def __init__(self, config, d_out, grouper, loss, unlabeled_loss, metric, n_train_steps):
featurizer, classifier = initialize_model(
config, d_out=d_out, is_featurizer=True
)
model = torch.nn.Sequential(featurizer, classifier)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
# algorithm hyperparameters
self.unlabeled_loss = unlabeled_loss
self.labeled_weight = config.self_training_labeled_weight
self.unlabeled_weight = config.self_training_unlabeled_weight
self.confidence_threshold = config.self_training_threshold
if config.process_outputs_function is not None:
self.process_outputs_function = process_outputs_functions[config.process_outputs_function]
self.soft_pseudolabels = config.soft_pseudolabels
# Additional logging
self.logged_fields.append("pseudolabels_kept_frac")
self.logged_fields.append("classification_loss")
self.logged_fields.append("consistency_loss")
def __init__(self, config, d_out, grouper, loss, unlabeled_loss, metric,
n_train_steps):
# initialize student model with dropout before last layer
featurizer, classifier = initialize_model(config,
d_out=d_out,
is_featurizer=True)
student_model = DropoutModel(featurizer, classifier,
config.dropout_rate).to(config.device)
# initialize module
super().__init__(
config=config,
model=student_model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
self.unlabeled_loss = unlabeled_loss
# additional logging
self.logged_fields.append("classification_loss")
self.logged_fields.append("consistency_loss")
self.logged_fields.append("pseudolabel_accuracy")
# used only for logging pseudolabel accuracy
if config.process_outputs_function is not None:
self.process_outputs_function = process_outputs_functions[
config.process_outputs_function]
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
self.num_domains = self.grouper.cardinality.item()
self.beta = config.hsic_beta
self.lamb = config.grad_penalty_lamb
self.label_cond = config.label_cond
self.d_out = d_out
# select the parameters to penalize
self.params_regex = config.params_regex
self.selected_param_names = list(dict(self.named_parameters()).keys())
self.selected_param_names = list(
filter(lambda name: re.match(self.params_regex, name) is not None,
self.selected_param_names))
print("The selected parameters are:\n", self.selected_param_names)
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
"""
Algorithm-specific arguments (in config):
- irm_lambda
- irm_penalty_anneal_iters
"""
# check config
assert config.train_loader == 'group'
assert config.uniform_over_groups
assert config.distinct_groups
# initialize model
model = initialize_model(config, d_out).to(config.device)
# initialize the module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
# additional logging
self.logged_fields.append('penalty')
# set IRM-specific variables
self.irm_lambda = config.irm_lambda
self.irm_penalty_anneal_iters = config.irm_penalty_anneal_iters
self.scale = torch.tensor(1.).to(self.device).requires_grad_()
self.update_count = 0
self.config = config # Need to store config for IRM because we need to re-init optimizer
assert isinstance(self.loss, ElementwiseMetric) or isinstance(
self.loss, MultiTaskMetric)
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
# check config
assert config.train_loader == 'group'
assert config.uniform_over_groups
assert config.distinct_groups
# initialize models
featurizer = initialize_model(config, d_out=None).to(config.device)
classifier = torch.nn.Linear(featurizer.d_out, d_out).to(config.device)
model = torch.nn.Sequential(featurizer, classifier).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
# algorithm hyperparameters
self.penalty_weight = config.coral_penalty_weight
# additional logging
self.logged_fields.append('penalty')
# set model components
self.featurizer = featurizer
self.classifier = classifier
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out=d_out)
model = model.to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
# algorithm hyperparameters
self.labeled_weight = config.self_training_labeled_weight
self.unlabeled_weight_scheduler = LinearScheduleWithWarmupAndThreshold(
max_value=config.self_training_unlabeled_weight,
step_every_batch=True, # step per batch
last_warmup_step=0,
threshold_step=config.pseudolabel_T2 * n_train_steps)
self.schedulers.append(self.unlabeled_weight_scheduler)
self.scheduler_metric_names.append(None)
self.confidence_threshold = config.self_training_threshold
if config.process_outputs_function is not None:
self.process_outputs_function = process_outputs_functions[
config.process_outputs_function]
# Additional logging
self.logged_fields.append("pseudolabels_kept_frac")
self.logged_fields.append("classification_loss")
self.logged_fields.append("consistency_loss")
def __init__(self, config, d_out, grouper, loss, domain_loss, metric,
n_train_steps):
# check config
assert config.train_loader == 'group'
assert config.uniform_over_groups
assert config.distinct_groups
# initialize models
featurizer, label_classifier = initialize_model(config,
d_out=d_out,
is_featurizer=True)
featurizer = featurizer.to(config.device)
reverse_gradient = RevGradLayer().to(config.device)
# mlp
if config.dann_domain_layers >= 1:
block = make_mlp(featurizer.d_out,
[32] * config.dann_domain_layers + [1])
domain_classifier = torch.nn.Sequential(reverse_gradient, *block)
else:
domain_classifier = torch.nn.Sequential(
reverse_gradient,
torch.nn.Linear(in_features=featurizer.d_out,
out_features=1)).to(config.device)
if config.dann_label_layers >= 1:
block = make_mlp(featurizer.d_out,
[32] * config.dann_label_layers + [d_out])
label_classifier = torch.nn.Sequential(*block).to(config.device)
else:
label_classifier = label_classifier.to(config.device)
classifier = BranchedModules(['lc', 'dc'], {
'lc': label_classifier,
'dc': domain_classifier
},
cat_dim=-1).to(config.device)
model = torch.nn.Sequential(featurizer, classifier).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
self.domain_loss = domain_loss
# algorithm hyperparameters
self.dann_lambda = config.dann_lambda
# additional logging
self.logged_fields.append('domain_classifier_loss')
# set model components
self.featurizer = featurizer
self.label_classifier = label_classifier
self.domain_classifier = domain_classifier
self.classifier = classifier
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
meta_model = l2l.algorithms.MAML(model,
lr=config.metalearning_adapt_lr,
first_order=config.maml_first_order)
# initialize module
super().__init__(
meta_model=meta_model,
config=config,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
self.num_domains = self.grouper.cardinality.item()
self.lamb = config.sd_penalty_lamb
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
for p in model.parameters():
p.requires_grad = False
*_, last = model.modules()
for p in last.parameters():
p.requires_grad = True
meta_model = l2l.algorithms.MAML(model,
lr=config.metalearning_adapt_lr)
# initialize module
super().__init__(
meta_model=meta_model,
config=config,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
def __init__(self, config, d_out, grouper, loss,
metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
# self.meta_model is the main model that lasts
# self.model is the task model that is frequently overwritten
meta_model = l2l.algorithms.MAML(
model,
lr=config.maml_adapt_lr,
first_order=config.maml_first_order
)
self.adaptation_steps = config.maml_n_adapt_steps
# initialize module
super().__init__(
config=config,
model=meta_model, # so that optim has correct params
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
self.meta_model = meta_model
del self.model
def __init__(self, config, d_out, grouper, loss,
metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
self.num_domains = grouper.cardinality.item()
self.lamb = config.dann_lamb
self.dc_name = config.dann_dc_name
self.d_out = d_out
# initialize domain classifier
model.domain_classifier = initialize_domain_classifier(name=self.dc_name,
num_features=model.n_outputs,
num_domains=self.num_domains).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps,
is_group_in_train):
# check config
assert config.uniform_over_groups
# initialize model
model = initialize_model(config, d_out).to(config.device)
# initialize module
super().__init__(
config=config,
model=model,
grouper=grouper,
loss=loss,
metric=metric,
n_train_steps=n_train_steps,
)
# additional logging
self.logged_fields.append('group_weight')
# step size
self.group_weights_step_size = config.group_dro_step_size
# initialize adversarial weights
self.group_weights = torch.zeros(grouper.n_groups)
self.group_weights[is_group_in_train] = 1
self.group_weights = self.group_weights / self.group_weights.sum()
self.group_weights = self.group_weights.to(self.device)
def main():
''' set default hyperparams in default_hyperparams.py '''
parser = argparse.ArgumentParser()
# Required arguments
parser.add_argument('-d',
'--dataset',
choices=wilds.supported_datasets,
required=True)
parser.add_argument('--algorithm',
required=True,
choices=supported.algorithms)
parser.add_argument(
'--root_dir',
required=True,
help=
'The directory where [dataset]/data can be found (or should be downloaded to, if it does not exist).'
)
parser.add_argument('--pretrained_model_path',
default=None,
type=str,
help="Specify a path to a pretrained model's weights")
# Dataset
parser.add_argument(
'--split_scheme',
help=
'Identifies how the train/val/test split is constructed. Choices are dataset-specific.'
)
parser.add_argument('--dataset_kwargs',
nargs='*',
action=ParseKwargs,
default={})
parser.add_argument(
'--download',
default=False,
type=parse_bool,
const=True,
nargs='?',
help=
'If true, tries to download the dataset if it does not exist in root_dir.'
)
parser.add_argument(
'--frac',
type=float,
default=1.0,
help=
'Convenience parameter that scales all dataset splits down to the specified fraction, for development purposes. Note that this also scales the test set down, so the reported numbers are not comparable with the full test set.'
)
parser.add_argument('--version', default=None, type=str)
# Loaders
parser.add_argument('--loader_kwargs',
nargs='*',
action=ParseKwargs,
default={})
parser.add_argument('--train_loader', choices=['standard', 'group'])
parser.add_argument('--uniform_over_groups',
type=parse_bool,
const=True,
nargs='?')
parser.add_argument('--distinct_groups',
type=parse_bool,
const=True,
nargs='?')
parser.add_argument('--n_groups_per_batch', type=int)
parser.add_argument('--unlabeled_n_groups_per_batch', type=int)
parser.add_argument('--batch_size', type=int)
parser.add_argument('--unlabeled_batch_size', type=int)
parser.add_argument('--eval_loader',
choices=['standard'],
default='standard')
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
'Number of batches to process before stepping optimizer and/or schedulers. If > 1, we simulate having a larger effective batch size (though batchnorm behaves differently).'
)
# Active Learning
parser.add_argument('--active_learning',
type=parse_bool,
const=True,
nargs='?')
parser.add_argument(
'--target_split',
default="test",
type=str,
help=
'Split from which to sample labeled examples and use as unlabeled data for self-training.'
)
parser.add_argument(
'--use_target_labeled',
type=parse_bool,
const=True,
nargs='?',
default=True,
help=
"If false, we sample target labels and remove them from the eval set, but don't actually train on them."
)
parser.add_argument(
'--use_source_labeled',
type=parse_bool,
const=True,
nargs='?',
default=False,
help=
"Train on labeled source examples (perhaps in addition to labeled target examples.)"
)
parser.add_argument(
'--upsample_target_labeled',
type=parse_bool,
const=True,
nargs='?',
default=False,
help=
"If concatenating source labels, upsample target labels s.t. our labeled batches are 1/2 src and 1/2 tgt."
)
parser.add_argument('--selection_function',
choices=supported.selection_functions)
parser.add_argument(
'--selection_function_kwargs',
nargs='*',
action=ParseKwargs,
default={},
help=
"keyword arguments for selection fn passed as key1=value1 key2=value2")
parser.add_argument(
'--selectby_fields',
nargs='+',
help=
"If set, acts like a grouper and n_shots are acquired per selection group (e.g. y x hospital selects K examples per y x hospital)."
)
parser.add_argument('--n_shots',
type=int,
help="number of shots (labels) to actively acquire")
# Model
parser.add_argument('--model', choices=supported.models)
parser.add_argument(
'--model_kwargs',
nargs='*',
action=ParseKwargs,
default={},
help=
'keyword arguments for model initialization passed as key1=value1 key2=value2'
)
parser.add_argument('--freeze_featurizer',
type=parse_bool,
const=True,
nargs='?',
help="Only train classifier weights")
parser.add_argument(
'--teacher_model_path',
type=str,
help=
'Path to teacher model weights. If this is defined, pseudolabels will first be computed for unlabeled data before anything else runs.'
)
parser.add_argument('--dropout_rate', type=float)
# Transforms
parser.add_argument('--transform', choices=supported.transforms)
parser.add_argument('--additional_labeled_transform',
type=parse_none,
choices=supported.additional_transforms)
parser.add_argument('--additional_unlabeled_transform',
type=parse_none,
nargs='+',
choices=supported.additional_transforms)
parser.add_argument(
'--target_resolution',
nargs='+',
type=int,
help=
'The input resolution that images will be resized to before being passed into the model. For example, use --target_resolution 224 224 for a standard ResNet.'
)
parser.add_argument('--resize_scale', type=float)
parser.add_argument('--max_token_length', type=int)
parser.add_argument(
'--randaugment_n',
type=int,
help=
'N parameter of RandAugment - the number of transformations to apply.')
# Objective
parser.add_argument('--loss_function', choices=supported.losses)
# Algorithm
parser.add_argument('--groupby_fields', nargs='+')
parser.add_argument('--group_dro_step_size', type=float)
parser.add_argument('--coral_penalty_weight', type=float)
parser.add_argument('--irm_lambda', type=float)
parser.add_argument('--irm_penalty_anneal_iters', type=int)
parser.add_argument('--maml_first_order',
type=parse_bool,
const=True,
nargs='?')
parser.add_argument('--metalearning_k', type=int)
parser.add_argument('--metalearning_adapt_lr', type=float)
parser.add_argument('--metalearning_kwargs',
nargs='*',
action=ParseKwargs,
default={})
parser.add_argument('--self_training_labeled_weight',
type=float,
help='Weight of labeled loss')
parser.add_argument('--self_training_unlabeled_weight',
type=float,
help='Weight of unlabeled loss')
parser.add_argument('--self_training_threshold', type=float)
parser.add_argument(
'--pseudolabel_T2',
type=float,
help=
'Percentage of total iterations at which to end linear scheduling and hold unlabeled weight at the max value'
)
parser.add_argument('--soft_pseudolabels',
default=False,
type=parse_bool,
const=True,
nargs='?')
parser.add_argument('--algo_log_metric')
# Model selection
parser.add_argument('--val_metric')
parser.add_argument('--val_metric_decreasing',
type=parse_bool,
const=True,
nargs='?')
# Optimization
parser.add_argument('--n_epochs', type=int)
parser.add_argument('--optimizer', choices=supported.optimizers)
parser.add_argument('--lr', type=float)
parser.add_argument('--weight_decay', type=float)
parser.add_argument('--max_grad_norm', type=float)
parser.add_argument('--optimizer_kwargs',
nargs='*',
action=ParseKwargs,
default={})
# Scheduler
parser.add_argument('--scheduler', choices=supported.schedulers)
parser.add_argument('--scheduler_kwargs',
nargs='*',
action=ParseKwargs,
default={})
parser.add_argument('--scheduler_metric_split',
choices=['train', 'val'],
default='val')
parser.add_argument('--scheduler_metric_name')
# Evaluation
parser.add_argument('--process_outputs_function',
choices=supported.process_outputs_functions)
parser.add_argument('--evaluate_all_splits',
type=parse_bool,
const=True,
nargs='?',
default=False)
parser.add_argument('--eval_splits', nargs='+', default=['val', 'test'])
parser.add_argument(
'--save_splits',
nargs='+',
default=['test'],
help=
'If save_pred_step or save_pseudo_step are set, then this sets which splits to save pred / pseudos for. Must be a subset of eval_splits.'
)
parser.add_argument('--eval_additional_every',
default=1,
type=int,
help='Eval additional splits every _ training epochs.')
parser.add_argument('--eval_only',
type=parse_bool,
const=True,
nargs='?',
default=False)
parser.add_argument(
'--eval_epoch',
default=None,
type=int,
help=
'If eval_only is set, then eval_epoch allows you to specify evaluating at a particular epoch. By default, it evaluates the best epoch by validation performance.'
)
# Misc
parser.add_argument('--device', type=int, nargs='+', default=[0])
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--log_dir', default='./logs')
parser.add_argument('--log_every', default=50, type=int)
parser.add_argument('--save_model_step', type=int)
parser.add_argument('--save_pred_step', type=int)
parser.add_argument('--save_pseudo_step', type=int)
parser.add_argument('--save_best',
type=parse_bool,
const=True,
nargs='?',
default=True)
parser.add_argument('--save_last',
type=parse_bool,
const=True,
nargs='?',
default=True)
parser.add_argument('--no_group_logging',
type=parse_bool,
const=True,
nargs='?')
parser.add_argument('--progress_bar',
type=parse_bool,
const=True,
nargs='?',
default=False)
parser.add_argument(
'--resume',
type=parse_bool,
const=True,
nargs='?',
default=False,
help=
'Whether to resume from the most recent saved model in the current log_dir.'
)
# Weights & Biases
parser.add_argument('--use_wandb',
type=parse_bool,
const=True,
nargs='?',
default=False)
parser.add_argument(
'--wandb_api_key_path',
type=str,
help=
"Path to Weights & Biases API Key. If use_wandb is set to True and this argument is not specified, user will be prompted to authenticate."
)
parser.add_argument('--wandb_kwargs',
nargs='*',
action=ParseKwargs,
default={},
help="Will be passed directly into wandb.init().")
config = parser.parse_args()
config = populate_defaults(config)
# Set device
if torch.cuda.is_available():
device_count = torch.cuda.device_count()
if len(config.device) > device_count:
raise ValueError(
f"Specified {len(config.device)} devices, but only {device_count} devices found."
)
config.use_data_parallel = len(config.device) > 1
try:
device_str = ",".join(map(str, config.device))
config.device = torch.device(f"cuda:{device_str}")
except RuntimeError as e:
print(
f"Failed to initialize CUDA. Using torch.device('cuda') instead. Error: {str(e)}"
)
config.device = torch.device("cuda")
else:
config.use_data_parallel = False
config.device = torch.device("cpu")
## Initialize logs
if os.path.exists(config.log_dir) and config.resume:
resume = True
config.mode = 'a'
elif os.path.exists(config.log_dir) and config.eval_only:
resume = False
config.mode = 'a'
else:
resume = False
config.mode = 'w'
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
logger = Logger(os.path.join(config.log_dir, 'log.txt'), config.mode)
# Record config
log_config(config, logger)
# Set random seed
set_seed(config.seed)
# Algorithms that use unlabeled data must be run in active learning mode,
# because otherwise we have no unlabeled data.
if config.algorithm in ["PseudoLabel", "FixMatch", "NoisyStudent"]:
assert config.active_learning
# Data
full_dataset = wilds.get_dataset(dataset=config.dataset,
version=config.version,
root_dir=config.root_dir,
download=config.download,
split_scheme=config.split_scheme,
**config.dataset_kwargs)
# In this project, we sometimes train on batches of mixed splits, e.g. some train labeled examples and test labeled examples
# Within each batch, we may want to sample uniformly across split, or log the train v. test label balance
# To facilitate this, we'll hack the WILDS dataset to include each point's split in the metadata array
add_split_to_wilds_dataset_metadata_array(full_dataset)
# To modify data augmentation, modify the following code block.
# If you want to use transforms that modify both `x` and `y`,
# set `do_transform_y` to True when initializing the `WILDSSubset` below.
train_transform = initialize_transform(
transform_name=config.transform,
config=config,
dataset=full_dataset,
additional_transform=config.additional_labeled_transform,
is_training=True)
eval_transform = initialize_transform(transform_name=config.transform,
config=config,
dataset=full_dataset,
is_training=False)
# Define any special transforms for the algorithms that use unlabeled data
# if config.algorithm == "FixMatch":
# # For FixMatch, we need our loader to return batches in the form ((x_weak, x_strong), m)
# # We do this by initializing a special transform function
# unlabeled_train_transform = initialize_transform(
# config.transform, config, full_dataset, is_training=True, additional_transform="fixmatch"
# )
# else:
unlabeled_train_transform = initialize_transform(
config.transform,
config,
full_dataset,
is_training=True,
additional_transform=config.additional_unlabeled_transform)
train_grouper = CombinatorialGrouper(dataset=full_dataset,
groupby_fields=config.groupby_fields)
datasets = defaultdict(dict)
for split in full_dataset.split_dict.keys():
if split == 'train':
transform = train_transform
verbose = True
elif split == 'val':
transform = eval_transform
verbose = True
else:
transform = eval_transform
verbose = False
data = full_dataset.get_subset(split,
frac=config.frac,
transform=transform)
datasets[split] = configure_split_dict(
data=data,
split=split,
split_name=full_dataset.split_names[split],
get_train=(split == 'train'),
get_eval=(split != 'train'),
verbose=verbose,
grouper=train_grouper,
batch_size=config.batch_size,
config=config)
pseudolabels = None
if config.algorithm == "NoisyStudent" and config.target_split == split:
# Infer teacher outputs on unlabeled examples in sequential order
# During forward pass, ensure we are not shuffling and not applying strong augs
print(
f"Inferring teacher pseudolabels on {config.target_split} for Noisy Student"
)
assert config.teacher_model_path is not None
if not config.teacher_model_path.endswith(".pth"):
# Use the best model
config.teacher_model_path = os.path.join(
config.teacher_model_path,
f"{config.dataset}_seed:{config.seed}_epoch:best_model.pth"
)
teacher_model = initialize_model(
config, infer_d_out(full_dataset)).to(config.device)
load(teacher_model,
config.teacher_model_path,
device=config.device)
# Infer teacher outputs on weakly augmented unlabeled examples in sequential order
weak_transform = initialize_transform(
transform_name=config.transform,
config=config,
dataset=full_dataset,
is_training=True,
additional_transform="weak")
unlabeled_split_dataset = full_dataset.get_subset(
split, transform=weak_transform, frac=config.frac)
sequential_loader = get_eval_loader(
loader=config.eval_loader,
dataset=unlabeled_split_dataset,
grouper=train_grouper,
batch_size=config.unlabeled_batch_size,
**config.loader_kwargs)
pseudolabels = infer_predictions(teacher_model, sequential_loader,
config)
del teacher_model
if config.active_learning and config.target_split == split:
datasets[split]['label_manager'] = LabelManager(
subset=data,
train_transform=train_transform,
eval_transform=eval_transform,
unlabeled_train_transform=unlabeled_train_transform,
pseudolabels=pseudolabels)
if config.use_wandb:
initialize_wandb(config)
# Logging dataset info
# Show class breakdown if feasible
if config.no_group_logging and full_dataset.is_classification and full_dataset.y_size == 1 and full_dataset.n_classes <= 10:
log_grouper = CombinatorialGrouper(dataset=full_dataset,
groupby_fields=['y'])
elif config.no_group_logging:
log_grouper = None
else:
log_grouper = train_grouper
log_group_data(datasets, log_grouper, logger)
## Initialize algorithm
## Schedulers are initialized as if we will iterate over "train" split batches.
## If we train on another split (e.g. labeled test), we'll re-initialize schedulers later using algorithm.change_n_train_steps()
algorithm = initialize_algorithm(config=config,
datasets=datasets,
train_grouper=train_grouper)
if config.freeze_featurizer: freeze_features(algorithm)
if config.active_learning:
select_grouper = CombinatorialGrouper(
dataset=full_dataset, groupby_fields=config.selectby_fields)
selection_fn = initialize_selection_function(
config, algorithm, select_grouper, algo_grouper=train_grouper)
# Resume from most recent model in log_dir
model_prefix = get_model_prefix(datasets['train'], config)
if not config.eval_only:
## If doing active learning, expects to load a model trained on source
resume_success = False
if config.resume:
save_path = model_prefix + 'epoch:last_model.pth'
if not os.path.exists(save_path):
epochs = [
int(file.split('epoch:')[1].split('_')[0])
for file in os.listdir(config.log_dir)
if file.endswith('.pth')
]
if len(epochs) > 0:
latest_epoch = max(epochs)
save_path = model_prefix + f'epoch:{latest_epoch}_model.pth'
try:
prev_epoch, best_val_metric = load(algorithm, save_path,
config.device)
# also load previous selections
epoch_offset = prev_epoch + 1
config.selection_function_kwargs[
'load_selection_path'] = config.log_dir
logger.write(
f'Resuming from epoch {epoch_offset} with best val metric {best_val_metric}\n'
)
resume_success = True
except FileNotFoundError:
pass
if resume_success == False:
epoch_offset = 0
best_val_metric = None
# Log effective batch size
logger.write((
f'\nUsing gradient_accumulation_steps {config.gradient_accumulation_steps} means that'
) + (
f' the effective labeled batch size is {config.batch_size * config.gradient_accumulation_steps}'
) + (
f' and the effective unlabeled batch size is {config.unlabeled_batch_size * config.gradient_accumulation_steps}'
if config.unlabeled_batch_size else '') + (
'. Updates behave as if torch loaders have drop_last=False\n'))
if config.active_learning:
# create new labeled/unlabeled test splits
train_split, unlabeled_split = run_active_learning(
selection_fn=selection_fn,
datasets=datasets,
grouper=train_grouper,
config=config,
general_logger=logger,
full_dataset=full_dataset)
# reset schedulers, which were originally initialized to schedule based on the 'train' split
# one epoch = one pass over labeled data
algorithm.change_n_train_steps(
new_n_train_steps=infer_n_train_steps(
datasets[train_split]['train_loader'], config),
config=config)
else:
train_split = "train"
unlabeled_split = None
train(algorithm=algorithm,
datasets=datasets,
train_split=train_split,
val_split="val",
unlabeled_split=unlabeled_split,
general_logger=logger,
config=config,
epoch_offset=epoch_offset,
best_val_metric=best_val_metric)
else:
if config.eval_epoch is None:
eval_model_path = model_prefix + 'epoch:best_model.pth'
else:
eval_model_path = model_prefix + f'epoch:{config.eval_epoch}_model.pth'
best_epoch, best_val_metric = load(algorithm, eval_model_path,
config.device)
if config.eval_epoch is None:
epoch = best_epoch
else:
epoch = config.eval_epoch
if config.active_learning:
# create new labeled/unlabeled test splits
config.selection_function_kwargs[
'load_selection_path'] = config.log_dir
run_active_learning(selection_fn=selection_fn,
datasets=datasets,
grouper=train_grouper,
config=config,
general_logger=logger,
full_dataset=full_dataset)
evaluate(algorithm=algorithm,
datasets=datasets,
epoch=epoch,
general_logger=logger,
config=config)
if config.use_wandb:
wandb.finish()
logger.close()
for split in datasets:
datasets[split]['eval_logger'].close()
datasets[split]['algo_logger'].close()