test.py

import argparse
import json

from torch.utils.data import DataLoader

from models import *
from utils.datasets import *
from utils.utils import *
# from profiler import Profiler
import time

def get_macs_and_params(backbone, branch_controller, branches, img_sz):
    from flops_counter import get_model_complexity_info
    with torch.cuda.device(0):
        if img_sz == 416:
            backbone_out_dim = 13
        else:
            backbone_out_dim = 10
        macs, params = get_model_complexity_info(backbone, (3, img_sz, img_sz), as_strings=True,
                                                print_per_layer_stat=True, verbose=True)
        print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
        print('{:<30}  {:<8}'.format('Number of parameters: ', params))
        macs, params = get_model_complexity_info(branch_controller, (1024, backbone_out_dim, backbone_out_dim), as_strings=True,
                                                print_per_layer_stat=True, verbose=True)
        print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
        print('{:<30}  {:<8}'.format('Number of parameters: ', params))
        macs, params = get_model_complexity_info(branches[0], (1024, backbone_out_dim, backbone_out_dim), as_strings=True,
                                                print_per_layer_stat=True, verbose=True,out=backbone.layer_outputs)
        print('{:<30}  {:<8}'.format('Computational complexity: ', macs))
        print('{:<30}  {:<8}'.format('Number of parameters: ', params))
        backbone.layer_outputs = []

def test(cfg,
         data,
         weights=None,
         batch_size=16,
         imgsz=416,
         conf_thres=0.001,
         iou_thres=0.6,  # for nms
         save_json=False,
         single_cls=False,
         augment=False,
         device=None,
         model=None,
         dataloader=None,
         multi_label=True,
         clusters=None,
         class_to_cluster_list=None,
         cluster_idx=0,
         profile=False,
         backbone=None):

    # Initialize/load model and set device
    if model is None:
        is_training = False
        device = torch_utils.select_device(device, batch_size=batch_size)
        verbose = True

        # Remove previous
        for f in glob.glob('test_batch*.jpg'):
            os.remove(f)

        # Initialize model
        model = Darknet(cfg, imgsz)

        # Load weights
        attempt_download(weights)
        if weights.endswith('.pt'):  # pytorch format
            model.load_state_dict(torch.load(weights, map_location=device)['model'])
        else:  # darknet format
            load_darknet_weights(model, weights)

        # Fuse
        model.fuse()
        model.to(device)

        if device.type != 'cpu' and torch.cuda.device_count() > 1:
            model = nn.DataParallel(model)
    else:  # called by train.py
        is_training = True
        device = next(model.parameters()).device  # get model device
        verbose = False

    # Configure run
    data = parse_data_cfg(data)
    nc = 1 if single_cls else int(data['classes'])  # number of classes
    path = data['valid']  # path to test images
    names = load_classes(data['names'])  # class names
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    iouv = iouv[0].view(1)  # comment for mAP@0.5:0.95
    niou = iouv.numel()

    # Dataloader
    if dataloader is None:
        dataset = LoadImagesAndLabels(path, imgsz, batch_size, rect=False, single_cls=single_cls, pad=0.5)
        batch_size = min(batch_size, len(dataset))
        dataloader = DataLoader(dataset,
                                batch_size=batch_size,
                                num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
                                pin_memory=True,
                                collate_fn=dataset.collate_fn)

    seen = 0
    model.eval()
    if backbone is not None:
        backbone.eval()
        backbone.to(device)

    coco91class = coco80_to_coco91_class()
    s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1')
    p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class = [], [], [], []
    latency = []
    if profile:
        profiler = Profiler(platform='nano')
        profiler.start()
    
    for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
        imgs = imgs.to(device).float() / 255.0  # uint8 to float32, 0 - 255 to 0.0 - 1.0
        targets = targets.to(device)
        nb, _, height, width = imgs.shape  # batch size, channels, height, width
        whwh = torch.Tensor([width, height, width, height]).to(device)

        # Disable gradients
        with torch.no_grad():
            # Run model
            t = time.time()
            if backbone is None:
                inf_out, train_out = model(imgs, augment=augment)  # inference and training outputs
                t0 += torch_utils.time_synchronized() - t

            else:
                backbone_out = backbone(imgs)
                inf_out, train_out = model(backbone_out, augment=augment,out=backbone.layer_outputs)  # inference and training outputs
                backbone.layer_outputs = []
                t0 += torch_utils.time_synchronized() - t

                targets = map_labels_to_cluster(targets, clusters, class_to_cluster_list, cluster_idx, device)
                if targets.shape[0] == 0:
                    continue

                full_detection = torch.zeros(inf_out.shape[0],inf_out.shape[1], nc+5, device=device)
                full_detection[:, :, 0:5] = inf_out[:, :, 0:5]
                new_indices = [5 + k for k in clusters[cluster_idx]]
                full_detection[:, :, new_indices] = inf_out[:, :, 5:]
                inf_out = full_detection

            # Compute loss
            if is_training:  # if model has loss hyperparameters
                loss += compute_loss(train_out, targets, model)[1][:3]  # GIoU, obj, cls

            # Run NMS
            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=multi_label)
            t1 += torch_utils.time_synchronized() - t
            latency.append(time.time() - t)
        
        if profile:
            if batch_i == 100:
                break
            else:
                continue
        
        # 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) + '\n') 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(imgs[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)  # target indices
                    pi = (cls == pred[:, 5]).nonzero().view(-1)  # prediction 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))

    latency = latency[50:]
    print("Average Latency",sum(latency)/len(latency))

    if profile:
        gpu_power, cpu_power, total_power = profiler.end()
        print("GPU power", gpu_power, "CPU power", cpu_power, "Total power", total_power)
        exit()

    # 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)
        if niou > 1:
            p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(1), ap[:, 0]  # [P, R, AP@0.5:0.95, AP@0.5]
        mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.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' + '%10.3g' * 6  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))

    # 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], ap[i], f1[i]))

    # Print speeds
    if verbose or save_json:
        t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Save JSON
    if save_json and map and len(jdict):
        print('\nCOCO mAP with pycocotools...')
        imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
        with open('results.json', '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('data/coco/annotations/instances_val*.json')[0])  # initialize COCO ground truth api
        cocoDt = cocoGt.loadRes('results.json')  # initialize COCO pred api
        cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
        cocoEval.params.imgIds = imgIds  # [:32]  # only evaluate these images
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()
        # mf1, map = cocoEval.stats[:2]  # update to pycocotools results (mAP@0.5:0.95, mAP@0.5)
        # except:
        #     print('WARNING: pycocotools must be installed with numpy==1.17 to run correctly. '
        #           'See https://github.com/cocodataset/cocoapi/issues/356')

    # Return results
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]

    return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps

def test_branches(data,
         batch_size=16,
         imgsz=416,
         conf_thres=0.001,
         iou_thres=0.4,  # for nms
         save_json=False,
         single_cls=False,
         augment=False,
         device=None,
         model=None,
         dataloader=None,
         profile=False,
         multi_label=True):

    clusters = parse_clusters_config(opt.clusters)
    common_classes = get_common_classes(clusters)

    # Load Backbone 
    backbone = Backbone(opt.backbone_cfg).to(device)
    backbone.load_darknet_weights(opt.backbone_weights, 100)
    count_parameters(backbone)

    branches = []
    for i, cfg in enumerate(opt.branches_cfg):
        branch = Darknet(cfg, imgsz)
        if opt.branches_weights:  # pytorch format
            branch.load_state_dict(torch.load(opt.branches_weights[i], map_location=device)['model'], strict=False)
        branch.to(device)
        count_parameters(branch)
        branch.eval()
        branches.append(branch)

    branch_controller = None
    if opt.branch_controller_cfg:
        branch_controller = BranchController(opt.branch_controller_cfg, len(clusters)).to(device)
        if opt.branch_controller_weights:
            branch_controller.load_state_dict(torch.load(opt.branch_controller_weights))
        count_parameters(branch_controller)
        branch_controller.eval()

    # Configure run
    data = parse_data_cfg(data)
    verbose = True
    nc = 1 if single_cls else int(data['classes'])  # number of classes
    path = data['valid']  # path to test images
    names = load_classes(data['names'])  # class names
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    iouv = iouv[0].view(1)  # comment for mAP@0.5:0.95
    niou = iouv.numel()

    # get_macs_and_params(backbone, branch_controller, branches, imgsz)
        
    # Dataloader
    if dataloader is None:
        dataset = LoadImagesAndLabels(path, imgsz, batch_size, rect=False, single_cls=single_cls, pad=0.5)
        batch_size = min(batch_size, len(dataset))
        dataloader = DataLoader(dataset,
                                batch_size=batch_size,
                                num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
                                pin_memory=True,
                                collate_fn=dataset.collate_fn)

    seen = 0
    backbone.eval()
    branches_num = 0
    # _ = model(torch.zeros((1, 3, imgsz, imgsz), device=device)) if device.type != 'cpu' else None  # run once
    coco91class = coco80_to_coco91_class()
    s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1')
    p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class = [], [], [], []
    latency = []
    if profile:
        profiler = Profiler(platform='nano')
        profiler.start()
        
    for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
        imgs = imgs.to(device).float() / 255.0  # uint8 to float32, 0 - 255 to 0.0 - 1.0
        targets = targets.to(device)
        nb, _, height, width = imgs.shape  # batch size, channels, height, width
        whwh = torch.Tensor([width, height, width, height]).to(device)

        # Disable gradients
        with torch.no_grad():
            # Run model
            t = time.time()
            backbone_out = backbone(imgs)
            # Select mode
            if not opt.oracle:
                if opt.single:
                    dominent_clus = [torch.argmax(branch_controller(backbone_out, []))]
                elif opt.multi:
                    class_out = branch_controller(backbone_out, [])
                    dominent_clus = torch.where(class_out > opt.bc_thres)[1]
                    if len(dominent_clus) == 0:
                        dominent_clus = [torch.argmax(class_out)]
            else:
                ts = targets[:, 1].tolist()
                cluster_cnt = np.zeros(len(clusters))
                for t in ts:
                    for i, cluster in enumerate(clusters):
                        if t in cluster and t not in common_classes:
                            cluster_cnt[i] += 1

                dominent_clus = [np.argmax(cluster_cnt)]
                dominent_clus = [idx for idx, val in enumerate(cluster_cnt) if val != 0]
                if len(dominent_clus) == 0:
                    dominent_clus = [0]
                    
            branches_num += len(dominent_clus)
            all_outputs = []
            for cluster_idx in dominent_clus:
                inf_out, _ = branches[cluster_idx](backbone_out, augment=augment,out=backbone.layer_outputs)  # inference and training outputs

                full_detection = torch.zeros(inf_out.shape[0],inf_out.shape[1], nc+5, device=device)
                full_detection[:, :, 0:5] = inf_out[:, :, 0:5]

                new_indices = [5 + k for k in clusters[cluster_idx]]
                full_detection[:, :, new_indices] = inf_out[:, :, 5:]
                all_outputs.append(full_detection)
            
            backbone.layer_outputs = []
            if len(all_outputs) == 0:
                continue
            inf_out = torch.cat(all_outputs, 1)
            t0 += torch_utils.time_synchronized() - t
            # Run NMS
            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=multi_label)
            t1 += torch_utils.time_synchronized() - t
            latency.append(time.time() - t)

        if profile:
            if batch_i == 100:
                break
            else:
                continue
    
        # 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) + '\n') 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(imgs[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)  # target indices
                    pi = (cls == pred[:, 5]).nonzero().view(-1)  # prediction 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))

    latency = latency[50:]
    print("Average Latency",sum(latency)/len(latency))

    if profile:
        gpu_power, cpu_power, total_power = profiler.end()
        print("GPU power", gpu_power, "CPU power", cpu_power, "Total power", total_power)
        exit()

    # 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)
        if niou > 1:
            p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(1), ap[:, 0]  # [P, R, AP@0.5:0.95, AP@0.5]
        mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)
    print("Branches/image = ", branches_num, seen, branches_num/seen)
    # Print results
    pf = '%20s' + '%10.3g' * 6  # print format

    # 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], ap[i], f1[i]))

    # Print speeds
    if verbose or save_json:
        t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Save JSON
    if save_json and map and len(jdict):
        print('\nCOCO mAP with pycocotools...')
        imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
        with open('results.json', '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('data/coco/annotations/instances_val*.json')[0])  # initialize COCO ground truth api
        cocoDt = cocoGt.loadRes('results.json')  # initialize COCO pred api
        cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
        cocoEval.params.imgIds = imgIds  # [:32]  # only evaluate these images
        cocoEval.evaluate()
        cocoEval.accumulate()
        cocoEval.summarize()
        # mf1, map = cocoEval.stats[:2]  # update to pycocotools results (mAP@0.5:0.95, mAP@0.5)
        # except:
        #     print('WARNING: pycocotools must be installed with numpy==1.17 to run correctly. '
        #           'See https://github.com/cocodataset/cocoapi/issues/356')

    # Return results
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    print("AVG mAP", np.mean(maps))
    return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps


if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='test.py')
    parser.add_argument('--data', type=str, default='data/coco2014.data', 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=512, 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('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
    parser.add_argument('--task', default='test_dynamic', help="'test_static', 'test_dynamic', 'test_dynamic_single'")
    parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--oracle', action='store_true', help='test the oracle model')
    parser.add_argument('--single', action='store_true', help='test the single branch model')
    parser.add_argument('--multi', action='store_true', help='test the multi branch model')
    parser.add_argument('--bc-thres', type=float, default=0.4, help='object confidence threshold')
    parser.add_argument('--adaptive', action='store_true', help='train adaptive model')
    parser.add_argument('--model', type=str, default='model.args', help='File for the model configurations')
    parser.add_argument('--profile', action='store_true', help='profile adaptive model')

    opt = parser.parse_args()
    opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])

    opt.data = check_file(opt.data)  # check file
    opt.model = check_file(opt.model)  # check file

    model_args = parse_model_args(opt.model)
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    if opt.adaptive:
        opt.clusters = check_file(model_args['clusters'])
        opt.backbone_cfg = check_file(model_args['backbone_cfg'])
        opt.backbone_weights = check_file(model_args['backbone_weights'])
        opt.branch_controller_cfg = check_file(model_args['branch_controller_cfg'])
        if 'branch_controller_weights' in model_args:
            opt.branch_controller_weights = check_file(model_args['branch_controller_weights'])
        else:
            opt.branch_controller_weights = None
        opt.branches_cfg = [check_file(f) for f in model_args['branches_cfg']]
        if 'branches_weights' in model_args:
            opt.branches_weights = [check_file(f) for f in model_args['branches_weights']]
        else:
            opt.branches_weights = None
    else:
        opt.cfg = check_file(model_args['cfg'])  # check file
        opt.weights = check_file(model_args['weights'])  # check file

    if opt.adaptive:  # (default) test normally
        if not opt.oracle and not opt.single:
            opt.multi = True
        test_branches(opt.data,
             1,
             opt.img_size,
             opt.conf_thres,
             opt.iou_thres,
             opt.save_json,
             opt.single_cls,
             opt.augment,
             profile=opt.profile,
             device=device)
    else:
        test(opt.cfg,
             opt.data,
             opt.weights,
             opt.batch_size,
             opt.img_size,
             opt.conf_thres,
             opt.iou_thres,
             opt.save_json,
             opt.single_cls,
             opt.augment,
             profile=opt.profile,
             device=device)