skp/models/segmentation/decoders/unet_3d/model.py

import torch.nn as nn

from typing import Optional, Union, List

from ...encoders.create import create_encoder
from ...base import (
    SegmentationModel,
    SegmentationHead_3D,
    ClassificationHead,
)
from .decoder import UnetDecoder_3D


class Unet_3D(SegmentationModel):
    """Unet_ is a fully convolution neural network for image semantic segmentation. Consist of *encoder*
    and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial
    resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *concatenation*
    for fusing decoder blocks with skip connections.

    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: List of integers which specify **in_channels** parameter for convolutions used in decoder.
            Length of the list should be the same as **encoder_depth**
        decoder_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers
            is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption.
            Available options are **True, False, "inplace"**
        decoder_attention_type: Attention module used in decoder of the model. Available options are
            **None** and **scse** (https://arxiv.org/abs/1808.08127).
        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**
        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``: Unet

    .. _Unet:
        https://arxiv.org/abs/1505.04597

    """

    def __init__(
        self,
        encoder_name: str,
        encoder_params: dict = {"pretrained": True, "depth": 5},
        decoder_use_batchnorm: bool = True,
        decoder_channels: List[int] = (256, 128, 64, 32, 16),
        decoder_attention_type: Optional[str] = None,
        deep_supervision: bool = False,
        dropout: float = 0.2,
        in_channels: int = 3,
        classes: int = 1,
        activation: Optional[Union[str, callable]] = None,
        upsampling: int = 1, 
        aux_params: Optional[dict] = None,
    ):
        super().__init__()

        encoder_depth = encoder_params.pop("depth", 5)
        self.encoder = create_encoder(
            name=encoder_name,
            encoder_params=encoder_params,
            in_channels=in_channels
        )

        assert decoder_attention_type in [None, "scse_3d"]

        self.decoder = UnetDecoder_3D(
            encoder_channels=self.encoder.out_channels,
            decoder_channels=decoder_channels,
            n_blocks=encoder_depth,
            use_batchnorm=decoder_use_batchnorm,
            center=True if encoder_name.startswith("vgg") else False,
            deep_supervision=deep_supervision,
            attention_type=decoder_attention_type,
        )

        self.segmentation_head = SegmentationHead_3D(
            in_channels=decoder_channels[-1],
            out_channels=classes,
            dropout=dropout,
            kernel_size=3,
            upsampling=upsampling,
        )

        self.deep_supervision = deep_supervision
        if self.deep_supervision:
            self.supervisor_heads = []
            self.supervisor_heads.append(
                SegmentationHead_3D(
                    in_channels=decoder_channels[-2],
                    out_channels=classes,
                    dropout=dropout,
                    kernel_size=3,
                    upsampling=upsampling,
                )
            )
            self.supervisor_heads.append(
                SegmentationHead_3D(
                    in_channels=decoder_channels[-3],
                    out_channels=classes,
                    dropout=dropout,
                    kernel_size=3,
                    upsampling=upsampling,
                )
            )
            self.supervisor_heads = nn.Sequential(*self.supervisor_heads)

        if aux_params is not None:
            self.classification_head = ClassificationHead(in_channels=self.encoder.out_channels[-1], **aux_params)
        else:
            self.classification_head = None

        self.name = "u-{}".format(encoder_name)
        self.initialize()