import torch
import torch.nn as nn
import torch.nn.functional as F
from hybra.utils import condition_number, audfilters, plot_response, circ_conv, circ_conv_transpose, frame_bounds
from hybra.utils import ISACgram as ISACgram_
from hybra._fit_dual import tight_hybra
from typing import Union
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class HybrA(nn.Module):
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def __init__(self,
kernel_size:int=128,
learned_kernel_size:int=23,
num_channels:int=40,
stride:Union[int,None]=None,
fc_max:Union[float,int,None]=None,
fs:int=16000,
L:int=16000,
supp_mult:float=1,
scale:str='mel',
tighten:bool=False,
det_init:bool=False,
verbose:bool=True):
"""Constructor for a HybrA filterbank.
Args:
kernel_size (int) - kernel size of the auditory filterbank
learned_kernel_size (int) - kernel size of the learned filterbank
num_channels (int) - number of channels
stride (int) - stride of the auditory filterbank. if 'None': 25% overlap
fc_max (float) - maximum frequency on the auditory scale. if 'None': fs//2.
fs (int) - sampling frequency
L (int) - signal length
supp_mult (float) - support multiplier.
scale (str) - auditory scale ('mel', 'erb', 'bark', 'log10', 'elelog'). elelog is a scale adapted to the hearing of elephants. Default: 'mel'.
tighten (bool) - whether to tighten the hybrid filterbank. Default: False.
det_init (bool) - whether to initialize the learned filters with diracs or randomly. Default: False.
"""
super().__init__()
[aud_kernels, d, fc, _, fc_max, kernel_min, kernel_size, Ls] = audfilters(
kernel_size=kernel_size, num_channels=num_channels, fc_max=fc_max, fs=fs, L=L, supp_mult=supp_mult, scale=scale
)
if stride is not None:
d = stride
Ls = int(torch.ceil(torch.tensor(L / d)) * d)
if verbose:
print(f"Max. kernel size: {kernel_size}")
print(f"Min. kernel size: {kernel_min}")
print(f"Number of channels: {num_channels}")
print(f"Stride for min. 25% overlap: {d}")
print(f"Signal length: {Ls}")
self.register_buffer('kernels', aud_kernels)
self.kernel_size = kernel_size
self.learned_kernel_size = learned_kernel_size
self.stride = d
self.num_channels = num_channels
self.fc = fc
self.Ls = Ls
self.fs = fs
if det_init:
learned_kernels = torch.zeros([self.num_channels, 1, self.learned_kernel_size])
learned_kernels[:,0,0] = 1.0
else:
learned_kernels = torch.randn([self.num_channels, 1, self.learned_kernel_size])/torch.sqrt(torch.tensor(self.learned_kernel_size*self.num_channels))
learned_kernels = learned_kernels / torch.norm(learned_kernels, p=1, dim=-1, keepdim=True)
learned_kernels = learned_kernels.to(self.kernels.dtype)
if tighten:
learned_kernels = tight_hybra(self.kernels, learned_kernels, d, Ls, fs, fit_eps = 1.0001, max_iter = 1000)
self.learned_kernels = nn.Parameter(learned_kernels, requires_grad=True)
self.hybra_kernels = F.conv1d(
self.kernels.squeeze(1),
self.learned_kernels,
groups=self.num_channels,
padding="same",
)
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def forward(self, x:torch.Tensor) -> torch.Tensor:
hybra_kernels = F.conv1d(
self.kernels.squeeze(1),
self.learned_kernels,
groups=self.num_channels,
padding="same",
)
self.hybra_kernels = hybra_kernels.clone().detach()
return circ_conv(x.unsqueeze(1), hybra_kernels, self.stride)
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def encoder(self, x:torch.Tensor):
"""
For learning use forward method!
"""
return circ_conv(x.unsqueeze(1), self.hybra_kernels, self.stride)
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def decoder(self, x:torch.Tensor) -> torch.Tensor:
_, B = frame_bounds(self.hybra_kernels.squeeze(1), self.stride, None)
return circ_conv_transpose(x, self.hybra_kernels / B, self.stride).squeeze(1)
# plotting methods
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def ISACgram(self, x):
with torch.no_grad():
coefficients = self.forward(x)
ISACgram_(coefficients, self.fc, self.Ls, self.fs)
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def plot_response(self):
plot_response((self.hybra_kernels).squeeze().cpu().detach().numpy(), self.fs)
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def plot_decoder_response(self):
plot_response((self.hybra_kernels).squeeze().cpu().detach().numpy(), self.fs, decoder=True)
@property
def condition_number(self, learnable:bool=False):
kernels = (self.hybra_kernels).squeeze()
if learnable:
return condition_number(kernels, self.stride, self.Ls)
else:
return condition_number(kernels, self.stride, self.Ls).item()