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yolov7/test.py

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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
# 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
"""
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