YOLOv 5 코드 상세 설명 (test. py 부분)
73180 단어 목표 검출
이 부분 은 train. py 를 실행 할 때 epoch 의 mAP. PS 를 계산 하 는 데 사 용 됩 니 다. train. py 와 비슷 한 부분 은 설명 하지 않 습 니 다.
2.1 초 파라미터 설정
가중치, 데이터, batch size, 이미지 크기, 어떤 그래 픽 카드 를 사용 하 는 지, 데이터 강화, mAP 를 계산 합 니 다.
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='test.py')
parser.add_argument('--weights', type=str, default='weights/best.pt', help='model.pt path')
parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--img-size', type=int, default=608, 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('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--task', default='', help="'val', 'test', 'study'")
parser.add_argument('--device', default='1', 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')
opt = parser.parse_args()
opt.img_size = check_img_size(opt.img_size)
opt.save_json = opt.save_json or opt.data.endswith('coco.yaml')
opt.data = check_file(opt.data) # check file
print(opt)
2.2 작업 설정 (검증, 테스트, 학습)
# task = 'val', 'test', 'study'
if opt.task in ['val','test']: # (default) 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)
elif opt.task == 'study': # run over a range of settings and save/plot
for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to
x = list(range(288, 896, 64)) # x axis
y = [] # y axis
for i in x: # img-size
print('
Running %s point %s...' % (f, i))
r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
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(f, x) # plot
'study' 는 예비 훈련 가중치 의 조작 을 수정 할 수 있 을 것 이다.
2.3 테스트 함수
2.3.1 모델 초기 화
모델 이 존재 하 는 지 여 부 를 판단 하고 존재 하지 않 으 면 가짜 로 훈련 하여 이전의 테스트 결 과 를 제거 하고 모델 을 다운로드 합 니 다.
# Initialize/load model and set device
if model is None:
training = False
device = torch_utils.select_device(opt.device, batch_size=batch_size)
half = device.type != 'cpu' # half precision only supported on CUDA
# Remove previous
for f in glob.glob('test_batch*.jpg'):
os.remove(f)
# Load model
google_utils.attempt_download(weights)
model = torch.load(weights, map_location=device)['model'].float() # load to FP32
torch_utils.model_info(model)
model.fuse()
model.to(device)
if half:
model.half() # to FP16
# Multi-GPU disabled, incompatible with .half()
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
else: # called by train.py
training = True
device = next(model.parameters()).device # get model device
# half disabled https://github.com/ultralytics/yolov5/issues/99
half = False # device.type != 'cpu' and torch.cuda.device_count() == 1
if half:
model.half() # to FP16
2.3.2 장치 유형 을 판단 하고 GPU 한 장만 사용 하여 테스트
장치 유형 을 판단 하고 GPU 만 절반 의 정밀 도 를 지원 합 니 다.
# Half
half = device.type != 'cpu' and torch.cuda.device_count() == 1 # half precision only supported on single-GPU
if half:
model.half() # to FP16
2.3.3 설정 파일 경로 와 파일 매개 변 수 를 가 져 옵 니 다.
프로필 Yml 의 nc 인 자 를 가 져 옵 니 다.
# Configure
model.eval()
with open(data) as f:
data = yaml.load(f, Loader=yaml.FullLoader) # model dict
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 [email protected]:0.95
niou = iouv.numel()
2.3.4 데이터 획득
# Dataloader
if dataloader is None: # not training
merge = opt.merge # use Merge NMS
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
dataloader = create_dataloader(path, imgsz, batch_size, int(max(model.stride)), opt,
hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0]
seen = 0
names = model.names if hasattr(model, 'names') else model.module.names
coco91class = coco80_to_coco91_class()
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', '[email protected]', '[email protected]:.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 = [], [], [], []
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
img = img.to(device)
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
whwh = torch.Tensor([width, height, width, height]).to(device)
# Disable gradients
with torch.no_grad():
# Run model
t = torch_utils.time_synchronized()
inf_out, train_out = model(img, augment=augment) # inference and training outputs
t0 += torch_utils.time_synchronized() - t
# Compute loss
if training: # if model has loss hyperparameters
loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # GIoU, obj, cls
# Run NMS
t = torch_utils.time_synchronized()
output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge)
t1 += torch_utils.time_synchronized() - t
# Statistics per image
for si, pred in enumerate(output):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
seen += 1
if pred is None:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
# Append to text file
# with open('test.txt', 'a') as file:
# [file.write('%11.5g' * 7 % tuple(x) + '
') for x in pred]
# Clip boxes to image bounds
clip_coords(pred, (height, width))
# 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(paths[si]).stem.split('_')[-1])
box = pred[:, :4].clone() # xyxy
scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape
box = xyxy2xywh(box) # 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])],
'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]) * whwh
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero().view(-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero().view(-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1) # best ious, indices
# Append detections
for j in (ious > iouv[0]).nonzero():
d = ti[i[j]] # detected target
if d not in detected:
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 batch_i < 1:
f = 'test_batch%g_gt.jpg' % batch_i # filename
plot_images(img, targets, paths, f, names) # ground truth
f = 'test_batch%g_pred.jpg' % batch_i
plot_images(img, output_to_target(output, width, height), paths, f, names) # predictions
2.3.5 계산 맵 데이터
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats):
p, r, ap, f1, ap_class = ap_per_class(*stats)
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, [email protected], [email protected]: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)
2.3.5 인쇄 결과 (그림, 속도), 결 과 를 json 에 저장 하고 결 과 를 되 돌려 줍 니 다.
# Print results
pf = '%20s' + '%12.3g' * 6 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
# Print results per class
if verbose 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)
# Save JSON
if save_json and map50 and len(jdict):
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
f = 'detections_val2017_%s_results.json' % \
(weights.split(os.sep)[-1].replace('.pt', '') if weights else '') # filename
print('
COCO mAP with pycocotools... saving %s...' % f)
with open(f, 'w') as file:
json.dump(jdict, file)
try:
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
cocoDt = cocoGt.loadRes(f) # initialize COCO pred api
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds # image IDs to evaluate
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
map, map50 = cocoEval.stats[:2] # update results ([email protected]:0.95, [email protected])
except:
print('WARNING: pycocotools must be installed with numpy==1.17 to run correctly. '
'See https://github.com/cocodataset/cocoapi/issues/356')
# Return results
model.float() # for training
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
좋은 웹페이지 즐겨찾기
