yolov5使用Grad-Cam进行热力图可视化_yolov5+grad cam

yolov5使用Grad-Cam进行热力图可视化

Grad-CAM(Class Activation Mapping-类别激活映射图)是非常常见的神经网络可视化的工具，用于探索模型的可解释性

grad-cam的计算，其实就是只需要两个值，一个是输出特征层，另一个是模型最后的某个类别对该特征层的梯度

类别激活映射图是一张图像，表示对预测输出的贡献分布，分数越高的地方表示原始图片对应区域对网络的响应越高、影响越大

论文：https://arxiv.org/abs/1610.02391

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
Localization

pip install grad-cam  -i https://pypi.tuna.tsinghua.edu.cn/simple
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pip install --upgrade numpy==1.23.5 -i https://pypi.tuna.tsinghua.edu.cn/simple
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# pip install grad-cam -i https://pypi.tuna.tsinghua.edu.cn/simple
import warnings

warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import torch, yaml, cv2, os, shutil
import numpy as np

np.random.seed(0)
import matplotlib.pyplot as plt
from tqdm import trange
from PIL import Image
from models.yolo import Model
from utils.general import intersect_dicts
from utils.augmentations import letterbox
from utils.general import xywh2xyxy
from pytorch_grad_cam import GradCAMPlusPlus, GradCAM, XGradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients


class yolov5_heatmap:
    def __init__(self, weight, cfg, device, method, layer, backward_type, conf_threshold, genCAMNum):
        device = torch.device(device)
        ckpt = torch.load(weight)
        model_names = ckpt['model'].names
        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
        model = Model(cfg, ch=3, nc=len(model_names)).to(device)
        csd = intersect_dicts(csd, model.state_dict(), exclude=['anchor'])  # intersect
        model.load_state_dict(csd, strict=False)  # load
        model.eval()
        print(f'Transferred {len(csd)}/{len(model.state_dict())} items')

        target_layers = [eval(layer)]
        method = eval(method)

        colors = np.random.uniform(0, 255, size=(len(model_names), 3)).astype(np.int)
        self.__dict__.update(locals())

    def post_process(self, result):
        logits_ = result[..., 4:]
        boxes_ = result[..., :4]
        # 对socre进行排序，获取排序后索引列表
        sorted, indices = torch.sort(logits_[..., 0], descending=True)
        return logits_[0][indices[0]], xywh2xyxy(boxes_[0][indices[0]]).cpu().detach().numpy()

    def draw_detections(self, box, color, name, img):
        xmin, ymin, xmax, ymax = list(map(int, list(box)))
        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), tuple(int(x) for x in color), 2)
        cv2.putText(img, str(name), (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, tuple(int(x) for x in color), 2,
                    lineType=cv2.LINE_AA)
        return img

    def __call__(self, img_path, save_path):
        # remove dir if exist
        if os.path.exists(save_path):
            shutil.rmtree(save_path)
        # make dir if not exist
        os.makedirs(save_path, exist_ok=True)

        # img process
        img = cv2.imread(img_path)
        img = letterbox(img)[0]
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = np.float32(img) / 255.0
        tensor = torch.from_numpy(np.transpose(img, axes=[2, 0, 1])).unsqueeze(0).to(self.device)

        # init ActivationsAndGradients
        grads = ActivationsAndGradients(self.model, self.target_layers, reshape_transform=None)

        # get ActivationsAndResult
        result = grads(tensor)
        activations = grads.activations[0].cpu().detach().numpy()

        # postprocess to yolo output
        post_result, post_boxes = self.post_process(result[0])


        for i in trange(self.genCAMNum):
            # if post_result[i][0] < self.conf_threshold:
            #     break

            self.model.zero_grad()
            if self.backward_type == 'conf':
                post_result[i, 0].backward(retain_graph=True)
            else:
                # get max probability for this prediction
                score = post_result[i, 1:].max()
                score.backward(retain_graph=True)

            # process heatmap
            gradients = grads.gradients[0]
            b, k, u, v = gradients.size()
            weights = self.method.get_cam_weights(self.method, None, None, None, activations,
                                                  gradients.detach().numpy())
            weights = weights.reshape((b, k, 1, 1))
            saliency_map = np.sum(weights * activations, axis=1)
            saliency_map = np.squeeze(np.maximum(saliency_map, 0))
            saliency_map = cv2.resize(saliency_map, (tensor.size(3), tensor.size(2)))
            saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max()
            if (saliency_map_max - saliency_map_min) == 0:
                continue
            saliency_map = (saliency_map - saliency_map_min) / (saliency_map_max - saliency_map_min)

            # add heatmap and box to image
            cam_image = show_cam_on_image(img.copy(), saliency_map, use_rgb=True)
            cam_image = self.draw_detections(post_boxes[i], self.colors[int(post_result[i, 1:].argmax())],
                                             f'{self.model_names[int(post_result[i, 1:].argmax())]} {post_result[i][0]:.2f}',
                                             cam_image)
            cam_image = Image.fromarray(cam_image)
            cam_image.save(f'{save_path}/{i}.png')


def get_params():
    params = {
        'weight': 'best.pt',  # 自己训练的模型权重
        'cfg': 'models/yolov5s.yaml',  # 模型对应配置文件
        'device': 'cuda:0',  # 设备
        'method': 'XGradCAM',  # 生成热力图方法GradCAMPlusPlus, GradCAM, XGradCAM
        'layer': 'model.model[-2]',  # 对那一层进行可视化，一般倒数第二层较佳
        'backward_type': 'conf',  # class or conf
        'conf_threshold': 0.1,  # 置信度阈值，被我注释了
        'genCAMNum': 10  # 生成热力图张数。（保存时，按模型输出预测得分从高到低排序保存）
    }
    return params


if __name__ == '__main__':
    model = yolov5_heatmap(**get_params())
    # 预测图像，保存文件夹路径
    model(r'.\WheatDataSet\images\train\1b43ca0a6.jpg', 'result')
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