def __call__(self, inputs: torch.Tensor, network: Callable[...,
torch.Tensor],
*args: Any, **kwargs: Any) -> torch.Tensor:
"""
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: optional args to be passed to ``network``.
kwargs: optional keyword args to be passed to ``network``.
"""
return sliding_window_inference(
inputs,
self.roi_size,
self.sw_batch_size,
network,
self.overlap,
self.mode,
self.sigma_scale,
self.padding_mode,
self.cval,
self.sw_device,
self.device,
self.progress,
*args,
**kwargs,
)
def __call__(self, inputs: torch.Tensor, network):
"""
Unified callable function API of Inferers.
Args:
inputs (torch.tensor): model input data for inference.
network (Network): target model to execute inference.
"""
# resize the input to the appropriate network input
orig_size = list(inputs.shape)
resized_size = copy.deepcopy(orig_size)
resized_size[2] = self.roi_size[0]
resized_size[3] = self.roi_size[1]
inputs_resize = torch.nn.functional.interpolate(inputs,
size=resized_size[2:],
mode='trilinear')
# convert the network to a callable that squeezes 3D slices to 2D before performing the network prediction
predictor_2d = Predict2DFrom3D(network)
outputs = sliding_window_inference(inputs_resize, self.roi_size,
self.sw_batch_size, predictor_2d,
self.overlap, self.mode)
# resize back to original size
outputs = torch.nn.functional.interpolate(outputs,
size=orig_size[2:],
mode='nearest')
return outputs
def __call__(self, inputs: torch.Tensor, network):
"""
Unified callable function API of Inferers.
Args:
inputs (torch.tensor): model input data for inference.
network (Network): target model to execute inference.
"""
return sliding_window_inference(inputs, self.roi_size,
self.sw_batch_size, network,
self.overlap, self.mode)
def __call__(self, inputs: torch.Tensor, network):
"""
Unified callable function API of Inferers.
Args:
inputs (torch.tensor): model input data for inference.
network (Network): target model to execute inference.
"""
# convert the network to a callable that squeezes 3D slices to 2D before performing the network prediction
predictor_2d = Predict2DFrom3D(network)
return sliding_window_inference(inputs, self.roi_size,
self.sw_batch_size, predictor_2d,
self.overlap, self.mode)
def __call__(self, inputs: torch.Tensor, network: Callable[[torch.Tensor], torch.Tensor]) -> torch.Tensor:
"""
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)``
"""
return sliding_window_inference(
inputs=inputs,
roi_size=self.roi_size,
sw_batch_size=self.sw_batch_size,
predictor=network,
overlap=self.overlap,
mode=self.mode,
sigma_scale=self.sigma_scale,
padding_mode=self.padding_mode,
cval=self.cval,
)
def __call__(self, inputs: torch.Tensor,
network: torch.nn.Module) -> torch.Tensor:
"""
Unified callable function API of Inferers.
Args:
inputs: model input data for inference.
network: target model to execute inference.
"""
return sliding_window_inference(
inputs=inputs,
roi_size=self.roi_size,
sw_batch_size=self.sw_batch_size,
predictor=network,
overlap=self.overlap,
mode=self.mode,
sigma_scale=self.sigma_scale,
padding_mode=self.padding_mode,
cval=self.cval,
)
