Skip to content

OutputConversionImageToColumn

Bases: _OutputConversionImage

Source code in src/safeds/ml/nn/_output_conversion_image.py 
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
class OutputConversionImageToColumn(_OutputConversionImage):

    def _data_conversion(self, input_data: ImageList, output_data: Tensor, **kwargs: Any) -> ImageDataset[Column]:
        import torch

        _init_default_device()

        if not isinstance(input_data, _SingleSizeImageList):
            raise ValueError("The given input ImageList contains images of different sizes.")  # noqa: TRY004
        if "column_name" not in kwargs or not isinstance(kwargs.get("column_name"), str):
            raise ValueError(
                "The column_name is not set. The data can only be converted if the column_name is provided as `str` in the kwargs.",
            )
        if "one_hot_encoder" not in kwargs or not isinstance(kwargs.get("one_hot_encoder"), OneHotEncoder):
            raise ValueError(
                "The one_hot_encoder is not set. The data can only be converted if the one_hot_encoder is provided as `OneHotEncoder` in the kwargs.",
            )
        one_hot_encoder: OneHotEncoder = kwargs["one_hot_encoder"]
        column_name: str = kwargs["column_name"]

        output = torch.zeros(len(input_data), len(one_hot_encoder.get_names_of_added_columns()))
        output[torch.arange(len(input_data)), output_data] = 1

        im_dataset: ImageDataset[Column] = ImageDataset[Column].__new__(ImageDataset)
        im_dataset._output = _ColumnAsTensor._from_tensor(output, column_name, one_hot_encoder)
        im_dataset._shuffle_tensor_indices = torch.LongTensor(list(range(len(input_data))))
        im_dataset._shuffle_after_epoch = False
        im_dataset._batch_size = 1
        im_dataset._next_batch_index = 0
        im_dataset._input_size = input_data.sizes[0]
        im_dataset._input = input_data
        return im_dataset