Source code for segmentation_models_pytorch.linknet.model

from typing import Optional, Union
from .decoder import LinknetDecoder
from ..base import SegmentationHead, SegmentationModel, ClassificationHead
from ..encoders import get_encoder

[docs]class Linknet(SegmentationModel): """Linknet_ 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 *sum* for fusing decoder blocks with skip connections. Note: This implementation by default has 4 skip connections (original - 3). 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 dimentions 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_use_batchnorm: If **True**, BatchNorm2d layer between Conv2D and Activation layers is used. If **"inplace"** InplaceABN will be used, allows to decrease memory consumption. Avaliable options are **True, False, "inplace"** 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. Avaliable options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"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``: **Linknet** .. _Linknet: """ def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_use_batchnorm: bool = True, in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, callable]] = None, aux_params: Optional[dict] = None, ): super().__init__() self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, ) self.decoder = LinknetDecoder( encoder_channels=self.encoder.out_channels, n_blocks=encoder_depth, prefinal_channels=32, use_batchnorm=decoder_use_batchnorm, ) self.segmentation_head = SegmentationHead( in_channels=32, out_channels=classes, activation=activation, kernel_size=1 ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None = "link-{}".format(encoder_name) self.initialize()