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Interactive-MEN-RT-Demo / nnInteractive /trainer /nnInteractiveTrainer.py

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from typing import Union, Tuple, List

from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
from torch import nn


class nnInteractiveTrainer_stub():
    def __init__(self, *args, **kwargs):
        pass

    @staticmethod
    def build_network_architecture(architecture_class_name: str,
                                   arch_init_kwargs: dict,
                                   arch_init_kwargs_req_import: Union[List[str], Tuple[str, ...]],
                                   num_input_channels: int,
                                   num_output_channels: int,
                                   enable_deep_supervision: bool = True) -> nn.Module:
        return nnUNetTrainer.build_network_architecture(
            architecture_class_name,
            arch_init_kwargs,
            arch_init_kwargs_req_import,
            num_input_channels + 7,
            2,  # nnunet handles one class segmentation still as CE so we need 2 outputs.
            enable_deep_supervision
        )

	from typing import Union, Tuple, List

	from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
	from torch import nn


	class nnInteractiveTrainer_stub():
	def __init__(self, args, *kwargs):
	pass

	@staticmethod
	def build_network_architecture(architecture_class_name: str,
	arch_init_kwargs: dict,
	arch_init_kwargs_req_import: Union[List[str], Tuple[str, ...]],
	num_input_channels: int,
	num_output_channels: int,
	enable_deep_supervision: bool = True) -> nn.Module:
	return nnUNetTrainer.build_network_architecture(
	architecture_class_name,
	arch_init_kwargs,
	arch_init_kwargs_req_import,
	num_input_channels + 7,
	2, # nnunet handles one class segmentation still as CE so we need 2 outputs.
	enable_deep_supervision
	)