model.py

from typing import Any, Optional

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 FPNDecoder


class FPN(SegmentationModel):
    """FPN_ is a fully convolution neural network for image semantic 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_pyramid_channels: A number of convolution filters in Feature Pyramid of FPN_
        decoder_segmentation_channels: A number of convolution filters in segmentation blocks of FPN_
        decoder_merge_policy: Determines how to merge pyramid features inside FPN. Available options are **add**
            and **cat**
        decoder_dropout: Spatial dropout rate in range (0, 1) for feature pyramid in FPN_
        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**
        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)
        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``: **FPN**

    .. _FPN:
        http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf

    """

    @supports_config_loading
    def __init__(
        self,
        encoder_name: str = "resnet34",
        encoder_depth: int = 5,
        encoder_weights: Optional[str] = "imagenet",
        decoder_pyramid_channels: int = 256,
        decoder_segmentation_channels: int = 128,
        decoder_merge_policy: str = "add",
        decoder_dropout: float = 0.2,
        decoder_interpolation: str = "nearest",
        in_channels: int = 3,
        classes: int = 1,
        activation: Optional[str] = None,
        upsampling: int = 4,
        aux_params: Optional[dict] = None,
        **kwargs: dict[str, Any],
    ):
        super().__init__()

        # validate input params
        if encoder_name.startswith("mit_b") and encoder_depth != 5:
            raise ValueError(
                "Encoder {} support only encoder_depth=5".format(encoder_name)
            )

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

        self.decoder = FPNDecoder(
            encoder_channels=self.encoder.out_channels,
            encoder_depth=encoder_depth,
            pyramid_channels=decoder_pyramid_channels,
            segmentation_channels=decoder_segmentation_channels,
            dropout=decoder_dropout,
            merge_policy=decoder_merge_policy,
            interpolation_mode=decoder_interpolation,
        )

        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

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