使用Python实现模型训练完成后自动向邮箱中发送训练结果。
最基础的发送邮件的代码
首先我们看最简单的用python实现发邮件功能的代码，运行以下代码可以实现向指定的接收邮箱发送一个主题为“Training finished”，正文内容为test的邮件。
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" # 邮箱的设置里的服务器地址
mail_user = "*****@163.com" # 发送邮件的邮箱
mail_pw = "*********" # 授权码，邮箱设置里开启POP3/SMTP服务，提供给你的密钥
sender = "******@163.com" # 发送邮件的邮箱，
receiver = "******@icloud.com" # 接收邮件的邮箱
# Create the container (outer) email message.
msg = MIMEText(content, "plain", "utf-8")
msg['Subject'] = subject
msg['From'] = sender
msg['To'] = receiver
try:
smtp = smtplib.SMTP_SSL(mail_host, 994) # 实例化smtp服务器
smtp.login(mail_user, mail_pw) # 登录
smtp.sendmail(sender, receiver, msg.as_string())
print("Email send successfully")
except smtplib.SMTPException:
print("Error: email send failed")
if __name__ == '__main__':
send_email(subject="Training finished", content="Test")其中，mail_host为邮箱的服务器地址，我们可以在邮箱的POP3/SMTP/IMAP设置页面里找到。
mail_user为发送邮件的邮箱，负责将你的训练结果作为邮件发出去。
mail_pw为邮箱的授权码，在POP3/SMTP/IMAP设置页面可以找到。选择开启IMAP/SMTP服务，安全识别后会提供给你密钥。
sender和mail_user一样，发送邮件的邮箱
receiver为接收邮件的邮箱。
运用到训练脚本
以下代码是将发送邮件的代码加入到swin transformer的运行脚本中。
代码的第13-38行，111，117，123，134行是加入的自动发送邮件的脚本。
实现的是全部的epoch训练结束后，将每个epoch的损失和准确率发送到指定的邮箱里。
如果需要每个epoch训练完都发送一次邮件的话，可以自己稍微改一下for循环那里。
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" # 邮箱的设置里的服务器地址
mail_user = "*****@163.com" # 发送邮件的邮箱
mail_pw = "*********" # 授权码，邮箱设置里开启POP3/SMTP服务，提供给你的密钥
sender = "******@163.com" # 发送邮件的邮箱，
receiver = "******@icloud.com" # 接收邮件的邮箱
# Create the container (outer) email message.
msg = MIMEText(content, "plain", "utf-8")
msg['Subject'] = subject
msg['From'] = sender
msg['To'] = receiver
try:
smtp = smtplib.SMTP_SSL(mail_host, 994) # 实例化smtp服务器
smtp.login(mail_user, mail_pw) # 登录
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])])}
# 实例化训练数据集
train_dataset = MyDataSet(images_path=train_images_path,
images_class=train_images_label,
transform=data_transform["train"])
# 实例化验证数据集
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]) # 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,
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"]
# 删除有关分类类别的权重
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():
# 除head外，其他权重全部冻结
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
train_loss, train_acc = train_one_epoch(model=model,optimizer=optimizer,data_loader=train_loader,device=device,epoch=epoch)
# 将训练损失和准确率保存为字符串
content+='[train epoch:{}]loss:{:.3f},acc:{:.3f}'.format(epoch,train_loss,train_acc) + '\n'
# validate
val_loss, val_acc = evaluate(model=model,data_loader=val_loader,device=device, epoch=epoch)
# 将验证的损失和准确率保存为字符串
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))
# email，调用函数，发送邮件
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)
# 数据集所在根目录
# https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
parser.add_argument('--data-path', type=str,default="/content/drive/MyDrive/transformer/double/skin_photos")
# 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='/content/drive/MyDrive/transformer/swin_transformer/swin_base_patch4_window7_224.pth',
help='initial weights path')
# 是否冻结权重
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)测试结果
训练完成后，邮箱可以收到如下邮件：
