CAM实践：基于pytorch的使用方法

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

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

# 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)