o oi@spddlmZddlmZmZddlmZddlZddlm Z m Z ddl m Z ddlmZd gZGd d d ZdS) ) annotations)AnyOptional)nnN)Moduletensor)segmentation_models_pytorch)SemanticSegmentationSegmentationModelsBuilderc@s6eZdZe      ddddZedddZdS)r Unetresnet34imagenetr softmax model_namestr encoder_nameencoder_weights Optional[str] in_channelsintclasses activationkwargsrreturnr c KsTtj|}t|}tt|d|||||d|} t| |t|d|dS)a3SegmentationModel is a module that wraps a segmentation model. This module uses SegmentationModel library for segmentation. Args: model_name: Name of the model to use. Valid options are: "Unet", "UnetPlusPlus", "MAnet", "LinkNet", "FPN", "PSPNet", "PAN", "DeepLabV3", "DeepLabV3Plus". encoder_name: Name of the encoder to use. encoder_depth: Depth of the encoder. encoder_weights: Weights of the encoder. decoder_channels: Number of channels in the decoder. in_channels: Number of channels in the input. classes: Number of classes to predict. activation: Type of activation layer. **kwargs: Additional arguments to pass to the model. Detailed arguments can be found at: https://github.com/qubvel-org/segmentation_models.pytorch/tree/main/segmentation_models_pytorch/decoders Note: Only encoder weights are available. Pretrained weights for the whole model are not available. )rrrrr_)model pre_processorpost_processornameN) smpencodersget_preprocessing_paramsr get_preprocessing_pipelinegetattrr rIdentity) rrrrrrrpreproc_params preprocessorsegmentation_modelr!r!b/home/ubuntu/.local/lib/python3.10/site-packages/kornia/models/segmentation/segmentation_models.pybuild"s"    zSegmentationModelsBuilder.buildr(dict[str, Any]-kornia.augmentation.container.ImageSequentialcCsg}|d}|dkr|tjn |dkrntd||d}|ddkr5|tjjdd d n|ddkrs