68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
from typing import Any, Dict, Tuple
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import torch
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import torch.nn as nn
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class PtnSmoothL1Loss:
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"""
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Ptn Smooth L1 Loss:
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A class to compute the Smooth L1 loss.
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Please see [PyTorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.SmoothL1Loss.html) for details and exact formulation.
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Args:
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reduction (str): Reduction method.
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`none`: no reduction will be applied done.
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`mean`: computes the mean.
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`sum`: computes the sum.
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beta (float): The threshold used to switch between adjusted L2 and L1 loss.
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category: PyTorch wrapper - Loss function
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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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"reduction": (("mean", "sum", "none"),),
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"beta": ("FLOAT", {"default":1.0, "min":0, "max":1e9, "step":0.0000000001}),
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}
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}
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RETURN_TYPES: Tuple[str] = ("PTLOSS",)
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FUNCTION: str = "f"
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CATEGORY: str = "Training"
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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,
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reduction: str,
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beta: float
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) -> Tuple[nn.Module]:
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"""
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A model to compute the Smooth L1 loss.
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Args:
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reduction: (str): Reduction method.
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`none`: no reduction will be applied done.
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`mean`: computes the mean.
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`sum`: computes the sum.
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beta (float): The threshold used to switch between adjusted L2 and L1 loss.
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Returns:
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Tuple[nn.Module]: A tuple containing the instantiated PyTorch model.
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"""
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with torch.inference_mode(False):
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model = nn.SmoothL1Loss(reduction=reduction, beta=beta)
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return (model,)
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