers=num_threads,
shuffle=True)
itemloader_dict['labeled_eval_st'] = ItemLoader(root=root,
meta_data=val_labeled_data,
name='l_eval',
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=False)
itemloader_dict['labeled_eval_te'] = ItemLoader(root=root,
meta_data=val_labeled_data,
name='l_eval',
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=False)
return DataProvider(itemloader_dict)
def init_data_provider(args, df_train, df_val, item_loaders, test_ds):
"""
function to initialize data provider for the autoencoder
Parameters
----------
args: Namespace
arguments for the whole network parsed using argparse
df_train: DataFrame
training data as pandas DataFrame
df_val: DataFrame
validation data as pandas DataFrame
item_loaders: dict
empty dictionary to be populated by data samplers
test_ds: DataFrame
test data for visualization as pandas DataFrame
Returns
-------
DataProvider
DataProvider object constructed from all the data samplers
"""
for stage, df in zip(['train', 'eval'], [df_train, df_val]):
item_loaders[f'mnist_{stage}'] = ItemLoader(
meta_data=df,
transform=init_mnist_transforms()[0],
parse_item_cb=parse_item_ae,
batch_size=args.bs,
num_workers=args.num_threads,
shuffle=True if stage == 'train' else False)
item_loaders['mnist_viz'] = ItemLoader(
meta_data=test_ds,
transform=init_mnist_transforms()[0],
parse_item_cb=parse_item_ae,
batch_size=args.bs,
num_workers=args.num_threads,
shuffle=True if stage == 'train' else False)
return DataProvider(item_loaders)
def gan_data_provider(g_network, item_loaders, train_ds, classes, latent_size,
transforms, parse_item, bs, num_threads, device):
"""
Default setting of data provider for GAN
"""
item_loaders['real'] = ItemLoader(meta_data=train_ds,
transform=transforms,
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=True)
item_loaders['fake'] = GANFakeSampler(g_network=g_network,
batch_size=bs,
latent_size=latent_size)
item_loaders['noise'] = GaussianNoiseSampler(batch_size=bs,
latent_size=latent_size,
device=device,
n_classes=len(classes))
return DataProvider(item_loaders)
num_workers=args.num_threads,
shuffle=False)
item_loaders['fake_unlabeled_gen'] = SSGANFakeSampler(
g_network=g_network,
batch_size=args.bs,
latent_size=args.latent_size,
n_classes=args.n_classes)
item_loaders['fake_unlabeled_latent'] = SSGANFakeSampler(
g_network=g_network,
batch_size=args.bs,
latent_size=args.latent_size,
n_classes=args.n_classes)
data_provider = DataProvider(item_loaders)
# Callbacks
g_callbacks_train = ClipGradCallback(g_network,
mode="norm",
max_norm=0.1,
norm_type=2)
d_callbacks_train = (SSValidityMeter(threshold=0.5,
sigmoid=False,
prefix="train/D",
name="ss_valid"),
SSAccuracyMeter(prefix="train/D", name="ss_acc"))
d_callbacks_eval = (SSValidityMeter(threshold=0.5,
sigmoid=False,
def mt_data_provider(st_model,
te_model,
train_labeled_data,
train_unlabeled_data,
val_labeled_data,
val_unlabeled_data,
transforms,
parse_item,
bs,
num_threads,
item_loaders=dict(),
n_augmentations=1,
output_type='logits',
root=""):
"""
Default setting of data provider for Mean-Teacher
"""
# Train
item_loaders["labeled_train_st"] = AugmentedGroupSampler(
root=root,
name='l_st',
meta_data=train_labeled_data,
model=st_model,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=True)
item_loaders["unlabeled_train_st"] = AugmentedGroupSampler(
root=root,
name='u_st',
model=st_model,
meta_data=train_unlabeled_data,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=True)
item_loaders["labeled_train_te"] = AugmentedGroupSampler(
root=root,
name='l_te',
meta_data=train_labeled_data,
model=te_model,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
detach=True,
shuffle=True)
item_loaders["unlabeled_train_te"] = AugmentedGroupSampler(
root=root,
name='u_te',
model=te_model,
meta_data=train_unlabeled_data,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
detach=True,
shuffle=True)
# Eval
item_loaders["labeled_eval_st"] = AugmentedGroupSampler(
root=root,
name='l_st',
meta_data=val_labeled_data,
model=st_model,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=False)
item_loaders["unlabeled_eval_st"] = AugmentedGroupSampler(
root=root,
name='u_st',
model=st_model,
meta_data=val_unlabeled_data,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=False)
item_loaders["labeled_eval_te"] = ItemLoader(root=root,
meta_data=val_labeled_data,
name='l_te_eval',
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=False)
return DataProvider(item_loaders)
def pimodel_data_provider(model,
train_labeled_data,
train_unlabeled_data,
val_labeled_data,
val_unlabeled_data,
transforms,
parse_item,
bs,
num_threads,
item_loaders=dict(),
root="",
n_augmentations=1,
output_type='logits'):
"""
Default setting of data provider for Pi-Model
"""
item_loaders["labeled_train"] = ItemLoader(root=root,
meta_data=train_labeled_data,
name='l',
transform=transforms[0],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=True)
# item_loaders["labeled_train"] = AugmentedGroupSampler(root=root, model=model, name='l', output_type=output_type,
# meta_data=train_labeled_data,
# n_augmentations=n_augmentations,
# augmentation=transforms[2],
# transform=transforms[1],
# parse_item_cb=parse_item,
# batch_size=bs, num_workers=num_workers,
# shuffle=True)
item_loaders["unlabeled_train"] = AugmentedGroupSampler(
root=root,
model=model,
name='u',
output_type=output_type,
meta_data=train_unlabeled_data,
n_augmentations=n_augmentations,
augmentation=transforms[2],
transform=transforms[0],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=True)
item_loaders["labeled_eval"] = ItemLoader(root=root,
meta_data=val_labeled_data,
name='l',
transform=transforms[1],
parse_item_cb=parse_item,
batch_size=bs,
num_workers=num_threads,
shuffle=False)
# item_loaders["labeled_eval"] = AugmentedGroupSampler(root=root, model=model, name='l', output_type=output_type,
# meta_data=val_labeled_data,
# n_augmentations=n_augmentations,
# augmentation=transforms[2],
# transform=transforms[1],
# parse_item_cb=parse_item,
# batch_size=bs, num_workers=num_workers,
# shuffle=False)
# item_loaders["unlabeled_eval"] = AugmentedGroupSampler(root=root, model=model, name='u', output_type=output_type,
# meta_data=val_unlabeled_data,
# n_augmentations=n_augmentations,
# augmentation=transforms[2],
# transform=transforms[1],
# parse_item_cb=parse_item,
# batch_size=bs, num_workers=num_workers,
# shuffle=False)
return DataProvider(item_loaders)
def worker_process(gpu, ngpus, sampling_config, strategy_config, args):
args.gpu = gpu # this line of code is not redundant
if args.distributed:
lr_m = float(args.batch_size * args.world_size) / 256.
else:
lr_m = 1.0
criterion = torch.nn.CrossEntropyLoss().to(gpu)
train_ds, classes = get_mnist(data_folder=args.save_data, train=True)
test_ds, _ = get_mnist(data_folder=args.save_data, train=False)
model = SimpleConvNet(bw=args.bw,
drop=args.dropout,
n_cls=len(classes),
n_channels=args.n_channels).to(gpu)
optimizer = torch.optim.Adam(params=model.parameters(),
lr=args.lr * lr_m,
weight_decay=args.wd)
args, model, optimizer = convert_according_to_args(args=args,
gpu=gpu,
ngpus=ngpus,
network=model,
optim=optimizer)
item_loaders = dict()
for stage, df in zip(['train', 'eval'], [train_ds, test_ds]):
if args.distributed:
item_loaders[f'mnist_{stage}'] = DistributedItemLoader(
meta_data=df,
transform=init_mnist_cifar_transforms(1, stage),
parse_item_cb=parse_item_mnist,
args=args)
else:
item_loaders[f'mnist_{stage}'] = ItemLoader(
meta_data=df,
transform=init_mnist_cifar_transforms(1, stage),
parse_item_cb=parse_item_mnist,
batch_size=args.batch_size,
num_workers=args.workers,
shuffle=True if stage == "train" else False)
data_provider = DataProvider(item_loaders)
if args.gpu == 0:
log_dir = args.log_dir
comment = args.comment
summary_writer = SummaryWriter(log_dir=log_dir,
comment='_' + comment + 'gpu_' +
str(args.gpu))
train_cbs = (RunningAverageMeter(prefix="train", name="loss"),
AccuracyMeter(prefix="train", name="acc"))
val_cbs = (RunningAverageMeter(prefix="eval", name="loss"),
AccuracyMeter(prefix="eval", name="acc"),
ScalarMeterLogger(writer=summary_writer),
ModelSaver(metric_names='eval/loss',
save_dir=args.snapshots,
conditions='min',
model=model))
else:
train_cbs = ()
val_cbs = ()
strategy = Strategy(data_provider=data_provider,
train_loader_names=tuple(
sampling_config['train']['data_provider'].keys()),
val_loader_names=tuple(
sampling_config['eval']['data_provider'].keys()),
data_sampling_config=sampling_config,
loss=criterion,
model=model,
n_epochs=args.n_epochs,
optimizer=optimizer,
train_callbacks=train_cbs,
val_callbacks=val_cbs,
device=torch.device('cuda:{}'.format(args.gpu)),
distributed=args.distributed,
use_apex=args.use_apex)
strategy.run()
def main(cfg):
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
data_dir = os.path.join(os.environ['PWD'], cfg.data_dir)
train_ds, classes = get_mnist(data_folder=data_dir, train=True)
n_classes = len(classes)
n_channels = 1
criterion = torch.nn.CrossEntropyLoss()
# Tensorboard visualization
log_dir = cfg.log_dir
comment = cfg.comment
summary_writer = SummaryWriter(log_dir=log_dir, comment=comment)
splitter = FoldSplit(train_ds, n_folds=5, target_col="target")
for fold_id, (df_train, df_val) in enumerate(splitter):
item_loaders = dict()
for stage, df in zip(['train', 'eval'], [df_train, df_val]):
item_loaders[f'loader_{stage}'] = ItemLoader(
meta_data=df,
transform=my_transforms()[stage],
parse_item_cb=parse_item,
batch_size=cfg.bs,
num_workers=cfg.num_threads,
shuffle=True if stage == "train" else False)
model = SimpleConvNet(bw=cfg.bw,
drop_rate=cfg.dropout,
n_classes=n_classes).to(device)
optimizer = torch.optim.Adam(params=model.parameters(),
lr=cfg.lr,
weight_decay=cfg.wd)
data_provider = DataProvider(item_loaders)
train_cbs = (RunningAverageMeter(name="loss"),
AccuracyMeter(name="acc"))
val_cbs = (RunningAverageMeter(name="loss"), AccuracyMeter(name="acc"),
ScalarMeterLogger(writer=summary_writer),
ModelSaver(metric_names='loss',
save_dir=cfg.snapshots,
conditions='min',
model=model),
ModelSaver(metric_names='acc',
save_dir=cfg.snapshots,
conditions='max',
model=model))
session = dict()
session['mymodel'] = Session(
data_provider=data_provider,
train_loader_names=cfg.sampling.train.data_provider.mymodel.keys(),
val_loader_names=cfg.sampling.eval.data_provider.mymodel.keys(),
module=model,
loss=criterion,
optimizer=optimizer,
train_callbacks=train_cbs,
val_callbacks=val_cbs)
strategy = Strategy(data_provider=data_provider,
data_sampling_config=cfg.sampling,
strategy_config=cfg.strategy,
sessions=session,
n_epochs=cfg.n_epochs,
device=device)
strategy.run()