| from torch import nn | |
| from typing import Optional | |
| from segmentation_models_pytorch.base import ( | |
| SegmentationModel, | |
| SegmentationHead, | |
| ClassificationHead, | |
| ) | |
| from segmentation_models_pytorch.encoders import get_encoder | |
| from .decoder import DeepLabV3Decoder, DeepLabV3PlusDecoder | |
| class DeepLabV3(SegmentationModel): | |
| """DeepLabV3_ implementation from "Rethinking Atrous Convolution for Semantic Image 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_channels: A number of convolution filters in ASPP module. Default is 256 | |
| 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 8 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``: **DeepLabV3** | |
| .. _DeeplabV3: | |
| https://arxiv.org/abs/1706.05587 | |
| """ | |
| def __init__( | |
| self, | |
| encoder_name: str = "resnet34", | |
| encoder_depth: int = 5, | |
| encoder_weights: Optional[str] = "imagenet", | |
| decoder_channels: int = 256, | |
| in_channels: int = 3, | |
| classes: int = 1, | |
| activation: Optional[str] = None, | |
| upsampling: int = 8, | |
| aux_params: Optional[dict] = None, | |
| ): | |
| super().__init__() | |
| self.encoder = get_encoder( | |
| encoder_name, | |
| in_channels=in_channels, | |
| depth=encoder_depth, | |
| weights=encoder_weights, | |
| output_stride=8, | |
| ) | |
| self.decoder = DeepLabV3Decoder( | |
| in_channels=self.encoder.out_channels[-1], out_channels=decoder_channels, | |
| ) | |
| 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 | |
| class DeepLabV3Plus(SegmentationModel): | |
| """DeepLabV3+ implementation from "Encoder-Decoder with Atrous Separable | |
| Convolution for Semantic Image 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) | |
| encoder_output_stride: Downsampling factor for last encoder features (see original paper for explanation) | |
| decoder_atrous_rates: Dilation rates for ASPP module (should be a tuple of 3 integer values) | |
| decoder_channels: A number of convolution filters in ASPP module. Default is 256 | |
| 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``: **DeepLabV3Plus** | |
| Reference: | |
| https://arxiv.org/abs/1802.02611v3 | |
| """ | |
| def __init__( | |
| self, | |
| encoder_name: str = "resnet34", | |
| encoder_depth: int = 5, | |
| encoder_weights: Optional[str] = "imagenet", | |
| encoder_output_stride: int = 16, | |
| decoder_channels: int = 256, | |
| decoder_atrous_rates: tuple = (12, 24, 36), | |
| in_channels: int = 3, | |
| classes: int = 1, | |
| activation: Optional[str] = None, | |
| upsampling: int = 4, | |
| aux_params: Optional[dict] = None, | |
| ): | |
| super().__init__() | |
| if encoder_output_stride not in [8, 16]: | |
| raise ValueError( | |
| "Encoder output stride should be 8 or 16, got {}".format( | |
| encoder_output_stride | |
| ) | |
| ) | |
| self.encoder = get_encoder( | |
| encoder_name, | |
| in_channels=in_channels, | |
| depth=encoder_depth, | |
| weights=encoder_weights, | |
| output_stride=encoder_output_stride, | |
| ) | |
| self.decoder = DeepLabV3PlusDecoder( | |
| encoder_channels=self.encoder.out_channels, | |
| out_channels=decoder_channels, | |
| atrous_rates=decoder_atrous_rates, | |
| output_stride=encoder_output_stride, | |
| ) | |
| 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 | |