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,

is_coco=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

# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99

# if device.type != 'cpu' and torch.cuda.device_count() > 1:

# model = nn.DataParallel(model)

# 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,

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) 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

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

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):

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))

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)})

# 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))

# 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()

# 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, 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]