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获取想要解释的那一层的特征图#xff0c;然后根据特征图梯度计算出权重值#xff0c;加在原图上面。
Demo 加上类激活(cam) 可以看到#xff0c;cam将模型认为有利于分类的特征标注了出来。 下面以ResNet50为例: Trick: 使用
for i in model._modules.items():可以…思路
获取想要解释的那一层的特征图然后根据特征图梯度计算出权重值加在原图上面。
Demo 加上类激活(cam) 可以看到cam将模型认为有利于分类的特征标注了出来。 下面以ResNet50为例: Trick: 使用
for i in model._modules.items():可以获得模型名称和对应层。
# coding: utf-8
import os
import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as pltimport torch
import torch.autograd as autograd
import torchvision.transforms as transformsimport torchvision.models as models# 训练过的模型路径
#resume_path rD:\TJU\GBDB\set113\cross_validation\test1\epoch_0257_checkpoint.pth.tar
# 输入图像路径
single_img_path rbicycle.jpg
# 绘制的热力图存储路径
save_path rheatmap/bicycle_layer4.jpg# 网络层的层名列表, 需要根据实际使用网络进行修改
layers_names [conv1, bn1, relu, maxpool, layer1, layer2, layer3, layer4, avgpool]
# 指定层名
out_layer_name layer4features_grad 0# 为了读取模型中间参数变量的梯度而定义的辅助函数
def extract(g):global features_gradfeatures_grad gdef draw_CAM(model, img_path, save_path, transformNone, visual_heatmapFalse, out_layerNone):绘制 Class Activation Map:param model: 加载好权重的Pytorch model:param img_path: 测试图片路径:param save_path: CAM结果保存路径:param transform: 输入图像预处理方法:param visual_heatmap: 是否可视化原始heatmap调用matplotlib:return:# 读取图像并预处理global layer2img Image.open(img_path).convert(RGB)if transform:img transform(img)img img.unsqueeze(0) # (1, 3, 448, 448)# model转为eval模式model.eval()# 获取模型层的字典layers_dict {layers_names[i]: None for i in range(len(layers_names))}for name,module in model._modules.items():#print(i, (name, module))layers_dict[name] module# 遍历模型的每一层, 获得指定层的输出特征图# features: 指定层输出的特征图, features_flatten: 为继续完成前端传播而设置的变量features imgstart_flatten Falsefeatures_flatten Nonefor name, layer in layers_dict.items():if name ! out_layer and start_flatten is False: # 指定层之前features layer(features)elif name out_layer and start_flatten is False: # 指定层features layer(features)start_flatten Trueelse: # 指定层之后if name fc:breakif features_flatten is None:features_flatten layer(features)else:features_flatten layer(features_flatten)#print(features_flatten.shape)features_flatten torch.flatten(features_flatten, 1)#print(features_flatten.shape)output model.fc(features_flatten)# 预测得分最高的那一类对应的输出scorepred torch.argmax(output, 1).item()pred_class output[:, pred]# 求中间变量features的梯度# 方法1# features.register_hook(extract)# pred_class.backward()# 方法2features_grad autograd.grad(pred_class, features, allow_unusedTrue)[0]grads features_grad # 获取梯度pooled_grads torch.nn.functional.adaptive_avg_pool2d(grads, (1, 1))# 此处batch size默认为1所以去掉了第0维batch size维pooled_grads pooled_grads[0]features features[0]print(pooled_grads:, pooled_grads.shape)print(features:, features.shape)# features.shape[0]是指定层feature的通道数for i in range(features.shape[0]):features[i, ...] * pooled_grads[i, ...]# 计算heatmapheatmap features.detach().cpu().numpy()heatmap np.mean(heatmap, axis0)heatmap np.maximum(heatmap, 0)heatmap / np.max(heatmap)# 可视化原始热力图if visual_heatmap:plt.matshow(heatmap)plt.show()img cv2.imread(img_path) # 用cv2加载原始图像heatmap cv2.resize(heatmap, (img.shape[1], img.shape[0])) # 将热力图的大小调整为与原始图像相同heatmap np.uint8(255 * heatmap) # 将热力图转换为RGB格式heatmap cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) # 将热力图应用于原始图像superimposed_img heatmap * 0.7 img # 这里的0.4是热力图强度因子cv2.imwrite(save_path, superimposed_img) # 将图像保存到硬盘if __name__ __main__:model models.resnet50(pretrainedTrue)#model.eval()transform transforms.Compose([transforms.Resize(448),transforms.ToTensor(),transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])# 构建模型并加载预训练参数#seresnet50 FineTuneSEResnet50(num_class113).cuda()#checkpoint torch.load(resume_path)#seresnet50.load_state_dict(checkpoint[state_dict])draw_CAM(model, single_img_path, save_path, transformtransform, visual_heatmapTrue, out_layerout_layer_name)
