用自己网络添加注意力机制后画出热力图-Toy模板网

这篇具有很好参考价值的文章主要介绍了用自己网络添加注意力机制后画出热力图。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

不知道大家有没有像我这样的困惑，就是加了注意力机制到自己网络里面之后，算法性能提升不大，不知道自己添加的注意力有没有关注自己所希望关注的地方，于是就想验证自己的注意力是否有用，然后又不知道怎么验证它。
反正我被这个问题困惑了好久，经过几天CSDN的“拾荒“”，我总算了解了如何将注意力热力图添加到自己的网络中，现在我将以一个通用的例子举例，然后再将其应用到我自己的网络中：

1.首先我们需要构建一个极简的网络：

from PIL import Image
import torchvision
import cv2
import numpy as np
from collections import OrderedDict
import torch
import torch.nn as nn

class MDNet(nn.Module):
    def __init__(self, model_path=None, K=1):
        super(MDNet, self).__init__()
        self.avgpool=nn.AdaptiveAvgPool2d(1)
        self.layers=nn.Sequential(OrderedDict([
                ('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
                                        nn.ReLU(inplace=True),
                                        nn.LocalResponseNorm(2),
                                        nn.MaxPool2d(kernel_size=3, stride=2))),
                ('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
                            nn.ReLU(inplace=True),
                            nn.LocalResponseNorm(2),
                            nn.MaxPool2d(kernel_size=3, stride=2))),
                ('features', nn.Sequential(nn.Conv2d(3, 512, kernel_size=3, stride=1),
                                  nn.ReLU(inplace=True))),
                ('fc4', nn.Sequential(nn.Linear(500, 512),
                                  nn.ReLU(inplace=True))),
                ('fc5', nn.Sequential(nn.Dropout(0.5),
                                  nn.Linear(500, 512),
                                  nn.ReLU(inplace=True)))
        ]))

    def forward(self, x):
        avg_result = self.avgpool(x)
        output = self.layers(x)
        return output
if __name__ == '__main__':
    net = MDNet()

其次将该模型保存下来，即在代码中添加：

def save_model(model):
    torch.save(obj=model, f='B.pth')

具体代码如下：

from PIL import Image
import torchvision
import cv2
import numpy as np
from collections import OrderedDict
import torch
import torch.nn as nn


class MDNet(nn.Module):
    def __init__(self, model_path=None, K=1):
        super(MDNet, self).__init__()
        self.avgpool=nn.AdaptiveAvgPool2d(1)
        self.layers=nn.Sequential(OrderedDict([
                ('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
                                        nn.ReLU(inplace=True),
                                        nn.LocalResponseNorm(2),
                                        nn.MaxPool2d(kernel_size=3, stride=2))),
                ('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
                            nn.ReLU(inplace=True),
                            nn.LocalResponseNorm(2),
                            nn.MaxPool2d(kernel_size=3, stride=2))),
                ('features', nn.Sequential(nn.Conv2d(3, 512, kernel_size=3, stride=1),
                                  nn.ReLU(inplace=True))),
                ('fc4', nn.Sequential(nn.Linear(500, 512),
                                  nn.ReLU(inplace=True))),
                ('fc5', nn.Sequential(nn.Dropout(0.5),
                                  nn.Linear(500, 512),
                                  nn.ReLU(inplace=True)))
        ]))

    def forward(self, x):
        avg_result = self.avgpool(x)
        output = self.layers(x)
        return output

def save_model(model):
    torch.save(obj=model, f='B.pth')

if __name__ == '__main__':
    net = MDNet()
    save_model(net)
    # model = torch.load(f="A.pth")

运行Python后可以看见生成了一个B.pth文件
用自己网络添加注意力机制后画出热力图

2.使用热红外图生成图片：

#图片路径
img_path = r'C:/Users/HP/Desktop/w/1.jpg'

#给图片进行标准化操作
img = Image.open(img_path).convert('RGB')
transforms = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize([0.5, ], [0.5, ])])
data = transforms(img).unsqueeze(0)

#用于加载Pycharm中封装好的网络框架
# model = torchvision.models.vgg11_bn(pretrained=True)
#用于加载1中生成的.pth文件
model = torch.load(f="B.pth")
#打印一下刚刚生成的.pth文件看看他的网络结构
print(model)
model.eval()

#读取他fc4层图片特征
features = net.layers.Conv1(data)
features.retain_grad()
# t = model.avgpool(features)
# t = t.reshape(1, -1)
# output = model.classifier(t)[0]


# pred = torch.argmax(output).item()
# pred_class = output[pred]
#
# pred_class.backward()
grads = features.grad


features = features[0]
# avg_grads = torch.mean(grads[0], dim=(1, 2))
# avg_grads = avg_grads.expand(features.shape[1], features.shape[2], features.shape[0]).permute(2, 0, 1)
# features *= avg_grads

heatmap = features.detach().cpu().numpy()
heatmap = np.mean(heatmap, axis=0)

heatmap = np.maximum(heatmap, 0)
heatmap /= (np.max(heatmap) + 1e-8)


img = cv2.imread(img_path)
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
superimposed_img = np.uint8(heatmap * 0.5 + img * 0.5)
cv2.imshow('1', superimposed_img)
cv2.waitKey(0)

3.总代码：

from PIL import Image
import torchvision
import cv2
import numpy as np
from collections import OrderedDict
import torch
import torch.nn as nn


class MDNet(nn.Module):
    def __init__(self, model_path=None, K=1):
        super(MDNet, self).__init__()
        self.avgpool=nn.AdaptiveAvgPool2d(1)
        self.layers=nn.Sequential(OrderedDict([
                ('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
                                        nn.ReLU(inplace=True),
                                        nn.LocalResponseNorm(2),
                                        nn.MaxPool2d(kernel_size=3, stride=2))),
                ('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
                            nn.ReLU(inplace=True),
                            nn.LocalResponseNorm(2),
                            nn.MaxPool2d(kernel_size=3, stride=2))),
                ('features', nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1),
                                  nn.ReLU(inplace=True))),
                # ('fc4', nn.Sequential(nn.Linear(500, 512),
                #                   nn.ReLU(inplace=True))),
                # ('fc5', nn.Sequential(nn.Dropout(0.5),
                #                   nn.Linear(500, 512),
                #                   nn.ReLU(inplace=True)))
        ]))

    def forward(self, x):
        avg_result = self.avgpool(x)
        output = self.layers(x)
        return output

def save_model(model):
    torch.save(obj=model, f='B.pth')

if __name__ == '__main__':
    # 图片路径
    img_path = r'I:/2.jpg'
    # 给图片进行标准化操作
    img = Image.open(img_path).convert('RGB')
    transforms = torchvision.transforms.Compose([
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize([0.5, ], [0.5, ])])
    data = transforms(img).unsqueeze(0)
    # 用于加载Pycharm中封装好的网络框架
    # model = torchvision.models.vgg11_bn(pretrained=True)
    # 用于加载1中生成的.pth文件
    model = torch.load(f="B.pth")
    # 打印一下刚刚生成的.pth文件看看他的网络结构
    print(model)
    model.eval()
    #实例化
    net = MDNet()
    save_model(net)
    features=MDNet.forward(net,data)
    # model = torch.load(f="A.pth")
    features.retain_grad()
    # t = model.avgpool(features)
    # t = t.reshape(1, -1)
    # output = model.classifier(t)[0]
    # pred = torch.argmax(output).item()
    # pred_class = output[pred]
    #
    # pred_class.backward()
    grads = features.grad

    features = features[0]
    # avg_grads = torch.mean(grads[0], dim=(1, 2))
    # avg_grads = avg_grads.expand(features.shape[1], features.shape[2], features.shape[0]).permute(2, 0, 1)
    # features *= avg_grads

    heatmap = features.detach().cpu().numpy()
    heatmap = np.mean(heatmap, axis=0)

    heatmap = np.maximum(heatmap, 0)
    heatmap /= (np.max(heatmap) + 1e-8)

    img = cv2.imread(img_path)
    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
    heatmap = np.uint8(255 * heatmap)
    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
    superimposed_img = np.uint8(heatmap * 0.5 + img * 0.5)
    cv2.imshow('1', superimposed_img)
    cv2.waitKey(0)