text: from torch import nn
  from typing import Optional
  
  from segmentation_models_pytorch.base import (
      SegmentationModel,
      SegmentationHead,
      ClassificationHead,
  )
  from segmentation_models_pytorch.encoders import get_encoder
  from .decoder import DeepLabV3Decoder, DeepLabV3PlusDecoder
  
  
  class DeepLabV3(SegmentationModel):
      """DeepLabV3_ implementation from "Rethinking Atrous Convolution for Semantic Image Segmentation"
  
      Args:
          encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
              to extract features of different spatial resolution
          encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features
              two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
              with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
              Default is 5
          encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and
              other pretrained weights (see table with available weights for each encoder_name)
          decoder_channels: A number of convolution filters in ASPP module. Default is 256
          in_channels: A number of input channels for the model, default is 3 (RGB images)
          classes: A number of classes for output mask (or you can think as a number of channels of output mask)
          activation: An activation function to apply after the final convolution layer.
              Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**,
                  **callable** and **None**.
              Default is **None**
          upsampling: Final upsampling factor. Default is 8 to preserve input-output spatial shape identity
          aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
              on top of encoder if **aux_params** is not **None** (default). Supported params:
                  - classes (int): A number of classes
                  - pooling (str): One of "max", "avg". Default is "avg"
                  - dropout (float): Dropout factor in [0, 1)
                  - activation (str): An activation function to apply "sigmoid"/"softmax"
                      (could be **None** to return logits)
      Returns:
          ``torch.nn.Module``: **DeepLabV3**
  
      .. _DeeplabV3:
          https://arxiv.org/abs/1706.05587
  
      """
  
      def __init__(
          self,
          encoder_name: str = "resnet34",
          encoder_depth: int = 5,
          encoder_weights: Optional[str] = "imagenet",
          decoder_channels: int = 256,
          in_channels: int = 3,
          classes: int = 1,
          activation: Optional[str] = None,
          upsampling: int = 8,
          aux_params: Optional[dict] = None,
          log_grad: bool = False
      ):
          super().__init__()
  
          self.encoder = get_encoder(
              encoder_name,
              in_channels=in_channels,
              depth=encoder_depth,
              weights=encoder_weights,
              output_stride=8,
          )
  
          self.decoder = DeepLabV3Decoder(
              in_channels=self.encoder.out_channels[-1],
              out_channels=decoder_channels,
              log_grad=log_grad
          )
  
          self.segmentation_head = SegmentationHead(
              in_channels=self.decoder.out_channels,
              out_channels=classes,
              activation=activation,
              kernel_size=1,
              upsampling=upsampling,
          )
  
          if aux_params is not None:
              self.classification_head = ClassificationHead(in_channels=self.encoder.out_channels[-1], **aux_params)
          else:
              self.classification_head = None
  
      @property
      def grad(self):
          return self.decoder.grad
  
  
  class DeepLabV3Plus(SegmentationModel):
      """DeepLabV3+ implementation from "Encoder-Decoder with Atrous Separable
      Convolution for Semantic Image Segmentation"
  
      Args:
          encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
              to extract features of different spatial resolution
          encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features
              two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
              with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
              Default is 5
          encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and
              other pretrained weights (see table with available weights for each encoder_name)
          encoder_output_stride: Downsampling factor for last encoder features (see original paper for explanation)
          decoder_atrous_rates: Dilation rates for ASPP module (should be a tuple of 3 integer values)
          decoder_channels: A number of convolution filters in ASPP module. Default is 256
          in_channels: A number of input channels for the model, default is 3 (RGB images)
          classes: A number of classes for output mask (or you can think as a number of channels of output mask)
          activation: An activation function to apply after the final convolution layer.
              Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**,
                  **callable** and **None**.
              Default is **None**
          upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity
          aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
              on top of encoder if **aux_params** is not **None** (default). Supported params:
                  - classes (int): A number of classes
                  - pooling (str): One of "max", "avg". Default is "avg"
                  - dropout (float): Dropout factor in [0, 1)
                  - activation (str): An activation function to apply "sigmoid"/"softmax"
                      (could be **None** to return logits)
      Returns:
          ``torch.nn.Module``: **DeepLabV3Plus**
  
      Reference:
          https://arxiv.org/abs/1802.02611v3
  
      """
  
      def __init__(
          self,
          encoder_name: str = "resnet34",
          encoder_depth: int = 5,
          encoder_weights: Optional[str] = "imagenet",
          encoder_output_stride: int = 16,
          decoder_channels: int = 256,
          decoder_atrous_rates: tuple = (12, 24, 36),
          in_channels: int = 3,
          classes: int = 1,
          activation: Optional[str] = None,
          upsampling: int = 4,
          aux_params: Optional[dict] = None,
      ):
          super().__init__()
  
          if encoder_output_stride not in [8, 16]:
              raise ValueError("Encoder output stride should be 8 or 16, got {}".format(encoder_output_stride))
  
          self.encoder = get_encoder(
              encoder_name,
              in_channels=in_channels,
              depth=encoder_depth,
              weights=encoder_weights,
              output_stride=encoder_output_stride,
          )
  
          self.decoder = DeepLabV3PlusDecoder(
              encoder_channels=self.encoder.out_channels,
              out_channels=decoder_channels,
              atrous_rates=decoder_atrous_rates,
              output_stride=encoder_output_stride,
          )
  
          self.segmentation_head = SegmentationHead(
              in_channels=self.decoder.out_channels,
              out_channels=classes,
              activation=activation,
              kernel_size=1,
              upsampling=upsampling,
          )
  
          if aux_params is not None:
              self.classification_head = ClassificationHead(in_channels=self.encoder.out_channels[-1], **aux_params)
          else:
              self.classification_head = None
  