Source code for farm.data_handler.dataset

import torch
from import TensorDataset

# TODO we need the option to handle different dtypes
[docs]def convert_features_to_dataset(features): """ Converts a list of feature dictionaries (one for each sample) into a PyTorch Dataset. :param features: A list of dictionaries. Each dictionary corresponds to one sample. Its keys are the names of the type of feature and the keys are the features themselves. :Return: a Pytorch dataset and a list of tensor names. """ tensor_names = list(features[0].keys()) all_tensors = [] for t_name in tensor_names: try: cur_tensor = torch.tensor( [sample[t_name] for sample in features], dtype=torch.long ) except ValueError: cur_tensor = torch.tensor( [sample[t_name] for sample in features], dtype=torch.float32 ) all_tensors.append(cur_tensor) dataset = TensorDataset(*all_tensors) return dataset, tensor_names