target_layer_names=["4"], use_cuda=args.use_cuda) img= cv2.imread(args.image_path, 1) img= np.float32(cv2.resize(img, (224, 224))) / 255input=preprocess_image(img)#If None, returns the map for the highest scoring category.#Otherwise, targets the requested index.target_index =None...
classGradCam:def__init__(self,model,feature_module,target_layer_names,use_cuda):''' model:resnet50 feature_module:module.layer4,即第4层 target_layer_names:2,即第4层中index=2的block '''self.model=model self.feature_module=feature_module self.model.eval()self.cuda=use_cudaifself.cuda:s...
grad_cam = GradCam(model=model, feature_module=model.layer4, \ target_layer_names=["2"], use_cuda=args.use_cuda) ''' 读取图片归一化为[0,1]矩阵,并转换为张量形式 ''' img = cv2.imread(args.image_path, 1) img = np.float32(cv2.resize(img, (224, 224))) / 255 input = preproc...
def __init__(self, model, feature_module, target_layer_names, use_cuda): self.model = model self.feature_module = feature_module self.model.eval() self.cuda = use_cuda if self.cuda: self.model =model.cuda() self.extractor = ModelOutputs(self.model, self.feature_module, target_layer_...
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_...
utils.model_targets import ClassifierOutputTarget torch.manual_seed(0) @pytest.fixture def numpy_image(): return cv2.imread("examples/both.png") @pytest.mark.parametrize("cnn_model,target_layer_names", [ (torchvision.models.resnet18, ["layer4[-1]"]) ]) @pytest.mark.param...
raise ValueError('Invalid layer name: {}'.format(target_layer)) ValueError: Invalid layer name: layer4.2 What's wrong to me? Thanks in advance. from @bemoregt.Owner kazuto1011 commented Mar 30, 2018 You can check valid layer names with this command: print(*list(models.resnet18().named...
GradCAM++算法针对Grad-CAM缺点的第1、2点进行了改进、ScoreCAM针对Grad-CAM缺点的第3、4、5点进行了改进、LayerCAM针对Grad-CAM缺点的第6点进行了改进。这些改进算法都十分有针对性,大家感兴趣也可以阅读阅读,本文的重点还是放到Grad-CAM算法上。 准备工作 ...
target_layers = [eval(layer)] method = eval(method) colors = np.random.uniform(0, 255, size=(len(model_names), 3)).astype(np.int) self.__dict__.update(locals()) def post_process(self, result): logits_ = result[..., 4:] ...
target_layer: The target convolutional layer for explanation, which can be tf.keras.layers.Layer or torch.nn.Module. mode: The task type, e.g., “classification” or “regression”.[5]: explainer = GradCAM( model=model, target_layer=model.layer4[-1], preprocess_function=preprocess ) #...