,即代码中target_layers,一般默认都是捕获最后一个卷积层的输出。接着还要指定我们感兴趣的类别id,即代码中的target_category,注意这里是直接使用官方在Imagenet数据上预训练好的模型,所以这里的类别id指的是Imagenet数据中1000个类别的id(代码中id默认从0开始),比如说对于tabby, tabby cat这个类别,它对应的target_c...
2. Activations from intermeddiate targetted layers. 3. Gradients from intermeddiate targetted layers. """ def __init__(self, model, feature_module, target_layers): self.model = model self.feature_module = feature_module self.feature_extractor = FeatureExtractor(self.feature_module, target_la...
cam = GradCAM(model=model, target_layers=target_layers, use_cuda=args.use_cuda) targets = [ClassifierOutputTarget(281)] grayscale_cam = cam(input_tensor=input_tensor, targets=targets) 输入的参数是图片对应的target,也就是one-hot标签里面的1的下标,但由于使用的是预训练模型,所以不知道具体的标签。
cam=GradCAM(model=model,target_layers=[model.blocks[-1].norm1],# 这里的target_layer要看模型情况, # 比如还有可能是:target_layers=[model.blocks[-1].ffn.norm]use_cuda=use_cuda,reshape_transform=reshape_transform) 接下来,我们需要准备一张输入图像,并将其转换为适合 ViT 的格式: 代码语言:javascri...
self.target_layers.register_forward_hook(self.forward_hook) self.target_layers.register_full_backward_hook(self.backward_hook) self.activations = [] self.grads = [] self.input_size = input_size def forward_hook(self, module, input, output): self.activations.append(output[0]) def backward_...
(model=model, target_layers=target_layers, use_cuda=args.use_cuda)# targets = [e.g ClassifierOutputTarget(281)]targets = None# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.grayscale_cam = cam(input_tensor=input_tensor,targets=targets,eigen_smooth=args.eigen...
target_layers = [model.layer4[-1]] 4. 处理输入图像 读取输入图像并进行预处理,以匹配预训练模型的输入要求: python data_transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224...
.target_layers: print(module) x.register_hook(self.save_gradient) outputs += [x] return outputs, x class ModelOutputs(): """ Class for making a forward pass, and getting: 1. The network output. 2. Activations from intermeddiate targetted layers. 3. Gradients from intermeddiate target...
if name in self.target_layers: x.register_hook(self.save_gradient) outputs += [x] return outputs, x class ModelOutputs(): """ Class for making a forward pass, and getting: 1. The network output. 2. Activations from intermeddiate targetted layers. ...
target_layer = model.layers[-1].blocks[-1].norm1 cam = GradCAMPlusPlus(model=model, target_layer=target_layer, reshape_transform=reshape_transform) rgb_img = cv2.imread(img_path, 1)[:, :, ::-1] rgb_img = cv2.resize(rgb_img, (224, 224)) ...