58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
from typing import Any, Dict
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import torch
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import ast
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from .utils import DTYPE_MAPPING
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class PtRand:
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"""
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Creates a PyTorch tensor with values sampled from a uniform distribution
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using the size entered in the text field.
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For example, if you want to create a 4D tensor of batch size=2, channel=3, height=96, width=32, enter:
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[2,3,96,32]
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category: PyTorch wrapper - Tensor creation
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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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"size": ("STRING", {"multiline": True}),
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"data_type": (("float32", "float16", "bfloat16", "float64"),)
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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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@classmethod
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def IS_CHANGED(cls, **kw):
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return float("NaN")
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def f(self, size: str, data_type: str) -> tuple:
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"""
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Creates a PyTorch tensor with values sampled from a uniform distribution.
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Args:
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size (str): String value in the text field.
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data_type (str): Desired floating-point data type of the tensor.
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Returns:
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tuple: A tuple containing the PyTorch tensor.
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"""
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# Convert string to Python list
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list_size = ast.literal_eval(size)
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# Convert list to PyTorch Size
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sz = torch.Size(list_size)
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tens = torch.rand(sz, dtype=DTYPE_MAPPING[data_type])
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return (tens,)
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