67 lines
2.2 KiB
Python
67 lines
2.2 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 PtFull:
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"""
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Creates a PyTorch tensor filled with a specified value 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 filled with 7, enter:
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[2,3,96,32] for size and 7 for value.
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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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"value": ("STRING", {"multiline": False}),
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"size": ("STRING", {"multiline": True}),
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"data_type": (("float32", "float16", "bfloat16", "float64", "uint8", "int8", "int16", "int32", "int64", "bool"),)
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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, value: str, size: str, data_type: str) -> tuple:
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"""
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Creates a PyTorch tensor filled with a specified value.
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Args:
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value (str): String representing the value to fill the tensor.
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size (str): String representation of the tensor size.
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data_type (str): Desired 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 the value to the appropriate type
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dtype = DTYPE_MAPPING[data_type]
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if dtype in {torch.float32, torch.float16, torch.bfloat16, torch.float64}:
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fill_value = float(value)
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elif dtype in {torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64}:
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fill_value = int(value)
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elif dtype == torch.bool:
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fill_value = value.lower() in {"true", "1"}
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else:
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raise ValueError(f"Unsupported data type: {data_type}")
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# Convert list to PyTorch Size
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sz = torch.Size(list_size)
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tens = torch.full(sz, fill_value, dtype=dtype)
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return (tens,)
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