XGradCAM'layer':'model.model[7]','backward_type':'all',# class, box, all'conf_threshold':0.3,'ratio':0.02# 0.02-0.1}returnparamsif__name__=='__main__':model=yolov8_heatmap(**get_params())model(r'small_0_0_8b134278-IMG_2195_JPG.rf.532f1f9c1d89fdf3723b06f4a581971c.JPG',...
Github上的这个heatmap库挺好用的，我做分割的时候一直在用它来产生heatmap GitHub - jacobgil/pytorch-...
class GradCAM: """ Class for generating GradCAM heatmaps for interpreting convolutional neural networks. Args: model (nn.Module): PyTorch model for which GradCAM will be generated. Attributes: model (nn.Module): PyTorch model. gradients (torch.Tensor): Gradients of the target layer. """ de...
import torch.nn.functional as Fimport matplotlib.pyplot as plt# weight the channels by corresponding gradientsfor i in range(activations.size()[1]): activations[:, i, :, :] *= pooled_gradients[i]# average the channels of the activationsheatmap = torch.mean(activations, dim=1).squeeze...
heatmap = cv2.applyColorMap((255 * cam).astype(np.uint8), cv2.COLORMAP_JET) heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB) image = cv2.resize(image, (w, h)) # 调整原始图像的大小与热图相同 image = image / image.max() heatmap = heatmap / heatmap.max() result = 0.4 *...
我们对正向激活map A进行加权组合,然后使用ReLU函数: 注意,这将得到一个与卷积特征图(如VGG[52]和AlexNet[33]网络的最后一个卷积层为14×14)相同大小的粗略heatmap。我们对maps的线性组合使用ReLU,因为我们只对对感兴趣的类别有positive影响的特征感兴趣,例如,为了增加yc,需要增加像素强度。negative像素很可能属于图...
COLORMAP_JET) heatmap = np.float32(heatmap) / 255 cam = heatmap + np.float32(img) cam = cam / np.max(cam) cam = np.uint8(255 * cam) if if_write: cv2.imwrite("camcam.jpg", cam) if if_show: # 要显示RGB的图片,如果是BGR的 热力图是反过来的 plt.imshow(cam[:, :, ::-...
return array def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None): ## First, we create a model that maps the input image to the activations ## of the last conv layer as well as the output predictions grad_model = keras.models.Model( model.inputs, [mode...
heatmap = np.maximum(heatmap, 0) heatmap /= np.max(heatmap) ``` 总之,Grad-CAM是解释深度神经网络预测的有用工具,在图像分类、目标检测和语义分割等领域有广泛用途。gradcam()方法是一种简单易用的Python构造函数,它可以可视化Grad-CAM的输出,让用户更好地理解深度神经网络在分类预测中的行为。©...
heatmap = np.float32(heatmap) / 255 cam = heatmap + np.float32(img) cam = cam / np.max(cam) cv2.imwrite("cam.jpg", np.uint8(255 * cam)) class GradCam: def __init__(self, model, feature_module, target_layer_names, use_cuda): ...