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| # | |
| # Authors: Wouter Van Gansbeke & Simon Vandenhende | |
| # Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/) | |
| import torch.nn as nn | |
| class ResnetDilated(nn.Module): | |
| def __init__(self, orig_resnet, dilate_scale=8): | |
| super(ResnetDilated, self).__init__() | |
| from functools import partial | |
| if dilate_scale == 8: | |
| orig_resnet.layer3.apply( | |
| partial(self._nostride_dilate, dilate=2)) | |
| orig_resnet.layer4.apply( | |
| partial(self._nostride_dilate, dilate=4)) | |
| elif dilate_scale == 16: | |
| orig_resnet.layer4.apply( | |
| partial(self._nostride_dilate, dilate=2)) | |
| self.conv1 = orig_resnet.conv1 | |
| self.bn1 = orig_resnet.bn1 | |
| self.relu = orig_resnet.relu | |
| self.maxpool = orig_resnet.maxpool | |
| self.layer1 = orig_resnet.layer1 | |
| self.layer2 = orig_resnet.layer2 | |
| self.layer3 = orig_resnet.layer3 | |
| self.layer4 = orig_resnet.layer4 | |
| def _nostride_dilate(self, m, dilate): | |
| classname = m.__class__.__name__ | |
| if classname.find('Conv') != -1: | |
| # the convolution with stride | |
| if m.stride == (2, 2): | |
| m.stride = (1, 1) | |
| if m.kernel_size == (3, 3): | |
| m.dilation = (dilate//2, dilate//2) | |
| m.padding = (dilate//2, dilate//2) | |
| # other convoluions | |
| else: | |
| if m.kernel_size == (3, 3): | |
| m.dilation = (dilate, dilate) | |
| m.padding = (dilate, dilate) | |
| def forward(self, x): | |
| x = self.relu(self.bn1(self.conv1(x))) | |
| x = self.maxpool(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| return x | |