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train.py
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from math import ceil
import os
import torch.utils.data
from torch.nn import DataParallel
from datetime import datetime
from model import LSCNN
from utils import init_log
from datasets.webface import CASIAWebFace
from datasets.lfw import LFW
from eval_lfw import evaluation_10_fold, getFeatureFromTorch
from torch.optim import lr_scheduler
import torch.optim as optim
import time
import numpy as np
import torchvision.transforms as transforms
import argparse
from torch.utils.tensorboard import SummaryWriter
def train(args):
writer = SummaryWriter()
# gpu init
multi_gpus = False
if len(args.gpus.split(',')) > 1:
multi_gpus = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# log init
save_dir = args.save_dir
if os.path.exists(save_dir):
if not args.resume:
raise NameError('model dir exists!')
else:
os.makedirs(save_dir)
logging = init_log(save_dir)
_print = logging.info
# dataset loader
train_transform = transforms.Compose([
transforms.RandomCrop(128),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)) # range [0.0, 1.0] -> [-1.0,1.0]
])
# validation dataset
train_set = CASIAWebFace(args.train_data_info, transform = train_transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=2, drop_last=False)
test_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(128),
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)) # range [0.0, 1.0] -> [-1.0,1.0]
])
lfw_dataset = LFW('./data/lfw_funneled', './data/lfw_funneled/pairs.txt', transform = test_transform)
lfw_dataloader = torch.utils.data.DataLoader(lfw_dataset, batch_size= 128, shuffle=False, num_workers=2, drop_last=False)
net = LSCNN(num_classes= 10559, growth_rate = 48)
if args.resume:
print('resume the model parameters from: ', args.net_path)
net.load_state_dict(torch.load(args.net_path)['net_state_dict'])
# define optimizers for different layer
criterion = torch.nn.CrossEntropyLoss().to(device)
optimizer_ft = optim.SGD(net.parameters(), lr = 0.1, weight_decay = 1e-4, momentum = 0.9, nesterov = True)
exp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer_ft, milestones=[10, 20, 30], gamma=0.1)
if multi_gpus:
net = DataParallel(net).to(device)
else:
net = net.to(device)
best_test_acc = 0.0
best_test_iters = 0
total_iters = 0
for epoch in range(1, args.total_epoch + 1):
exp_lr_scheduler.step()
# train model
_print('Train Epoch: {}/{} ...'.format(epoch, args.total_epoch))
net.train()
since = time.time()
for data in train_loader:
img, label = data[0].to(device), data[1].to(device)
optimizer_ft.zero_grad()
output = net(img)
total_loss = criterion(output, label)
total_loss.backward()
optimizer_ft.step()
total_iters += 1
# print train information
if total_iters % 100 == 0:
# current training accuracy
_, predict = torch.max(output.data, 1)
total = label.size(0)
correct = (np.array(predict.cpu()) == np.array(label.data.cpu())).sum()
time_cur = (time.time() - since) / 100
since = time.time()
writer.add_scalar("Accuracy/train", correct / total , total_iters)
writer.add_scalar("Loss/train", total_loss, total_iters)
_print("Iters: {:0>6d}/[{:0>2d}], loss: {:.4f}, train_accuracy: {:.4f}, time: {:.2f} s/iter, learning rate: {}".format(total_iters, epoch, total_loss.item(), correct/total, time_cur, exp_lr_scheduler.get_last_lr()))
# save model
if total_iters % args.save_freq == 0:
msg = 'Saving checkpoint: {}'.format(total_iters)
_print(msg)
if multi_gpus:
net_state_dict = net.module.state_dict()
else:
net_state_dict = net.state_dict()
if not os.path.exists(save_dir):
os.mkdir(save_dir)
torch.save({
'iters': total_iters,
'net_state_dict': net_state_dict},
os.path.join(save_dir, 'Iter_%06d_net.ckpt' % total_iters))
# test accuracy
if total_iters % args.test_freq == 0:
with torch.no_grad():
net.eval()
getFeatureFromTorch('./result/cur_epoch_lfw_result.mat', net, device, lfw_dataset, lfw_dataloader)
lfw_accuracy = evaluation_10_fold('./result/cur_epoch_lfw_result.mat')
lfw_accuracy = np.mean(lfw_accuracy) * 100
writer.add_scalar("Accuracy/test", lfw_accuracy, total_iters)
_print(f'LFW Ave Accuracy: {lfw_accuracy.item():.4f}')
if best_test_acc <= lfw_accuracy.item() :
best_test_acc = lfw_accuracy.item()
best_test_iters = total_iters
_print(f'Current Best Accuracy: test: {best_test_acc:.4f} in iters: {best_test_iters}')
net.train()
_print('Finally Best Accuracy: val: {:.4f} in iters: {}'.format(best_test_acc, best_test_iters))
print('finishing training')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch for deep face recognition')
parser.add_argument('--train_data_info', type=str, default='./data/img_info.csv', help='train image info csv')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--total_epoch', type=int, default=25, help='total epochs')
parser.add_argument('--save_freq', type=int, default=3500, help='save frequency')
parser.add_argument('--test_freq', type=int, default=3500, help='test frequency')
parser.add_argument('--resume', type=bool, default=False, help='resume model')
parser.add_argument('--net_path', type=str, default='', help='resume model')
parser.add_argument('--save_dir', type=str, default='./model', help='model save dir')
parser.add_argument('--gpus', type=str, default='0', help='model prefix')
args = parser.parse_args()
train(args)