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main_audio_lrs3_new.py
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main_audio_lrs3_new.py
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import wandb, shutil
import numpy as np
import torch
import torch.optim as optim
import torch.nn.functional as F
import os, yaml, pprint, warnings
from yaml import Loader as CLoader
from tqdm import tqdm
import torch.multiprocessing as mp
from multiprocessing import Process, Manager
from torch.utils.data import DataLoader
import datasets_new as datasets
from models.loss import AAMsoftmax
from models.network_new import AudioModel
from utils.eval_metrics_new import *
################# GLOBAL CONFIGURATION ##############################
MODE = 'train' # train | test | finetune
#####################################################################
SEED = 1022 # fixed random seed for fair comparison
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
warnings.filterwarnings('ignore')
with open('./conf/savedsolution_audio_lrs3_IT.yaml', 'r') as f:
OPTS = yaml.load(f, Loader=CLoader)
# shutil.copyfile('./conf/config_audio_lrs3_new.yaml', './conf/savedsolution_audio_lrs3_IT.yaml')
# Basic functions for train and test phase
def get_model(model):
return AudioModel(model, C=512)
class Trainer(object):
def __init__(self):
self.stageopts = OPTS[MODE]
self.modelname = self.stageopts['model']
self.modelopts = OPTS[self.modelname]
self.resume = self.stageopts['resume']
self.audioemb_dim = self.modelopts['embedding_dim']
# neural network
audiomodel = get_model(self.modelname)
print('audio model parameters_count: %.2fM' % (sum(p.numel() for p in audiomodel.parameters() if p.requires_grad)/1e6))
device_ids, device_num = self.stageopts['gpus'], len(self.stageopts['gpus'])
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu) for gpu in device_ids])
self.device_ids = [x for x in range(len(device_ids))]
self.device = torch.device('cuda') # : +str(device_ids[0])
self.audiomodel = torch.nn.DataParallel(audiomodel.to(self.device), device_ids=self.device_ids)
self.featureopts = OPTS[self.modelopts['feature']]
self.dataopts = {}
# self.dataopts = {**{'seconds':self.stageopts['seconds']}, **{'sample_rate':self.featureopts['sample_rate']} }
self.dataopts = {'seconds':self.stageopts['seconds'],'sample_rate':self.featureopts['sample_rate']}
for aug in self.stageopts['augmentation'].keys():
# self.dataopts = {**self.dataopts, **{aug:self.stageopts['augmentation'][aug]}}
self.dataopts[aug] = self.stageopts['augmentation'][aug]
for traindata in ['train_manifest', 'train_audiodir', 'musan_path', 'rir_path']: # for train and aug
# self.dataopts = {**self.dataopts, **{traindata:OPTS[traindata]}}
self.dataopts[traindata] = OPTS[traindata]
for testdata in ['test_trial', 'test_audiodir']: # for val
# self.dataopts = {**self.dataopts, **{testdata:OPTS['test_lrs3'][testdata]}}
self.dataopts[testdata] = OPTS['test_lrs3'][testdata]
self.trainset = datasets.AudioTrainset(self.dataopts)
self.trainloader = DataLoader(self.trainset, shuffle=True, batch_size=self.stageopts['batchsize'], num_workers=4*device_num, drop_last=True)
times = len(self.stageopts['augmentation']['speedperturb']) # whether do speaker augmentation by speed perturb
self.audiocriterion = AAMsoftmax(n_class=self.trainset.n_spk*times, m=self.stageopts['margins'][1], s=self.stageopts['scale'], em_dim=self.audioemb_dim).to(self.device)
param_groups = [{'params': self.audiomodel.parameters()},
{'params': self.audiocriterion.parameters()},
]
if self.stageopts['optimizer'] == 'sgd':
self.optimopts = OPTS['sgd']
self.optim = optim.SGD(param_groups, self.optimopts['init_lr'], nesterov=self.optimopts['nesterov'], momentum = self.optimopts['momentum'], weight_decay = self.optimopts['weight_decay'])
elif self.stageopts['optimizer'] == 'adam':
self.optimopts = OPTS['adam']
self.optim = optim.Adam(param_groups, lr=self.optimopts['init_lr'] , weight_decay=self.optimopts['weight_decay'])
if self.stageopts['lr_scheduler'] == 'steplr':
# self.lr_scheduler = optim.lr_scheduler.StepLR(self.optim, step_size=2, gamma=0.8)
# self.lr_scheduler = optim.lr_scheduler.StepLR(self.optim, step_size=1, gamma=0.97)
self.lr_scheduler = optim.lr_scheduler.MultiStepLR(self.optim, milestones=[10,15], gamma=0.1)
elif self.stageopts['lr_scheduler'] == 'cycliclr':
self.lr_scheduler = optim.lr_scheduler.CyclicLR(self.optim, cycle_momentum=False, base_lr=0.000001, max_lr=0.001,step_size_up=2000,step_size_down=2000)
self.eers = []
self.dcfs = []
self.current_epoch, self.epochs = 0, self.stageopts['epochs']
if self.resume == 'None':
# sp = 'sp'+str(len(self.stageopts['augmentation']['speedperturb']))
self.exp = '_'.join(['lrs3', 'independent-training', self.modelname, 'noisespec3blocks'])
else:
self.exp = self.resume.split('/')[1]
# continue training
if self.resume != 'None' and os.path.exists(self.resume):
self._load()
else:
print('Train from scratch: there is no specified resume.')
self.wandb = self.stageopts['wandb']
if self.wandb != False:
config = {
"learning_rate": self.optimopts['init_lr'],
"epochs": self.epochs,
"batch_size": self.stageopts['batchsize']
}
wandb.init(project="Short-Short-New", config=config)
def _load(self):
print('loading model from {}'.format(self.resume))
if not os.path.exists(self.resume):
print('No pretrained model exists!')
ckpt = torch.load(self.resume)
if 'audiostate_dict' in ckpt.keys():
self.audiomodel.load_state_dict(ckpt['audiostate_dict'])
self.current_epoch = ckpt['epoch']
if 'optimizer' in ckpt.keys():
self.optim.load_state_dict(ckpt['optimizer'])
if 'audiocriterion' in ckpt.keys():
self.audiocriterion = ckpt['audiocriterion']
def _train(self):
start_epoch = self.current_epoch
for epoch in range(start_epoch + 1, self.epochs + 1):
self.current_epoch = epoch
self._train_epoch()
self.lr_scheduler.step()
def _train_epoch(self):
self.audiomodel.train()
self.audiocriterion.train()
audiosum_loss, audiosum_samples, audiocorrect = 0, 0, 0
progress_bar = tqdm(self.trainloader)
for batch_idx, (audiofeats, targets_label) in enumerate(progress_bar):
self.optim.zero_grad()
audiofeats = audiofeats.to(self.device)
targets_label = targets_label.to(self.device)
_, output_audio_ = self.audiomodel(audiofeats, aug=True)
audioloss, audiologits = self.audiocriterion(output_audio_, targets_label)
audioloss.backward()
audiosum_samples += len(audiofeats)
_, audioprediction = torch.max(audiologits, dim = 1)
audiocorrect += (audioprediction == targets_label).sum().item()
# linear warm up for 2000 steps
# if self.current_epoch == 1 and batch_idx < 2000:
# lr_scale = min(1., float(batch_idx + 1) / float(2000))
# for _, pg in enumerate(self.optim.param_groups):
# pg['lr'] = lr_scale * 0.001
self.optim.step()
audiosum_loss += audioloss.item() * len(targets_label)
progress_bar.set_description(
'Train Epoch: {:3d} [{:4d}/{:4d} ({:3.3f}%)] audioLoss: {:.4f} audioAcc: {:.4f}%' #
.format(self.current_epoch, batch_idx + 1,
len(self.trainloader), 100. * (batch_idx + 1) / len(self.trainloader),
audiosum_loss / audiosum_samples, 100. * audiocorrect / audiosum_samples))
if self.wandb != False:
wandb.log({"audioLoss":audiosum_loss/audiosum_samples,
"lr": self.optim.state_dict()['param_groups'][0]['lr'],
"audioAcc":100.*audiocorrect/audiosum_samples})
self._save('exp/{}/net_{}.pth'.format(self.exp, self.current_epoch))
# flexible log
val_step = 1
if self.current_epoch % val_step == 0:
eer, minDCF = self._eval_network(stage='val', num_workers=1)
self.eers.append(eer)
self.dcfs.append(minDCF)
if not os.path.isdir('log/'):
os.mkdir('log')
with open('log/'+self.exp+'-training.log', "a+") as score_file:
score_file.write("%d epoch, LR %f, LOSS %f, ACC %2.2f%%, EER %2.2f%%, minDCF %2.4f, bestEER %2.2f%%, bestminDCF %2.4f\n" \
%(self.current_epoch, self.optim.state_dict()['param_groups'][0]['lr'], audiosum_loss / audiosum_samples, \
100. * audiocorrect / audiosum_samples, self.eers[-1], self.dcfs[-1], min(self.eers), min(self.dcfs)))
score_file.flush()
return audiosum_loss / audiosum_samples
def _save(self, modelpath):
torch.save({'epoch': self.current_epoch,
'audiostate_dict': self.audiomodel.state_dict(),
'audiocriterion': self.audiocriterion,
'optimizer': self.optim.state_dict()},
modelpath)
def _extract_embedding(self, stage, filelist): # stage='val'
testset = datasets.AudioTestset(self.dataopts, filelist, stage='val')
testloader = DataLoader(testset, batch_size=1, shuffle=False, num_workers=1)
localmodel = self.audiomodel.module
localmodel = localmodel.to(self.device)
localmodel.eval()
# print('Extracting test embeddings for {}: '.format(dataset))
emb_dir = os.path.join('exp/{}/{}_emb'.format(self.exp, stage))
os.makedirs(emb_dir, exist_ok = True)
with torch.no_grad():
for signal_global, signal_local, utt in tqdm(testloader):
utt = utt[0]
spk_dir = os.path.join(emb_dir, os.path.dirname(utt))
os.makedirs(spk_dir, exist_ok=True)
signal_global = signal_global.squeeze(0).to(self.device)
signal_local = signal_local.squeeze(0).to(self.device)
_, embedding_global = localmodel(signal_global, aug=False)
_, embedding_local = localmodel(signal_local, aug=False)
embedding_global = embedding_global.cpu().numpy()
embedding_local = embedding_local.cpu().numpy()
np.savez_compressed(os.path.join(spk_dir, os.path.basename(utt.split('.')[0])), [embedding_global, embedding_local])
del localmodel
torch.cuda.empty_cache()
def _score_embedding(self, stage, trials, score_dict):
# GRID scoring for global and local embeddings and save score in score/MODELDIR/score_TRIAL.txt
emb_dir = os.path.join('exp/{}/{}_emb'.format(self.exp, stage))
for line in trials:
embedding_11 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-2]+'.npz'), allow_pickle=True)['arr_0'][0])
embedding_12 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-2]+'.npz'), allow_pickle=True)['arr_0'][1])
embedding_21 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-1]+'.npz'), allow_pickle=True)['arr_0'][0])
embedding_22 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-1]+'.npz'), allow_pickle=True)['arr_0'][1])
embedding_11, embedding_12, embedding_21, embedding_22 = F.normalize(embedding_11),F.normalize(embedding_12),F.normalize(embedding_21),F.normalize(embedding_22)
# Compute the scores
score_1 = torch.mean(torch.matmul(embedding_11, embedding_21.T)) # higher is positive
score_2 = torch.mean(torch.matmul(embedding_12, embedding_22.T))
score = (score_1 + score_2) / 2
score = score.detach().cpu().numpy()
score_dict[line] = [score, int(line.split()[0])] # score, label
def _eval_network(self, stage, num_workers=1):
# prepare for multiprocessing of embedding extraction
files, utts = [], []
trial = self.dataopts['test_trial']
lines = open(trial).read().splitlines()
for line in lines:
files.append(line.split()[-2])
files.append(line.split()[-1])
setfiles = list(set(files))
setfiles.sort()
part = list(range(0, len(setfiles)+1, int(len(setfiles)//num_workers)))
part[-1] = len(setfiles)
utts = [setfiles[part[i]:part[i+1]] for i in range(num_workers)]
self.audiomodel.cpu()
torch.cuda.empty_cache()
if num_workers == 1:
self._extract_embedding(stage, utts[0])
else:
# extract embeddings and save in exp/MODELDIR/test_emb/
args = [(stage, utts[i]) for i in range(num_workers)]
ctx = mp.get_context("spawn")
jobs = [ctx.Process(target=self._extract_embedding, args=(a)) for a in args]
for j in jobs: j.start()
for j in jobs: j.join()
self.audiomodel = self.audiomodel.to(self.device)
num_workers = 40
score_dict = Manager().dict()
part = list(range(0, len(lines)+1, int(len(lines)//num_workers)))
part[-1] = len(lines)
trials = [lines[part[i]:part[i+1]] for i in range(num_workers)]
args = [(stage, trials[i], score_dict) for i in range(num_workers)]
jobs = [Process(target=self._score_embedding, args=(a)) for a in args]
for j in jobs: j.start()
for j in jobs: j.join()
scores, labels = [], []
for line in lines:
scores.append(score_dict[line][0])
labels.append(score_dict[line][1])
# Coumpute EER and minDCF
EER = tuneThresholdfromScore(scores, labels, [1, 0.1])[1]
fnrs, fprs, thresholds = ComputeErrorRates(scores, labels)
minDCF, _ = ComputeMinDcf(fnrs, fprs, thresholds, 0.05, 1, 1)
print("EER: {:.6f}%, minDCF: {:.6f}".format(EER, minDCF))
return EER, minDCF
def _print_config(self, opts):
pp = pprint.PrettyPrinter(indent = 2)
pp.pprint(opts)
def __call__(self):
print("[Model is saved in: {}]".format(self.exp))
# print("Data opts: ")
# self._print_config(self.dataopts)
# print("Model opts: ")
# self._print_config(self.modelopts)
# print("Stage opts: ")
# self._print_config(self.stageopts)
os.makedirs('exp/{}'.format(self.exp), exist_ok = True)
self._train()
class Tester(object):
def __init__(self):
self.stageopts = OPTS[MODE]
self.modelname = self.stageopts['model']
self.modelopts = OPTS[self.modelname]
self.audioemb_dim = self.modelopts['embedding_dim']
self.resume = self.stageopts['resume']
self.exp = self.resume.split('/')[1]
self.gpus = self.stageopts['gpus']
self.device_ids = [x for x in range(len(self.gpus))]
self.featureopts = OPTS[self.modelopts['feature']]
self.dataopts = {}
for data in ['train_manifest', 'train_audiodir', 'cohort_manifest']: # for submean, cohort, and test
# self.dataopts = {**self.dataopts, **{data:OPTS[data]}}
self.dataopts[data] = OPTS[data]
for testdata in ['test_trial', 'test_audiodir',]: # for test
# self.dataopts = {**self.dataopts, **{testdata:OPTS[self.stageopts['data']][testdata]}}
self.dataopts[testdata] = OPTS[self.stageopts['data']][testdata]
def _extract_submeanembedding(self, stage):
self.submeanset = datasets.AudioSubmeanset(self.dataopts)
self.submeanloader = DataLoader(self.submeanset, batch_size = 400, shuffle=False, num_workers=4, drop_last=True)
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([str(gpu) for gpu in self.gpus])
device = torch.device('cuda')
testmodel = get_model(self.modelname)
testmodel = torch.nn.DataParallel(testmodel.to(device), device_ids=[x for x in range(len(self.gpus))])
ckpt = torch.load(self.resume)
if 'audiostate_dict' in ckpt.keys():
testmodel.load_state_dict(ckpt['audiostate_dict'], strict=False)
testmodel.eval()
print('Extracting submean embedding: ')
os.makedirs('exp/{}/{}_emb/'.format(self.exp, stage), exist_ok = True)
sum_output_audio = None
with torch.no_grad():
dataloader = self.submeanloader
for audiofeat, utt in tqdm(dataloader):
audiofeat = audiofeat.to(device)
utt = utt[0]
_, output_audio_ = testmodel(audiofeat, aug=False)
output_audio_ = output_audio_.cpu().numpy()
if sum_output_audio is None:
sum_output_audio = np.mean(output_audio_, axis=0)
else:
sum_output_audio += np.mean(output_audio_, axis=0)
avg_output_audio = sum_output_audio / len(dataloader)
np.save(os.path.join('exp/{}/{}_emb/'.format(self.exp, stage),'submean.npy'), avg_output_audio)
def _extract_embedding(self, stage, filelist, gpu): # test | cohort | submean
testset = datasets.AudioTestset(self.dataopts, filelist, stage)
testloader = DataLoader(testset, batch_size=1, shuffle=False, num_workers=1)
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu)
device = torch.device('cuda')
testmodel = get_model(self.modelname)
testmodel = torch.nn.DataParallel(testmodel.to(device), device_ids=[0])
ckpt = torch.load(self.resume)
if 'audiostate_dict' in ckpt.keys():
testmodel.load_state_dict(ckpt['audiostate_dict'], strict=False)
testmodel.module.eval()
# print('Extracting test embeddings for {}: '.format(dataset))
emb_dir = os.path.join('exp/{}/{}_emb'.format(self.exp, stage))
os.makedirs(emb_dir, exist_ok = True)
with torch.no_grad():
for signal_global, signal_local, utt in tqdm(testloader):
utt = utt[0]
spk_dir = os.path.join(emb_dir, os.path.dirname(utt))
os.makedirs(spk_dir, exist_ok=True)
signal_global = signal_global.squeeze(0).to(device)
signal_local = signal_local.squeeze(0).to(device)
_, embedding_global = testmodel(signal_global, aug=False)
_, embedding_local = testmodel(signal_local, aug=False)
embedding_global = embedding_global.cpu().numpy()
embedding_local = embedding_local.cpu().numpy()
np.savez_compressed(os.path.join(spk_dir, os.path.basename(utt.split('.')[0])), [embedding_global, embedding_local])
def _score_embedding(self, stage, trials, score_dict):
emb_dir = os.path.join('exp/{}/{}_emb'.format(self.exp, stage))
for line in trials:
embedding_11 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-2].split('.')[0]+'.npz'), allow_pickle=True)['arr_0'][0])
embedding_12 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-2].split('.')[0]+'.npz'), allow_pickle=True)['arr_0'][1])
embedding_21 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-1].split('.')[0]+'.npz'), allow_pickle=True)['arr_0'][0])
embedding_22 = torch.FloatTensor(np.load(os.path.join(emb_dir, line.split()[-1].split('.')[0]+'.npz'), allow_pickle=True)['arr_0'][1])
embedding_11, embedding_12, embedding_21, embedding_22 = F.normalize(embedding_11),F.normalize(embedding_12),F.normalize(embedding_21),F.normalize(embedding_22)
# Compute the scores
score_1 = torch.mean(torch.matmul(embedding_11, embedding_21.T)) # higher is positive
score_2 = torch.mean(torch.matmul(embedding_12, embedding_22.T))
score = (score_1 + score_2) / 2
score = score.detach().cpu().numpy()
if len(line.split()) == 3:
score_dict[line] = [score, int(line.split()[0])]
elif len(line.split()) == 2:
score_dict[line] = [score]
def _eval_network(self, stage, num_workers=1):
# prepare for multiprocessing of embedding extraction
files, utts = [], []
if stage == 'test':
trial = self.dataopts['test_trial']
lines = open(trial).read().splitlines()
for line in lines:
files.append(line.split()[-2])
files.append(line.split()[-1])
setfiles = list(set(files))
setfiles.sort()
part = list(range(0, len(setfiles)+1, int(len(setfiles)//num_workers)))
part[-1] = len(setfiles)
utts = [setfiles[part[i]:part[i+1]] for i in range(num_workers)]
elif stage == 'cohort':
trial = self.dataopts['cohort_manifest']
lines = open(trial).read().splitlines()
for line in lines:
files.append(line)
setfiles = list(set(files))
setfiles.sort()
part = list(range(0, len(setfiles)+1, int(len(setfiles)//num_workers)))
part[-1] = len(setfiles)
utts = [setfiles[part[i]:part[i+1]] for i in range(num_workers)]
print('loading model from {}'.format(self.resume))
if num_workers == 1:
self._extract_embedding(stage, utts[0], 0)
else:
# extract embeddings and save in exp/MODELDIR/test_emb/
args = [(stage, utts[i], self.gpus[i%len(self.gpus)]) for i in range(num_workers)]
jobs = [Process(target=self._extract_embedding, args=(a)) for a in args]
for j in jobs: j.start()
for j in jobs: j.join()
if stage == 'cohort':
print('finished')
return
num_workers = 20
score_dict = Manager().dict()
part = list(range(0, len(lines)+1, int(len(lines)//num_workers)))
part[-1] = len(lines)
trials = [lines[part[i]:part[i+1]] for i in range(num_workers)]
args = [(stage, trials[i], score_dict) for i in range(num_workers)]
jobs = [Process(target=self._score_embedding, args=(a)) for a in args]
for j in jobs: j.start()
for j in jobs: j.join()
# GRID scoring for global and local embeddings and save score in score/MODELDIR/score_TRIAL.txt
scores, labels = [], []
score_fname = os.path.join('score', self.exp, 'score_'+os.path.basename(trial))
os.makedirs(os.path.join('score', self.exp), exist_ok = True)
with open(score_fname, 'w+') as fout:
for line in lines:
scores.append(score_dict[line][0])
if len(line.split()) == 3:
labels.append(score_dict[line][1])
if self.stageopts['write_score']:
if len(line.split()) == 3:
fout.write('{} {} {:.5f} {}\n'.format(line.split()[-2], line.split()[-1], score_dict[line][0], line.split()[0]))
elif len(line.split()) == 2:
fout.write('{} {} {:.5f}\n'.format(line.split()[-2], line.split()[-1], score_dict[line][0]))
if len(line.split()) == 3:
# Coumpute EER and minDCF
EER = tuneThresholdfromScore(scores, labels, [1, 0.1])[1]
fnrs, fprs, thresholds = ComputeErrorRates(scores, labels)
minDCF, _ = ComputeMinDcf(fnrs, fprs, thresholds, 0.05, 1, 1)
print("EER: {:.6f}%, minDCF: {:.6f}".format(EER, minDCF))
def _print_config(self, opts):
pp = pprint.PrettyPrinter(indent = 2)
pp.pprint(opts)
if __name__ == '__main__':
if MODE == 'train' or MODE == 'finetune':
trainer = Trainer()
trainer()
# voxceleb dataset test
elif MODE == 'test':
stage = 'test' # test | cohort | submean
tester = Tester()
if stage in ['test', 'cohort']:
tester._eval_network(stage=stage, num_workers=6)
if stage in ['submean']:
tester._extract_submeanembedding(stage=stage)
# elif MODE == 'extract_submean_embedding':
# trainer.extract_submeanembedding()
# elif MODE == 'featurefusion':
# eer, minDCF = utils.eer_cos_featurefusion(trainer.log_time, dataset='lrs3', trial=trainer.data_opts['test_lrs3']['test_trial_lrs3'])
# print("EER: {:.6f}%, minDCF: {:.6f}".format(eer * 100, minDCF))
# elif MODE == 'scorefusion':
# eer, minDCF = utils.eer_cos_scorefusion('score_v_FaceResNet101-JointTraining.txt','score_v_MiniFace-JointTraining.txt')
# print("EER: {:.6f}%, minDCF: {:.6f}".format(eer * 100, minDCF))