classification-main/block/train_with_watermark.py

import os

import cv2
import tqdm
import wandb
import torch
import numpy as np
from PIL import Image
from torch import nn
from torchvision import transforms
from watermark_codec import ModelEncoder

from block.val_get import val_get
from block.model_ema import model_ema
from block.lr_get import adam, lr_adjust


def train_embed(args, model_dict, loss, secret):
    # 加载模型
    model = model_dict['model'].to(args.device, non_blocking=args.latch)
    print(model)
    # 选择加密层并初始化白盒水印编码器
    conv_list = []
    for module in model.modules():
        if isinstance(module, nn.Conv2d):
            conv_list.append(module)
    conv_list = conv_list[0:2]
    model_dict['enc_layers'] = conv_list  # 将加密层保存至权重文件中
    encoder = ModelEncoder(layers=conv_list, secret=secret, key_path=args.key_path, device=args.device)

    # 数据集
    print("加载训练集至内存中...")
    train_transform = transforms.Compose([
        transforms.RandomHorizontalFlip(),  # 随机水平翻转
        transforms.RandomCrop(32, padding=4),  # 随机裁剪并填充
        transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),  # 颜色抖动
        transforms.ToTensor(),  # 将图像转换为PyTorch张量
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # 标准化
    ])
    train_dataset = CustomDataset(data_dir=args.train_dir, transform=train_transform)
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None
    train_shuffle = False if args.distributed else True  # 分布式设置sampler后shuffle要为False
    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch, shuffle=train_shuffle,
                                                   drop_last=True, pin_memory=args.latch, num_workers=args.num_worker,
                                                   sampler=train_sampler)
    print("加载验证集至内存中...")
    val_transform = transforms.Compose([
        transforms.ToTensor(),  # 将图像转换为PyTorch张量
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])  # 标准化
    ])
    val_dataset = CustomDataset(data_dir=args.test_dir, transform=val_transform)
    val_sampler = None  # 分布式时数据合在主GPU上进行验证
    val_batch = args.batch // args.device_number  # 分布式验证时batch要减少为一个GPU的量
    val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=val_batch, shuffle=False,
                                                 drop_last=False, pin_memory=args.latch, num_workers=args.num_worker,
                                                 sampler=val_sampler)

    # 学习率
    optimizer = adam(args.regularization, args.r_value, model.parameters(), lr=args.lr_start, betas=(0.937, 0.999))
    optimizer.load_state_dict(model_dict['optimizer_state_dict']) if model_dict['optimizer_state_dict'] else None
    train_len = train_dataset.__len__()
    step_epoch = train_len // args.batch // args.device_number * args.device_number  # 每轮的步数
    print(train_len // args.batch)
    print(step_epoch)
    optimizer_adjust = lr_adjust(args, step_epoch, model_dict['epoch_finished'])  # 学习率调整函数
    optimizer = optimizer_adjust(optimizer)  # 学习率初始化
    # 使用平均指数移动(EMA)调整参数(不能将ema放到args中，否则会导致模型保存出错)
    ema = model_ema(model) if args.ema else None
    if args.ema:
        ema.updates = model_dict['ema_updates']

    # 分布式初始化
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
                                                      output_device=args.local_rank) if args.distributed else model
    # wandb
    if args.wandb and args.local_rank == 0:
        wandb_image_list = []  # 记录所有的wandb_image最后一起添加(最多添加args.wandb_image_num张)
    epoch_base = model_dict['epoch_finished'] + 1  # 新的一轮要+1
    for epoch in range(epoch_base, args.epoch + 1):  # 训练
        print(f'\n-----------------------第{epoch}轮-----------------------') if args.local_rank == 0 else None
        model.train()
        train_loss = 0  # 记录损失
        train_embed_loss = 0
        if args.local_rank == 0:  # tqdm
            tqdm_show = tqdm.tqdm(total=step_epoch)
        for index, (image_batch, true_batch) in enumerate(train_dataloader):
            if args.wandb and args.local_rank == 0 and len(wandb_image_list) < args.wandb_image_num:
                wandb_image_batch = (image_batch * 255).cpu().numpy().astype(np.uint8).transpose(0, 2, 3, 1)
            image_batch = image_batch.to(args.device, non_blocking=args.latch)
            true_batch = true_batch.to(args.device, non_blocking=args.latch)
            if args.amp:
                with torch.cuda.amp.autocast():
                    pred_batch = model(image_batch)
                    loss_batch = loss(pred_batch, true_batch)
                    embed_loss = encoder.get_embeder_loss()  # 获取水印嵌入损失
                    loss_batch = embed_loss + loss_batch  # 修改原始损失
                args.amp.scale(loss_batch).backward()
                args.amp.step(optimizer)
                args.amp.update()
                optimizer.zero_grad()
            else:
                pred_batch = model(image_batch)
                loss_batch = loss(pred_batch, true_batch)
                embed_loss = encoder.get_embeder_loss()  # 获取水印嵌入损失
                loss_batch = embed_loss + loss_batch  # 修改原始损失
                loss_batch.backward()
                optimizer.step()
                optimizer.zero_grad()
            # 调整参数，ema.updates会自动+1
            ema.update(model) if args.ema else None
            # 记录损失
            train_loss += loss_batch.item()
            train_embed_loss += embed_loss.item()
            # 调整学习率
            optimizer = optimizer_adjust(optimizer)
            # tqdm
            if args.local_rank == 0:
                tqdm_show.set_postfix({'train_loss': loss_batch.item(), 'embed_loss': embed_loss.item(),
                                       'lr': optimizer.param_groups[0]['lr']})  # 添加显示
                tqdm_show.update(args.device_number)  # 更新进度条
            # wandb
            if args.wandb and args.local_rank == 0 and epoch == 0 and len(wandb_image_list) < args.wandb_image_num:
                cls = true_batch.cpu().numpy().tolist()
                for i in range(len(wandb_image_batch)):  # 遍历每一张图片
                    image = wandb_image_batch[i]
                    text = ['{:.0f}'.format(_) for _ in cls[i]]
                    text = text[0] if len(text) == 1 else '--'.join(text)
                    image = np.ascontiguousarray(image)  # 将数组的内存变为连续存储(cv2画图的要求)
                    cv2.putText(image, text, (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                    wandb_image = wandb.Image(image)
                    wandb_image_list.append(wandb_image)
                    if len(wandb_image_list) == args.wandb_image_num:
                        break
        # tqdm
        if args.local_rank == 0:
            tqdm_show.close()
        # 计算平均损失
        train_loss /= index + 1
        train_embed_loss /= index + 1
        if args.local_rank == 0:
            print(f'\n| 训练损失 | train_loss:{train_loss:.4f} | 水印损失 | train_embed_loss:{train_embed_loss:.4f} | lr:{optimizer.param_groups[0]["lr"]:.6f} |\n')
        # 清理显存空间
        del image_batch, true_batch, pred_batch, loss_batch
        torch.cuda.empty_cache()
        # 验证
        if args.local_rank == 0:  # 分布式时只验证一次
            val_loss, accuracy = val_get(args, val_dataloader, model, loss, ema, val_dataset.__len__())
        # 保存
        if args.local_rank == 0:  # 分布式时只保存一次
            model_dict['model'] = model.module if args.distributed else model
            model_dict['epoch_finished'] = epoch
            model_dict['optimizer_state_dict'] = optimizer.state_dict()
            model_dict['ema_updates'] = ema.updates if args.ema else model_dict['ema_updates']
            model_dict['train_loss'] = train_loss
            model_dict['val_loss'] = val_loss
            model_dict['val_accuracy'] = accuracy
            torch.save(model_dict, args.save_path_last if not args.prune else 'prune_last.pt')  # 保存最后一次训练的模型
            if accuracy > 0.5 and accuracy > model_dict['standard']:
                model_dict['standard'] = accuracy
                save_path = args.save_path if not args.prune else args.prune_save
                torch.save(model_dict, save_path)  # 保存最佳模型
                print(f'| 保存最佳模型:{save_path} | accuracy:{accuracy:.4f} |')
            # wandb
            if args.wandb:
                wandb_log = {}
                if epoch == 0:
                    wandb_log.update({f'image/train_image': wandb_image_list})
                wandb_log.update({'metric/train_loss': train_loss,
                                  'metric/val_loss': val_loss,
                                  'metric/val_accuracy': accuracy
                                  })
                args.wandb_run.log(wandb_log)
        torch.distributed.barrier() if args.distributed else None  # 分布式时每轮训练后让所有GPU进行同步，快的GPU会在此等待


class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, data_dir, image_size=(32, 32), transform=None):
        self.data_dir = data_dir
        self.image_size = image_size
        self.transform = transform

        self.images = []
        self.labels = []

        # 遍历指定目录下的子目录，每个子目录代表一个类别
        class_dirs = sorted(os.listdir(data_dir))
        for index, class_dir in enumerate(class_dirs):
            class_path = os.path.join(data_dir, class_dir)

            # 遍历当前类别目录下的图像文件
            for image_file in os.listdir(class_path):
                image_path = os.path.join(class_path, image_file)

                # 使用PIL加载图像并调整大小
                image = Image.open(image_path).convert('RGB')
                image = image.resize(image_size)

                self.images.append(np.array(image))
                self.labels.append(index)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        image = self.images[idx]
        label = self.labels[idx]

        if self.transform:
            image = self.transform(Image.fromarray(image))

        return image, label