test_fp.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_false_positive_rate(pred, labels, iou_threshold=0.5):
    """
    计算假阳性率 (False Positive Rate, FPR)
    
    参数:
    pred -- 模型预测的边界框和类别标签
    labels -- 实际标签，包含真实边界框和类别标签
    iou_threshold -- IOU 阈值，用于确定是否为误报
    
    返回:
    fpr -- 假阳性率
    """
    false_positives = 0
    total_negatives = len(pred)  # 假设所有预测都是负例
    print('-------------------------')
    print('len pred ', len(pred))
    print(labels)
    print(pred)
    print('-------------------------')
    for i, pred_bbox in enumerate(pred):
        ious = box_iou(pred_bbox.unsqueeze(0), labels[:, 1:5])  # Calculate IOU with ground truth
        if ious.max() < iou_threshold:  # No match found above IOU threshold
            false_positives += 1

    fpr = false_positives / total_negatives if total_negatives > 0 else 0  # 防止除以零
    return fpr

def test(data,
         weights=None,
         batch_size=32,
         imgsz=640,
         conf_thres=0.001,
         iou_thres=0.6,
         save_json=False,
         single_cls=False,
         augment=False,
         verbose=False,
         model=None,
         dataloader=None,
         save_dir=Path(''),
         save_txt=False,
         save_hybrid=False,
         save_conf=False,
         plots=True,
         wandb_logger=None,
         compute_loss=None,
         half_precision=True,
         trace=False,
         is_coco=False,
         v5_metric=False):
    training = model is not None
    if training:  
        device = next(model.parameters()).device
    else:  
        set_logging()
        device = select_device(opt.device, batch_size=batch_size)
        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
        model = attempt_load(weights, map_location=device)
        gs = max(int(model.stride.max()), 32)
        imgsz = check_img_size(imgsz, s=gs)
        
        if trace:
            model = TracedModel(model, device, imgsz)

    half = device.type != 'cpu' and half_precision  
    if half:
        model.half()

    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)  
    nc = 1 if single_cls else int(data['nc'])
    iouv = torch.linspace(0.5, 0.95, 10).to(device)
    niou = iouv.numel()

    log_imgs = 0
    if wandb_logger and wandb_logger.wandb:
        log_imgs = min(wandb_logger.log_imgs, 100)

    if not training:
        if device.type != 'cpu':
            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))
        task = opt.task if opt.task in ('train', 'val', 'test') else 'val'
        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, prefix=colorstr(f'{task}: '))[0]

    seen = 0
    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 = [], [], [], [], []

    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()
        img /= 255.0
        targets = targets.to(device)
        nb, _, height, width = img.shape

        with torch.no_grad():
            t = time_synchronized()
            out, train_out = model(img, augment=augment)
            t0 += time_synchronized() - t

            if compute_loss:
                loss += compute_loss([x.float() for x in train_out], targets)[1][:3]

            targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)
            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []
            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

        for si, pred in enumerate(out):
            labels = targets[targets[:, 0] == si, 1:]
            fpr = calculate_false_positive_rate(pred, labels)  # 计算假阳性率
            print(f"False Positive Rate for image {si}: {fpr}")

            path = Path(paths[si])
            seen += 1

            if len(pred) == 0:
                if len(labels):
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

            predn = pred.clone()
            scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])

            correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
            if len(labels):
                detected = []
                tcls_tensor = labels[:, 0]
                tbox = xywh2xyxy(labels[:, 1:5])
                scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])
                if plots:
                    confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))

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

                    if pi.shape[0]:
                        ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1)

                        detected_set = set()
                        for j in (ious > iouv[0]).nonzero(as_tuple=False):
                            d = ti[i[j]]
                            if d.item() not in detected_set:
                                detected_set.add(d.item())
                                detected.append(d)
                                correct[pi[j]] = ious[j] > iouv

            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

    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,
             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