예제 #1
0
def main(args):
    torch.set_grad_enabled(False)

    device = torch.device(args.device)

    # Initialize the net and load the model
    print('Loading pretrained model from {}'.format(args.trained_model))
    net = YuFaceDetectNet(phase='test', size=None)
    net = load_model(net, args.trained_model)
    net.eval()
    if device.type == 'cuda':
        cudnn.benchmark = True
        net = net.to(device)
    print('Finished loading model!')

    # init data loader for WIDER Face
    print('Loading data for {}...'.format(args.widerface_split))
    widerface = WIDERFace(args.widerface_root, split=args.widerface_split)
    print('Finished loading data!')

    # start testing
    scales = []
    if args.multi_scale:
        scales = [0.25, 0.50, 0.75, 1.25, 1.50, 1.75, 2.0]
    print('Performing testing with scales: 1. {}, conf_threshold: {}'.format(
        str(scales), args.confidence_threshold))
    priors_dict = {}
    for idx in tqdm(range(len(widerface))):
        img, event, name = widerface[idx]  # img_subpath = '0--Parade/XXX.jpg'
        if img.shape in priors_dict:
            priors = priors_dict[img.shape]
        else:
            height, width, _ = img.shape
            priors = PriorBox(cfg, image_size=(height, width)).forward()
            priors_dict[img.shape] = priors
        dets = detect_face(net, img, priors, device)
        available_scales = get_available_scales(img.shape[0], img.shape[1],
                                                scales)
        for available_scale in available_scales:
            det = detect_face(net, img, None, device, scale=available_scale)
            if det.shape[0] != 0:
                dets = np.row_stack((dets, det))
        # nms
        dets = nms_opencv(dets,
                          score_thresh=args.confidence_threshold,
                          nms_thresh=args.nms_threshold,
                          top_k=args.top_k,
                          keep_top_k=args.keep_top_k)
        save_res(dets, event, name)

    # widerface_eval
    print('Evaluating:')
    evaluation(args.res_dir,
               os.path.join(args.widerface_root, 'eval_tools/ground_truth'))
예제 #2
0
파일: test.py 프로젝트: Swinsie/cv-rep-fork
def main(args):
    torch.set_grad_enabled(False)

    device = torch.device(args.device)

    # Initialize the net and load the model
    print('Loading pretrained model from {}'.format(args.trained_model))
    net = YuFaceDetectNet(phase='test', size=None)
    net = load_model(net, args.trained_model)
    net.eval()
    if device.type == 'cuda':
        cudnn.benchmark = True
        net = net.to(device)
    print('Finished loading model!')

    # init data loader for WIDER Face
    print('Loading data for {}...'.format(args.widerface_split))
    widerface = WIDERFace(args.widerface_root, split=args.widerface_split)
    print('Finished loading data!')

    # start testing
    scales = [1.]
    if args.multi_scale:
        scales = [0.25, 0.50, 0.75, 1.25, 1.50, 1.75, 2.0]
    print('Performing testing with scales: {}, conf_threshold: {}'.format(
        str(scales), args.confidence_threshold))
    for idx in tqdm(range(len(widerface))):
        img, event, name = widerface[idx]  # img_subpath = '0--Parade/XXX.jpg'

        dets = detect_face(net,
                           img,
                           device,
                           conf_thresh=args.confidence_threshold)
        available_scales = get_available_scales(img.shape[0], img.shape[1],
                                                scales)
        for available_scale in available_scales:
            det = detect_face(net,
                              img,
                              device,
                              scale=available_scale,
                              conf_thresh=args.confidence_threshold)
            if det is not None: dets = np.row_stack((dets, det))

        # nms
        dets = simple_nms(dets)
        # dets = bbox_vote(dets)

        save_res(dets, event, name)
예제 #3
0
    net = load_model(net, args.trained_model, True)

    #net.load_state_dict(torch.load(args.trained_model))
    net.eval()

    print('Finished loading model!')

    ## Print model's state_dict
    #print("Model's state_dict:")
    #for param_tensor in net.state_dict():
    #    print(param_tensor, "\t", net.state_dict()[param_tensor].size())

    #print(net)

    cudnn.benchmark = True
    net = net.to(device)

    _t = {'forward_pass': Timer(), 'misc': Timer()}

    # testing begin
    img_raw = cv2.imread(args.image_file, cv2.IMREAD_COLOR)
    img = np.float32(img_raw)
    im_height, im_width, _ = img.shape

    #img -= (104, 117, 123)
    img = img.transpose(2, 0, 1)
    img = torch.from_numpy(img).unsqueeze(0)
    img = img.to(device)

    scale = torch.Tensor([
        im_width, im_height, im_width, im_height, im_width, im_height,