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train_transformer_flower.py
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############################################################################################################
# 项目讲解链接：https://www.bilibili.com/video/BV1px4y1W7zs/?spm_id_from=333.999.0.0&vd_source=6528929ff3772e61e9f5baf9b8ab1e64
# 增添了以下功能：
# 1. 使用argparse类实现可以在训练的启动命令中指定超参数
# 2. 可以通过在启动命令中指定 --seed 来固定网络的初始化方式，以达到结果可复现的效果
# 3. 使用了更高级的学习策略 cosine warm up：在训练的第一轮使用一个较小的lr（warm_up），从第二个epoch开始，随训练轮数逐渐减小lr。
# 4. 可以通过在启动命令中指定 --model 来选择使用的模型
# 5. 使用amp包实现半精度训练，在保证准确率的同时尽可能的减小训练成本
# 6. 实现了数据加载类的自定义实现
# 7. 可以通过在启动命令中指定 --tensorboard 来进行tensorboard可视化, 默认不启用。
# 注意，使用tensorboad之前需要使用命令 "tensorboard --logdir= log_path"来启动，结果通过网页 http://localhost:6006/'查看可视化结果
############################################################################################################
# --model 可选的超参如下：
# alexnet zfnet vgg vgg_tiny vgg_small vgg_big googlenet xception resnet_small resnet resnet_big resnext resnext_big
# densenet_tiny densenet_small densenet densenet_big mobilenet_v3 mobilenet_v3_large shufflenet_small shufflenet
# efficient_v2_small efficient_v2 efficient_v2_large convnext_tiny convnext_small convnext convnext_big convnext_huge
# kansformer1 kansformer2 vision_transformer1 vision_transformer2 vision_transformer_small vision_transformer_big swin_transformer_tiny swin_transformer_small swin_transformer
# 训练命令示例： # python train.py --model alexnet --num_classes 5
############################################################################################################
import os
import argparse
import math
import shutil
import random
import numpy as np
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import classic_models
from utils.lr_methods import warmup
from dataload.dataload_five_flower import Five_Flowers_Load
from utils.train_engin import train_one_epoch, evaluate
parser = argparse.ArgumentParser()
parser.add_argument('--num_classes', type=int, default=5, help='the number of classes')
parser.add_argument('--epochs', type=int, default=50, help='the number of training epoch')
parser.add_argument('--batch_size', type=int, default=32, help='batch_size for training')
parser.add_argument('--lr', type=float, default=0.0002, help='star learning rate')
parser.add_argument('--lrf', type=float, default=0.0001, help='end learning rate')
parser.add_argument('--seed', default=False, action='store_true', help='fix the initialization of parameters')
parser.add_argument('--tensorboard', default=False, action='store_true', help=' use tensorboard for visualization')
parser.add_argument('--use_amp', default=False, action='store_true', help=' training with mixed precision')
parser.add_argument('--data_path', type=str, default=r"data/flower")
parser.add_argument('--model', type=str, default="vision_transformer1", help=' select a model for training')
parser.add_argument('--device', default='cuda', help='device id (i.e. 0 or 0,1 or cpu)')
# parser.add_argument('--weights', type=str, default=r'model_pth\vit_base_patch16_224.pth', help='initial weights path')
opt = parser.parse_args()
if opt.seed:
def seed_torch(seed=7):
random.seed(seed) # Python random module.
os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现
np.random.seed(seed) # Numpy module.
torch.manual_seed(seed) # 为CPU设置随机种子
torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
# 设置cuDNN：cudnn中对卷积操作进行了优化，牺牲了精度来换取计算效率。如果需要保证可重复性，可以使用如下设置:
# torch.backends.cudnn.benchmark = False
# torch.backends.cudnn.deterministic = True
# 实际上这个设置对精度影响不大，仅仅是小数点后几位的差别。所以如果不是对精度要求极高，其实不太建议修改，因为会使计算效率降低。
print('random seed has been fixed')
seed_torch()
def main(args):
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
print(args)
if opt.tensorboard:
# 这是存放你要使用tensorboard显示的数据的绝对路径
log_path = os.path.join('./results/tensorboard' , args.model)
print('Start Tensorboard with "tensorboard --logdir={}"'.format(log_path))
if os.path.exists(log_path) is False:
os.makedirs(log_path)
print("tensorboard log save in {}".format(log_path))
else:
shutil.rmtree(log_path) #当log文件存在时删除文件夹。记得在代码最开始import shutil
# 实例化一个tensorboard
tb_writer = SummaryWriter(log_path)
data_transform = {
"train": transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
"val": transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
# 对标pytorch封装好的ImageFlolder，我们自己实现了一个数据加载类 Five_Flowers_Load，并使用指定的预处理操作来处理图像，结果会同时返回图像和对应的标签。
train_dataset = Five_Flowers_Load(os.path.join(args.data_path , 'train'), transform=data_transform["train"])
val_dataset = Five_Flowers_Load(os.path.join(args.data_path , 'val'), transform=data_transform["val"])
if args.num_classes != train_dataset.num_class:
raise ValueError("dataset have {} classes, but input {}".format(train_dataset.num_class, args.num_classes))
nw = min([os.cpu_count(), args.batch_size if args.batch_size > 1 else 0, 8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
# 使用 DataLoader 将加载的数据集处理成批量（batch）加载模式
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=0, collate_fn=train_dataset.collate_fn)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=0, collate_fn=val_dataset.collate_fn)
# create model
model = classic_models.find_model_using_name(opt.model, num_classes=opt.num_classes,has_logits=False).to(device)
# 若需要加载预训练权重将下面这段注释打开即可
# 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)
# # 删除不需要的权重
# del_keys = ['head.weight', 'head.bias'] if model.has_logits \
# else ['pre_logits.fc.weight', 'pre_logits.fc.bias', 'head.weight', 'head.bias']
# for k in del_keys:
# del weights_dict[k]
# print(model.load_state_dict(weights_dict, strict=False))
pg = [p for p in model.parameters() if p.requires_grad]
optimizer = optim.Adam(pg, lr=args.lr)
# 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_acc = 0.
# save parameters path
save_path = os.path.join(os.getcwd(), 'results/weights', args.model)
if os.path.exists(save_path) is False:
os.makedirs(save_path)
for epoch in range(args.epochs):
# train
mean_loss, train_acc = train_one_epoch(model=model, optimizer=optimizer, data_loader=train_loader, device=device, epoch=epoch, use_amp=args.use_amp, lr_method= warmup)
scheduler.step()
# validate
val_acc = evaluate(model=model, data_loader=val_loader, device=device)
print('[epoch %d] train_loss: %.3f train_acc: %.3f val_accuracy: %.3f' % (epoch + 1, mean_loss, train_acc, val_acc))
with open(os.path.join(save_path, "vision_transformer_flower_log.txt"), 'a') as f:
f.writelines('[epoch %d] train_loss: %.3f train_acc: %.3f val_accuracy: %.3f' % (epoch + 1, mean_loss, train_acc, val_acc) + '\n')
if opt.tensorboard:
tags = ["train_loss", "train_acc", "val_accuracy", "learning_rate"]
tb_writer.add_scalar(tags[0], mean_loss, epoch)
tb_writer.add_scalar(tags[1], train_acc, epoch)
tb_writer.add_scalar(tags[2], val_acc, epoch)
tb_writer.add_scalar(tags[3], optimizer.param_groups[0]["lr"], epoch)
# 判断当前验证集的准确率是否是最大的，如果是，则更新之前保存的权重
if val_acc > best_acc:
best_acc = val_acc
torch.save(model.state_dict(), os.path.join(save_path, "vision_transformer_flower.pth"))
main(opt)