Faster RCNN 源码解析

源码

在Pytorch官网找到源码

pascal_voc_classes.json:

pascal_voc标签文件。?一个类别对应一个整形数字。在目标检测中“0”是专门留给背景的。

制作自己的voc数据集

加载模型?

    # create model num_classes equal background + 20 classes
    model = create_model(num_classes=21)

transform

import random
from torchvision.transforms import functional as F


class Compose(object):
    """组合多个transform函数"""
    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, image, target):
        for t in self.transforms:
            image, target = t(image, target)
        return image, target


class ToTensor(object):
    """将PIL图像转为Tensor"""
    def __call__(self, image, target):
        image = F.to_tensor(image)
        return image, target


class RandomHorizontalFlip(object):
    """随机水平翻转图像以及bboxes"""
    def __init__(self, prob=0.5):
        self.prob = prob

    def __call__(self, image, target):
        if random.random() < self.prob:
            height, width = image.shape[-2:]
            image = image.flip(-1)  # 水平翻转图片
            bbox = target["boxes"]
            # bbox: xmin, ymin, xmax, ymax
            bbox[:, [0, 2]] = width - bbox[:, [2, 0]]  # 翻转对应bbox坐标信息
            target["boxes"] = bbox
        return image, target

mask_rcnn的transform

import random
from torchvision.transforms import functional as F


class Compose(object):
    """组合多个transform函数"""
    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, image, target):
        for t in self.transforms:
            image, target = t(image, target)
        return image, target


class ToTensor(object):
    """将PIL图像转为Tensor"""
    def __call__(self, image, target):
        image = F.to_tensor(image)
        return image, target


class RandomHorizontalFlip(object):
    """随机水平翻转图像以及bboxes"""
    def __init__(self, prob=0.5):
        self.prob = prob

    def __call__(self, image, target):
        if random.random() < self.prob:
            height, width = image.shape[-2:]
            image = image.flip(-1)  # 水平翻转图片
            bbox = target["boxes"]
            # bbox: xmin, ymin, xmax, ymax
            bbox[:, [0, 2]] = width - bbox[:, [2, 0]]  # 翻转对应bbox坐标信息
            target["boxes"] = bbox
            if "masks" in target:
                target["masks"] = target["masks"].flip(-1)
        return image, target

比较

相比之下，mask_rcnn就在随机水平翻转的函数里多了这几行代码

            if "masks" in target:
                target["masks"] = target["masks"].flip(-1)

?predict.py

注意自己的backbone是mobileNetv2还是resNet50+fpn，然后再选择模型创建的代码

def create_model(num_classes):
    # mobileNetv2+faster_RCNN
    backbone = MobileNetV2().features
    backbone.out_channels = 1280
    
    anchor_generator = AnchorsGenerator(sizes=((32, 64, 128, 256, 512),),
                                        aspect_ratios=((0.5, 1.0, 2.0),))
    
    roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
                                                    output_size=[7, 7],
                                                    sampling_ratio=2)
    
    model = FasterRCNN(backbone=backbone,
                       num_classes=num_classes,
                       rpn_anchor_generator=anchor_generator,
                       box_roi_pool=roi_pooler)

    # resNet50+fpn+faster_RCNN
    # 注意，这里的norm_layer要和训练脚本中保持一致
    # backbone = resnet50_fpn_backbone(norm_layer=torch.nn.BatchNorm2d)
    # model = FasterRCNN(backbone=backbone, num_classes=num_classes, rpn_score_thresh=0.5)

    return model

验证并显示待输入的图片

    # read class_indict
    label_json_path = './pascal_voc_classes.json'
    assert os.path.exists(label_json_path), "json file {} dose not exist.".format(label_json_path)
    with open(label_json_path, 'r') as f:
        class_dict = json.load(f)

    category_index = {str(v): str(k) for k, v in class_dict.items()}

    # load image
    original_img = Image.open("./test.jpg")

    # from pil image to tensor, do not normalize image
    data_transform = transforms.Compose([transforms.ToTensor()])
    img = data_transform(original_img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    model.eval()  # 进入验证模式
    with torch.no_grad():
        # init
        img_height, img_width = img.shape[-2:]
        init_img = torch.zeros((1, 3, img_height, img_width), device=device)
        model(init_img)

        t_start = time_synchronized()
        predictions = model(img.to(device))[0]
        t_end = time_synchronized()
        print("inference+NMS time: {}".format(t_end - t_start))

        predict_boxes = predictions["boxes"].to("cpu").numpy()
        predict_classes = predictions["labels"].to("cpu").numpy()
        predict_scores = predictions["scores"].to("cpu").numpy()

        if len(predict_boxes) == 0:
            print("没有检测到任何目标!")

        plot_img = draw_objs(original_img,
                             predict_boxes,
                             predict_classes,
                             predict_scores,
                             category_index=category_index,
                             box_thresh=0.5,
                             line_thickness=3,
                             font='arial.ttf',
                             font_size=20)
        plt.imshow(plot_img)
        plt.show()
        # 保存预测的图片结果
        plot_img.save("test_result.jpg")