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from dynamic_network_architectures.architectures.unet import PlainConvUNet, ResidualEncoderUNet |
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from dynamic_network_architectures.building_blocks.helper import get_matching_instancenorm, convert_dim_to_conv_op |
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from dynamic_network_architectures.initialization.weight_init import init_last_bn_before_add_to_0 |
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from nnunetv2.utilities.network_initialization import InitWeights_He |
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from nnunetv2.utilities.plans_handling.plans_handler import ConfigurationManager, PlansManager |
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from torch import nn |
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def get_network_from_plans(plans_manager: PlansManager, |
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dataset_json: dict, |
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configuration_manager: ConfigurationManager, |
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num_input_channels: int, |
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deep_supervision: bool = True): |
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""" |
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we may have to change this in the future to accommodate other plans -> network mappings |
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num_input_channels can differ depending on whether we do cascade. Its best to make this info available in the |
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trainer rather than inferring it again from the plans here. |
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""" |
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num_stages = len(configuration_manager.conv_kernel_sizes) |
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dim = len(configuration_manager.conv_kernel_sizes[0]) |
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conv_op = convert_dim_to_conv_op(dim) |
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label_manager = plans_manager.get_label_manager(dataset_json) |
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segmentation_network_class_name = configuration_manager.UNet_class_name |
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mapping = { |
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'PlainConvUNet': PlainConvUNet, |
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'ResidualEncoderUNet': ResidualEncoderUNet |
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} |
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kwargs = { |
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'PlainConvUNet': { |
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'conv_bias': True, |
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'norm_op': get_matching_instancenorm(conv_op), |
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'norm_op_kwargs': {'eps': 1e-5, 'affine': True}, |
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'dropout_op': None, 'dropout_op_kwargs': None, |
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'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True}, |
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}, |
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'ResidualEncoderUNet': { |
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'conv_bias': True, |
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'norm_op': get_matching_instancenorm(conv_op), |
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'norm_op_kwargs': {'eps': 1e-5, 'affine': True}, |
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'dropout_op': None, 'dropout_op_kwargs': None, |
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'nonlin': nn.LeakyReLU, 'nonlin_kwargs': {'inplace': True}, |
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} |
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} |
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assert segmentation_network_class_name in mapping.keys(), 'The network architecture specified by the plans file ' \ |
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'is non-standard (maybe your own?). Yo\'ll have to dive ' \ |
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'into either this ' \ |
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'function (get_network_from_plans) or ' \ |
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'the init of your nnUNetModule to accomodate that.' |
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network_class = mapping[segmentation_network_class_name] |
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conv_or_blocks_per_stage = { |
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'n_conv_per_stage' |
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if network_class != ResidualEncoderUNet else 'n_blocks_per_stage': configuration_manager.n_conv_per_stage_encoder, |
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'n_conv_per_stage_decoder': configuration_manager.n_conv_per_stage_decoder |
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} |
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model = network_class( |
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input_channels=num_input_channels, |
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n_stages=num_stages, |
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features_per_stage=[min(configuration_manager.UNet_base_num_features * 2 ** i, |
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configuration_manager.unet_max_num_features) for i in range(num_stages)], |
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conv_op=conv_op, |
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kernel_sizes=configuration_manager.conv_kernel_sizes, |
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strides=configuration_manager.pool_op_kernel_sizes, |
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num_classes=label_manager.num_segmentation_heads, |
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deep_supervision=deep_supervision, |
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**conv_or_blocks_per_stage, |
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**kwargs[segmentation_network_class_name] |
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) |
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model.apply(InitWeights_He(1e-2)) |
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if network_class == ResidualEncoderUNet: |
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model.apply(init_last_bn_before_add_to_0) |
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return model |
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