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import os
import time
import functools
import numpy as np
from math import cos, pi, floor, sin
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from stft_loss import MultiResolutionSTFTLoss
def flatten(v):
return [x for y in v for x in y]
def rescale(x):
return (x - x.min()) / (x.max() - x.min())
def find_max_epoch(path):
"""
Find latest checkpoint
Returns:
maximum iteration, -1 if there is no (valid) checkpoint
"""
files = os.listdir(path)
epoch = -1
for f in files:
if len(f) <= 4:
continue
if f[-4:] == '.pkl':
number = f[:-4]
try:
epoch = max(epoch, int(number))
except:
continue
return epoch
def print_size(net, keyword=None):
"""
Print the number of parameters of a network
"""
if net is not None and isinstance(net, torch.nn.Module):
module_parameters = filter(lambda p: p.requires_grad, net.parameters())
params = sum([np.prod(p.size()) for p in module_parameters])
print("{} Parameters: {:.6f}M".format(
net.__class__.__name__, params / 1e6), flush=True, end="; ")
if keyword is not None:
keyword_parameters = [p for name, p in net.named_parameters() if p.requires_grad and keyword in name]
params = sum([np.prod(p.size()) for p in keyword_parameters])
print("{} Parameters: {:.6f}M".format(
keyword, params / 1e6), flush=True, end="; ")
print(" ")
####################### lr scheduler: Linear Warmup then Cosine Decay #############################
# Adapted from https://github.com/rosinality/vq-vae-2-pytorch
# Original Copyright 2019 Kim Seonghyeon
# MIT License (https://opensource.org/licenses/MIT)
def anneal_linear(start, end, proportion):
return start + proportion * (end - start)
def anneal_cosine(start, end, proportion):
cos_val = cos(pi * proportion) + 1
return end + (start - end) / 2 * cos_val
class Phase:
def __init__(self, start, end, n_iter, cur_iter, anneal_fn):
self.start, self.end = start, end
self.n_iter = n_iter
self.anneal_fn = anneal_fn
self.n = cur_iter
def step(self):
self.n += 1
return self.anneal_fn(self.start, self.end, self.n / self.n_iter)
def reset(self):
self.n = 0
@property
def is_done(self):
return self.n >= self.n_iter
class LinearWarmupCosineDecay:
def __init__(
self,
optimizer,
lr_max,
n_iter,
iteration=0,
divider=25,
warmup_proportion=0.3,
phase=('linear', 'cosine'),
):
self.optimizer = optimizer
phase1 = int(n_iter * warmup_proportion)
phase2 = n_iter - phase1
lr_min = lr_max / divider
phase_map = {'linear': anneal_linear, 'cosine': anneal_cosine}
cur_iter_phase1 = iteration
cur_iter_phase2 = max(0, iteration - phase1)
self.lr_phase = [
Phase(lr_min, lr_max, phase1, cur_iter_phase1, phase_map[phase[0]]),
Phase(lr_max, lr_min / 1e4, phase2, cur_iter_phase2, phase_map[phase[1]]),
]
if iteration < phase1:
self.phase = 0
else:
self.phase = 1
def step(self):
lr = self.lr_phase[self.phase].step()
for group in self.optimizer.param_groups:
group['lr'] = lr
if self.lr_phase[self.phase].is_done:
self.phase += 1
if self.phase >= len(self.lr_phase):
for phase in self.lr_phase:
phase.reset()
self.phase = 0
return lr
####################### model util #############################
def std_normal(size):
"""
Generate the standard Gaussian variable of a certain size
"""
return torch.normal(0, 1, size=size).cuda()
def weight_scaling_init(layer):
"""
weight rescaling initialization from https://arxiv.org/abs/1911.13254
"""
w = layer.weight.detach()
alpha = 10.0 * w.std()
layer.weight.data /= torch.sqrt(alpha)
layer.bias.data /= torch.sqrt(alpha)
@torch.no_grad()
def sampling(net, noisy_audio):
"""
Perform denoising (forward) step
"""
return net(noisy_audio)
def loss_fn(net, X, ell_p, ell_p_lambda, stft_lambda, mrstftloss, **kwargs):
"""
Loss function in CleanUNet
Parameters:
net: network
X: training data pair (clean audio, noisy_audio)
ell_p: \ell_p norm (1 or 2) of the AE loss
ell_p_lambda: factor of the AE loss
stft_lambda: factor of the STFT loss
mrstftloss: multi-resolution STFT loss function
Returns:
loss: value of objective function
output_dic: values of each component of loss
"""
assert type(X) == tuple and len(X) == 2
clean_audio, noisy_audio = X
B, C, L = clean_audio.shape
output_dic = {}
loss = 0.0
# AE loss
denoised_audio = net(noisy_audio)
if ell_p == 2:
ae_loss = nn.MSELoss()(denoised_audio, clean_audio)
elif ell_p == 1:
ae_loss = F.l1_loss(denoised_audio, clean_audio)
else:
raise NotImplementedError
loss += ae_loss * ell_p_lambda
output_dic["reconstruct"] = ae_loss.data * ell_p_lambda
if stft_lambda > 0:
sc_loss, mag_loss = mrstftloss(denoised_audio.squeeze(1), clean_audio.squeeze(1))
loss += (sc_loss + mag_loss) * stft_lambda
output_dic["stft_sc"] = sc_loss.data * stft_lambda
output_dic["stft_mag"] = mag_loss.data * stft_lambda
return loss, output_dic
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