yolov7/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, append_to_txt
from utils.torch_utils import select_device, time_synchronized, TracedModel


def test(data,
         weights=None,
         batch_size=32,
         imgsz=640,
         conf_thres=0.001,
         iou_thres=0.6,  # used 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,
         **kwargs):
    # Initialize/load model and set device

    hyp = kwargs['hyp']
    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

        if opt.save_path == '':
            save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
        else:
            save_dir = Path(increment_path(os.path.join(opt.save_path, Path(opt.project) , opt.name), exist_ok=opt.exist_ok))

        try: # no suduer can fail
            (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
        except Exception as e:
            print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!",e)

        # 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, opt.input_channels)

    #torch.backends.cudnn.benchmark = True  ##uses the inbuilt cudnn auto-tuner to find the fastest convolution algorithms. -
    # Half
    half = device.type != 'cpu' and half_precision  # half precision only supported on CUDA @@ HK : TODO what are the consequences  add :
    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, opt.input_channels, 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
        hyp = dict()
        hyp['person_size_small_medium_th'] = 32 * 32
        hyp['car_size_small_medium_th'] = 44 * 44

        hyp['img_percentile_removal'] = 0.3
        hyp['beta'] = 0.3
        hyp['gamma'] = 80 # dummy anyway augmentation is disabled
        hyp['gamma_liklihood'] = 0.01
        hyp['random_pad'] = True
        hyp['copy_paste'] = False
        # augment=False explicit no augmentation to test
        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, hyp, pad=0.5, augment=False, rect=False, #rect was True  # HK@@@ TODO : why pad =0.5?? only effective in rect=True in test time ? https://github.com/ultralytics/ultralytics/issues/13271
                                       prefix=colorstr(f'{task}: '), rel_path_images=data['path'], num_cls=data['nc'])[0]

    if v5_metric:
        print("Testing with YOLOv5 AP metric...")
    
    seen = 0
    confusion_matrix = ConfusionMatrix(nc=nc, conf=conf_thres, iou_thres=iou_thres) # HK per conf per iou_thresh
    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 = [], [], [], [], []
    stats_all_large, stats_person_medium = [], []
    for batch_i, (img, targets, paths, shapes) 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 c# already done inside dataloader
        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(4 coordination, obj conf, cls conf ) and training outputs(batch_size, anchor per scale, x,y dim of scale out 40x40 ,n_classes-conf+1-objectness+4-bbox ) over 3 scales diferent outputs (2,2,80,80,7), (2,2,40,40,7)  : 640/8=40
            t0 += time_synchronized() - t
            # out coco 80 classes : [1, 25200, 85] [batch, proposals_3_scales,4_box__coord+1_obj_score + n x classes]
            # 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
            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) # Does thresholding for class  : list of detections, on (n,6) tensor per image [xyxy, conf, cls]
            # out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=False) # Does thresholding for class  : list of detections, on (n,6) tensor per image [xyxy, conf, cls]
            t1 += time_synchronized() - t

        # Statistics per image
        for si, pred in enumerate(out): # [bbox_coors, objectness_logit, class]
            labels = targets[targets[:, 0] == si, 1:]
            nl = len(labels)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            path = Path(paths[si])
            seen += 1

            if len(pred) == 0:
                if nl:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))    #niou for COCO 0.5:0.05:1
                    stats_all_large.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                    stats_person_medium.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Predictions
            predn = pred.clone() # *xyxy, conf, cls in predn  [x y ,w ,h, conf, cls] taking top 300 after NMS
            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)})

            # Assign all predictions as incorrect ; pred takes top 300 predictions conf over 10 ious [0.5:0.95:0.05]
            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): # iouv[0]=0.5 IOU for dectetions iouv in general are all 0.5:0.05:.. for COCO
                            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_objectness, pcls, tcls) Predicted class is Max-Likelihood among all classes logit and threshol goes over the objectness only
            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # correct @ IOU=0.5 of pred box with target
            if not training or 1:
                # assert len(pred[:, :4]) == 1
                x, y, w, h = xyxy2xywh(pred[:, :4])[0]
                if w * h > hyp['person_size_small_medium_th'] and  w * h <= hyp['car_size_small_medium_th']:
                    stats_person_medium.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
                if w * h > hyp['car_size_small_medium_th']:
                    stats_all_large.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

        # Plot images  aa = np.repeat(img[0,:,:,:].cpu().permute(1,2,0).numpy(), 3, axis=2).astype('float32') cv2.imwrite('test/exp40/test_batch88_labels__.jpg', aa*255)
        if plots and batch_i > 10 or 1:
            # conf_thresh_plot = 0.1 # the plot threshold the connfidence
            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
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy

    if not training or 1:
        stats_person_medium = [np.concatenate(x, 0) for x in zip(*stats_person_medium)]  # to numpy
        stats_all_large = [np.concatenate(x, 0) for x in zip(*stats_all_large)]  # to numpy

    if len(stats) and stats[0].any(): # P, R @  # max F1 index
        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names) #based on correct @ IOU=0.5 of pred box with target
        if not training or 1:
            if bool(stats_person_medium):
                p_med, r_med, ap_med, f1_med, ap_class_med = ap_per_class(*stats_person_medium, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names, tag='small_objects')
                ap50_med, ap_med = ap_med[:, 0], ap_med.mean(1)  # AP@0.5, AP@0.5:0.95
                mp_med, mr_med, map50_med, map_med = p_med.mean(), r_med.mean(), ap50_med.mean(), ap_med.mean()
                nt_med = np.bincount(stats_person_medium[3].astype(np.int64), minlength=nc)  # number of targets per class
            if bool(stats_all_large):
                p_large, r_large, ap_large, f1_large, ap_class_large = ap_per_class(*stats_all_large, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names, tag='large_objects')
                ap50_large, ap_large = ap_large[:, 0], ap_large.mean(1)  # AP@0.5, AP@0.5:0.95
                mp_large, mr_large, map50_large, map_large = p_large.mean(), r_large.mean(), ap50_large.mean(), ap_large.mean()
                nt_large = np.bincount(stats_all_large[3].astype(np.int64), minlength=nc)  # number of targets per class

        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)
        nt_med = torch.zeros(1)
        nt_large = torch.zeros(1)

    # Print results
    pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
    if not training or 1:
        if bool(stats_person_medium):
            try:
                print(pf % ('all_medium', seen, nt_med.sum(), mp_med, mr_med, map50_med, map_med))
            except Exception as e:
                print(e)

        if bool(stats_all_large):
            try:
                print(pf % ('all_large', seen, nt_large.sum(), mp_large, mr_large, map50_large, map_large))
            except Exception as e:
                print(e)

    file_path = os.path.join(save_dir, 'class_stats.txt') #'Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95'
    append_to_txt(file_path, 'all', seen, nt.sum(), mp, mr, map50, map)

    # Print results per class
    if 1 or (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]))
            append_to_txt(file_path, names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])
        try:
            if bool(stats_person_medium):
                for i, c in enumerate(ap_class_med):
                    print(pf % (names[c]+ '_med', seen, nt_med[c], p_med[i], r_med[i], ap50_med[i], ap_med[i]))
                    append_to_txt(file_path, names[c] + '_med', seen, nt_med[c], p_med[i], r_med[i], ap50_med[i], ap_med[i])
        except Exception as e:
            print(e)

        try:
            if bool(stats_all_large):
                for i, c in enumerate(ap_class_large):
                    print(pf % (names[c]+ '_large', seen, nt_large[c], p_large[i], r_large[i], ap50_large[i], ap_large[i]))
                    append_to_txt(file_path, names[c] + '_large', seen, nt_large[c], p_large[i], r_large[i], ap50_large[i], ap_large[i])
        except Exception as e:
            print(e)

    # 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): # @@ HK TODO:
        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=0.6, 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')
    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')
    parser.add_argument('--norm-type', type=str, default='standardization',
                        choices=['standardization', 'single_image_0_to_1', 'single_image_mean_std','single_image_percentile_0_255',
                                 'single_image_percentile_0_1', 'remove+global_outlier_0_1'],
                        help='Normalization approach')

    parser.add_argument('--no-tir-signal', action='store_true', help='')

    parser.add_argument('--tir-channel-expansion', action='store_true', help='drc_per_ch_percentile')

    parser.add_argument('--input-channels', type=int, default=3, help='')

    parser.add_argument('--save-path', default='', help='save to project/name')


    opt = parser.parse_args()

    if opt.tir_channel_expansion: # operates over 3 channels
        opt.input_channels = 3

    if opt.tir_channel_expansion and opt.norm_type != 'single_image_percentile_0_1': # operates over 3 channels
        print('Not a good combination')

    opt.save_json |= opt.data.endswith('coco.yaml')
    opt.data = check_file(opt.data)  # check file
    print(opt)
    #check_requirements()
    hyp = dict()

    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,
             hyp=hyp)

    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
"""

--weights ./yolov7/yolov7.pt --device 0 --batch-size 16 --data data/coco_2_tir.yaml --img-size 640 --conf 0.6 --verbose --save-txt --save-hybrid --norm-type single_image_percentile_0_1
test based on RGB coco model
--weights ./yolov7/yolov7.pt --device 0 --batch-size 64 --data data/coco_2_tir.yaml --img-size 640 --conf 0.25 --verbose --save-txt --norm-type single_image_percentile_0_1 --project test --task train

--weights ./yolov7/yolov7.pt --device 0 --batch-size 64 --data data/tir_od.yaml --img-size 640 --conf 0.25 --verbose --save-txt --norm-type single_image_percentile_0_1 --project test --task val
# Using pretrained model
--weights /mnt/Data/hanoch/runs/train/yolov7434/weights/epoch_099.pt --device 0 --batch-size 4 --data data/tir_od_test_set.yaml --img-size 640 --conf 0.25 --verbose --norm-type single_image_percentile_0_1 --project test --task test
#vbased on 7555 mAP=82.3
--weights /mnt/Data/hanoch/runs/train/yolov7563/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --conf 0.02 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --iou-thres 0.4

/home/hanoch/projects/tir_od/runs/train/yolov7563/weights


--weights /mnt/Data/hanoch/runs/train/yolov7575/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --conf 0.001 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --iou-thres 0.6
--weights /home/hanoch/projects/tir_od/runs/gpu02/yolov74/weights --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --conf 0.001 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --iou-thres 0.6

"""