采用在Imagenet上预训练的VGG16模型进行分类测试

预训练权重下载

    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',

    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',

    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',

    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',

    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',

    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',

    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',

    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',

 进行分类测试

def img_transform(img_rgb, transform=None):    """    将数据转换为模型读取的形式    :param img_rgb: PIL Image    :param transform: torchvision.transform    :return: tensor    """    if transform is None: raise ValueError("找不到transform！必须有transform对img进行处理")    img_t = transform(img_rgb)    return img_tdef process_img(path_img):    # hard code    norm_mean = [0.485, 0.456, 0.406]    norm_std = [0.229, 0.224, 0.225]    inference_transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop((224, 224)), transforms.ToTensor(), transforms.Normalize(norm_mean, norm_std),    ])    # path --> img    img_rgb = Image.open(path_img).convert('RGB')    # img --> tensor    img_tensor = img_transform(img_rgb, inference_transform)    img_tensor.unsqueeze_(0) # chw --> bchw    img_tensor = img_tensor.to(device)    return img_tensor, img_rgbdef load_class_names(p_clsnames, p_clsnames_cn):    """    加载标签名    :param p_clsnames:    :param p_clsnames_cn:    :return:    """    with open(p_clsnames, "r") as f: class_names = json.load(f)    with open(p_clsnames_cn, encoding='UTF-8') as f:  # 设置文件对象 class_names_cn = f.readlines()    return class_names, class_names_cnif __name__ == "__main__":    # config    path_state_dict = os.path.join(BASE_DIR, "..", "data", "vgg16-397923af.pth")    # path_img = os.path.join(BASE_DIR, "..", "..", "Data","Golden Retriever from baidu.jpg")    path_img = os.path.join(BASE_DIR, "..",  "data", "Golden Retriever from baidu.jpg")    path_classnames = os.path.join(BASE_DIR, "..", "data", "imagenet1000.json")    path_classnames_cn = os.path.join(BASE_DIR, "..", "data","imagenet_classnames.txt")    # load class names    cls_n, cls_n_cn = load_class_names(path_classnames, path_classnames_cn)    # 1/5 load img    img_tensor, img_rgb = process_img(path_img)    # 2/5 load model    vgg_model = get_vgg16(path_state_dict, device, True)    # 3/5 inference  tensor --> vector    with torch.no_grad(): time_tic = time.time() outputs = vgg_model(img_tensor) time_toc = time.time()    # 4/5 index to class names    _, pred_int = torch.max(outputs.data, 1)    _, top5_idx = torch.topk(outputs.data, 5, dim=1)    pred_idx = int(pred_int.cpu().numpy())    pred_str, pred_cn = cls_n[pred_idx], cls_n_cn[pred_idx]    print("img: {} is: {}\n{}".format(os.path.basename(path_img), pred_str, pred_cn))    print("time consuming:{:.2f}s".format(time_toc - time_tic))    # 5/5 visualization    plt.imshow(img_rgb)    plt.title("predict:{}".format(pred_str))    top5_num = top5_idx.cpu().numpy().squeeze()    text_str = [cls_n[t] for t in top5_num]    for idx in range(len(top5_num)): plt.text(5, 15+idx*30, "top {}:{}".format(idx+1, text_str[idx]), bbox=dict(fc='yellow'))    plt.show()

结果展示