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()