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from typing import Optional, Union, List
from segmentation_models_pytorch.encoders import get_encoder
from segmentation_models_pytorch.base import (
SegmentationModel,
SegmentationHead,
ClassificationHead,
)
from .decoder import MAnetDecoder
class MAnet(SegmentationModel):
"""MAnet_ : Multi-scale Attention Net. The MA-Net can capture rich contextual dependencies based on
the attention mechanism, using two blocks:
- Position-wise Attention Block (PAB), which captures the spatial dependencies between pixels in a global view
- Multi-scale Fusion Attention Block (MFAB), which captures the channel dependencies between any feature map by
multi-scale semantic feature fusion
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: List of integers which specify **in_channels** parameter for convolutions used in decoder.
Length of the list should be the same as **encoder_depth**
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"**
decoder_pab_channels: A number of channels for PAB module in decoder.
Default is 64.
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``: **MAnet**
.. _MAnet:
https://ieeexplore.ieee.org/abstract/document/9201310
"""
def __init__(
self,
encoder_name: str = "resnet34",
encoder_depth: int = 5,
encoder_weights: Optional[str] = "imagenet",
decoder_use_batchnorm: bool = True,
decoder_channels: List[int] = (256, 128, 64, 32, 16),
decoder_pab_channels: int = 64,
in_channels: int = 3,
classes: int = 1,
activation: Optional[Union[str, callable]] = None,
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 = MAnetDecoder(
encoder_channels=self.encoder.out_channels,
decoder_channels=decoder_channels,
n_blocks=encoder_depth,
use_batchnorm=decoder_use_batchnorm,
pab_channels=decoder_pab_channels,
)
self.segmentation_head = SegmentationHead(
in_channels=decoder_channels[-1],
out_channels=classes,
activation=activation,
kernel_size=3,
)
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 = "manet-{}".format(encoder_name)
self.initialize()
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