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