yolov5test.py注释

import argparse
import json
import os
from pathlib import Path
from threading import Thread

os.environ['KMP_DUPLICATE_LIB_OK']='True'

import warnings
warnings.filterwarnings('ignore')

import numpy as np
import torch
import yaml
from tqdm import tqdm

from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
    box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronized


def test(data,
         weights=None,
         batch_size=8,
         imgsz=640,
         conf_thres=0.001,
         iou_thres=0.6,  # for NMS
         save_json=False,
         single_cls=False,
         augment=False,
         verbose=False,
         model=None,
         dataloader=None,
         save_dir=Path(''),  # for saving images
         save_txt=False,  # for auto-labelling
         save_hybrid=False,  # for hybrid auto-labelling
         save_conf=False,  # save auto-label confidences
         plots=True,
         wandb_logger=None,
         compute_loss=None,
         half_precision=True,
         is_coco=False):
    # Initialize/load model and set device
    # 判断是否在训练时调用test，如果是则获取训练时的设备
    training = model is not None
    if training:  # called by train.py
        device = next(model.parameters()).device  # get model device

    else:  # called directly
        # 选择设备
        set_logging()
        device = select_device(opt.device, batch_size=batch_size)

        # Directories
        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

        # Load model
        # 加载模型
        model = attempt_load(weights, map_location=device)  # load FP32 model
        gs = max(int(model.stride.max()), 32)  # grid size (max stride)
        # 检查输入图片分辨率是否能被32整除
        imgsz = check_img_size(imgsz, s=gs)  # check img_size

        # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
        # if device.type != 'cpu' and torch.cuda.device_count() > 1:
        #     model = nn.DataParallel(model)

    # Half
    half = device.type != 'cpu' and half_precision  # half precision only supported on CUDA
    # 如果设备不是cpu并且gpu数目为1，则将模型由Float32转为Float16，提高前向传播的速度
    if half:
        model.half()

    # Configure
    model.eval()
    # 加载数据配置信息
    if isinstance(data, str):
        is_coco = data.endswith('coco.yaml')
        with open(data) as f:
            data = yaml.load(f, Loader=yaml.SafeLoader)
    check_dataset(data)  # check
    nc = 1 if single_cls else int(data['nc'])  # number of classes
    # 设置iou阈值，从0.5~0.95，每间隔0.05取一次
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    # iou个数
    niou = iouv.numel()

    # Logging
    log_imgs = 0
    if wandb_logger and wandb_logger.wandb:
        log_imgs = min(wandb_logger.log_imgs, 100)
    # Dataloader
    if not training:
        if device.type != 'cpu':
            # 创建一个全0数组测试一下前向传播是否正常运行
            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
        task = opt.task if opt.task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
        # 创建dataloader
        # 注意这里rect参数为True，yolov5的测试评估是基于矩形推理的
        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
                                       prefix=colorstr(f'{task}: '))[0]

    seen = 0
    # 获取类别的名字
    confusion_matrix = ConfusionMatrix(nc=nc)
    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
    coco91class = coco80_to_coco91_class()
    # 设置tqdm进度条的显示信息
    s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
    p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
    # 初始化测试集的损失
    loss = torch.zeros(3, device=device)
    # 初始化json文件的字典，统计信息，ap
    jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
    for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
        img = img.to(device, non_blocking=True)
        # 图片也由Float32->Float16
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        targets = targets.to(device)
        nb, _, height, width = img.shape  # batch size, channels, height, width

        with torch.no_grad():
            # Run model
            """
                time_synchronized()函数里面进行了torch.cuda.synchronize()，再返回的time.time()
                torch.cuda.synchronize()等待gpu上完成所有的工作
                总的来说就是这样测试时间会更准确 
            """
            t = time_synchronized()
            # 前向传播
            # inf_out为预测结果, train_out训练结果
            out, train_out = model(img, augment=augment)  # inference and training outputs
            # t0累计前向传播的时间
            t0 += time_synchronized() - t

            # Compute loss
            # 如果是在训练时进行的test，则通过训练结果计算并返回测试集的GIoU, obj, cls损失
            if compute_loss:
                loss += compute_loss([x.float() for x in train_out], targets)[1][:3]  # box, obj, cls

            # Run NMS
            targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
            t = time_synchronized()
            """
                        non_max_suppression进行非极大值抑制;
                        conf_thres为置信度阈值，iou_thres为iou阈值
                        merge为是否合并框
            """
            out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True)
            t1 += time_synchronized() - t

        # Statistics per image
        # 为每一张图片做统计, 写入预测信息到txt文件, 生成json文件字典, 统计tp等
        for si, pred in enumerate(out):
            # 获取第si张图片的标签信息, 包括class,x,y,w,h
            # targets[:, 0]为标签属于哪一张图片的编号
            labels = targets[targets[:, 0] == si, 1:]
            nl = len(labels)
            # 获取标签类别
            tcls = labels[:, 0].tolist() if nl else []  # target class
            path = Path(paths[si])
            # 统计测试图片数量
            seen += 1
            # 如果预测为空，则添加空的信息到stats里
            if len(pred) == 0:
                if nl:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Predictions
            predn = pred.clone()
            scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])  # native-space pred

            # Append to text file
            # 保存预测结果为txt文件
            if save_txt:
                # 获得对应图片的长和宽
                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                for *xyxy, conf, cls in predn.tolist():
                    # xyxy格式->xywh, 并对坐标进行归一化处理
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                    # 保存预测类别和坐标到txt文件
                    with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
                        f.write(('%g ' * len(line)).rstrip() % line + '\n')

            # W&B logging - Media Panel Plots
            if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0:  # Check for test operation
                if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
                    box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                                 "class_id": int(cls),
                                 "box_caption": "%s %.3f" % (names[cls], conf),
                                 "scores": {"class_score": conf},
                                 "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                    boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
                    wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
            wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None

            # Append to pycocotools JSON dictionary
            # 保存coco格式的json文件字典
            if save_json:
                # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
                # 获取图片id
                image_id = int(path.stem) if path.stem.isnumeric() else path.stem
                box = xyxy2xywh(predn[:, :4])  # xywh
                """
                   xyxy格式为左上角右下角的坐标                   
                    xywh是中心点坐标和长和宽
                   而coco的json格式中的框坐标格式为xywh,此处的xy为左上角坐标
                   也就是coco的json格式的坐标格式为：左上角坐标+长宽
                   所以下面一行代码就是将：中心点坐标->左上角
                """
                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                """
                   image_id：图片id, 即属于哪张图
                   category_id: 类别, coco91class()从索引0~79映射到索引0~90
                   bbox：框的坐标
                   score：置信度
                """
                for p, b in zip(pred.tolist(), box.tolist()):
                    jdict.append({'image_id': image_id,
                                  'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
                                  'bbox': [round(x, 3) for x in b],
                                  'score': round(p[4], 5)})

            # Assign all predictions as incorrect
            # 初始化预测评定，niou为iou阈值的个数
            correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
            if nl:
                # detected用来存放已检测到的目标
                detected = []  # target indices
                tcls_tensor = labels[:, 0]

                # target boxes
                # 获得xyxy格式的框并乘以wh
                tbox = xywh2xyxy(labels[:, 1:5])
                scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])  # native-space labels
                if plots:
                    confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))

                # Per target class
                # 对图片中的每个类单独处理
                for cls in torch.unique(tcls_tensor):
                    # 标签框该类别的索引
                    ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)  # prediction indices
                    # 预测框该类别的索引
                    pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices

                    # Search for detections
                    if pi.shape[0]:
                        # Prediction to target ious
                        # box_iou计算预测框与标签框的iou值，max(1)选出最大的ious值,i为对应的索引
                        """
                        pred shape[N, 4]
                        tbox shape[M, 4]
                        box_iou shape[N, M]
                        ious shape[N, 1]
                        i shape[N, 1], i里的值属于0~M
                        """
                        ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1)  # best ious, indices

                        # Append detections
                        detected_set = set()
                        for j in (ious > iouv[0]).nonzero(as_tuple=False):
                            # 获得检测到的目标
                            d = ti[i[j]]  # detected target
                            if d.item() not in detected_set:
                                # 添加d到detected
                                detected_set.add(d.item())
                                detected.append(d)
                                # iouv为以0.05为步长 0.5到0.95的序列
                                # 获得不同iou阈值下的true positive
                                correct[pi[j]] = ious[j] > iouv  # iou_thres is 1xn
                                if len(detected) == nl:  # all targets already located in image
                                    break

            # Append statistics (correct, conf, pcls, tcls)
            # 每张图片的结果统计到stats里
            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

        # Plot images
        # 画出第1个batch的图片的ground truth和预测框并保存
        if plots and batch_i < 3:
            f = save_dir / f'test_batch{batch_i}_labels.jpg'  # labels
            Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
            f = save_dir / f'test_batch{batch_i}_pred.jpg'  # predictions
            Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()

    # Compute statistics
    # 将stats列表的信息拼接到一起
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
    if len(stats) and stats[0].any():
        # 根据上面得到的tp等信息计算指标
        # 精准度TP/TP+FP，召回率TP/P，map，f1分数，类别
        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
        ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
        # nt是一个列表，测试集每个类别有多少个标签框
        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

    # Print results
    # 打印指标结果
    pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))

    # Print results per class
    # 细节展示每一个类别的指标
    if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
        for i, c in enumerate(ap_class):
            print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))

    # Print speeds
    # 打印前向传播耗费的时间、nms的时间、总时间
    t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
    if not training:
        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Plots
    if plots:
        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
        if wandb_logger and wandb_logger.wandb:
            val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
            wandb_logger.log({"Validation": val_batches})
    if wandb_images:
        wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})

    # Save JSON
    # 采用之前保存的json格式预测结果，通过cocoapi评估指标
    # 需要注意的是 测试集的标签也需要转成coco的json格式
    if save_json and len(jdict):
        # 获取图片id
        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
        # 获取预测框的json文件路径并打开
        anno_json = '../coco/annotations/instances_val2017.json'  # annotations json
        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
        print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
        with open(pred_json, 'w') as f:
            json.dump(jdict, f)

        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            from pycocotools.coco import COCO
            from pycocotools.cocoeval import COCOeval

            anno = COCO(anno_json)  # init annotations api
            pred = anno.loadRes(pred_json)  # init predictions api
            eval = COCOeval(anno, pred, 'bbox')
            if is_coco:
                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate
            eval.evaluate()
            eval.accumulate()
            eval.summarize()
            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
        except Exception as e:
            print(f'pycocotools unable to run: {e}')

    # Return results
    model.float()  # for training
    if not training:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t


if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='test.py')
    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
    parser.add_argument('--batch-size', type=int, default=4, help='size of each image batch')
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
    parser.add_argument('--task', default='val', help='train, val, test, speed or study')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--verbose', action='store_true', help='report mAP by class')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
    parser.add_argument('--project', default='runs/test', help='save to project/name')
    parser.add_argument('--name', default='exp', help='save to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    opt = parser.parse_args()
    opt.save_json |= opt.data.endswith('coco.yaml')
    opt.data = check_file(opt.data)  # check file
    print(opt)
    check_requirements()

    if opt.task in ('train', 'val', 'test'):  # run normally
        test(opt.data,
             opt.weights,
             opt.batch_size,
             opt.img_size,
             opt.conf_thres,
             opt.iou_thres,
             opt.save_json,
             opt.single_cls,
             opt.augment,
             opt.verbose,
             save_txt=opt.save_txt | opt.save_hybrid,
             save_hybrid=opt.save_hybrid,
             save_conf=opt.save_conf,
             )

    elif opt.task == 'speed':  # speed benchmarks
        for w in opt.weights:
            test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False)
    # task == 'study'时，就评估yolov5系列和yolov3-spp各个模型在各个尺度下的指标并可视化
    elif opt.task == 'study':  # run over a range of settings and save/plot
        # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
        x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
        for w in opt.weights:
            f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
            y = []  # y axis
            for i in x:  # img-size
                print(f'\nRunning {f} point {i}...')
                r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
                               plots=False)
                y.append(r + t)  # results and times
            np.savetxt(f, y, fmt='%10.4g')  # save
        os.system('zip -r study.zip study_*.txt')
        plot_study_txt(x=x)  # plot

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原文链接：https://blog.csdn.net/Recursions/article/details/122934873