| from typing import Optional, Union, List |
| from .decoder import MAnetDecoder |
| from ..encoders import get_encoder |
| from ..base import SegmentationModel |
| from ..base import SegmentationHead, ClassificationHead |
|
|
|
|
| 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() |
|
|
