Source code for segmentation_models_pytorch.decoders.fpn.model

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


[docs] 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()