# Save one txt result

gn = torch.tensor(shape)[[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(file, 'a') as f:

# Save one JSON result {"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(predn.tolist(), box.tolist()):

jdict.append({

'image_id': image_id,

'category_id': class_map[int(p[5])],

'bbox': [round(x, 3) for x in b],

"""

Return correct prediction matrix

Arguments:

detections (array[N, 6]), x1, y1, x2, y2, conf, class

labels (array[M, 5]), class, x1, y1, x2, y2

Returns:

correct (array[N, 10]), for 10 IoU levels

"""

correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)

iou = box_iou(labels[:, 1:], detections[:, :4])

correct_class = labels[:, 0:1] == detections[:, 5]

for i in range(len(iouv)):

x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match

if x[0].shape[0]:

matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]

if x[0].shape[0] > 1:

matches = matches[matches[:, 2].argsort()[::-1]]

matches = matches[np.unique(matches[:, 1], return_index=True)[1]]

# matches = matches[matches[:, 2].argsort()[::-1]]

matches = matches[np.unique(matches[:, 0], return_index=True)[1]]

correct[matches[:, 1].astype(int), i] = True

data,

weights=None, # model.pt path(s)

batch_size=32, # batch size

imgsz=640, # inference size (pixels)

conf_thres=0.001, # confidence threshold

iou_thres=0.6, # NMS IoU threshold

max_det=300, # maximum detections per image

task='val', # train, val, test, speed or study

device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu

workers=8, # max dataloader workers (per RANK in DDP mode)

single_cls=False, # treat as single-class dataset

augment=False, # augmented inference

verbose=False, # verbose output

save_txt=False, # save results to *.txt

save_hybrid=False, # save label prediction hybrid results to *.txt

save_conf=False, # save confidences in --save-txt labels

save_json=False, # save a COCO-JSON results file

project=ROOT / 'runs/val', # save to project/name

name='exp', # save to project/name

exist_ok=False, # existing project/name ok, do not increment

half=True, # use FP16 half-precision inference

dnn=False, # use OpenCV DNN for ONNX inference

model=None,

dataloader=None,

save_dir=Path(''),

plots=True,

callbacks=Callbacks(),

compute_loss=None,

):

# Initialize/load model and set device

training = model is not None

if training: # called by train.py

device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model

half &= device.type != 'cpu' # half precision only supported on CUDA

model.half() if half else model.float()

else: # called directly

device = select_device(device, batch_size=batch_size)

# Directories

save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run

(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir

# Load model

model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)

stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine

imgsz = check_img_size(imgsz, s=stride) # check image size

half = model.fp16 # FP16 supported on limited backends with CUDA

if engine:

batch_size = model.batch_size

else:

device = model.device

if not (pt or jit):

batch_size = 1 # export.py models default to batch-size 1

LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')

# Data

data = check_dataset(data) # check

# Configure

model.eval()

cuda = device.type != 'cpu'

is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset

nc = 1 if single_cls else int(data['nc']) # number of classes

iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for [email protected]:0.95

niou = iouv.numel()

# Dataloader

if not training:

if pt and not single_cls: # check --weights are trained on --data

ncm = model.model.nc

assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \

f'classes). Pass correct combination of --weights and --data that are trained together.'

model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup

pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks

task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images

dataloader = create_dataloader(data[task],

imgsz,

batch_size,

stride,

single_cls,

pad=pad,

rect=rect,

workers=workers,

prefix=colorstr(f'{task}: '))[0]

seen = 0

confusion_matrix = ConfusionMatrix(nc=nc)

names = model.names if hasattr(model, 'names') else model.module.names # get class names

if isinstance(names, (list, tuple)): # old format

names = dict(enumerate(names))

class_map = coco80_to_coco91_class() if is_coco else list(range(1000))

s = ('"s' 's' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95')

tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0

dt = Profile(), Profile(), Profile() # profiling times

loss = torch.zeros(3, device=device)

jdict, stats, ap, ap_class = [], [], [], []

callbacks.run('on_val_start')

pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar

for batch_i, (im, targets, paths, shapes) in enumerate(pbar):

callbacks.run('on_val_batch_start')

with dt[0]:

if cuda:

im = im.to(device, non_blocking=True)

targets = targets.to(device)

im = im.half() if half else im.float() # uint8 to fp16/32

im /= 255 # 0 - 255 to 0.0 - 1.0

nb, _, height, width = im.shape # batch size, channels, height, width

# Inference

with dt[1]:

preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)

# Loss

if compute_loss:

loss = compute_loss(train_out, targets)[1] # box, obj, cls

# NMS

targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels

lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling

with dt[2]:

preds = non_max_suppression(preds,

conf_thres,

iou_thres,

labels=lb,

multi_label=True,

agnostic=single_cls,

max_det=max_det)

# Metrics

for si, pred in enumerate(preds):

labels = targets[targets[:, 0] == si, 1:]

nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions

path, shape = Path(paths[si]), shapes[si][0]

correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init

seen = 1

if npr == 0:

if nl:

stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))

if plots:

confusion_matrix.process_batch(detections=None, labels=labels[:, 0])

continue

# Predictions

if single_cls:

pred[:, 5] = 0

predn = pred.clone()

scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred

# Evaluate

if nl:

tbox = xywh2xyxy(labels[:, 1:5]) # target boxes

scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels

labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels

correct = process_batch(predn, labelsn, iouv)

if plots:

confusion_matrix.process_batch(predn, labelsn)

stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)

# Save/log

if save_txt:

save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')

if save_json:

save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary

callbacks.run('on_val_image_end', pred, predn, path, names, im[si])

# Plot images

if plots and batch_i < 3:

plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels

plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred

callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds)

# Compute metrics

stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy

if len(stats) and stats[0].any():

tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)

ap50, ap = ap[:, 0], ap.mean(1) # [email protected], [email protected]:0.95

mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()

nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class

# Print results

pf = '"s' 'i' * 2 '.3g' * 4 # print format

LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))

if nt.sum() == 0:

LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')

# 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):

LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))

# Print speeds

t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image

if not training:

shape = (batch_size, 3, imgsz, imgsz)

LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)

# Plots

if plots:

confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))

callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)

# 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 = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations

pred_json = str(save_dir / f'{w}_predictions.json') # predictions

LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')

with open(pred_json, 'w') as f:

json.dump(jdict, f)

try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb

check_requirements('pycocotools>=2.0.6')

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.im_files] # image IDs to evaluate

eval.evaluate()

eval.accumulate()

eval.summarize()

map, map50 = eval.stats[:2] # update results ([email protected]:0.95, [email protected])

except Exception as e:

LOGGER.info(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 ''

LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")

maps = np.zeros(nc) map

for i, c in enumerate(ap_class):

maps[c] = ap[i]

parser = argparse.ArgumentParser()

parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')

parser.add_argument('--weights', nargs=' ', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')

parser.add_argument('--batch-size', type=int, default=32, help='batch size')

parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')

parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')

parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')

parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')

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('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')

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 COCO-JSON results file')

parser.add_argument('--project', default=ROOT / 'runs/val', 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('--half', action='store_true', help='use FP16 half-precision inference')

parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')

opt = parser.parse_args()

opt.data = check_yaml(opt.data) # check YAML

opt.save_json |= opt.data.endswith('coco.yaml')

opt.save_txt |= opt.save_hybrid

print_args(vars(opt))

check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))

if opt.task in ('train', 'val', 'test'): # run normally

if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466

LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')

if opt.save_hybrid:

LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')

run(**vars(opt))

else:

weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]

opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results

if opt.task == 'speed': # speed benchmarks

# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...

opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False

for opt.weights in weights:

run(**vars(opt), plots=False)

elif opt.task == 'study': # speed vs mAP benchmarks

# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...

for opt.weights in weights:

f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to

x, y = list(range(256, 1536 128, 128)), [] # x axis (image sizes), y axis

for opt.imgsz in x: # img-size

LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')

r, _, t = run(**vars(opt), plots=False)

y.append(r t) # results and times

np.savetxt(f, y, fmt='.4g') # save

subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt'])

plot_val_study(x=x) # plot

else: