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train_mask.py
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train_mask.py
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import os
import math
import argparse
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import transforms as T
from torch.optim.lr_scheduler import LambdaLR
from my_dataset import FS2K_DataSet_With_Seg
from models.swin import FaceAttrModel
from utils import read_train_test, train_one_epoch, evaluate, checkpoint_load
def linear_warmup_cosine_lr_scheduler(optimizer, warmup_time_ratio, T_max):
T_warmup = int(T_max * warmup_time_ratio)
def lr_lambda(epoch):
# linear warm up
if epoch < T_warmup:
return epoch / T_warmup
else:
progress_0_1 = (epoch - T_warmup) / (T_max - T_warmup)
cosine_decay = 0.5 * (1 + math.cos(math.pi * progress_0_1))
return cosine_decay
return LambdaLR(optimizer, lr_lambda=lr_lambda)
def main(args):
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
if os.path.exists("./weights") is False:
os.makedirs("./weights")
train_attrs, test_attrs = read_train_test()
# 实例化训练数据集
data_transform = {
"train": T.Compose([
T.RandomResize(224),
T.RandomCrop(224),
T.RandomHorizontalFlip(0.5)]),
"test": T.Compose([
T.CenterCrop(224),
])}
img_data_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
train_dataset = FS2K_DataSet_With_Seg(attrs=train_attrs, transform=data_transform["train"], t2=img_data_transform)
val_dataset = FS2K_DataSet_With_Seg(attrs=test_attrs, transform=data_transform["test"], t2=img_data_transform)
batch_size = args.batch_size
nw = 6 # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True,
num_workers=nw)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
num_workers=nw)
model = FaceAttrModel().to(device)
if args.weights != "":
checkpoint_load(model, args.weights, device)
pg = [p for p in model.parameters() if p.requires_grad]
optimizer = optim.AdamW(pg, lr=args.lr, weight_decay=args.weight_decay)
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
best_map = 0
for epoch in range(args.epochs):
# train
train_loss, accuracy_hair, accuracy_hair_color, accuracy_gender, accuracy_earring, \
accuracy_smile, accuracy_frontal_face, accuracy_style = train_one_epoch(model=model, optimizer=optimizer,
data_loader=train_loader, device=device,
epoch=epoch)
# 平均准确率
train_mAP = (accuracy_hair.item() + accuracy_gender.item() + accuracy_earring.item()
+ accuracy_smile.item() + accuracy_frontal_face.item() + accuracy_style.item()) / 6
scheduler.step()
# validate
val_loss, val_accuracy_hair, val_accuracy_hair_color, val_accuracy_gender, \
val_accuracy_earring, val_accuracy_smile, val_accuracy_frontal_face, val_accuracy_style \
= evaluate(model=model, data_loader=val_loader, device=device, epoch=epoch)
val_mAP1 = (
val_accuracy_hair.item() + val_accuracy_gender.item() + val_accuracy_earring.item() +
val_accuracy_smile.item() + val_accuracy_frontal_face.item() + val_accuracy_style.item()) / 6
val_mAP = (
val_accuracy_hair.item() + val_accuracy_gender.item() +
val_accuracy_smile.item() + val_accuracy_frontal_face.item() + val_accuracy_style.item()) / 5
print(
"train: hair: {:.3f}, hair_color: {:.3f}, gender: {:.3f}, earring: {:.3f}, smile: {:.3f}, frontal_face: {:.3f}, style: {:.3f},train_map:{:.3f}".format(
accuracy_hair.item(), accuracy_hair_color.item(), accuracy_gender.item(), accuracy_earring.item(),
accuracy_smile.item(), accuracy_frontal_face.item(), accuracy_style.item(), train_mAP))
print(
"test: hair: {:.3f}, hair_color: {:.3f}, gender: {:.3f}, earring: {:.3f}, smile: {:.3f}, frontal_face: {:.3f}, style: {:.3f},val_map1:{:.3f} val_map:{:.3f}".format(
val_accuracy_hair.item(), val_accuracy_hair_color.item(), val_accuracy_gender.item(),
val_accuracy_earring.item(),
val_accuracy_smile.item(), val_accuracy_frontal_face.item(), val_accuracy_style.item(), val_mAP1, val_mAP))
if val_mAP > best_map:
best_map = val_mAP
torch.save(model.state_dict(), "./weights/model_best.pth")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--lrf', type=float, default=0.01)
parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: None, use opt default)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='Optimizer momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--clip-mode', type=str, default='norm',
help='Gradient clipping mode. One of ("norm", "value", "agc")')
parser.add_argument('--lr', type=float, default=1e-5, metavar='LR',
help='learning rate (default: 2.5e-4)')
# 预训练权重路径，如果不想载入就设置为空字符
parser.add_argument('--weights', type=str, default='',
help='initial weights path')
# 是否冻结权重
parser.add_argument('--freeze-layers', type=bool, default=True)
parser.add_argument('--device', default='cuda:3', help='device id (i.e. 0 or 0,1 or cpu)')
opt = parser.parse_args()
main(opt)