def save_tensors(collected_tensors, output_directory): filenames = [] for tensor_name, tensor_value in collected_tensors: np_filename = os.path.join(output_directory, make_safe_filename(tensor_name) + ".npy") np.save(np_filename, tensor_value.detach().cpu().numpy()) filenames.append(np_filename) return filenames
def save_tensors(collected_tensors, experiment_dir_name): filenames = [] for tensor_name, tensor_value in collected_tensors: np_filename = os.path.join(experiment_dir_name, make_safe_filename(tensor_name) + '.npy') np.save(np_filename, tensor_value.numpy()) filenames.append(np_filename) return filenames
def _save_as_numpy(predictions, output_directory, saved_keys, backend): predictions = predictions[[ c for c in predictions.columns if c not in saved_keys ]] npy_filename = os.path.join(output_directory, "{}.npy") numpy_predictions = to_numpy_dataset(predictions, backend) for k, v in numpy_predictions.items(): k = k.replace("<", "[").replace( ">", "]") # Replace <UNK> and <PAD> with [UNK], [PAD] if k not in saved_keys: if has_remote_protocol(output_directory): with open_file(npy_filename.format(make_safe_filename(k)), mode="wb") as f: np.save(f, v) else: np.save(npy_filename.format(make_safe_filename(k)), v) saved_keys.add(k)
def save_tensors(collected_tensors, experiment_dir_name): for tensor_name, tensor_values in collected_tensors.items(): np_filename = os.path.join(experiment_dir_name, make_safe_filename(tensor_name) + '.npy') np.save(np_filename, tensor_values)