utils_cv/tracking/references/fairmot/models/networks/dlav0.py [72:97]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self.bn2 = BatchNorm(bottle_planes)
        self.conv3 = nn.Conv2d(bottle_planes, planes,
                               kernel_size=1, bias=False)
        self.bn3 = BatchNorm(planes)
        self.relu = nn.ReLU(inplace=True)
        self.stride = stride

    def forward(self, x, residual=None):
        if residual is None:
            residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        out += residual
        out = self.relu(out)

        return out
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



utils_cv/tracking/references/fairmot/models/networks/dlav0.py [116:141]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self.bn2 = BatchNorm(bottle_planes)
        self.conv3 = nn.Conv2d(bottle_planes, planes,
                               kernel_size=1, bias=False)
        self.bn3 = BatchNorm(planes)
        self.relu = nn.ReLU(inplace=True)
        self.stride = stride

    def forward(self, x, residual=None):
        if residual is None:
            residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        out += residual
        out = self.relu(out)

        return out
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -