RemoteSensingChangeDetection-RSCD.CTTF
/
rscd
/models
/decoderheads
/lgpnet
/SpatialPyramidModule.py
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch | |
| """ | |
| This code refers to "Pyramid scene parsing network". | |
| """ | |
| class SPM(nn.Module): | |
| def __init__(self, features, out_features=1024, sizes=(1, 2, 3, 6)): | |
| super().__init__() | |
| self.stages = [] | |
| self.stages = nn.ModuleList([self._make_stage(features, size) for size in sizes]) | |
| self.bottleneck = nn.Conv2d(features * (len(sizes) + 1), out_features, kernel_size=1) | |
| self.relu = nn.ReLU() | |
| def _make_stage(self, features, size): | |
| prior = nn.AdaptiveAvgPool2d(output_size=(size, size)) | |
| conv = nn.Conv2d(features, features, kernel_size=1, bias=False) | |
| return nn.Sequential(prior, conv) | |
| def forward(self, feats): | |
| h, w = feats.size(2), feats.size(3) | |
| priors = [F.interpolate(input=stage(feats), size=(h, w), mode='bilinear', align_corners=False) for stage in self.stages] + [feats] | |
| bottle = self.bottleneck(torch.cat(priors, 1)) | |
| return self.relu(bottle) |