| from typing import Optional, Union |
| from .decoder import PANDecoder |
| from ..encoders import get_encoder |
| from ..base import SegmentationModel |
| from ..base import SegmentationHead, ClassificationHead |
|
|
|
|
| class PAN(SegmentationModel): |
| """ Implementation of PAN_ (Pyramid Attention Network). |
| |
| Note: |
| Currently works with shape of input tensor >= [B x C x 128 x 128] for pytorch <= 1.1.0 |
| and with shape of input tensor >= [B x C x 256 x 256] for pytorch == 1.3.1 |
| |
| 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_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: 16 or 32, if 16 use dilation in encoder last layer. |
| Doesn't work with ***ception***, **vgg***, **densenet*`** backbones.Default is 16. |
| decoder_channels: A number of convolution layer filters in decoder blocks |
| 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``: **PAN** |
| |
| .. _PAN: |
| https://arxiv.org/abs/1805.10180 |
| |
| """ |
|
|
| def __init__( |
| self, |
| encoder_name: str = "resnet34", |
| encoder_weights: Optional[str] = "imagenet", |
| encoder_output_stride: int = 16, |
| decoder_channels: int = 32, |
| in_channels: int = 3, |
| classes: int = 1, |
| activation: Optional[Union[str, callable]] = None, |
| upsampling: int = 4, |
| aux_params: Optional[dict] = None |
| ): |
| super().__init__() |
|
|
| if encoder_output_stride not in [16, 32]: |
| raise ValueError("PAN support output stride 16 or 32, got {}".format(encoder_output_stride)) |
|
|
| self.encoder = get_encoder( |
| encoder_name, |
| in_channels=in_channels, |
| depth=5, |
| weights=encoder_weights, |
| output_stride=encoder_output_stride, |
| ) |
|
|
| self.decoder = PANDecoder( |
| encoder_channels=self.encoder.out_channels, |
| decoder_channels=decoder_channels, |
| ) |
|
|
| self.segmentation_head = SegmentationHead( |
| in_channels=decoder_channels, |
| out_channels=classes, |
| activation=activation, |
| kernel_size=3, |
| 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 |
|
|
| self.name = "pan-{}".format(encoder_name) |
| self.initialize() |
|
|
