test.py

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

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, TracedModel
from scipy.spatial import distance_matrix
import os
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
def calculate_fpr(pred, gt, iou_threshold=0.5):
    """
    计算目标检测的假阳性率 (False Positive Rate, FPR)

    参数:
    pred -- 模型预测的边界框和类别标签 (形状为 Nx6 的张量，包含 [class, confidence, x, y, w, h] 列)
    gt -- 实际的标签，包含真实边界框和类别标签 (形状为 Mx5 的张量，包含 [class, x, y, w, h] 列)
    iou_threshold -- IoU 阈值，用于确定是否为误报

    返回:
    fpr -- 假阳性率
    """
    # 初始化假阳性计数器和总预测数
    false_positives = 0
    total_predictions = len(pred)

    # 如果没有预测，直接返回 FPR 为 0
    if total_predictions == 0:
        return 0.0

    # 遍历每个预测边界框
    for p in pred:
        pred_class, _, px, py, pw, ph = p  # 提取预测类别和边界框
        pred_bbox = torch.tensor([px, py, px + pw, py + ph])  # 转换为 [x1, y1, x2, y2] 格式

        # 查找与预测类别相同的所有真实边界框，假设 `gt` 格式为 [class, conf, x, y, w, h]
        gt_bboxes = gt[gt[:, 0] == pred_class][:, 2:]  # 取出 [x, y, w, h]
        gt_bboxes = torch.cat((gt_bboxes[:, :2], gt_bboxes[:, :2] + gt_bboxes[:, 2:]), dim=1)  # 转换为 [x1, y1, x2, y2]

        if gt_bboxes.shape[0] == 0:
            false_positives += 1
            continue

        ious = box_iou(pred_bbox.unsqueeze(0), gt_bboxes)
        max_iou = ious.max()

        if max_iou < iou_threshold:
            false_positives += 1

    fpr = false_positives / total_predictions
    return fpr
def calculate_normalized_distance(gt_points, pred_points, image_width, image_height):
    """
    计算归一化距离（ND）和匹配点。
    
    参数:
    gt_points: 真实中心点的坐标 (n, 2)
    pred_points: 预测中心点的坐标 (m, 2)
    image_width: 图像宽度
    image_height: 图像高度
    
    返回:
    ND: 归一化距离 (m,)
    matched_indices: 每个预测点对应的真实点的索引 (m,)
    """
    # 计算距离矩阵，行是预测点，列是真实点
    dists = distance_matrix(pred_points, gt_points)

    # 对于每个预测点，找到最近的真实点的索引
    matched_indices = np.argmin(dists, axis=1)

    # 计算每个预测点到其匹配的真实点的欧几里得距离
    min_dists = dists[np.arange(len(pred_points)), matched_indices]

    # 计算图像的对角线长度
    diagonal_length = np.sqrt(image_width**2 + image_height**2)

    # 计算归一化距离 ND
    ND = min_dists / diagonal_length
    mND = np.mean(ND)
    return mND, ND, matched_indices

def calculate_mse(pred_points, gt_points, image_width, image_height):
    """
    计算预测点与真实点之间的均方误差 (MSE)
    
    参数:
    pred_points -- 预测点坐标 (形状为 Nx2 的张量或 numpy 数组)
    gt_points -- 真实点坐标 (形状为 Mx2 的张量或 numpy 数组)
    image_width -- 图像的宽度
    image_height -- 图像的高度
    
    返回:
    mse -- 计算得到的均方误差
    """
    # 如果输入是 PyTorch 张量，转换为 numpy 数组
    if isinstance(pred_points, torch.Tensor):
        pred_points = pred_points.numpy()
    if isinstance(gt_points, torch.Tensor):
        gt_points = gt_points.numpy()

    # 计算距离矩阵，行是预测点，列是真实点
    dists = distance_matrix(pred_points, gt_points)

    # 对于每个预测点，找到最近的真实点的索引
    matched_indices = np.argmin(dists, axis=1)

    # 获取匹配的真实点
    matched_gt_points = gt_points[matched_indices]

    # 计算每个预测点到其匹配的真实点的欧几里得距离平方
    squared_dists = np.sum((pred_points - matched_gt_points) ** 2, axis=1)

    # 计算图像的对角线长度
    diagonal_length = np.sqrt(image_width**2 + image_height**2)

    # 归一化平方距离 (相对于图像对角线长度)
    normalized_squared_dists = squared_dists / (diagonal_length ** 2)

    # 计算均方误差 (MSE)
    mse = np.mean(normalized_squared_dists)
    
    return mse, normalized_squared_dists, matched_indices

def test(data,
         weights=None,
         batch_size=32,
         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,
         trace=False,
         is_coco=False,
         v5_metric=False):
    # Initialize/load model and set device
    training = model is not None
    if training:  # called by train.py
        device = next(model.parameters()).device  # get model device

    else:  # called directly
        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
        model = attempt_load(weights, map_location=device)  # load FP32 model
        gs = max(int(model.stride.max()), 32)  # grid size (max stride)
        imgsz = check_img_size(imgsz, s=gs)  # check img_size
        
        if trace:
            model = TracedModel(model, device, imgsz)

    # Half
    half = device.type != 'cpu' and half_precision  # half precision only supported on CUDA
    if half:
        model.half()

    # Configure
    model.eval()
    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
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    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':
            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
        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, # rect=True,
                                       prefix=colorstr(f'{task}: '))[0]
    """
                     create_dataloader(train_path, imgsz, batch_size, gs, opt,
                                                            hyp=hyp, augment=False, cache=opt.cache_images, rect=opt.rect, rank=rank,
                                                            world_size=opt.world_size, workers=opt.workers,
                                                            image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
    """
    if v5_metric:
        print("Testing with YOLOv5 AP metric...")
    
    seen = 0
    all_MNDs, all_fpr = [], []
    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()
    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.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class, wandb_images, nds = [], [], [], [], [], []
    for batch_i, (img, targets, paths, shapes, points) in enumerate(tqdm(dataloader, desc=s)):
        img = img.to(device, non_blocking=True)
        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
            t = time_synchronized()
            
            out, train_out = model(img, augment=augment)  # inference and training outputs
            #o = torch.concat([out, train_out], dim=-1)
            """
            p_ = torch.split(out, [8, 2], dim=-1)[0] 
            pred_pts = torch.split(out, [8, 2], dim=-1)[1] 
            out = p_
            """
            t0 += time_synchronized() - t

            # Compute loss
            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
            points = points[:,1:].to(device)
            #out = o 
            # print('target s ', targets ) 
            #print('points shape ', points.shape, out.shape)
            points[:, :] *= torch.Tensor([width, height]).to(device)
            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
 
            t = time_synchronized()
            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
        for si, pred in enumerate(out):
            #print('pred shape ', pred.shape)
            p_ = torch.split(pred, [6, 2], dim=-1)[0] 
            pred_pts = torch.split(pred, [6, 2], dim=-1)[1] 
            pred = p_
            
            labels = targets[targets[:, 0] == si, 1:]
            nl = len(labels)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            zero_indices = [i for i, val in enumerate(tcls) if val == 0]
            
            # 使用这些索引从张量中提取对应的元素
            points = points[zero_indices]
            if points.numel() > 0:
                #mND, ND, matched_indices = mean_squared_error(points.cpu(), pred_pts.cpu())# calculate_normalized_distance(points.cpu(), pred_pts.cpu(), width, height)
                mse, _,  matched_indices = calculate_mse(points.cpu(), pred_pts.cpu(), width, height)# calculate_normalized_distance(points.cpu(), pred_pts.cpu(), width, height)
                all_MNDs.append(mse)
                
                
            
            # print('target cls ', len(tcls), points.shape)
            path = Path(paths[si])
            seen += 1
            # ---
            # fpr 
            all_fpr.append(calculate_fpr(pred, targets))
            
            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
            if save_txt:
                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                for *xyxy, conf, cls in predn.tolist():
                    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
                    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
            if save_json:
                # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
                image_id = int(path.stem) if path.stem.isnumeric() else path.stem
                box = xyxy2xywh(predn[:, :4])  # xywh
                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                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)})
            # compute ND
            #print('pred pts ', pred_pts.shape)
            #print('points ', points.shape)
            # Assign all predictions as incorrect
            correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
            if nl:
                detected = []  # target indices
                tcls_tensor = labels[:, 0]

                # target boxes
                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
                for cls in torch.unique(tcls_tensor):
                    ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)  # prediction indices
                    pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices

                    # Search for detections
                    if pi.shape[0]:
                        # Prediction to target ious
                        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):
                            d = ti[i[j]]  # detected target
                            if d.item() not in detected_set:
                                detected_set.add(d.item())
                                detected.append(d)
                                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)
            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
            nds.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
        # Plot images
        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()
    # print("all_mse : ", all_MNDs)        
    # all_MNDs_flat = np.concatenate(all_MNDs)
    average_MND = np.mean(all_MNDs)
    print("Average mse:", average_MND)
    average_fpr = np.mean(all_fpr)
    print("Average fpr:", average_fpr)
    
    # Compute statistics
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
    if len(stats) and stats[0].any():
        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, v5_metric=v5_metric, 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 = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

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

    # Print results per class
    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
    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
    if save_json and len(jdict):
        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
        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='yolov7.pt', help='model.pt path(s)')
    parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
    parser.add_argument('--batch-size', type=int, default=32, 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=0.65, 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')
    parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
    parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation')
    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,
             trace=not opt.no_trace,
             plots=False, 
             v5_metric=opt.v5_metric
             )

    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, v5_metric=opt.v5_metric)

    elif opt.task == 'study':  # run over a range of settings and save/plot
        # python test.py --task study --data coco.yaml --iou 0.65 --weights yolov7.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, v5_metric=opt.v5_metric)
                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