def __call__( # type: ignore
self,
inputs: torch.Tensor,
network: nn.Module,
*args: Any,
**kwargs: Any,
):
"""Unified callable function API of Inferers.
Args:
inputs: model input data for inference.
network: target model to execute inference.
supports callables such as ``lambda x: my_torch_model(x, additional_config)``
args: other optional args to be passed to the `__call__` of cam.
kwargs: other optional keyword args to be passed to `__call__` of cam.
"""
cam: Union[CAM, GradCAM, GradCAMpp]
if self.cam_name == "cam":
cam = CAM(network, self.target_layers, *self.args, **self.kwargs)
elif self.cam_name == "gradcam":
cam = GradCAM(network, self.target_layers, *self.args,
**self.kwargs)
else:
cam = GradCAMpp(network, self.target_layers, *self.args,
**self.kwargs)
return cam(inputs, self.class_idx, *args, **kwargs)
def test_shape(self, input_data, expected_shape):
if input_data["model"] == "densenet2d":
model = densenet121(spatial_dims=2, in_channels=1, out_channels=3)
if input_data["model"] == "densenet3d":
model = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=3,
init_features=2,
growth_rate=2,
block_config=(6, ))
if input_data["model"] == "senet2d":
model = se_resnet50(spatial_dims=2, in_channels=3, num_classes=4)
if input_data["model"] == "senet3d":
model = se_resnet50(spatial_dims=3, in_channels=3, num_classes=4)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
cam = GradCAMpp(nn_module=model,
target_layers=input_data["target_layers"])
image = torch.rand(input_data["shape"], device=device)
result = cam(x=image, layer_idx=-1)
fea_shape = cam.feature_map_size(input_data["shape"], device=device)
self.assertTupleEqual(fea_shape, input_data["feature_shape"])
self.assertTupleEqual(result.shape, expected_shape)