Source code for pywick.models.segmentation.dunet

# Source: https://github.com/Tramac/awesome-semantic-segmentation-pytorch/blob/master/core/models/dunet.py (License: Apache 2.0)

"""
Implementation of `Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation <https://arxiv.org/pdf/1903.02120>`_
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from pywick.models.segmentation.da_basenets.segbase import SegBaseModel
from pywick.models.segmentation.da_basenets.fcn import _FCNHead

__all__ = ['DUNet', 'DUNet_Resnet50', 'DUNet_Resnet101', 'DUNet_Resnet152']


# The model may be wrong because lots of details missing in paper.
[docs]class DUNet(SegBaseModel): """Decoders Matter for Semantic Segmentation Reference: Zhi Tian, Tong He, Chunhua Shen, and Youliang Yan. "Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation." CVPR, 2019 """ def __init__(self, num_classes, pretrained=True, backbone='resnet101', aux=False, **kwargs): super(DUNet, self).__init__(num_classes, pretrained=pretrained, aux=aux, backbone=backbone, **kwargs) self.head = _DUHead(2144, **kwargs) self.dupsample = DUpsampling(256, num_classes, scale_factor=8, **kwargs) if aux: self.auxlayer = _FCNHead(1024, 256, **kwargs) self.aux_dupsample = DUpsampling(256, num_classes, scale_factor=8, **kwargs) self.__setattr__('exclusive', ['dupsample', 'head', 'auxlayer', 'aux_dupsample'] if aux else ['dupsample', 'head']) def forward(self, x): c1, c2, c3, c4 = self.base_forward(x) outputs = [] x = self.head(c2, c3, c4) x = self.dupsample(x) outputs.append(x) if self.aux and self.training: auxout = self.auxlayer(c3) auxout = self.aux_dupsample(auxout) outputs.append(auxout) return tuple(outputs) else: return outputs[0]
class FeatureFused(nn.Module): """Module for fused features""" def __init__(self, inter_channels=48, norm_layer=nn.BatchNorm2d, **kwargs): super(FeatureFused, self).__init__() self.conv2 = nn.Sequential( nn.Conv2d(512, inter_channels, 1, bias=False), norm_layer(inter_channels), nn.ReLU(True) ) self.conv3 = nn.Sequential( nn.Conv2d(1024, inter_channels, 1, bias=False), norm_layer(inter_channels), nn.ReLU(True) ) def forward(self, c2, c3, c4): size = c4.size()[2:] c2 = self.conv2(F.interpolate(c2, size, mode='bilinear', align_corners=True)) c3 = self.conv3(F.interpolate(c3, size, mode='bilinear', align_corners=True)) fused_feature = torch.cat([c4, c3, c2], dim=1) return fused_feature class _DUHead(nn.Module): def __init__(self, in_channels, norm_layer=nn.BatchNorm2d, **kwargs): super(_DUHead, self).__init__() self.fuse = FeatureFused(norm_layer=norm_layer, **kwargs) self.block = nn.Sequential( nn.Conv2d(in_channels, 256, 3, padding=1, bias=False), norm_layer(256), nn.ReLU(True), nn.Conv2d(256, 256, 3, padding=1, bias=False), norm_layer(256), nn.ReLU(True) ) def forward(self, c2, c3, c4): fused_feature = self.fuse(c2, c3, c4) out = self.block(fused_feature) return out class DUpsampling(nn.Module): """DUsampling module""" def __init__(self, in_channels, out_channels, scale_factor=2, **kwargs): super(DUpsampling, self).__init__() self.scale_factor = scale_factor self.conv_w = nn.Conv2d(in_channels, out_channels * scale_factor * scale_factor, 1, bias=False) def forward(self, x): x = self.conv_w(x) n, c, h, w = x.size() # N, C, H, W --> N, W, H, C x = x.permute(0, 3, 2, 1).contiguous() # N, W, H, C --> N, W, H * scale, C // scale x = x.view(n, w, h * self.scale_factor, c // self.scale_factor) # N, W, H * scale, C // scale --> N, H * scale, W, C // scale x = x.permute(0, 2, 1, 3).contiguous() # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2) x = x.view(n, h * self.scale_factor, w * self.scale_factor, c // (self.scale_factor * self.scale_factor)) # N, H * scale, W * scale, C // (scale ** 2) -- > N, C // (scale ** 2), H * scale, W * scale x = x.permute(0, 3, 1, 2) return x def get_dunet(num_classes=1, backbone='resnet50', pretrained=True, **kwargs): r"""Decoders Matter for Semantic Segmentation Parameters ---------- num_classes : int (default: 1) - number of classes backbone : str - type of backbone to use (one of `{resnet50, resnet101, resnet152}`) pretrained : bool (default: True) - whether to load pretrained backbone network, that was trained on ImageNet. """ model = DUNet(num_classes=num_classes, backbone=backbone, pretrained=pretrained, **kwargs) return model
[docs]def DUNet_Resnet50(num_classes=1, **kwargs): return get_dunet(num_classes=num_classes, backbone='resnet50', **kwargs)
[docs]def DUNet_Resnet101(num_classes=1, **kwargs): return get_dunet(num_classes=num_classes, backbone='resnet101', **kwargs)
[docs]def DUNet_Resnet152(num_classes=1, **kwargs): return get_dunet(num_classes=num_classes, backbone='resnet152', **kwargs)
if __name__ == '__main__': img = torch.randn(2, 3, 256, 256) model = DUNet_Resnet50() outputs = model(img)