GradCAM

class tensornet.gradcam.GradCAM(model: torch.nn.modules.module.Module, layer_name: str)

Calculate GradCAM salinecy map.

Note: The current implemenation supports only ResNet models. The class can be extended to add support for other models.

Parameters

• model (torch.nn.Module) – A model instance.

• layer_name (str) – Name of the layer in model for which the map will be calculated.

saliency_map_size(*input_size)

Returns the shape of the saliency map.

__call__(input: tuple, class_idx: Optional[int] = None, retain_graph: bool = False) → Tuple[torch.Tensor]

Parameters

• input (tuple) – Input image with shape of (1, 3, H, W)

• class_idx (int, optional) – Class index for calculating GradCAM. If not specified, the class index that makes the highest model prediction score will be used.

Returns

2-element tuple containing

• (torch.tensor): saliency map of the same spatial dimension with input.

• (torch.tensor): model output.

class tensornet.gradcam.GradCAMPP(model: torch.nn.modules.module.Module, layer_name: str)

Calculate GradCAM++ salinecy map.

It inherits the GradCAM class so the definition for all the methods is exactly the same as its parent class.

Note: The current implemenation supports only ResNet models. The class can be extended to add support for other models.

class tensornet.gradcam.GradCAMView(model: torch.nn.modules.module.Module, layers: List[str], device: Union[str, torch.device], mean: Union[float, tuple], std: Union[float, tuple])

Create GradCAM and GradCAM++.

Note: The current implemenation of GradCAM and GradCAM++ supports only ResNet models. The class can be extended to add support for other models.

Parameters

• model (torch.nn.Module) – Trained model.

• layers (list) – List of layers to show GradCAM on.

• device (str or torch.device) – GPU or CPU.

• mean (float or tuple) – Mean of the dataset.

• std (float or tuple) – Standard Deviation of the dataset.

switch_mode()

Switch between GradCAM and GradCAM++.

cam(norm_img_class_list: List[Union[Dict[str, Union[torch.Tensor, int]], torch.Tensor]])

Get CAM for a list of images.

Parameters

norm_img_class_list (list) – List of dictionaries or list of images. If dict, each dict contains keys ‘image’ and ‘class’ having values ‘normalized_image’ and ‘class_idx’ respectively. class_idx is optional. If class_idx is not given then the model prediction will be used and the parameter should just be a list of images. Each image should be of type torch.Tensor

__call__(norm_img_class_list: List[Union[Dict[str, Union[torch.Tensor, int]], torch.Tensor]]) → List[Dict[str, Union[numpy.ndarray, Dict[str, numpy.ndarray]]]]

Get GradCAM for a list of images.

Parameters

norm_img_class_list (list) – List of dictionaries or list of images. If dict, each dict contains keys ‘image’ and ‘class’ having values ‘normalized_image’ and ‘class_idx’ respectively. class_idx is optional. If class_idx is not given then the model prediction will be used and the parameter should just be a list of images. Each image should be of type torch.Tensor

tensornet.gradcam.visualize_cam(mask: torch.Tensor, img: torch.Tensor, alpha: float = 1.0) → Tuple[torch.Tensor]

Make heatmap from mask and synthesize GradCAM result image using heatmap and img.

Parameters

• mask (torch.tensor) – mask shape of (1, 1, H, W) and each element has value in range [0, 1]

• img (torch.tensor) – img shape of (1, 3, H, W) and each pixel value is in range [0, 1]

Returns

2-element tuple containing

• (torch.tensor): heatmap img shape of (3, H, W)

• (torch.tensor): synthesized GradCAM result of same shape with heatmap.