43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
from typing import TypeVar, Union, Tuple, Optional
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from .. import Tensor
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# Create some useful type aliases
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# Template for arguments which can be supplied as a tuple, or which can be a scalar which PyTorch will internally
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# broadcast to a tuple.
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# Comes in several variants: A tuple of unknown size, and a fixed-size tuple for 1d, 2d, or 3d operations.
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T = TypeVar('T')
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_scalar_or_tuple_any_t = Union[T, Tuple[T, ...]]
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_scalar_or_tuple_1_t = Union[T, Tuple[T]]
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_scalar_or_tuple_2_t = Union[T, Tuple[T, T]]
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_scalar_or_tuple_3_t = Union[T, Tuple[T, T, T]]
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_scalar_or_tuple_4_t = Union[T, Tuple[T, T, T, T]]
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_scalar_or_tuple_5_t = Union[T, Tuple[T, T, T, T, T]]
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_scalar_or_tuple_6_t = Union[T, Tuple[T, T, T, T, T, T]]
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# For arguments which represent size parameters (eg, kernel size, padding)
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_size_any_t = _scalar_or_tuple_any_t[int]
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_size_1_t = _scalar_or_tuple_1_t[int]
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_size_2_t = _scalar_or_tuple_2_t[int]
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_size_3_t = _scalar_or_tuple_3_t[int]
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_size_4_t = _scalar_or_tuple_4_t[int]
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_size_5_t = _scalar_or_tuple_5_t[int]
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_size_6_t = _scalar_or_tuple_6_t[int]
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# For arguments which represent optional size parameters (eg, adaptive pool parameters)
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_size_any_opt_t = _scalar_or_tuple_any_t[Optional[int]]
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_size_2_opt_t = _scalar_or_tuple_2_t[Optional[int]]
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_size_3_opt_t = _scalar_or_tuple_3_t[Optional[int]]
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# For arguments that represent a ratio to adjust each dimension of an input with (eg, upsampling parameters)
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_ratio_2_t = _scalar_or_tuple_2_t[float]
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_ratio_3_t = _scalar_or_tuple_3_t[float]
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_ratio_any_t = _scalar_or_tuple_any_t[float]
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_tensor_list_t = _scalar_or_tuple_any_t[Tensor]
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# For the return value of max pooling operations that may or may not return indices.
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# With the proposed 'Literal' feature to Python typing, it might be possible to
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# eventually eliminate this.
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_maybe_indices_t = _scalar_or_tuple_2_t[Tensor]
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