53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
from typing import Any, Dict
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import torch
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import ast
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class PtdmSample:
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"""
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Samples from the input distribution.
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Args:
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distribution (torch.distributions.distribution.Distribution): Distribution.
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sample_shape (torch.Size): Shape of the sample.
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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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"sample_shape": ("STRING", {"default": ""})
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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,
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distribution:torch.distributions.distribution.Distribution,
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sample_shape: str) -> tuple:
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"""
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Samples from the input distribution.
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Args:
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distribution (torch.distributions.distribution.Distribution): Distribution.
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sample_shape (torch.Size): Shape of the sample.
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Returns:
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tuple containing the sample
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"""
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shape = ast.literal_eval(sample_shape)
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if isinstance(shape, int):
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shape = (shape,)
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return (distribution.sample(shape),)
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