utils_cv/tracking/references/fairmot/models/networks/resnet_dcn.py [154:193]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        for head in self.heads:
            classes = self.heads[head]
            if head_conv > 0:
                fc = nn.Sequential(
                  nn.Conv2d(64, head_conv,
                    kernel_size=3, padding=1, bias=True),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(head_conv, classes, 
                    kernel_size=1, stride=1, 
                    padding=0, bias=True))
                if 'hm' in head:
                    fc[-1].bias.data.fill_(-2.19)
                else:
                    fill_fc_weights(fc)
            else:
                fc = nn.Conv2d(64, classes, 
                  kernel_size=1, stride=1, 
                  padding=0, bias=True)
                if 'hm' in head:
                    fc.bias.data.fill_(-2.19)
                else:
                    fill_fc_weights(fc)
            self.__setattr__(head, fc)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



utils_cv/tracking/references/fairmot/models/networks/resnet_fpn_dcn.py [160:199]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        for head in self.heads:
            classes = self.heads[head]
            if head_conv > 0:
                fc = nn.Sequential(
                  nn.Conv2d(64, head_conv,
                    kernel_size=3, padding=1, bias=True),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(head_conv, classes, 
                    kernel_size=1, stride=1, 
                    padding=0, bias=True))
                if 'hm' in head:
                    fc[-1].bias.data.fill_(-2.19)
                else:
                    fill_fc_weights(fc)
            else:
                fc = nn.Conv2d(64, classes, 
                  kernel_size=1, stride=1, 
                  padding=0, bias=True)
                if 'hm' in head:
                    fc.bias.data.fill_(-2.19)
                else:
                    fill_fc_weights(fc)
            self.__setattr__(head, fc)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -