main.py train训练代码阅读心得

阅读代码官方的ImageNet例子

设备处理

args = parser.parse_args()

if args.seed is not None:
    random.seed(args.seed)
    torch.manual_seed(args.seed)
    # 由于计算中有随机性，每次网络前馈结果有差异
		# 为了避免这种结果波动，设置 cudnn.deterministic = True
    cudnn.deterministic = True
    warnings.warn('You have chosen to seed training. '
                    'This will turn on the CUDNN deterministic setting, '
                    'which can slow down your training considerably! '
                    'You may see unexpected behavior when restarting '
                    'from checkpoints.')

if args.gpu is not None:
    warnings.warn('You have chosen a specific GPU. This will completely '
                    'disable data parallelism.')
    
# world_size 代表多少个计算节点
if args.dist_url == "env://" and args.world_size == -1:
    args.world_size = int(os.environ["WORLD_SIZE"])

# 判断是否需要并行训练
args.distributed = args.world_size > 1 or args.multiprocessing_distributed

# 获取节点gpu设备数量
ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
    # Since we have ngpus_per_node processes per node, the total world_size
    # needs to be adjusted accordingly
    args.world_size = ngpus_per_node * args.world_size
    # Use torch.multiprocessing.spawn to launch distributed processes: the
    # main_worker process function
    # 通过torch.multiprocessing.spawn来开启多进程
    mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
else:
    # Simply call main_worker function
    main_worker(args.gpu, ngpus_per_node, args)

def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    args.gpu = gpu

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
        		# rank 表示节点编号(n台节点即：0,1,2,..,n-1)
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        # dist-backend gpu上一般设置为nccl，cpu上设置为gloo
        # 根据官网的介绍, 如果是使用cpu的分布式计算, 建议使用gloo, 因为表中可以看到 gloo对cpu的支持是最好的, 
        # 然后如果使用gpu进行分布式计算, 建议使用nccl, 实际测试中我也感觉到, 当使用gpu的时候, nccl的效率是高于gloo的. 
        # 根据博客和官网的态度, 好像都不怎么推荐在多gpu的时候使用mpi
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)
    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
    else:
        print("=> creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
          	## 在生成网络对象之前：！！！
            # CUDA使用指定显卡，类似export CUDA_VISIBLE_DEVICES=0
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # When using a single GPU per process and per
            # DistributedDataParallel, we need to divide the batch size
            # ourselves based on the total number of GPUs we have
            args.batch_size = int(args.batch_size / ngpus_per_node)
            args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
            # pytorch的官网建议使用DistributedDataParallel来代替DataParallel, 
            # 据说是因为DistributedDataParallel比DataParallel运行的更快, 然后显存分屏的更加均衡. 
            # 而且DistributedDataParallel功能更加强悍, 例如分布式的模型(一个模型太大, 以至于无法放到一个GPU上运行, 
            # 需要分开到多个GPU上面执行). 只有DistributedDataParallel支持分布式的模型像单机模型那样可以进行多机多卡的运算.
            
            # 注意要提前把模型加载到gpu, 然后才可以加载到DistributedDataParallel
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()
   # ................

数据读取

# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                    std=[0.229, 0.224, 0.225])

train_dataset = datasets.ImageFolder(
    traindir,
    transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        normalize,
    ]))

# 这步很重要，分布式训练的话需要将train_dataset进行分布式操作
# 将train_dataset送到了DistributedSampler中创造了一个train_sampler, 然后在构造train_loader的时候, 
# 参数中传入了一个sampler=train_sampler. 使用这些的意图是, 让不同节点的机器加载自己本地的数据进行训练, 
# 也就是说进行多机多卡训练的时候, 不再是从主节点分发数据到各个从节点, 而是各个从节点自己从自己的硬盘上读取数据.

# 使用DistributedSampler来创造一个sampler提供给DataLoader, sampler的作用自定义一个数据的编号, 
# 然后让DataLoader按照这个编号来提取数据放入到模型中训练, 其中sampler参数和shuffle参数不能同时指定, 
# 如果这个时候还想要可以随机的输入数据, 我们可以在DistributedSampler中指定shuffle参数,
if args.distributed:
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
    train_sampler = None

# 加载训练数据集 pin_memory用于锁页内存，提升训练速度，若gpu显存不够，修改为False
train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
    num_workers=args.workers, pin_memory=True, sampler=train_sampler)

val_loader = torch.utils.data.DataLoader(
    datasets.ImageFolder(valdir, transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize,
    ])),
    batch_size=args.batch_size, shuffle=False,
    num_workers=args.workers, pin_memory=True)

if args.evaluate:
    validate(val_loader, model, criterion, args)
    return

torchvision.datasets.ImageFolder函数解释

class ImageFolder(DatasetFolder):
    """A generic data loader where the images are arranged in this way: ::

        root/dog/xxx.png
        root/dog/xxy.png
        root/dog/xxz.png

        root/cat/123.png
        root/cat/nsdf3.png
        root/cat/asd932_.png

    Args:
    		# 图片存储的根目录，即各类别文件夹所在目录的上一级目录。
        root (string): Root directory path.
        # 对图片进行预处理的操作（函数），原始图片作为输入，返回一个转换后的图片。
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
   			# 对图片类别进行预处理的操作，输入为 target，输出对其的转换。如果不传该参数，即对 target 
   			# 不做任何转换，返回的顺序索引0,1, 2…
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        # 表示数据集加载方式，通常默认加载方式即可。
        loader (callable, optional): A function to load an image given its path.
        # 获取图像文件的路径并检查该文件是否为有效文件的函数(用于检查损坏文件)
        is_valid_file (callable, optional): A function that takes path of an Image file
            and check if the file is a valid_file (used to check of corrupt files)

     Attributes:
     		# 用一个 list 保存类别名称
        classes (list): List of the class names.
        # 类别对应的索引，与不做任何转换返回的 target 对应
        class_to_idx (dict): Dict with items (class_name, class_index).
        # 保存(img-path, class) tuple的 list
        imgs (list): List of (image path, class_index) tuples
    """

    def __init__(self, root, transform=None, target_transform=None,
                 loader=default_loader, is_valid_file=None):
        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,
                                          transform=transform,
                                          target_transform=target_transform,
                                          is_valid_file=is_valid_file)
        self.imgs = self.samples

torch.utils.data.DataLoader()函数解释

class torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, num_workers=0, collate_fn=<function default_collate>, pin_memory=False, drop_last=False)

数据加载器。组合数据集和采样器，并在数据集上提供单进程或多进程迭代器。

参数：

• dataset(Dataset) – 加载数据的数据集。
• batch_size(int, optional) – 每个batch加载多少个样本(默认: 1)。
• shuffle(bool, optional) – 设置为True时会在每个epoch重新打乱数据(默认: False).
• sampler(Sampler, optional) – 定义从数据集中提取样本的策略。如果指定，则忽略shuffle参数。
• num_workers(int, optional) – 用多少个子进程加载数据。0表示数据将在主进程中加载(默认: 0)
• collate_fn(callable, optional) –
• pin_memory(bool, optional) – 锁页内存，创建DataLoader时，设置pin_memory=True，则意味着生成的Tensor数据最开始是属于内存中的锁页内存，这样将内存的Tensor转义到GPU的显存就会更快一些。
• drop_last(bool, optional) – 如果数据集大小不能被batch size整除，则设置为True后可删除最后一个不完整的batch。如果设为False并且数据集的大小不能被batch size整除，则最后一个batch将更小。(默认: False)

模型选择

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.models as models

# 获取torch.models中已有的models并通过首字母排序
model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))
#输出结果
# ['alexnet', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'googlenet', 'inception_v3', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3', 'mobilenet_v2', 'resnet101', 'resnet152', 'resnet18', 'resnet34', 'resnet50', 'resnext101_32x8d', 'resnext50_32x4d', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0', 'squeezenet1_0', 'squeezenet1_1', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', 'wide_resnet101_2', 'wide_resnet50_2']

# 选取模型
if args.pretrained:
  	print("=> using pre-trained model '{}'".format(args.arch))
  	model = models.__dict__[args.arch](pretrained=True)
else:
  	print("=> creating model '{}'".format(args.arch))
  	model = models.__dict__[args.arch]()
    
# define loss function (criterion) and optimizer
# 损失函数使用交叉熵，记得cuda
criterion = nn.CrossEntropyLoss().cuda(args.gpu)

# 优化器使用SGD
optimizer = torch.optim.SGD(model.parameters(), args.lr,
                            momentum=args.momentum,
                            weight_decay=args.weight_decay)

分布式训练

# 通过mp.spawn来进行分布式训练。
# multiprocessing包用于在相同数据的不同进程中共享视图。

import torch.multiprocessing as mp

ngpus_per_node = torch.cuda.device_count()
if args.multiprocessing_distributed:
    # Since we have ngpus_per_node processes per node, the total world_size
    # needs to be adjusted accordingly
    args.world_size = ngpus_per_node * args.world_size
    # Use torch.multiprocessing.spawn to launch distributed processes: the
    # main_worker process function
    # 通过torch.multiprocessing.spawn来开启多进程
  	mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
else:
    # Simply call main_worker function
    main_worker(args.gpu, ngpus_per_node, args)

for epoch in range(args.start_epoch, args.epochs):
    if args.distributed:
        train_sampler.set_epoch(epoch)
    adjust_learning_rate(optimizer, epoch, args)

    # train for one epoch
    train(train_loader, model, criterion, optimizer, epoch, args)

    # evaluate on validation set
    acc1 = validate(val_loader, model, criterion, args)

    # remember best acc@1 and save checkpoint
    # 记录最好的结果
    is_best = acc1 > best_acc1
    best_acc1 = max(acc1, best_acc1)

    # rank：表示当前进程id，用于进程间通讯。0代表主机
    # ngpus_per_node：表示每个节点的gpu数量。
    # (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0)代表只需要保存一次
    # 不写的话会保存多次模型
    if not args.multiprocessing_distributed or (args.multiprocessing_distributed
            and args.rank % ngpus_per_node == 0):
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_acc1': best_acc1,
            'optimizer' : optimizer.state_dict(),
        }, is_best)
        
     
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')

def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    # 启用 Batch Normalization 和 Dropout
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        if torch.cuda.is_available():
            target = target.cuda(args.gpu, non_blocking=True)

        # compute output
        output = model(images)
        
        # loss += (label[k] - h) * (label[k] - h) / 2
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        # 梯度置零，也就是把loss关于weight的导数变成0
        optimizer.zero_grad()
        # 反向传播求梯度
        # d_weights = [d_weights[j] + (label[k] - h) * input[k][j] for j in range(n)]
        loss.backward()
        # 更新所有参数
        # weights = [weights[k] + alpha * d_weights[k] for k in range(n)]
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)

模型加载

# optionally resume from a checkpoint
if args.resume:
    if os.path.isfile(args.resume):
        print("=> loading checkpoint '{}'".format(args.resume))
        if args.gpu is None:
            checkpoint = torch.load(args.resume)
        else:
            # Map model to be loaded to specified single gpu.
            loc = 'cuda:{}'.format(args.gpu)
            checkpoint = torch.load(args.resume, map_location=loc)
        args.start_epoch = checkpoint['epoch']
        best_acc1 = checkpoint['best_acc1']
        if args.gpu is not None:
            # best_acc1 may be from a checkpoint from a different GPU
            best_acc1 = best_acc1.to(args.gpu)
        model.load_state_dict(checkpoint['state_dict'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        print("=> loaded checkpoint '{}' (epoch {})"
                .format(args.resume, checkpoint['epoch']))
    else:
        print("=> no checkpoint found at '{}'".format(args.resume))

模型保存

    is_best = acc1 > best_acc1
      	best_acc1 = max(acc1, best_acc1)  
  	# rank：表示当前进程id，用于进程间通讯。0代表主机
    # ngpus_per_node：表示每个节点的gpu数量。
    # (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0)代表只需要保存一次
    # 不写的话会保存多次模型
    if not args.multiprocessing_distributed or (args.multiprocessing_distributed
            and args.rank % ngpus_per_node == 0):
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_acc1': best_acc1,
            'optimizer' : optimizer.state_dict(),
        }, is_best)

    def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
        torch.save(state, filename)
        if is_best:
            shutil.copyfile(filename, 'model_best.pth.tar')

CUDNN加速

import torch.backends.cudnn as cudnn

# 设置这个 flag 可以让内置的 cuDNN 的 auto-tuner 自动寻找最适合当前配置的高效算法，来达到优化运行效率的问题。
# 如果网络的输入数据维度或类型上变化不大，可以增加运行效率；
# 如果网络的输入数据在每次 iteration 都变化的话，会导致 cnDNN 每次都会去寻找一遍最优配置，这样反而会降低运行效率。
cudnn.benchmark = True

# 由于计算中有随机性，每次网络前馈结果有差异
# 为了避免这种结果波动，设置
cudnn.deterministic = True

验证函数

def validate(val_loader, model, criterion, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    # 不启用 Batch Normalization 和 Dropout。
    model.eval()
		
    # 不会track梯度
    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            if torch.cuda.is_available():
                target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))

    return top1.avg

class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)
 
class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'
     
  
def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

lr动态更新

每30轮，学习率缩小10倍

def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr