47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
from typing import Any, Dict
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import torch
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class PtdmPdfTensor:
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"""
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Computes the probability density for the input distribution. This nodes accepts a tensor so it can be used to compute pdf for multiple values contained in a tensor.
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Args:
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distribution (torch.distributions.distribution.Distribution): Distribution.
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tens (torch.Tensor): Value(s) in Tensor.
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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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"distribution": ("PTDISTRIBUTION", {}),
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"tens": ("TENSOR", {})
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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 = "Distribution"
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def f(self, distribution: torch.distributions.Distribution, tens: torch.Tensor) -> tuple:
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"""
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Computes the probability for the input distribution.
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Args:
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distribution (torch.distributions.Distribution): Distribution.
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tens (torch.Tensor): Value(s) in Tensor.
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Returns:
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tuple containing the probability
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"""
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return (torch.exp(distribution.log_prob(tens)),)
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