def main(): parser = argparse.ArgumentParser() add_test_args(parser) add_common_args(parser) args = parser.parse_args() device = model_utils.get_device() assert args.name is not None os.makedirs(f'{args.save_dir}/{args.name}') # Load dataset from disk dev_dl = model_utils.load_test_data(args) dev_dl = dev_dl.take(args.num_images) # Initialize a model model = models.get_model(args.model)(args.size) # load from checkpoint if path specified assert args.load_path is not None model = model_utils.load_model(model, args.load_path) model.eval() # Move model to GPU if necessary model.to(device) # test! make_images( dev_dl, model, args, )
def main(): parser = argparse.ArgumentParser() add_test_args(parser) add_common_args(parser) args = parser.parse_args() device = model_utils.get_device() # Load dataset from disk x_dev, y_dev, ground_truths, container = model_utils.load_test_data( args.dataset_dir, dev_frac=args.dev_frac, max_entries=args.dataset_cap) dev_dl = data.DataLoader( data.TensorDataset(x_dev, y_dev, ground_truths), batch_size=args.batch_size, shuffle=False, ) # Initialize a model model = models.get_model(args.model)() # load from checkpoint if path specified assert args.load_path is not None model = model_utils.load_model(model, args.load_path) model.eval() # Move model to GPU if necessary model.to(device) # test! test_model( dev_dl, model, args, container, )
def main(): parser = argparse.ArgumentParser() add_train_args(parser) add_common_args(parser) args = parser.parse_args() add_experiment(args) device = model_utils.get_device() # Load dataset from disk train_ds, dev_ds = model_utils.load_training_data(args) # Initialize a model model = models.get_model(args.model)(size=args.size) # load from checkpoint if path specified if args.load_path is not None: model = model_utils.load_model(model, args.load_path) # Move model to GPU if necessary model.to(device) # Initialize optimizer optimizer = optim.Adam( model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay, ) # Scheduler scheduler = optim.lr_scheduler.StepLR(optimizer, 5, 0.1, verbose=True) os.makedirs(f'{args.save_path}/{args.experiment}') print(f'Created new experiment: {args.experiment}') save_arguments(args, f'{args.save_path}/{args.experiment}/args.txt') # Train! trained_model = train_model( train_ds, dev_ds, model, optimizer, scheduler, args, ) # Save trained model filename = f'{args.save_path}/{args.experiment}/{model.__class__.__name__}_trained.checkpoint' model_utils.save_model(trained_model, filename)
def main(): parser = argparse.ArgumentParser() add_test_args(parser) add_common_args(parser) add_wav_args(parser) args = parser.parse_args() model = models.get_model(args.model)() assert args.load_path is not None, "Did not specify model load path" model = model_utils.load_model(model, args.load_path) model = model.cpu() model.eval() assert args.input_file is not None, "Did not specify input file!" print("Processing file...") audio_data = TrumpRemover.process_through_model(args.input_file, args.output_file, model, args) play(audio_data) print("Done!")
def main(): parser = argparse.ArgumentParser() add_test_args(parser) add_common_args(parser) args = parser.parse_args() device = model_utils.get_device() assert args.load_path is not None or args.load_dir is not None if args.load_dir is not None: prev_args: Dict = load_arguments(f'{args.load_dir}/args.txt') args.model = prev_args['model'] args.size = prev_args['size'] args.name = prev_args['experiment'] assert args.name is not None os.makedirs(f'{args.save_dir}/{args.name}') # Load dataset from disk dev_dl = model_utils.load_test_data(args) # Initialize a model model = models.get_model(args.model)(size=args.size) # load from checkpoint if path specified if args.load_path is not None: model = model_utils.load_model(model, args.load_path) else: model = model_utils.load_model( model, f'{args.load_dir}/{args.model}_best_val.checkpoint') model.eval() # Move model to GPU if necessary model.to(device) # test! test_model( dev_dl, model, args, )
def main(): parser = argparse.ArgumentParser() add_train_args(parser) add_common_args(parser) args = parser.parse_args() add_experiment(args) device = model_utils.get_device() # Load dataset from disk x_train, y_train_biden, y_train_trump, mask_train, x_dev, y_dev_biden, y_dev_trump, mask_dev, container = model_utils.load_data( args.dataset_dir, dev_frac=args.dev_frac, max_entries=args.dataset_cap) train_dl = data.DataLoader( data.TensorDataset(x_train, y_train_biden, y_train_trump, mask_train), batch_size=args.train_batch_size, shuffle=True, ) dev_dl = data.DataLoader( data.TensorDataset(x_dev, y_dev_biden, y_dev_trump, mask_dev), batch_size=args.val_batch_size, shuffle=False, ) # Initialize a model model = models.get_model(args.model)() # load from checkpoint if path specified if args.load_path is not None: model = model_utils.load_model(model, args.load_path) # Move model to GPU if necessary model.to(device) # Initialize optimizer optimizer = optim.Adam( model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay, ) # Scheduler scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode='min', factor=0.5, patience=30, verbose=True, ) os.makedirs(f'{args.save_path}/{args.experiment}') print(f'Created new experiment: {args.experiment}') save_arguments(args, f'{args.save_path}/{args.experiment}/args.txt') # Train! trained_model = train_model( train_dl, dev_dl, model, optimizer, scheduler, args, ) # Save trained model filename = f'{args.save_path}/{args.experiment}/{model.__class__.__name__}_trained.checkpoint' model_utils.save_model(trained_model, filename)