63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
from typing import Any, Dict
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import torch
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import ast
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class PtReshape:
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"""
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Reshapes a PyTorch tensor into a specified shape using `torch.reshape()`.
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The total number of elements must remain unchanged.
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For example, if a tensor has shape (2, 3, 4), and `new_shape="[6, 4]"`,
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the output will have shape (6, 4).
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Use -1 to automatically infer a dimension, e.g.:
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```
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[2, -1]
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```
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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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"new_shape": ("STRING", {"multiline": True})
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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, new_shape: str) -> tuple:
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"""
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Reshapes the input tensor based on the specified shape.
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Args:
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tens (torch.Tensor): The input tensor.
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new_shape (str): A string representation of the desired shape,
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e.g., "[2, -1]" or "(6, 4)".
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Returns:
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tuple: A tuple containing the reshaped PyTorch tensor.
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"""
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# Convert string to a tuple of integers
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shape_order = tuple(ast.literal_eval(new_shape))
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# Ensure the parsed shape is valid
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if not isinstance(shape_order, tuple) or not all(isinstance(i, int) for i in shape_order):
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raise ValueError(f"Invalid format for new_shape: {new_shape}. Must be a tuple or list of integers.")
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# Apply reshape operation
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tens = tens.reshape(*shape_order)
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return (tens,)
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