model.py

import warnings
from typing import Any, Dict, Optional, Union, Sequence, Callable

from segmentation_models_pytorch.base import (
    ClassificationHead,
    SegmentationHead,
    SegmentationModel,
)
from segmentation_models_pytorch.encoders import get_encoder
from segmentation_models_pytorch.base.hub_mixin import supports_config_loading

from .decoder import MAnetDecoder


class MAnet(SegmentationModel):
    """MAnet_ :  Multi-scale Attention Net. The MA-Net can capture rich contextual dependencies based on
    the attention mechanism, using two blocks:
     - Position-wise Attention Block (PAB), which captures the spatial dependencies between pixels in a global view
     - Multi-scale Fusion Attention Block (MFAB), which  captures the channel dependencies between any feature map by
       multi-scale semantic feature fusion

    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_norm: Specifies normalization between Conv2D and activation.
            Accepts the following types:
            - **True**: Defaults to `"batchnorm"`.
            - **False**: No normalization (`nn.Identity`).
            - **str**: Specifies normalization type using default parameters. Available values:
              `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`.
            - **dict**: Fully customizable normalization settings. Structure:
              ```python
              {"type": <norm_type>, **kwargs}
              ```
              where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation.

            **Example**:
            ```python
            decoder_use_norm={"type": "layernorm", "eps": 1e-2}
            ```
        decoder_pab_channels: A number of channels for PAB module in decoder.
            Default is 64.
        decoder_interpolation: Interpolation mode used in decoder of the model. Available options are
            **"nearest"**, **"bilinear"**, **"bicubic"**, **"area"**, **"nearest-exact"**. Default is **"nearest"**.
        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)
        kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing.

    Returns:
        ``torch.nn.Module``: **MAnet**

    .. _MAnet:
        https://ieeexplore.ieee.org/abstract/document/9201310

    """

    @supports_config_loading
    def __init__(
        self,
        encoder_name: str = "resnet34",
        encoder_depth: int = 5,
        encoder_weights: Optional[str] = "imagenet",
        decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm",
        decoder_channels: Sequence[int] = (256, 128, 64, 32, 16),
        decoder_pab_channels: int = 64,
        decoder_interpolation: str = "nearest",
        in_channels: int = 3,
        classes: int = 1,
        activation: Optional[Union[str, Callable]] = None,
        aux_params: Optional[dict] = None,
        **kwargs: dict[str, Any],
    ):
        super().__init__()

        decoder_use_batchnorm = kwargs.pop("decoder_use_batchnorm", None)
        if decoder_use_batchnorm is not None:
            warnings.warn(
                "The usage of decoder_use_batchnorm is deprecated. Please modify your code for decoder_use_norm",
                DeprecationWarning,
                stacklevel=2,
            )
            decoder_use_norm = decoder_use_batchnorm

        self.encoder = get_encoder(
            encoder_name,
            in_channels=in_channels,
            depth=encoder_depth,
            weights=encoder_weights,
            **kwargs,
        )

        self.decoder = MAnetDecoder(
            encoder_channels=self.encoder.out_channels,
            decoder_channels=decoder_channels,
            n_blocks=encoder_depth,
            use_norm=decoder_use_norm,
            pab_channels=decoder_pab_channels,
            interpolation_mode=decoder_interpolation,
        )

        self.segmentation_head = SegmentationHead(
            in_channels=decoder_channels[-1],
            out_channels=classes,
            activation=activation,
            kernel_size=3,
        )

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