| 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() | |