YOLOv5代码详解 (第一部分）

• 1. train.py
• 1.1 使用nvidia的apex接口计算混合精度训练
• 1.2 获取文件路径
• 1.3 获取数据路径
• 1.4 移除之前的结果
• 1.5 创建模型
• 1.6 检查训练和测试图片尺寸
• 1.7 设置优化器参数
• 1.8 加载预训练模型和权重，并写入训练结果到results.txt
• 1.9 把混合精度训练加载入训练中
• 1.10 设置cosine调度器，定义学习率衰减
• 1.11 定义并初始化分布式训练
• 1.12 载入训练集和测试集
• 1.13 模型参数
• 1.14 类别统计
• 1.15 检查anchors是否存在
• 1.16 指数移动平均
• 1.17 开始训练
• 1.17.1 获取参数
• 1.17.2 训练开始
• 1.18 定义模型文件名字
• 1.19 训练结束，返回结果

1. train.py

1.1 使用nvidia的apex接口计算混合精度训练

mixed_precision = True
try:  # Mixed precision training https://github.com/NVIDIA/apex
    from apex import amp
except:
    print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
    mixed_precision = False  # not installed

1.2 获取文件路径

wdir = 'weights' + os.sep  # weights dir
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = 'results.txt'

1.3 获取数据路径

# Configure
    init_seeds(1)
    with open(opt.data) as f:
        data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
    train_path = data_dict['train']
    test_path = data_dict['val']
    nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes

1.4 移除之前的结果

# Remove previous results
    for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
        os.remove(f)

1.5 创建模型

# Create model
    model = Model(opt.cfg).to(device)
    assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (opt.data, nc, opt.cfg, model.md['nc'])
    model.names = data_dict['names']

assert是一个判断表达式，在assert后面成立时创建模型。
参考链接

1.6 检查训练和测试图片尺寸

# Image sizes
    gs = int(max(model.stride))  # grid size (max stride)
    imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples

1.7 设置优化器参数

# Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
    for k, v in model.named_parameters():
        if v.requires_grad:
            if '.bias' in k:
                pg2.append(v)  # biases
            elif '.weight' in k and '.bn' not in k:
                pg1.append(v)  # apply weight decay
            else:
                pg0.append(v)  # all else

    optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \
        optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
    optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
    optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
    print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
    del pg0, pg1, pg2

Optimizer groups: 102 .bias, 108 conv.weight, 99 other
del并非删除数据，而是删除变量（删除指向数据的链接）参考链接

1.8 加载预训练模型和权重，并写入训练结果到results.txt

# Load Model
    google_utils.attempt_download(weights)
    start_epoch, best_fitness = 0, 0.0
    if weights.endswith('.pt'):  # pytorch format
        ckpt = torch.load(weights, map_location=device)  # load checkpoint

        # load model
        try:
            ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items()
                             if model.state_dict()[k].shape == v.shape}  # to FP32, filter
            model.load_state_dict(ckpt['model'], strict=False)
        except KeyError as e:
            s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s." \
                % (opt.weights, opt.cfg, opt.weights)
            raise KeyError(s) from e

        # load optimizer
        if ckpt['optimizer'] is not None:
            optimizer.load_state_dict(ckpt['optimizer'])
            best_fitness = ckpt['best_fitness']

        # load results
        if ckpt.get('training_results') is not None:
            with open(results_file, 'w') as file:
                file.write(ckpt['training_results'])  # write results.txt

        start_epoch = ckpt['epoch'] + 1
        del ckpt

1.9 把混合精度训练加载入训练中

若之前mixed_precision=False则不会加入混合精度训练至训练中。

if mixed_precision:
        model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)

opt_level=‘O1’ ，这里不是‘零1’，而是“O1”（偶1）

1.10 设置cosine调度器，定义学习率衰减

# Scheduler https://arxiv.org/pdf/1812.01187.pdf
    lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1  # cosine
    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
    scheduler.last_epoch = start_epoch - 1  # do not move

1.11 定义并初始化分布式训练

# Initialize distributed training
    if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
        dist.init_process_group(backend='nccl',  # distributed backend
                                init_method='tcp://127.0.0.1:9999',  # init method
                                world_size=1,  # number of nodes
                                rank=0)  # node rank
        model = torch.nn.parallel.DistributedDataParallel(model)

当满足上面三个条件（非CPU、cuda设备大于1、分布式torch可用）时，就可以进行分布式训练了。
作者使用一张卡训练，不满足这个条件，也没有使用分布式训练。 ————————————————————————————————————————————————
nn.distributedataparallel()支持模型多进程并行，适用于单机或多机，每个进程都具备独立的优化器，执行自己的更新过程。
参考链接

1.12 载入训练集和测试集

# Trainloader
    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect)
    mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
    assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg)

    # Testloader
    testloader = create_dataloader(test_path, imgsz_test, batch_size, gs, opt,
                                            hyp=hyp, augment=False, cache=opt.cache_images, rect=True)[0]

dataloader和testloader不同之处在于：

1. testloader：没有数据增强，rect=True（大概是测试图片保留了原图的长宽比）
2. dataloader：数据增强，保留了矩形框训练。

1.13 模型参数

# Model parameters
    hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
    model.nc = nc  # attach number of classes to model
    model.hyp = hyp  # attach hyperparameters to model
    model.gr = 1.0  # giou loss ratio (obj_loss = 1.0 or giou)
    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights

1.14 类别统计

# Class frequency
    labels = np.concatenate(dataset.labels, 0)
    c = torch.tensor(labels[:, 0])  # classes
    # cf = torch.bincount(c.long(), minlength=nc) + 1.
    # model._initialize_biases(cf.to(device))
    if tb_writer:
        plot_labels(labels)
        tb_writer.add_histogram('classes', c, 0)

1.15 检查anchors是否存在

# Check anchors
    if not opt.noautoanchor:
        check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)

1.16 指数移动平均

# Exponential moving average
    ema = torch_utils.ModelEMA(model)

在深度学习中，经常会使用EMA（指数移动平均）这个方法对模型的参数做平均，以求提高测试指标并增加模型鲁棒。参考博客

1.17 开始训练

1.17.1 获取参数

获取开始时间，batch size数量，epochs数量，图片数量。

# Start training
    t0 = time.time() # start time
    nb = len(dataloader)  # number of batches
    n_burn = max(3 * nb, 1e3)  # burn-in iterations, max(3 epochs, 1k iterations)
    maps = np.zeros(nc)  # mAP per class
    results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
    print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
    print('Using %g dataloader workers' % dataloader.num_workers)
    print('Starting training for %g epochs...' % epochs)
    # torch.autograd.set_detect_anomaly(True)

1.17.2 训练开始

加载图片权重（可选），定义进度条，设置偏差Burn-in，使用多尺度，前向传播，损失函数，反向传播，优化器，打印进度条，保存训练参数至tensorboard，计算mAP，保存结果到results.txt，保存模型（最好和最后）。

    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
        model.train()

        # Update image weights (optional)
        if dataset.image_weights:
            w = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
            image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
            dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n)  # rand weighted idx

        # Update mosaic border
        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders

        mloss = torch.zeros(4, device=device)  # mean losses
        print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
        pbar = tqdm(enumerate(dataloader), total=nb)  # progress bar
        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
            ni = i + nb * epoch  # number integrated batches (since train start)
            imgs = imgs.to(device).float() / 255.0  # uint8 to float32, 0 - 255 to 0.0 - 1.0

            # Burn-in
            if ni <= n_burn:
                xi = [0, n_burn]  # x interp
                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # giou loss ratio (obj_loss = 1.0 or giou)
                accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
                for j, x in enumerate(optimizer.param_groups):
                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                    x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
                    if 'momentum' in x:
                        x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])

            # Multi-scale
            if opt.multi_scale:
                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
                sf = sz / max(imgs.shape[2:])  # scale factor
                if sf != 1:
                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
                    imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)

            # Forward
            pred = model(imgs)

            # Loss
            loss, loss_items = compute_loss(pred, targets.to(device), model)
            if not torch.isfinite(loss):
                print('WARNING: non-finite loss, ending training ', loss_items)
                return results

            # Backward
            if mixed_precision:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
            else:
                loss.backward()

            # Optimize
            if ni % accumulate == 0:
                optimizer.step()
                optimizer.zero_grad()
                ema.update(model)

            # Print
            mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
            mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
            s = ('%10s' * 2 + '%10.4g' * 6) % (
                '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
            pbar.set_description(s)

            # Plot
            if ni < 3:
                f = 'train_batch%g.jpg' % ni  # filename
                result = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
                if tb_writer and result is not None:
                    tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
                    # tb_writer.add_graph(model, imgs)  # add model to tensorboard

            # end batch ------------------------------------------------------------------------------------------------

        # Scheduler
        scheduler.step()

        # mAP
        ema.update_attr(model)
        final_epoch = epoch + 1 == epochs
        if not opt.notest or final_epoch:  # Calculate mAP
            results, maps, times = test.test(opt.data,
                                             batch_size=batch_size,
                                             imgsz=imgsz_test,
                                             save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
                                             model=ema.ema,
                                             single_cls=opt.single_cls,
                                             dataloader=testloader)

        # Write
        with open(results_file, 'a') as f:
            f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
        if len(opt.name) and opt.bucket:
            os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name))

        # Tensorboard
        if tb_writer:
            tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
                    'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
                    'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
            for x, tag in zip(list(mloss[:-1]) + list(results), tags):
                tb_writer.add_scalar(tag, x, epoch)

        # Update best mAP
        fi = fitness(np.array(results).reshape(1, -1))  # fitness_i = weighted combination of [P, R, mAP, F1]
        if fi > best_fitness:
            best_fitness = fi

        # Save model
        save = (not opt.nosave) or (final_epoch and not opt.evolve)
        if save:
            with open(results_file, 'r') as f:  # create checkpoint
                ckpt = {'epoch': epoch,
                        'best_fitness': best_fitness,
                        'training_results': f.read(),
                        'model': ema.ema.module if hasattr(model, 'module') else ema.ema,
                        'optimizer': None if final_epoch else optimizer.state_dict()}

            # Save last, best and delete
            torch.save(ckpt, last)
            if (best_fitness == fi) and not final_epoch:
                torch.save(ckpt, best)
            del ckpt

        # end epoch ----------------------------------------------------------------------------------------------------
    # end training

Image sizes 608 train, 608 test（设置训练和测试图片的size）
Using 8 dataloader workers（设置batch size 为8，即一次性输入8张图片训练）
Starting training for 100 epochs… （设置为100个epochs）
—————————————————————————————————————————————
tqdm是一个快速，可扩展的Python进度条，可以在 Python 长循环中添加一个进度提示信息，用户只需要封装任意的迭代器 tqdm(iterator)。
参考博客
tqdm进度条
python pbar = tqdm(enumerate(dataloader), total=nb)表示进度条，total=nb预期的迭代次数，即你上面设置的epochs。
—————————————————————————————————————————————
results.txt保存结果：
0/49 6.44G 0.09249 0.07952 0.05631 0.2283 6 608 0.1107 0.1954 0.1029 0.03088 0.07504 0.06971 0.03865
epoch, best_fitness, training_results, model, optimizer, img-size, P, R, mAP, F1, test_losses=(GIoU, obj, cls)
（有点不对，以后再补充）

1.18 定义模型文件名字

    n = opt.name
    if len(n):
        n = '_' + n if not n.isnumeric() else n
        fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
        for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]):
            if os.path.exists(f1):
                os.rename(f1, f2)  # rename
                ispt = f2.endswith('.pt')  # is *.pt
                strip_optimizer(f2) if ispt else None  # strip optimizer
                os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None  # upload

1.19 训练结束，返回结果

    if not opt.evolve:
        plot_results()  # save as results.png
    print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
    dist.destroy_process_group() if device.type != 'cpu' and torch.cuda.device_count() > 1 else None
    torch.cuda.empty_cache()
    return results

50 epochs completed in 11.954 hours.