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"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import torch
from torch import nn
import torch.nn.functional as F
from utils.endoscopy import write_data
from utils.ada_conv import adaconv_kernel
from utils.softquant import soft_quant
class LimitedAdaptiveConv1d(nn.Module):
COUNTER = 1
def __init__(self,
in_channels,
out_channels,
kernel_size,
feature_dim,
frame_size=160,
overlap_size=40,
padding=None,
name=None,
gain_limits_db=[-6, 6],
shape_gain_db=0,
norm_p=2,
softquant=False,
apply_weight_norm=False,
**kwargs):
"""
Parameters:
-----------
in_channels : int
number of input channels
out_channels : int
number of output channels
feature_dim : int
dimension of features from which kernels, biases and gains are computed
frame_size : int
frame size
overlap_size : int
overlap size for filter cross-fade. Cross-fade is done on the first overlap_size samples of every frame
use_bias : bool
if true, biases will be added to output channels
padding : List[int, int]
"""
super(LimitedAdaptiveConv1d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.feature_dim = feature_dim
self.kernel_size = kernel_size
self.frame_size = frame_size
self.overlap_size = overlap_size
self.gain_limits_db = gain_limits_db
self.shape_gain_db = shape_gain_db
self.norm_p = norm_p
if name is None:
self.name = "limited_adaptive_conv1d_" + str(LimitedAdaptiveConv1d.COUNTER)
LimitedAdaptiveConv1d.COUNTER += 1
else:
self.name = name
norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x
# network for generating convolution weights
self.conv_kernel = norm(nn.Linear(feature_dim, in_channels * out_channels * kernel_size))
if softquant:
self.conv_kernel = soft_quant(self.conv_kernel)
self.shape_gain = min(1, 10**(shape_gain_db / 20))
self.filter_gain = norm(nn.Linear(feature_dim, out_channels))
log_min, log_max = gain_limits_db[0] * 0.11512925464970229, gain_limits_db[1] * 0.11512925464970229
self.filter_gain_a = (log_max - log_min) / 2
self.filter_gain_b = (log_max + log_min) / 2
if type(padding) == type(None):
self.padding = [kernel_size // 2, kernel_size - 1 - kernel_size // 2]
else:
self.padding = padding
self.overlap_win = nn.Parameter(.5 + .5 * torch.cos((torch.arange(self.overlap_size) + 0.5) * torch.pi / overlap_size), requires_grad=False)
def flop_count(self, rate):
frame_rate = rate / self.frame_size
overlap = self.overlap_size
overhead = overlap / self.frame_size
count = 0
# kernel computation and filtering
count += 2 * (frame_rate * self.feature_dim * self.kernel_size)
count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate)
# gain computation
count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels
# windowing
count += 3 * overlap * frame_rate * self.out_channels
return count
def forward(self, x, features, debug=False):
""" adaptive 1d convolution
Parameters:
-----------
x : torch.tensor
input signal of shape (batch_size, in_channels, num_samples)
feathres : torch.tensor
frame-wise features of shape (batch_size, num_frames, feature_dim)
"""
batch_size = x.size(0)
num_frames = features.size(1)
num_samples = x.size(2)
frame_size = self.frame_size
overlap_size = self.overlap_size
kernel_size = self.kernel_size
win1 = torch.flip(self.overlap_win, [0])
win2 = self.overlap_win
if num_samples // self.frame_size != num_frames:
raise ValueError('non matching sizes in AdaptiveConv1d.forward')
conv_kernels = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channels, self.kernel_size))
# normalize kernels (TODO: switch to L1 and normalize over kernel and input channel dimension)
conv_kernels = conv_kernels / (1e-6 + torch.norm(conv_kernels, p=self.norm_p, dim=[-2, -1], keepdim=True))
# limit shape
id_kernels = torch.zeros_like(conv_kernels)
id_kernels[..., self.padding[1]] = 1
conv_kernels = self.shape_gain * conv_kernels + (1 - self.shape_gain) * id_kernels
# calculate gains
conv_gains = torch.exp(self.filter_gain_a * torch.tanh(self.filter_gain(features)) + self.filter_gain_b)
if debug and batch_size == 1:
key = self.name + "_gains"
write_data(key, conv_gains.permute(0, 2, 1).detach().squeeze().cpu().numpy(), 16000 // self.frame_size)
key = self.name + "_kernels"
write_data(key, conv_kernels.detach().squeeze().cpu().numpy(), 16000 // self.frame_size)
conv_kernels = conv_kernels * conv_gains.view(batch_size, num_frames, self.out_channels, 1, 1)
conv_kernels = conv_kernels.permute(0, 2, 3, 1, 4)
output = adaconv_kernel(x, conv_kernels, win1, fft_size=256)
return output
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