Source code for segmentation_models_pytorch.decoders.deeplabv3.model

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


[docs]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
[docs]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