def enhance_with_model(experiment_name, loadpath, cuda=False, samples=None): checkpoint = load_to_cpu(loadpath) p = checkpoint['p'] model = p['model_class'](**p['model_kwargs']) model.load_state_dict(checkpoint['state_dict']) model.transform = p['input_transform'] model.transform.target_transform = None model.output_transform = p['output_transform'] model.inverse_transform = istft model.experiment_name = experiment_name # this is a hack clean_lre17_dev(model, cuda=cuda, samples=samples) clean_lre17_eval(model, cuda=cuda, samples=samples) clean_dataset_4_eval(model, cuda=cuda, samples=samples) clean_lre17tel_dev(model, cuda=cuda, samples=samples) clean_lre17tel_eval(model, cuda=cuda, samples=samples)
def clean_overfit(experiment_name, loadpath, p): model = p['model_class'](**p['model_kwargs']) model.load_state_dict(load_to_cpu(loadpath)['state_dict']) model.transform = p['input_transform'] model.transform.target_transform = None model.output_transform = p['output_transform'] model.inverse_transform = istft model.experiment_name = experiment_name # this is a hack samples = None # slice(-1,None,-1) cuda = True if cuda: model.cuda() clean_lre17_dev(model, cuda=cuda, samples=samples) # clean_lre17_eval(model, cuda=cuda, samples=samples) clean_lre17tel_dev(model, cuda=cuda, samples=samples)
def clean_overfit(experiment_name, loadpath): from src.models.BLSTM_A5 import p model = p['model_class'](**p['model_kwargs']) model.load_state_dict(load_to_cpu(loadpath)['state_dict']) model.experiment_name = experiment_name model.transform = p['input_transform'] model.transform.mode = 'runtime' model.apply_mask = apply_mask model.inverse_transform = istft samples = None # slice(-1,None,-1) cuda = True if cuda: model.cuda() clean_lre17_dev(model, cuda=cuda, samples=samples) clean_lre17_eval(model, cuda=cuda, samples=samples) clean_lre17tel_dev(model, cuda=cuda, samples=samples) clean_lre17tel_eval(model, cuda=cuda, samples=samples) clean_dataset_4_eval(model, cuda=cuda, samples=samples)
import os from src.features.features_functions import istft from src.evaluation.eval_BLSTM_A0 import ( clean_lre17_dev, clean_lre17_eval, clean_dataset_3_eval, clean_dataset_4_eval, clean_lre17tel_dev, clean_lre17tel_eval, clean_lre17tel_train) from src.models.model_functions import apply_mask, load_to_cpu if __name__ == '__main__': from src.models.BLSTM_A11 import p model = p['model_class'](**p['model_kwargs']) loadpath = os.path.join('models', 'BLSTM_A11', 'BLSTM_A11_epoch_32.state') model.load_state_dict(load_to_cpu(loadpath)['state_dict']) model.transform = p['input_transform'] model.transform.mode = 'runtime' model.apply_mask = apply_mask model.inverse_transform = istft model.experiment_name = p['experiment_name'] # this is a hack samples = None # slice(-1,None,-1) cuda = True if cuda: model.cuda() clean_lre17_dev(model, cuda=cuda, samples=samples) clean_lre17_eval(model, cuda=cuda, samples=samples)
output_dir = os.path.join('data', 'interim', 'dataset_4_val', model.experiment_name) enhance_Datafolder(model, input_dir, output_dir, batch_size=10, cuda=cuda, samples=samples) if __name__ == '__main__': # experiment_name = 'BLSTM_A5_27' # loadpath = os.path.join('models', 'BLSTM_A5', # 'BLSTM_A5_epoch_27.state') # enhance_with_model(experiment_name, loadpath) experiment_name, loadpath, cuda = False, samples = None checkpoint = load_to_cpu(loadpath) p = checkpoint['p'] model = p['model_class'](**p['model_kwargs']) model.load_state_dict(checkpoint['state_dict']) model.transform = p['input_transform'] model.transform.target_transform = None model.output_transform = p['output_transform'] model.inverse_transform = istft model.experiment_name = experiment_name # this is a hack clean_dataset_4_eval(model, cuda=cuda, samples=samples)