train.py

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
from vit_pytorch import ViT
from fairscale.nn.data_parallel import FullyShardedDataParallel
from my_dataset import MyDataSet
from vit_model import vit_base_patch16_224_in21k as create_model
from utils import read_split_data, train_one_epoch, evaluate
# 新增1:依赖
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def main(args):

    local_rank = args.local_rank

    # 新增3：DDP backend初始化
    #   a.根据local_rank来设定当前使用哪块GPU
    # torch.cuda.set_device(local_rank)
    torch.cuda.set_device('cuda:' + local_rank)
    #   b.初始化DDP，使用默认backend(nccl)就行。如果是CPU模型运行，需要选择其他后端。
    dist.init_process_group(backend='nccl')

    # 新增4：定义并把模型放置到单独的GPU上，需要在调用`model=DDP(model)`前做哦。
    #       如果要加载模型，也必须在这里做哦。
    device = torch.device("cuda:" + local_rank)


    # 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)

    data_transform = {
        "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                     transforms.RandomHorizontalFlip(),
                                     transforms.ToTensor(),
                                     transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
        "val": transforms.Compose([transforms.Resize(256),
                                   transforms.CenterCrop(224),
                                   transforms.ToTensor(),
                                   transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])}

    # 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))


    # 新增1：使用DistributedSampler，DDP帮我们把细节都封装起来了。用，就完事儿！
    #       sampler的原理，后面也会介绍。
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    # 需要注意的是，这里的batch_size指的是每个进程下的batch_size。也就是说，总batch_size是这里的batch_size再乘以并行数(world_size)。
    val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)
    # 需要注意的是，这里的batch_size指的是每个进程下的batch_size。也就是说，总batch_size是这里的batch_size再乘以并行数(world_size)。


    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=batch_size,
                                               sampler=train_sampler,
                                               pin_memory=True,
                                               num_workers=nw,
                                               collate_fn=train_dataset.collate_fn)

    val_loader = torch.utils.data.DataLoader(val_dataset,
                                             batch_size=batch_size,
                                             sampler=val_sampler,
                                             pin_memory=True,
                                             num_workers=nw,
                                             collate_fn=val_dataset.collate_fn)

    # model = create_model(num_classes=5, has_logits=False).to(device)
    model = ViT(
    image_size = 224,    # 图像大小
    patch_size = 16,     # patch大小（分块的大小）
    num_classes = 5,  # imagenet数据集1000分类
    dim = 768,          # position embedding的维度
    depth = 12,           # encoder和decoder中block层数是6
    heads = 12,          # multi-head中head的数量为16
    mlp_dim = 2048,
    dropout = 0.1,       #
    emb_dropout = 0.1
    ).to(device)
    model = DDP(model, device_ids=[int(local_rank)], output_device=int(local_rank),find_unused_parameters=True) # 此处必须要加find_unused这个奇怪的参数。。？诡异。暂时没研究原理
    print('enter before FSDP')
    model = FullyShardedDataParallel(model, flatten_parameters=False)
    print('enter after FSDP')
    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))

    if args.freeze_layers:
        for name, para in model.named_parameters():
            # 除head, pre_logits外，其他权重全部冻结
            if "head" not in name and "pre_logits" 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.SGD(pg, lr=args.lr, momentum=0.9, weight_decay=5E-5)
    # 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)

    for epoch in range(args.epochs):
        train_loader.sampler.set_epoch(epoch)
        # train
        print('enter one epoch')
        train_loss, train_acc = train_one_epoch(model=model,
                                                optimizer=optimizer,
                                                data_loader=train_loader,
                                                device=device,
                                                epoch=epoch)

        scheduler.step()

        # validate
        val_loss, val_acc = evaluate(model=model,
                                     data_loader=val_loader,
                                     device=device,
                                     epoch=epoch)

        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))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--num_classes', type=int, default=5)
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=8)
    parser.add_argument('--lr', type=float, default=0.001)
    parser.add_argument('--lrf', type=float, default=0.01)

    # 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="../flower_photos")
    parser.add_argument('--model-name', default='', help='create model name')

    # 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='./vit_base_patch16_224_in21k.pth',
    #                     help='initial weights path')
    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:0', help='device id (i.e. 0 or 0,1 or cpu)')
    parser.add_argument("--local_rank", default=-1)
    opt = parser.parse_args()

    main(opt)