| from typing import Optional, Union | |
| from segmentation_models_pytorch.base import ( | |
| SegmentationHead, | |
| SegmentationModel, | |
| ClassificationHead, | |
| ) | |
| from segmentation_models_pytorch.encoders import get_encoder | |
| from .decoder import LinknetDecoder | |
| 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 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_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"** | |
| 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``: **Linknet** | |
| .. _Linknet: | |
| https://arxiv.org/abs/1707.03718 | |
| """ | |
| 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__() | |
| if encoder_name.startswith("mit_b"): | |
| raise ValueError( | |
| "Encoder `{}` is not supported for Linknet".format(encoder_name) | |
| ) | |
| 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 | |
| self.name = "link-{}".format(encoder_name) | |
| self.initialize() | |