45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
from typing import Any, Dict
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import torch
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class PtUnsqueeze:
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"""
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Adds a singleton dimension at the specified position in the input tensor.
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For example, if a tensor has shape (3, 4) and `dim=1`, the output will have shape (3, 1, 4).
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category: PyTorch wrapper - Transform
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Any]:
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"""
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Defines the input types for the function.
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Returns:
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Dict[str, Any]: A dictionary specifying required input types.
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"""
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return {
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"required": {
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"tens": ("TENSOR", {}),
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"dim": ("INT", {"default": -1, "min": -10, "max": 10})
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}
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}
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RETURN_TYPES: tuple = ("TENSOR",)
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FUNCTION: str = "f"
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CATEGORY: str = "Data Analysis"
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def f(self, tens: torch.Tensor, dim: int) -> tuple:
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"""
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Adds a singleton dimension at the specified position.
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Args:
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tens (torch.Tensor): Input tensor.
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dim (int): Dimension at which to insert the new singleton dimension.
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Returns:
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tuple: A tuple containing the transformed PyTorch tensor.
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"""
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tens = torch.unsqueeze(tens, dim)
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return (tens,)
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