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| import os import argparse
import torch import torch.optim as optim from torch.utils.tensorboard import SummaryWriter from torchvision import transforms
from my_dataset import MyDataSet from model import swin_base_patch4_window7_224 as create_model from utils import read_split_data, train_one_epoch, evaluate
import smtplib from email.mime.text import MIMEText from email.header import Header from email.mime.image import MIMEImage from email.mime.multipart import MIMEMultipart
def send_email(subject="No subject", content="I am boring"): mail_host = "smtp.163.com" mail_user = "*****@163.com" mail_pw = "*********" sender = "******@163.com" receiver = "******@icloud.com"
msg = MIMEText(content, "plain", "utf-8") msg['Subject'] = subject msg['From'] = sender msg['To'] = receiver
try: smtp = smtplib.SMTP_SSL(mail_host, 994) smtp.login(mail_user, mail_pw) smtp.sendmail(sender, receiver, msg.as_string()) print("Email send successfully") except smtplib.SMTPException: print("Error: email send failed")
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")
tb_writer = SummaryWriter()
train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)
img_size = 224 data_transform = { "train": transforms.Compose([transforms.RandomResizedCrop(img_size), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), "val": transforms.Compose([transforms.Resize(int(img_size * 1.143)), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
train_dataset = MyDataSet(images_path=train_images_path, images_class=train_images_label, transform=data_transform["train"])
val_dataset = MyDataSet(images_path=val_images_path, images_class=val_images_label, transform=data_transform["val"])
batch_size = args.batch_size nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) 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, collate_fn=train_dataset.collate_fn)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=nw, collate_fn=val_dataset.collate_fn)
model = create_model(num_classes=args.num_classes).to(device)
if args.weights != "": assert os.path.exists(args.weights), "weights file: '{}' not exist.".format(args.weights) weights_dict = torch.load(args.weights, map_location=device)["model"] for k in list(weights_dict.keys()): if "head" in k: del weights_dict[k] print(model.load_state_dict(weights_dict, strict=False))
if args.freeze_layers: for name, para in model.named_parameters(): if "head" not in name: para.requires_grad_(False) else: print("training {}".format(name))
pg = [p for p in model.parameters() if p.requires_grad] optimizer = optim.AdamW(pg, lr=args.lr, weight_decay=5E-2) content='' for epoch in range(args.epochs): train_loss, train_acc = train_one_epoch(model=model,optimizer=optimizer,data_loader=train_loader,device=device,epoch=epoch) content+='[train epoch:{}]loss:{:.3f},acc:{:.3f}'.format(epoch,train_loss,train_acc) + '\n' val_loss, val_acc = evaluate(model=model,data_loader=val_loader,device=device, epoch=epoch) content+='[val epoch:{}]loss:{:.3f},acc:{:.3f}'.format(epoch,val_loss,val_acc) + '\n' tags = ["train_loss", "train_acc", "val_loss", "val_acc", "learning_rate"] tb_writer.add_scalar(tags[0], train_loss, epoch) tb_writer.add_scalar(tags[1], train_acc, epoch) tb_writer.add_scalar(tags[2], val_loss, epoch) tb_writer.add_scalar(tags[3], val_acc, epoch) tb_writer.add_scalar(tags[4], optimizer.param_groups[0]["lr"], epoch)
torch.save(model.state_dict(), "./weights/model-{}.pth".format(epoch)) send_email(subject='Training finished',content=content)
if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--num_classes', type=int, default=2) parser.add_argument('--epochs', type=int, default=10) parser.add_argument('--batch-size', type=int, default=32) parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--data-path', type=str,default="/content/drive/MyDrive/transformer/double/skin_photos")
parser.add_argument('--weights', type=str, default='/content/drive/MyDrive/transformer/swin_transformer/swin_base_patch4_window7_224.pth', help='initial weights path') parser.add_argument('--freeze-layers', type=bool, default=False) parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')
opt = parser.parse_args()
main(opt)
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