Source code for segmentation_models_pytorch.pan.model

from typing import Optional, Union
from .decoder import PANDecoder
from ..encoders import get_encoder
from ..base import SegmentationModel
from ..base import SegmentationHead, ClassificationHead

[docs]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: """ 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 = "pan-{}".format(encoder_name) self.initialize()