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