utils_cv/tracking/references/fairmot/models/networks/dlav0.py [120:149]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        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


class Root(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, residual):
        super(Root, self).__init__()
        self.conv = nn.Conv2d(
            in_channels, out_channels, 1,
            stride=1, bias=False, padding=(kernel_size - 1) // 2)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



utils_cv/tracking/references/fairmot/models/networks/pose_dla_dcn.py [122:151]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        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


class Root(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, residual):
        super(Root, self).__init__()
        self.conv = nn.Conv2d(
            in_channels, out_channels, 1,
            stride=1, bias=False, padding=(kernel_size - 1) // 2)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -