target_category = None # 可以指定一个类别,或者使用 None 表示最高概率的类别 grayscale_cam = cam(input_tensor=input_tensor, target_category=target_category) #将 grad-cam 的输出叠加到原始图像上 visualization = show_cam_on_image(rgb_img, grayscale_cam) # 保存可视化结果 cv2.imwrite('cam.jpg'...
,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...
首先解释一下这个ClassifierOutputTarget(),我们按住Ctrl,点击ClassifierOutputTarget,跳转到pytorch_grad_cam\utils\model_targets.py文件ClassifierOutputTarget类: classClassifierOutputTarget:def__init__(self,category):self.category=categorydef__call__(self,model_output):# 若模型输出单列iflen(model_output.shap...
(class_out, one_hot) target_category_loss = fluid.layers.reduce_sum(one_hot) # 因为没有优化器,所以手动添加反向 p_g_list = fluid.backward.append_backward(target_category_loss) #从loss 出发,计算指定feature_map的梯度 gradients_map = fluid.gradients(target_category_loss, conv)[0] # print(...
one_hot[0][target_category] = 1 one_hot = torch.from_numpy(one_hot).requires_grad_(True) if self.cuda: one_hot = one_hot.cuda() one_hot = torch.sum(one_hot * output) self.feature_module.zero_grad() self.model.zero_grad() ...
XGradCAM,EigenCAM,FullGradfrompytorch_grad_cam.utils.model_targetsimportClassifierOutputTargetfrom...
g ClassifierOutputTarget(281)] # You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing. grayscale_cam = cam(input_tensor=input_tensor, target_category=target_category) # In this example grayscale_cam has only one image in the batch: grayscale_cam = grayscale_cam[...
target_category=0, eigen_smooth=True, aug_smooth=True) grayscale_cam = grayscale_cam[0, :] cam_image = show_cam_on_image(rgb_img, grayscale_cam) cv2.imwrite('save_path.jpg', cam_image) Original Image ViT GradCam++ Swin Transformer GradCAM++ ...
parametrize("target_category", [ 100 ]) @pytest.mark.parametrize("aug_smooth", [ False ]) @pytest.mark.parametrize("eigen_smooth", [ False ]) @pytest.mark.parametrize("cam_method", [GradCAM]) def test_memory_usage_in_loop(numpy_image, batch_size, width, height, cnn_...
target layer.Methods that return weights channels,will typically need to only implement this function. """@staticmethoddef get_cam_weights(grads):return np.mean(grads, axis=(2, 3), keepdims=True)@staticmethoddef get_loss(output, target_category):loss = 0for i in range(len(target_category)...