| from typing import Optional, Union | |
| from .decoder import FPNDecoder | |
| from ..base import SegmentationModel, SegmentationHead, ClassificationHead | |
| from ..encoders import get_encoder | |
| 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_ | |
| 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) | |
| Returns: | |
| ``torch.nn.Module``: **FPN** | |
| .. _FPN: | |
| http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf | |
| """ | |
| 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, | |
| in_channels: int = 3, | |
| classes: int = 1, | |
| activation: Optional[str] = None, | |
| upsampling: int = 4, | |
| 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 = 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, | |
| ) | |
| 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() | |