def __init__(self, exp_dict):
super().__init__()
self.backbone = models.vgg16(
pretrained=exp_dict["imagenet_pretraining"], progress=True)
num_ftrs = self.backbone.classifier[-1].in_features
self.backbone.classifier[-1] = torch.nn.Linear(num_ftrs,
exp_dict["num_classes"])
self.backbone = patch_module(self.backbone)
self.initial_weights = deepcopy(self.backbone.state_dict())
self.backbone.cuda()
self.batch_size = exp_dict['batch_size']
self.calibrate = exp_dict.get('calibrate', False)
self.learning_epoch = exp_dict['learning_epoch']
self.optimizer = torch.optim.SGD(self.backbone.parameters(),
lr=exp_dict['lr'],
weight_decay=5e-4,
momentum=0.9,
nesterov=True)
self.criterion = CrossEntropyLoss()
shuffle_prop = exp_dict.get('shuffle_prop', 0.0)
max_sample = -1
self.heuristic = get_heuristic(exp_dict['heuristic'],
shuffle_prop=shuffle_prop)
self.wrapper = ModelWrapper(self.backbone, criterion=self.criterion)
self.wrapper.add_metric(
'cls_report',
lambda: ClassificationReport(exp_dict["num_classes"]))
self.wrapper.add_metric('accuracy', lambda: Accuracy())
self.loop = ActiveLearningLoop(None,
self.wrapper.predict_on_dataset,
heuristic=self.heuristic,
ndata_to_label=exp_dict['query_size'],
batch_size=self.batch_size,
iterations=exp_dict['iterations'],
use_cuda=True,
max_sample=max_sample)
self.calib_set = get_dataset('calib', exp_dict['dataset'])
self.valid_set = get_dataset('val', exp_dict['dataset'])
self.calibrator = DirichletCalibrator(
self.wrapper,
exp_dict["num_classes"],
lr=0.001,
reg_factor=exp_dict['reg_factor'],
mu=exp_dict['mu'])
self.active_dataset = None
self.active_dataset_settings = None
def _build_model(self):
# We use `patch_module` to swap Dropout modules in the model
# for our implementation which enables MC-Dropou
self.vgg16 = patch_module(vgg16(num_classes=self.hparams.num_classes))
def main():
args = parse_args()
use_cuda = torch.cuda.is_available()
torch.backends.cudnn.benchmark = True
random.seed(1337)
torch.manual_seed(1337)
if not use_cuda:
print("warning, the experiments would take ages to run on cpu")
hyperparams = vars(args)
active_set, test_set = get_datasets(hyperparams["initial_pool"])
heuristic = get_heuristic(hyperparams["heuristic"],
hyperparams["shuffle_prop"])
criterion = CrossEntropyLoss()
model = vgg16(pretrained=False, num_classes=10)
weights = load_state_dict_from_url(
"https://download.pytorch.org/models/vgg16-397923af.pth")
weights = {k: v for k, v in weights.items() if "classifier.6" not in k}
model.load_state_dict(weights, strict=False)
# change dropout layer to MCDropout
model = patch_module(model)
if use_cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(),
lr=hyperparams["lr"],
momentum=0.9)
# Wraps the model into a usable API.
model = ModelWrapper(model, criterion)
logs = {}
logs["epoch"] = 0
# for prediction we use a smaller batchsize
# since it is slower
active_loop = ActiveLearningLoop(
active_set,
model.predict_on_dataset,
heuristic,
hyperparams.get("query_size", 1),
batch_size=10,
iterations=hyperparams["iterations"],
use_cuda=use_cuda,
)
# We will reset the weights at each active learning step.
init_weights = deepcopy(model.state_dict())
for epoch in tqdm(range(args.epoch)):
# Load the initial weights.
model.load_state_dict(init_weights)
model.train_on_dataset(
active_set,
optimizer,
hyperparams["batch_size"],
hyperparams["learning_epoch"],
use_cuda,
)
# Validation!
model.test_on_dataset(test_set, hyperparams["batch_size"], use_cuda)
metrics = model.metrics
should_continue = active_loop.step()
if not should_continue:
break
val_loss = metrics["test_loss"].value
logs = {
"val": val_loss,
"epoch": epoch,
"train": metrics["train_loss"].value,
"labeled_data": active_set.labelled,
"Next Training set size": len(active_set),
}
print(logs)
def main():
args = parse_args()
use_cuda = torch.cuda.is_available()
torch.backends.cudnn.benchmark = True
random.seed(1337)
torch.manual_seed(1337)
if not use_cuda:
print("warning, the experiments would take ages to run on cpu")
hyperparams = vars(args)
heuristic = get_heuristic(hyperparams['heuristic'],
hyperparams['shuffle_prop'])
model = BertForSequenceClassification.from_pretrained(
pretrained_model_name_or_path=hyperparams["model"])
tokenizer = BertTokenizer.from_pretrained(
pretrained_model_name_or_path=hyperparams["model"])
# In this example we use tokenizer once only in the beginning since it would
# make the whole process faster. However, it is also possible to input tokenizer
# in trainer.
active_set, test_set = get_datasets(hyperparams['initial_pool'], tokenizer)
# change dropout layer to MCDropout
model = patch_module(model)
if use_cuda:
model.cuda()
init_weights = deepcopy(model.state_dict())
training_args = TrainingArguments(
output_dir='/app/baal/results', # output directory
num_train_epochs=hyperparams['learning_epoch'], # total # of training epochs
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=64, # batch size for evaluation
weight_decay=0.01, # strength of weight decay
logging_dir='/app/baal/logs', # directory for storing logs
)
# We wrap the huggingface Trainer to create an Active Learning Trainer
model = BaalTransformersTrainer(model=model,
args=training_args,
train_dataset=active_set,
eval_dataset=test_set,
tokenizer=None)
logs = {}
logs['epoch'] = 0
# In this case, nlp data is fast to process and we do NoT need to use a smaller batch_size
active_loop = ActiveLearningLoop(active_set,
model.predict_on_dataset,
heuristic,
hyperparams.get('n_data_to_label', 1),
iterations=hyperparams['iterations'])
for epoch in tqdm(range(args.epoch)):
# we use the default setup of HuggingFace for training (ex: epoch=1).
# The setup is adjustable when BaalHuggingFaceTrainer is defined.
model.train()
# Validation!
eval_metrics = model.evaluate()
# We reorder the unlabelled pool at the frequency of learning_epoch
# This helps with speed while not changing the quality of uncertainty estimation.
should_continue = active_loop.step()
# We reset the model weights to relearn from the new trainset.
model.load_state_dict(init_weights)
model.lr_scheduler = None
if not should_continue:
break
active_logs = {"epoch": epoch,
"labeled_data": active_set._labelled,
"Next Training set size": len(active_set)}
logs = {**eval_metrics, **active_logs}
print(logs)
def __init__(self, active_dataset: ActiveLearningDataset,
hparams: Namespace, network: nn.Module):
super().__init__(active_dataset, hparams, network)
self.network = patch_module(self.network)
# Uses an ActiveLearningDataset to help us split labelled and unlabelled examples.
al_dataset = ActiveLearningDataset(
train_ds, pool_specifics={"transform": test_transform})
al_dataset.label_randomly(200) # Start with 200 items labelled.
# Creates an MLP to classify MNIST
model = nn.Sequential(
nn.Flatten(),
nn.Linear(784, 512),
nn.Dropout(),
nn.Linear(512, 512),
nn.Dropout(),
nn.Linear(512, 10),
)
model = patch_module(model) # Set dropout layers for MC-Dropout.
if use_cuda:
model = model.cuda()
wrapper = ModelWrapper(model=model, criterion=nn.CrossEntropyLoss())
optimizer = optim.SGD(model.parameters(),
lr=0.001,
momentum=0.9,
weight_decay=5e-4)
# We will use BALD as our heuristic as it is a great tradeoff between performance and efficiency.
bald = BALD()
# Setup our active learning loop for our experiments
al_loop = ActiveLearningLoop(
dataset=al_dataset,
get_probabilities=wrapper.predict_on_dataset,
heuristic=bald,
