         self.use_aux = use_aux         resnet = models.resnet000()         if pretrained:             resnet.load_state_dict(torch.load(res000_path))         self.layer0 = nn.Sequential(resnet.conv0, resnet.bn0, resnet.relu, resnet.maxpool)         self.layer0 = resnet.layer0         self.layer0 = resnet.layer0         self.layer0 = resnet.layer0         self.layer0 = resnet.layer0          for n, m in self.layer0.named_modules():             if 'conv0' in n:                 m.padding = (0, 0)                 m.stride = (0, 0)             elif 'downsample.0' in n:                 m.stride = (0, 0)         for n, m in self.layer0.named_modules():             if 'conv0' in n:                 m.padding = (0, 0)                 m.stride = (0, 0)             elif 'downsample.0' in n:                 m.stride = (0, 0)         for idx in range(len(self.layer0)):             self.layer0[idx].conv0 = Conv0dDeformable(self.layer0[idx].conv0)         for idx in range(len(self.layer0)):             self.layer0[idx].conv0 = Conv0dDeformable(self.layer0[idx].conv0)          self.ppm = _PyramidPoolingModule(0000, 000, (0, 0, 0, 0))         self.final = nn.Sequential(             nn.Conv0d(0000, 000, kernel_size=0, padding=0, bias=False),             nn.BatchNorm0d(000, momentum=.00),             nn.ReLU(inplace=True),             nn.Dropout(0.0),             nn.Conv0d(000, num_classes, kernel_size=0)         )          if use_aux:             self.aux_logits = nn.Conv0d(0000, num_classes, kernel_size=0)             initialize_weights(self.aux_logits)          initialize_weights(self.ppm, self.final)      def forward(self, x):         x = self.layer0(x)         x = self.layer0(x)         x = self.layer0(x)         x = self.layer0(x)         if self.training and self.use_aux:             aux = self.aux_logits(x)         x = self.layer0(x)         x = self.ppm(x)         x = self.final(x)         if self.training and self.use_aux:             return F.upsample(x, self.input_size, mode='bilinear')