1.下载Dark Face数据集，使用track2.2_test_sample文件中图片进行人脸检测测试。

2.修改DSFD源码中demo.py部分：

test_oneimage()：

def test_oneimage():
    # load net

    # 影响网络的自动求导机制，使网络前向传播后不进行求导和反向传播（仅测试时使用）
    torch.set_grad_enabled(False)

    # 加载config配置参数
    cfg = widerface_640
    # 分类的类别数目---widerface.py
    num_classes = len(WIDERFace_CLASSES) + 1 # +1 background
    # 加载SSD网络模型，返回一个SSD实例
    net = build_ssd('test', cfg['min_dim'], num_classes) # initialize SSD
    # 加载预训练模型train_model
    net.load_state_dict(torch.load(args.trained_model))
    net.cuda()
    # 表示进入评估模式，神经网络中有train(),eval()两种模式，使用eval()可关闭dropout
    net.eval()
    print('Finished loading model!')

    # evaluation
    cuda = args.cuda
    transform = TestBaseTransform((104, 117, 123))
    thresh=cfg['conf_thresh']
    #save_path = args.save_folder
    #num_images = len(testset)

    # load data，从指定路径加载待测图像

    ''' 
    以此为界，前半部分为网络模型加载和初始化，后半部分为单张图片的人脸检测。此处为测试图片的路            
    径设置过程。

    修改部分，不适用arg.imag_root作为测试图像路径.
    遍历darkface数据集100张图片，依次读取并进行测试
    '''

    folder = './data/'

    #img_id = 'face'
    for i in range(100):

        img = cv2.imread(folder + str(i) + '_fake_B.jpg', cv2.IMREAD_COLOR)
        img_id = 'test' + str(i)
        

        # 单张图片的测试过程
        max_im_shrink = ( (2000.0*2000.0) / (img.shape[0] * img.shape[1])) ** 0.5
        shrink = max_im_shrink if max_im_shrink < 1 else 1

        det0 = infer(net , img , transform , thresh , cuda , shrink)
        det1 = infer_flip(net , img , transform , thresh , cuda , shrink)
        # shrink detecting and shrink only detect big face
        st = 0.5 if max_im_shrink >= 0.75 else 0.5 * max_im_shrink
        det_s = infer(net , img , transform , thresh , cuda , st)
        index = np.where(np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1) > 30)[0]
        det_s = det_s[index, :]
        # enlarge one times
        factor = 2
        bt = min(factor, max_im_shrink) if max_im_shrink > 1 else (st + max_im_shrink) / 2
        det_b = infer(net , img , transform , thresh , cuda , bt)
        # enlarge small iamge x times for small face
        if max_im_shrink > factor:
            bt *= factor
            while bt < max_im_shrink:
                det_b = np.row_stack((det_b, infer(net , img , transform , thresh , cuda , bt)))
                bt *= factor
            det_b = np.row_stack((det_b, infer(net , img , transform , thresh , cuda , max_im_shrink) ))
        # enlarge only detect small face
        if bt > 1:
            index = np.where(np.minimum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
            det_b = det_b[index, :]
        else:
            index = np.where(np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
            det_b = det_b[index, :]
        det = np.row_stack((det0, det1, det_s, det_b))
        det = bbox_vote(det)
        vis_detections(img , det , img_id, args.visual_threshold)

3.运行demo.py即可。能在arg.save_folder处得到100张dark_face人脸检测结果。

补充：

DSFD只接收输入格式为jpg的图片，因此对darkface数据集进行批量转换。

import os
from PIL import Image

dirname_read="/home/...DSFD/darkface_png/"   # png格式图片的输入路径
dirname_write="/home/...DSFD/data/"    # jpg图片的输出路径
names=os.listdir(dirname_read)
count=0
for name in names:
    img=Image.open(dirname_read+name)
    name=name.split(".")
    if name[-1] == "png":
        name[-1] = "jpg"
        name = str.join(".", name)
        #r,g,b,a=img.split()
        #img=Image.merge("RGB",(r,g,b))
        to_save_path = dirname_write + name
        img.save(to_save_path)
        count+=1
        print(to_save_path, "------conut：", count)
    else:
        continue