71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import torch
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from torch.types import Number
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@torch.no_grad()
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def amp_to_db(
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x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40
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) -> torch.Tensor:
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"""
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Convert the input tensor from amplitude to decibel scale.
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Arguments:
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x {[torch.Tensor]} -- [Input tensor.]
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Keyword Arguments:
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eps {[float]} -- [Small value to avoid numerical instability.]
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(default: {torch.finfo(torch.float64).eps})
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top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
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` (default: {40})
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Returns:
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[torch.Tensor] -- [Output tensor in decibel scale.]
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"""
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x_db = 20 * torch.log10(x.abs() + eps)
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return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))
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@torch.no_grad()
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def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
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"""
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Apply a sigmoid function with temperature scaling.
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Arguments:
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x {[torch.Tensor]} -- [Input tensor.]
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x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.]
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temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.]
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Returns:
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[torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.]
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"""
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return torch.sigmoid((x - x0) / temp_coeff)
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@torch.no_grad()
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def linspace(
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start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs
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) -> torch.Tensor:
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"""
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Generate a linearly spaced 1-D tensor.
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Arguments:
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start {[Number]} -- [The starting value of the sequence.]
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stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
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In that case, the sequence consists of all but the last of ``num + 1``
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evenly spaced samples, so that `stop` is excluded. Note that the step
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size changes when `endpoint` is False.]
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Keyword Arguments:
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num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
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endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
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Default is True.]
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**kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]
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Returns:
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[torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
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"""
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if endpoint:
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return torch.linspace(start, stop, num, **kwargs)
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else:
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return torch.linspace(start, stop, num + 1, **kwargs)[:-1]
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