utils_cv/tracking/references/fairmot/models/networks/resnet_dcn.py [220:251]:
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            fc = DCN(self.inplanes, planes, 
                    kernel_size=(3,3), stride=1,
                    padding=1, dilation=1, deformable_groups=1)
            # fc = nn.Conv2d(self.inplanes, planes,
            #         kernel_size=3, stride=1, 
            #         padding=1, dilation=1, bias=False)
            # fill_fc_weights(fc)
            up = nn.ConvTranspose2d(
                    in_channels=planes,
                    out_channels=planes,
                    kernel_size=kernel,
                    stride=2,
                    padding=padding,
                    output_padding=output_padding,
                    bias=self.deconv_with_bias)
            fill_up_weights(up)

            layers.append(fc)
            layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
            layers.append(nn.ReLU(inplace=True))
            layers.append(up)
            layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
            layers.append(nn.ReLU(inplace=True))
            self.inplanes = planes

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
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utils_cv/tracking/references/fairmot/models/networks/resnet_fpn_dcn.py [222:253]:
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        fc = DCN(self.inplanes, planes,
                kernel_size=(3,3), stride=1,
                padding=1, dilation=1, deformable_groups=1)
        # fc = nn.Conv2d(self.inplanes, planes,
        #         kernel_size=3, stride=1,
        #         padding=1, dilation=1, bias=False)
        # fill_fc_weights(fc)
        up = nn.ConvTranspose2d(
                in_channels=planes,
                out_channels=planes,
                kernel_size=kernel,
                stride=2,
                padding=padding,
                output_padding=output_padding,
                bias=self.deconv_with_bias)
        fill_up_weights(up)

        layers.append(fc)
        layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
        layers.append(nn.ReLU(inplace=True))
        layers.append(up)
        layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
        layers.append(nn.ReLU(inplace=True))
        self.inplanes = planes

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
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