wehub-resource-sync
107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
from typing import Any
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from typing import Callable
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from typing import List
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from typing import Union
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import numpy as np
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import scipy.sparse
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import torch
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def to_tensor(
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X: Union[list, np.ndarray, torch.Tensor, scipy.sparse.csr_matrix]
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) -> torch.Tensor:
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r"""Convert ``List``, ``numpy.ndarray``, ``scipy.sparse.csr_matrix`` to ``torch.Tensor``.
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Args:
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``X`` (``Union[List, np.ndarray, torch.Tensor, scipy.sparse.csr_matrix]``): Input.
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Examples:
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>>> import easygraph.datapipe as dd
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>>> X = [[0.1, 0.2, 0.5],
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[0.5, 0.2, 0.3],
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[0.3, 0.2, 0]]
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>>> dd.to_tensor(X)
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tensor([[0.1000, 0.2000, 0.5000],
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[0.5000, 0.2000, 0.3000],
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[0.3000, 0.2000, 0.0000]])
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"""
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if isinstance(X, list):
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X = torch.tensor(X)
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elif isinstance(X, scipy.sparse.csr_matrix):
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X = X.todense()
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X = torch.tensor(X)
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elif isinstance(X, scipy.sparse.coo_matrix):
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X = X.todense()
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X = torch.tensor(X)
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elif isinstance(X, np.ndarray):
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X = torch.tensor(X)
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else:
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X = torch.tensor(X)
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return X.float()
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def to_bool_tensor(X: Union[List, np.ndarray, torch.Tensor]) -> torch.BoolTensor:
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r"""Convert ``List``, ``numpy.ndarray``, ``torch.Tensor`` to ``torch.BoolTensor``.
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Args:
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``X`` (``Union[List, np.ndarray, torch.Tensor]``): Input.
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Examples:
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>>> import easygraph.datapipe as dd
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>>> X = [[0.1, 0.2, 0.5],
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[0.5, 0.2, 0.3],
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[0.3, 0.2, 0]]
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>>> dd.to_bool_tensor(X)
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tensor([[ True, True, True],
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[ True, True, True],
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[ True, True, False]])
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"""
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if isinstance(X, list):
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X = torch.tensor(X)
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elif isinstance(X, np.ndarray):
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X = torch.tensor(X)
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else:
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X = torch.tensor(X)
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return X.bool()
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def to_long_tensor(X: Union[List, np.ndarray, torch.Tensor]) -> torch.LongTensor:
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r"""Convert ``List``, ``numpy.ndarray``, ``torch.Tensor`` to ``torch.LongTensor``.
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Args:
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``X`` (``Union[List, np.ndarray, torch.Tensor]``): Input.
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Examples:
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>>> import easygraph.datapipe as dd
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>>> X = [[1, 2, 5],
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[5, 2, 3],
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[3, 2, 0]]
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>>> dd.to_long_tensor(X)
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tensor([[1, 2, 5],
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[5, 2, 3],
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[3, 2, 0]])
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"""
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if isinstance(X, list):
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X = torch.tensor(X)
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elif isinstance(X, np.ndarray):
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X = torch.tensor(X)
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else:
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X = torch.tensor(X)
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return X.long()
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def compose_pipes(*pipes: Callable) -> Callable:
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r"""Compose datapipe functions.
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Args:
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``pipes`` (``Callable``): Datapipe functions to compose.
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"""
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def composed_pipes(X: Any) -> torch.Tensor:
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for pipe in pipes:
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X = pipe(X)
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return X
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return composed_pipes
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