275 lines
8.9 KiB
Python
275 lines
8.9 KiB
Python
import math
|
|
import torch
|
|
from torch.nn import functional as F
|
|
|
|
def median_pool1d(signal, kernel_size, dim=-1, stride=1):
|
|
"""
|
|
Apply a 1D median filter along a specified dimension for a 3D tensor.
|
|
|
|
Args:
|
|
signal (torch.Tensor): The input tensor of shape (batch_size, F, T).
|
|
kernel_size (int): The size of the median filter kernel.
|
|
dim (int): The dimension along which to apply the median filter.
|
|
stride (int): The stride of the sliding window.
|
|
|
|
Returns:
|
|
torch.Tensor: The filtered tensor.
|
|
"""
|
|
# Ensure the input tensor is 3D (batch_size, F, T)
|
|
if signal.dim() != 3:
|
|
raise ValueError(f"Input tensor {signal.shape} must be 3D (batch_size, F, T)")
|
|
|
|
# Adjust kernel size if larger than input dimension
|
|
input_size = signal.size(dim)
|
|
if kernel_size > input_size:
|
|
kernel_size = input_size
|
|
|
|
# Pad the input to handle edge cases
|
|
padding = (kernel_size - 1) // 2
|
|
signal = F.pad(signal, (padding, padding), mode='reflect')
|
|
if dim == -1:
|
|
signal = F.pad(signal, (padding, padding), mode='reflect')
|
|
elif dim == -2:
|
|
signal = F.pad(signal, (0, 0, padding, padding), mode="reflect")
|
|
|
|
# Unfold the input to create sliding windows
|
|
unfolded = signal.unfold(dim, kernel_size, stride)
|
|
|
|
# Compute the median along the last dimension
|
|
median = unfolded.median(dim=-1).values
|
|
|
|
return median
|
|
|
|
def autocorrelation1d(log_magnitude, max_lag=None):
|
|
"""
|
|
Computes the autocorrelation of log-magnitude spectrogram.
|
|
:param log_magnitude: Log magnitude spectrogram (batch, channels, time)
|
|
:param max_lag: Maximum lag for which to compute autocorrelation.
|
|
:return: Autocorrelation of the log-magnitude spectrogram.
|
|
"""
|
|
if max_lag is None:
|
|
max_lag = log_magnitude.size(-1) // 2 # Default to half the signal length
|
|
|
|
# Ensure the signal is zero-mean
|
|
log_magnitude = log_magnitude - log_magnitude.mean(dim=-1, keepdim=True)
|
|
|
|
# Compute autocorrelation using FFT-based convolution
|
|
padded_log_magnitude = F.pad(log_magnitude, (max_lag, 0), mode='reflect')
|
|
|
|
# Ensure the kernel has the correct shape for conv1d
|
|
kernel = log_magnitude
|
|
padded_log_magnitude = padded_log_magnitude
|
|
|
|
autocorr = F.conv1d(padded_log_magnitude, kernel, groups=log_magnitude.size(1))
|
|
|
|
# Take only the relevant part
|
|
autocorr = F.normalize(autocorr[:,:max_lag + 1], p=2, dim=-1)
|
|
|
|
return autocorr.nan_to_num(0)
|
|
|
|
def minmax_scale(tensor: torch.Tensor, eps=1e-8):
|
|
tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min() + eps)
|
|
return tensor
|
|
|
|
def compute_correlation(x: torch.Tensor, y: torch.Tensor, eps=1e-8):
|
|
"""
|
|
Calculate the Pearson correlation coefficient between two batched vectors.
|
|
|
|
Args:
|
|
x (torch.Tensor): A tensor of shape (batch_size, num_features).
|
|
y (torch.Tensor): A tensor of shape (batch_size, num_features).
|
|
|
|
Returns:
|
|
torch.Tensor: A tensor of shape (batch_size,) containing the correlation coefficients.
|
|
"""
|
|
# Ensure the inputs are float tensors
|
|
x = x.float()
|
|
y = y.float()
|
|
|
|
# Calculate the mean of each batch
|
|
mean_x = torch.mean(x, dim=-1, keepdim=True)
|
|
mean_y = torch.mean(y, dim=-1, keepdim=True)
|
|
|
|
# Subtract the mean from each element
|
|
x_centered = x - mean_x
|
|
y_centered = y - mean_y
|
|
|
|
# Calculate the covariance
|
|
covariance = torch.sum(x_centered * y_centered, dim=-1)
|
|
|
|
# Calculate the standard deviations
|
|
std_x = torch.sqrt(torch.sum(x_centered ** 2, dim=-1)+eps).nan_to_num(eps)
|
|
std_y = torch.sqrt(torch.sum(y_centered ** 2, dim=-1)+eps).nan_to_num(eps)
|
|
|
|
# Calculate the correlation coefficient
|
|
correlation = covariance / (std_x * std_y + eps)
|
|
|
|
return correlation.nan_to_num(eps)
|
|
|
|
def init_weights(m, mean=0.0, std=0.01):
|
|
classname = m.__class__.__name__
|
|
if classname.find("Conv") != -1:
|
|
m.weight.data.normal_(mean, std)
|
|
|
|
|
|
def get_padding(kernel_size, dilation=1):
|
|
return int((kernel_size * dilation - dilation) / 2)
|
|
|
|
|
|
def convert_pad_shape(pad_shape):
|
|
l = pad_shape[::-1]
|
|
pad_shape = [item for sublist in l for item in sublist]
|
|
return pad_shape
|
|
|
|
def intersperse(lst, item):
|
|
result = [item] * (len(lst) * 2 + 1)
|
|
result[1::2] = lst
|
|
return result
|
|
|
|
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
|
"""KL(P||Q)"""
|
|
kl = (logs_q - logs_p) - 0.5
|
|
kl += (
|
|
0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
|
|
)
|
|
return kl
|
|
|
|
|
|
def rand_gumbel(shape):
|
|
"""Sample from the Gumbel distribution, protect from overflows."""
|
|
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
|
return -torch.log(-torch.log(uniform_samples))
|
|
|
|
|
|
def rand_gumbel_like(x):
|
|
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
|
return g
|
|
|
|
|
|
def slice_segments(x, ids_str, segment_size=4):
|
|
ret = torch.zeros_like(x[:, :, :segment_size])
|
|
for i in range(x.size(0)):
|
|
idx_str = ids_str[i]
|
|
idx_end = idx_str + segment_size
|
|
ret[i] = x[i, :, idx_str:idx_end]
|
|
return ret
|
|
|
|
|
|
def slice_segments2(x, ids_str, segment_size=4):
|
|
ret = torch.zeros_like(x[:, :segment_size])
|
|
for i in range(x.size(0)):
|
|
idx_str = ids_str[i]
|
|
idx_end = idx_str + segment_size
|
|
ret[i] = x[i, idx_str:idx_end]
|
|
return ret
|
|
|
|
|
|
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
|
b, d, t = x.size()
|
|
if x_lengths is None:
|
|
x_lengths = t
|
|
ids_str_max = x_lengths - segment_size + 1
|
|
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
|
ret = slice_segments(x, ids_str, segment_size)
|
|
return ret, ids_str
|
|
|
|
|
|
def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
|
position = torch.arange(length, dtype=torch.float)
|
|
num_timescales = channels // 2
|
|
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
|
|
num_timescales - 1
|
|
)
|
|
inv_timescales = min_timescale * torch.exp(
|
|
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
|
|
)
|
|
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
|
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
|
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
|
signal = signal.view(1, channels, length)
|
|
return signal
|
|
|
|
|
|
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
|
b, channels, length = x.size()
|
|
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
|
return x + signal.to(dtype=x.dtype, device=x.device)
|
|
|
|
|
|
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
|
b, channels, length = x.size()
|
|
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
|
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
|
|
|
|
|
def subsequent_mask(length):
|
|
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
|
return mask
|
|
|
|
|
|
@torch.jit.script
|
|
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
|
n_channels_int = n_channels[0]
|
|
in_act = input_a + input_b
|
|
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
|
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
|
acts = t_act * s_act
|
|
return acts
|
|
|
|
|
|
def convert_pad_shape(pad_shape):
|
|
l = pad_shape[::-1]
|
|
pad_shape = [item for sublist in l for item in sublist]
|
|
return pad_shape
|
|
|
|
|
|
def shift_1d(x):
|
|
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
|
return x
|
|
|
|
|
|
def sequence_mask(length, max_length=None):
|
|
if max_length is None:
|
|
max_length = length.max()
|
|
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
|
return x.unsqueeze(0) < length.unsqueeze(1)
|
|
|
|
|
|
def generate_path(duration, mask):
|
|
"""
|
|
duration: [b, 1, t_x]
|
|
mask: [b, 1, t_y, t_x]
|
|
"""
|
|
device = duration.device
|
|
|
|
b, _, t_y, t_x = mask.shape
|
|
cum_duration = torch.cumsum(duration, -1)
|
|
|
|
cum_duration_flat = cum_duration.view(b * t_x)
|
|
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
|
path = path.view(b, t_x, t_y)
|
|
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
|
path = path.unsqueeze(1).transpose(2, 3) * mask
|
|
return path
|
|
|
|
|
|
def clip_grad_value_(parameters, clip_value, norm_type=2, batch_size=1):
|
|
if isinstance(parameters, torch.Tensor):
|
|
parameters = [parameters]
|
|
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
|
norm_type = float(norm_type)
|
|
if clip_value is not None:
|
|
clip_value = float(clip_value)
|
|
|
|
total_norm = 0
|
|
for p in parameters:
|
|
param_norm = p.grad.data.norm(norm_type)/batch_size
|
|
total_norm += param_norm.item() ** norm_type
|
|
if clip_value is not None:
|
|
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
|
total_norm = total_norm ** (1.0 / norm_type)
|
|
return total_norm
|
|
|
|
def serialize_tensor(tensor):
|
|
if hasattr(tensor,"item"): return tensor.item()
|
|
else: return float(f"{float(tensor):.3f}") |