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train_dist.py
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#! /usr/bin/env python3
import os
import sys
import time
import random
import numpy as np
import argparse
import torch
import torch.nn as nn
import torchaudio
import model.resnet as model_2d
import model.tdnn as model_1d
import model.classifier as classifiers
from torch.utils.data import DataLoader
from dataset import WavDataset
from tools.utils import get_eer, get_lr, change_lr
import torch.nn.functional as F
from config.config_ecapatdnn_dist import Config
from torch import distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.backends.cudnn as cudnn
from torch.utils.data.distributed import DistributedSampler
from visualdl import LogWriter
parser = argparse.ArgumentParser(description='Network Parser')
parser.add_argument('--local_rank', default=-1, type=int)
args = parser.parse_args()
def main():
local_rank = args.local_rank
print('local_rank is ', local_rank)
torch.cuda.set_device(local_rank)
torch.distributed.init_process_group(backend='nccl')
opt = Config()
init_seeds(opt.seed+local_rank)
# training dataset
train_dataset = WavDataset(opt=opt, train_mode=True)
train_sampler = DistributedSampler(train_dataset)
train_loader = DataLoader(train_dataset,
num_workers=opt.workers,
batch_size=opt.batch_size,
sampler=train_sampler,
pin_memory=True,
drop_last=True)
# validation dataset
if dist.get_rank() == 0:
val_dataset = WavDataset(opt=opt, train_mode=False)
val_dataloader = DataLoader(
val_dataset, num_workers=opt.workers, pin_memory=True, batch_size=1)
if opt.conv_type == '1D':
model = getattr(model_1d, opt.model)(in_dim=opt.in_planes, embedding_size=opt.embd_dim,
hidden_dim=opt.hidden_dim).cuda() # tdnn, ecapa_tdnn
elif opt.conv_type == '2D':
model = getattr(model_2d, opt.model)(
in_planes=opt.in_planes, embedding_size=opt.embd_dim).cuda() # resnet
classifier = getattr(classifiers, opt.classifier)(opt.embd_dim, len(opt.spk2int)*3 if opt.spk_aug else len(opt.spk2int),
device_id=[local_rank],
m=opt.angular_m, s=opt.angular_s).cuda() # arcface
optimizer = torch.optim.SGD(list(model.parameters()) + list(classifier.parameters()),
lr=opt.lr, momentum=0.9, weight_decay=1e-3)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, [10, 20, 30], gamma=0.1, last_epoch=-1)
os.system('mkdir -p exp/%s' % opt.save_dir)
epochs, start_epoch = opt.epochs, opt.start_epoch
if start_epoch != 0:
print('Load exp/%s/model_%d.pkl' % (opt.save_dir, start_epoch-1))
checkpoint = torch.load('exp/%s/model_%d.pkl' %
(opt.save_dir, start_epoch-1))
model.load_state_dict(checkpoint['model'])
if opt.load_classifier:
classifier.load_state_dict(checkpoint['classifier'])
logs = open('exp/%s/train.out' % opt.save_dir, 'a')
else:
logs = open('exp/%s/train.out' % opt.save_dir, 'w')
logs.write(str(model) + '\n' + str(classifier) + '\n')
criterion = nn.CrossEntropyLoss()
batch_per_epoch = len(train_loader)
def lr_lambda(x): return opt.lr / \
(batch_per_epoch * opt.warm_up_epoch) * (x + 1)
model = DDP(model, device_ids=[
local_rank], output_device=local_rank, find_unused_parameters=True)
classifier = DDP(classifier, device_ids=[
local_rank], output_device=local_rank, find_unused_parameters=True)
for epoch in range(start_epoch, epochs):
writer=LogWriter(logdir=f"./exp/{opt.save_dir}/log")
train_sampler.set_epoch(epoch)
model.train()
classifier.train()
end = time.time()
for i, (feats, key) in enumerate(train_loader):
data_time = time.time() - end
if epoch < opt.warm_up_epoch:
change_lr(optimizer, lr_lambda(len(train_loader) * epoch + i))
feats, key = feats.cuda(), key.cuda()
outputs = classifier(model(feats), key)
loss = criterion(outputs, key)
optimizer.zero_grad()
loss.backward()
optimizer.step()
output_pre = np.argmax(outputs.data.cpu().numpy(), axis=1)
acc = np.mean((output_pre == key.cpu().numpy()).astype(int))
batch_time = time.time() - end
end = time.time()
if opt.visual:
print('Epoch [%d][%d/%d]\t ' % (epoch, i+1, len(train_loader)) +
'Length %d\t' % (feats.shape[2]) +
'Time [%.3f/%.3f]\t' % (batch_time, data_time) +
'Loss %.4f\t' % (loss.data.item()) +
'Accuracy %3.3f\t' % (acc*100) +
'LR %.6f\n' % get_lr(optimizer))
if dist.get_rank() == 0:
writer.add_scalar(tag='train/loss', step=epoch,value=loss.data.item())
writer.add_scalar(tag='train/acc', step=epoch, value=acc*100)
writer.add_scalar(tag='train/lr', step=epoch,value=get_lr(optimizer))
logs.write('Epoch [%d][%d/%d]\t ' % (epoch, i+1, len(train_loader)) +
'Length %d\t' % (feats.shape[2]) +
'Time [%.3f/%.3f]\t' % (batch_time, data_time) +
'Loss %.4f\t' % (loss.data.item()) +
'Accuracy %3.3f\t' % (acc*100) +
'LR %.6f\n' % get_lr(optimizer))
logs.flush()
if dist.get_rank() == 0:
save_model('exp/%s' % opt.save_dir, epoch, model, classifier, optimizer, scheduler)
# strongly recommend the following validate code when implement finetuning step.
eer, cost = validate(model, val_dataloader, epoch, opt)
print('Epoch %d\t lr %f\t EER %.4f\t cost %.4f\n' % (epoch, get_lr(optimizer), eer*100, cost))
logs.write('Epoch %d\t lr %f\t EER %.4f\t cost %.4f\n' % (epoch, get_lr(optimizer), eer*100, cost))
writer.add_scalar(tag='val/eer', step=epoch, value=eer*100)
writer.add_scalar(tag='val/cost', step=epoch, value=cost)
scheduler.step()
def validate(model, val_dataloader, epoch, opt):
model.eval()
embd_dict = {}
with torch.no_grad():
for j, (feat, utt) in enumerate(val_dataloader):
outputs = model(feat.cuda())
for i in range(len(utt)):
# print(j, utt[i],feat.shape[2])
embd_dict[utt[i]] = outputs[i, :].cpu().numpy()
eer, _, cost, _ = get_eer(embd_dict, trial_file='data/%s/trials' % opt.val_dir)
np.save('exp/%s/test_%s.npy' % (opt.save_dir, epoch), embd_dict)
return eer, cost
def save_model(chk_dir, epoch, model, classifier, optimizer, scheduler):
torch.save({'model': model.module.state_dict(),
'classifier': classifier.module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict()
}, os.path.join(chk_dir, 'model_%d.pkl' % epoch))
def init_seeds(seed=0, cuda_deterministic=True):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if cuda_deterministic: # slower, more reproducible
cudnn.deterministic = True
cudnn.benchmark = False
else: # faster, less reproducible
cudnn.deterministic = False
cudnn.benchmark = True
if __name__ == '__main__':
main()