53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
from typing import Any, Dict
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import ast
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import torch
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from torch.distributions import Uniform
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class PtdUniform:
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"""
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Instantiates a Uniform distribution object.
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Args:
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low (str): Minimum (inclusive). You can specify a scalar or a tuple of float.
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high (str): Maximum (exclusive). You can specify a scalar or a tuple of float.
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category: PyTorch wrapper - Distribution
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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 the required input types.
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"""
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return {
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"required": {
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"low": ("STRING", {"default": ""}),
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"high": ("STRING", {"default": ""})
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}
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}
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RETURN_TYPES: tuple = ("PTDISTRIBUTION",)
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FUNCTION: str = "f"
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CATEGORY: str = "Distribution"
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def f(self, low: str, high: str) -> tuple:
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"""
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Instantiates a Uniform distribution object.
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Args:
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low (str): Minimum (inclusive). You can specify a scalar or a tuple of float.
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high (str): Maximum (exclusive). You can specify a scalar or a tuple of float.
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Returns:
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tuple: (torch.distributions.binomial.Uniform)
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"""
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l = ast.literal_eval(low)
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h = ast.literal_eval(high)
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dist = Uniform(
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low=torch.tensor(l, dtype=torch.float32), high=torch.tensor(h, dtype=torch.float32))
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return (dist,)
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