CAM方法获取显著图：基于pytorch的实现

骑猪看日落 2024-02-19 17:43 15阅读 0赞

#### CAM方法获取显著图：基于pytorch的实现 ####

*  1、下载代码
 *  2、具体代码
 *   *  （1）导入需要的模块
     *  （2）具体代码详解
     *  （3）生成结果

**转：CAM实践：基于pytorch的使用方法**  
**原文地址：https://cloud.tencent.com/developer/article/1370937**  
**非原创，建议到原创作者观看，仅做个人查阅**

## 1、下载代码 ##

git clone https://github.com/metalbubble/CAM

文件夹下有一个pytorch\_CAM.py的文件，利用这个便可以生成CAM了。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pzeDE3MTMzNjYyNDk_size_16_color_FFFFFF_t_70]

## 2、具体代码 ##

### （1）导入需要的模块 ###

import io
    import requests
    from PIL import Image
    from torchvision import models, transforms
    from torch.autograd import Variable
    from torch.nn import functional as F
    import numpy as np
    import cv2
    import pdb
    import json

### （2）具体代码详解 ###

# input image
    LABELS_URL = 'https://s3.amazonaws.com/outcome-blog/imagenet/labels.json'
    IMG_URL = 'http://media.mlive.com/news_impact/photo/9933031-large.jpg'
    # 使用本地的图片和下载到本地的labels.json文件
    # LABELS_PATH = "labels.json"
    # networks such as googlenet, resnet, densenet already use global average pooling at the end, so CAM could be used directly.
    model_id = 1
    # 选择使用的网络
    if model_id == 1:
     net = models.squeezenet1_1(pretrained=True)
     finalconv_name = 'features' # this is the last conv layer of the network
    elif model_id == 2:
     net = models.resnet18(pretrained=True)
     finalconv_name = 'layer4'
    elif model_id == 3:
     net = models.densenet161(pretrained=True)
     finalconv_name = 'features'
    # 有固定参数的作用，如norm的参数
    net.eval()
    # 获取特定层的feature map
    # hook the feature extractor
    features_blobs = []
    def hook_feature(module, input, output):
     features_blobs.append(output.data.cpu().numpy())
    net._modules.get(finalconv_name).register_forward_hook(hook_feature)
    # 得到softmax weight,
    params = list(net.parameters()) # 将参数变换为列表
    weight_softmax = np.squeeze(params[-2].data.numpy()) # 提取softmax 层的参数
    # 生成CAM图的函数，完成权重和feature相乘操作
    # CAMs = returnCAM(features_blobs[0], weight_softmax, [idx[0]])
    def returnCAM(feature_conv, weight_softmax, class_idx):
     # generate the class activation maps upsample to 256x256
     size_upsample = (256, 256)
     bz, nc, h, w = feature_conv.shape # 获取feature_conv特征的尺寸
     output_cam = []
     # class_idx为预测分值较大的类别的数字表示的数组，一张图片中有N类物体则数组中N个元素
     for idx in class_idx:
     # weight_softmax中预测为第idx类的参数w乘以feature_map(为了相乘，故reshape了map的形状)
     cam = weight_softmax[idx].dot(feature_conv.reshape((nc, h*w)))
     # 将feature_map的形状reshape回去
     cam = cam.reshape(h, w)
     # 归一化操作（最小的值为0，最大的为1）
     cam = cam - np.min(cam)
     cam_img = cam / np.max(cam)
     # 转换为图片的255的数据
     cam_img = np.uint8(255 * cam_img)
     # resize 图片尺寸与输入图片一致
     output_cam.append(cv2.resize(cam_img, size_upsample))
     return output_cam
    # 数据处理，先缩放尺寸到（224*224），再变换数据类型为tensor,最后normalize
    normalize = transforms.Normalize(
     mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]
    )
    preprocess = transforms.Compose([
     transforms.Resize((224,224)),
     transforms.ToTensor(),
     normalize
    ])
    # 通过requests库获取图片并保存，若是本地图片则只需要设置本地图片的保存地址，以便后来提取便好
    # img_path = "/../..jpg"
    # img_pil = Image.open(img_path)
    response = requests.get(IMG_URL)
    img_pil = Image.open(io.BytesIO(response.content))
    img_pil.save('test.jpg')
    # 将图片数据处理成所需要的可用的数据
    img_tensor = preprocess(img_pil)
    # 处理图片为Variable数据
    img_variable = Variable(img_tensor.unsqueeze(0))
    # 将图片输入网络得到预测类别分值
    logit = net(img_variable)
    # download the imagenet category list
    # 下载imageNet 分类标签列表，并存储在classes中（数字类别，类别名称）
    classes = {int(key):value for (key, value)
     in requests.get(LABELS_URL).json().items()}
    # # 使用本地的 LABELS_PATH
    # with open(LABELS_PATH) as f:
    # data = json.load(f).items()
    # classes = {int(key):value for (key, value) in data}
    # 使用softmax打分
    h_x = F.softmax(logit, dim=1).data.squeeze() # 分类分值
    # 对分类的预测类别分值排序，输出预测值和在列表中的位置
    probs, idx = h_x.sort(0, True) 
    # 转换数据类型
    probs = probs.numpy()
    idx = idx.numpy()
    # 输出预测分值排名在前五的五个类别的预测分值和对应类别名称
    for i in range(0, 5):
     print('{:.3f} -> {}'.format(probs[i], classes[idx[i]]))
    # generate class activation mapping for the top1 prediction
    # 输出与图片尺寸一致的CAM图片
    CAMs = returnCAM(features_blobs[0], weight_softmax, [idx[0]])
    # render the CAM and output
    print('output CAM.jpg for the top1 prediction: %s'%classes[idx[0]])
    # 将图片和CAM拼接在一起展示定位结果结果
    img = cv2.imread('test.jpg')
    height, width, _ = img.shape
    # 生成热度图
    heatmap = cv2.applyColorMap(cv2.resize(CAMs[0],(width, height)), cv2.COLORMAP_JET)
    result = heatmap * 0.3 + img * 0.5
    cv2.imwrite('CAM.jpg', result)

### （3）生成结果 ###

注意：如果为了快一点，不使用网络的图片以及文件的话，记得更改图片地址和已下载文件地址!  
![测试图片][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pzeDE3MTMzNjYyNDk_size_16_color_FFFFFF_t_70 1]  
![结果图片][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pzeDE3MTMzNjYyNDk_size_16_color_FFFFFF_t_70 2]

**CAM论文地址：**

http://arxiv.org/pdf/1512.04150.pdf

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pzeDE3MTMzNjYyNDk_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/01/29/1c2c183c42ce4d8581c3d4ef7c1bdd25.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pzeDE3MTMzNjYyNDk_size_16_color_FFFFFF_t_70 1]: https://image.dandelioncloud.cn/pgy_files/images/2024/01/29/9c2cb6d9fa2a4bda81bed1487fffa72e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pzeDE3MTMzNjYyNDk_size_16_color_FFFFFF_t_70 2]: https://image.dandelioncloud.cn/pgy_files/images/2024/01/29/930d1e5b844f463ca58affd4a1728da6.png