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#!/usr/bin/python
#-*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy, math, pdb, sys, random
import time, os, itertools, shutil, importlib
from tuneThreshold import tuneThresholdfromScore
from DatasetLoader import test_dataset_loader, loadWAV
import pickle
import numpy as np
import time
from tqdm import tqdm
import soundfile
class WrappedModel(nn.Module):
## The purpose of this wrapper is to make the model structure consistent between single and multi-GPU
def __init__(self, model):
super(WrappedModel, self).__init__()
self.module = model
def forward(self, x, x_clean=None, label=None,l2_reg_dict=None, epoch=-1):
return self.module(x, x_clean, label, epoch=epoch)
class SpeakerNet(nn.Module):
def __init__(self, model, optimizer, trainfunc, nPerSpeaker, **kwargs):
super(SpeakerNet, self).__init__()
SpeakerNetModel = importlib.import_module('models.'+model).__getattribute__('MainModel')
self.__S__ = SpeakerNetModel(**kwargs);
LossFunction = importlib.import_module('loss.'+trainfunc).__getattribute__('LossFunction')
self.__L__ = LossFunction(**kwargs);
self.nPerSpeaker = nPerSpeaker
self.weight_finetuning_reg = kwargs['weight_finetuning_reg']
def forward(self, data, data_clean=None, label=None, l2_reg_dict=None, epoch=-1):
if label is None:
data_reshape = data[0].cuda()
outp = self.__S__.forward([data_reshape, data[1]])
return outp
elif len(data) == 3 and data[2] == "gen_ps":
data_reshape = data[0].reshape(-1,data[0].size()[-1]).cuda()
outp = self.__S__.forward([data_reshape, data[1]])
pseudo_labels = self.__L__.get_pseudo_labels(outp, label)
return pseudo_labels
else:
data_reshape = data[0].reshape(-1,data[0].size()[-1]).cuda()
data_clean_reshape = data_clean.reshape(-1,data_clean.size()[-1]).cuda()
outp = self.__S__.forward([data_reshape, data[1]])
outp_clean = self.__S__.forward([data_clean_reshape, data[1]])
nloss, prec1, ce = self.__L__.forward(outp, outp_clean, label, epoch)
if l2_reg_dict is not None:
Learned_dict = l2_reg_dict
l2_reg = 0
for name,param in self.__S__.model.named_parameters():
if name in Learned_dict:
l2_reg = l2_reg + torch.norm(param-Learned_dict[name].cuda(),2)
tloss = nloss/nloss.detach() + self.weight_finetuning_reg*l2_reg/(l2_reg.detach()+1e-5)
else:
tloss = nloss
print("Without L2 Reg")
return tloss, prec1, nloss, ce
class ModelTrainer(object):
def __init__(self, speaker_model, optimizer, scheduler, gpu, mixedprec, **kwargs):
self.__model__ = speaker_model
WavLM_params = list(map(id, self.__model__.module.__S__.model.parameters()))
Backend_params = filter(lambda p: id(p) not in WavLM_params, self.__model__.module.parameters())
self.path = kwargs['pretrained_model_path']
Optimizer = importlib.import_module('optimizer.'+optimizer).__getattribute__('Optimizer')
# Define the initial param groups
param_groups = [{'params': Backend_params, 'lr': kwargs['LR_MHFA']}]
# Extract the encoder layers
encoder_layers = self.__model__.module.__S__.model.encoder.layers
# Iterate over the encoder layers to create param groups
for i in range(12): # Assuming 12 layers from 0 to 11 (for BASE model, when it comes to LARGE model, 12->24)
lr = kwargs['LR_Transformer'] * (kwargs['LLRD_factor'] ** i)
param_groups.append({'params': encoder_layers[i].parameters(), 'lr': lr})
# Initialize the optimizer with these param groups
self.__optimizer__ = Optimizer(param_groups, **kwargs)
# self.__optimizer__ = Optimizer(self.__model__.parameters(), **kwargs)
# print('scheduler.'+scheduler)
Scheduler = importlib.import_module('scheduler.'+scheduler).__getattribute__('Scheduler')
# print(kwargs)
try:
self.__scheduler__, self.lr_step = Scheduler(self.__optimizer__, **kwargs)
except:
self.__scheduler__, self.lr_step = Scheduler(self.__optimizer__, lr_decay=0.9, **kwargs)
# self.scaler = GradScaler()
self.gpu = gpu
self.mixedprec = mixedprec
print("Mix prec: %s"%(self.mixedprec))
assert self.lr_step in ['epoch', 'iteration']
# ## ===== ===== ===== ===== ===== ===== ===== =====
# ## Train network
# ## ===== ===== ===== ===== ===== ===== ===== =====
def update_lgl_threshold(self, lgl_threshold):
self.__model__.module.__L__.lgl_threshold = lgl_threshold
# """
def train_network(self, loader, loss_vals_path, epoch, verbose):
if torch.distributed.is_initialized():
rank = torch.distributed.get_rank()
unique_loss_vals_path = f"{loss_vals_path.split('.')[0]}_rank{rank}.txt"
else:
unique_loss_vals_path = loss_vals_path
self.__model__.train();
stepsize = loader.batch_size;
counter = 0;
index = 0;
loss = 0;
top1 = 0 # EER or accuracy
tstart = time.time()
Learned_dict = {}
checkpoint = torch.load(self.path)
for name, param in checkpoint['model'].items():
if 'w2v_encoder.w2v_model.' in name:
newname = name.replace('w2v_encoder.w2v_model.', '')
else:
newname = name
Learned_dict[newname] = param;
# for data_clean, data, data_label, data_path in loader:
# telapsed = time.time() - tstart
# tstart = time.time()
# counter += 1;
# index += stepsize
# sys.stdout.write("\rProcessing (%d) "%(index));
# sys.stdout.write("Loss %f TEER/TAcc %2.3f%% - %.2f Hz "%(loss/counter, top1/counter, stepsize/telapsed));
# if counter % 100 == 0:
# sys.stdout.flush()
with open(unique_loss_vals_path, 'w') as loss_vals_file:
for data_clean, data, data_label, data_path in loader:
data_clean = data_clean.transpose(1,0)
data = data.transpose(1,0)
self.__model__.zero_grad()
label = torch.LongTensor(data_label).cuda()
nloss, prec1, spkloss, ce = self.__model__([data,"train"], data_clean, label, Learned_dict, epoch=epoch)
for ce_val, path in zip(ce.detach().cpu().numpy(), data_path):
loss_vals_file.write(f'{ce_val} {"/".join(path.split("/")[5:])}\n')
nloss.backward()
self.__optimizer__.step();
loss += spkloss.detach().cpu()
top1 += prec1.detach().cpu()
counter += 1;
index += stepsize;
telapsed = time.time() - tstart
tstart = time.time()
if verbose:
sys.stdout.write("\rProcessing (%d) "%(index));
sys.stdout.write("Loss %f TEER/TAcc %2.3f%% - %.2f Hz "%(loss/counter, top1/counter, stepsize/telapsed));
sys.stdout.flush();
if self.lr_step == 'iteration': self.__scheduler__.step()
if self.lr_step == 'epoch': self.__scheduler__.step()
sys.stdout.write("\n");
return (loss/counter, top1/counter);
# """
## ===== ===== ===== ===== ===== ===== ===== =====
## Evaluate from list
## ===== ===== ===== ===== ===== ===== ===== =====
def evaluateFromList(self, test_list, test_path, nDataLoaderThread, print_interval=10, num_eval=15, **kwargs):
self.__model__.eval();
lines = []
files = []
feats = {}
tstart = time.time()
## Read all lines
with open(test_list) as f:
lines = f.readlines()
## Get a list of unique file names
files = sum([x.strip().split()[-2:] for x in lines],[])
setfiles = list(set(files))
setfiles.sort()
## Define test data loader
test_dataset = test_dataset_loader(setfiles, test_path, num_eval=num_eval, **kwargs)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=nDataLoaderThread,
drop_last=False,
)
ref_feat_list = []
ref_feat_2_list = []
max_len = 0
forward = 0
## Extract features for every image
for idx, data in enumerate(test_loader):
inp1 = data[0][0].cuda()
inp2 = data[1][0].cuda()
telapsed_2 = time.time()
b,utt_l = inp2.shape
if utt_l > max_len:
max_len = utt_l
ref_feat = self.__model__([inp1, "test"]).cuda()
ref_feat = ref_feat.detach().cpu()
ref_feat_2 = self.__model__([inp2[:,:700000], "test"]).cuda() # The reason why here is set to 700000 is due to GPU memory size.
ref_feat_2 = ref_feat_2.detach().cpu()
feats[data[2][0]] = [ref_feat,ref_feat_2]
ref_feat_list.extend(ref_feat.numpy())
ref_feat_2_list.extend(ref_feat_2.numpy())
telapsed = time.time() - tstart
forward = forward + time.time() - telapsed_2
if idx % print_interval == 0:
sys.stdout.write("\rReading %d of %d: %.2f Hz, forward speed: %.2f Hz, embedding size %d, max_len %d"%(idx,len(setfiles),idx/telapsed,idx/forward, ref_feat.size()[-1],max_len));
print('')
all_scores = [];
all_labels = [];
all_trials = [];
all_scores_1 = [];
all_scores_2 = [];
tstart = time.time()
ref_feat_list = numpy.array(ref_feat_list)
ref_feat_2_list = numpy.array(ref_feat_2_list)
ref_feat_list_mean = 0
ref_feat_2_list_mean = 0
## Read files and compute all scores
for idx, line in enumerate(lines):
data = line.split();
## Append random label if missing
if len(data) == 2: data = [random.randint(0,1)] + data
ref_feat,ref_feat_2 = feats[data[1]]
com_feat,com_feat_2 = feats[data[2]]
# if self.__model__.module.__L__.test_normalize:
ref_feat = F.normalize(ref_feat-ref_feat_list_mean, p=2, dim=1) # B, D
com_feat = F.normalize(com_feat-ref_feat_list_mean, p=2, dim=1)
ref_feat_2 = F.normalize(ref_feat_2-ref_feat_2_list_mean, p=2, dim=1) # B, D
com_feat_2 = F.normalize(com_feat_2-ref_feat_2_list_mean, p=2, dim=1)
score_1 = torch.mean(torch.matmul(ref_feat, com_feat.T)) # higher is positive
score_2 = torch.mean(torch.matmul(ref_feat_2, com_feat_2.T))
score = (score_1 + score_2) / 2
score = score.detach().cpu().numpy()
all_scores.append(score);
all_scores_1.append(score_1);
all_scores_2.append(score_2);
all_labels.append(int(data[0]));
all_trials.append(data[1]+" "+data[2])
if idx % (10*print_interval) == 0:
telapsed = time.time() - tstart
sys.stdout.write("\rComputing %d of %d: %.2f Hz"%(idx,len(lines),idx/telapsed));
sys.stdout.flush();
print('')
return (all_scores, all_labels, all_trials,all_scores_1,all_scores_2);
def generate_embeddings(self, wav_files, output, device):
res = {}
for file in tqdm(wav_files):
wav, sr = soundfile.read(file)
wav = torch.from_numpy(wav).float().to(device)
with torch.no_grad():
embedding = self.__model__([wav.unsqueeze(0), "test"]).detach().cpu()
key = '/'.join(file.split('/')[-3:])
res[key] = embedding
torch.save(res, output)
def saveParameters(self, path):
torch.save(self.__model__.module.state_dict(), path);
## ===== ===== ===== ===== ===== ===== ===== =====
## Load parameters
## ===== ===== ===== ===== ===== ===== ===== =====
def loadParameters(self, path):
self_state = self.__model__.module.state_dict();
loaded_state = torch.load(path, map_location="cuda:%d"%self.gpu);
# loaded_state = torch.load(path, map_location="cpu");
for name, param in loaded_state.items():
origname = name;
if name not in self_state:
name = name.replace("module.", "");
if name not in self_state:
print("%s is not in the model."%origname);
continue;
if self_state[name].size() != loaded_state[origname].size():
print("Wrong parameter length: %s, model: %s, loaded: %s"%(origname, self_state[name].size(), loaded_state[origname].size()));
continue;
self_state[name].copy_(param);
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