def network_initialization()

in models/trunks/spconv/models/resnet.py [0:0]

• 43 lines of code
• 3 McCabe index (conditional complexity)

  def network_initialization(self, in_channels, out_channels, D):

    def space_n_time_m(n, m):
      return n if D == 3 else [n, n, n, m]

    if D == 4:
      self.OUT_PIXEL_DIST = space_n_time_m(self.OUT_PIXEL_DIST, 1)

    dilations = config.dilations
    bn_momentum = config.bn_momentum
    self.inplanes = self.INIT_DIM
    self.conv1 = conv(
        in_channels,
        self.inplanes,
        kernel_size=space_n_time_m(config.conv1_kernel_size, 1),
        stride=1,
        D=D)

    self.bn1 = get_norm(NormType.BATCH_NORM, self.inplanes, D=self.D, bn_momentum=bn_momentum)
    self.relu = ME.MinkowskiReLU(inplace=True)
    self.pool = sum_pool(kernel_size=space_n_time_m(2, 1), stride=space_n_time_m(2, 1), D=D)

    self.layer1 = self._make_layer(
        self.BLOCK,
        self.PLANES[0],
        self.LAYERS[0],
        stride=space_n_time_m(2, 1),
        dilation=space_n_time_m(dilations[0], 1))
    self.layer2 = self._make_layer(
        self.BLOCK,
        self.PLANES[1],
        self.LAYERS[1],
        stride=space_n_time_m(2, 1),
        dilation=space_n_time_m(dilations[1], 1))
    self.layer3 = self._make_layer(
        self.BLOCK,
        self.PLANES[2],
        self.LAYERS[2],
        stride=space_n_time_m(2, 1),
        dilation=space_n_time_m(dilations[2], 1))
    self.layer4 = self._make_layer(
        self.BLOCK,
        self.PLANES[3],
        self.LAYERS[3],
        stride=space_n_time_m(2, 1),
        dilation=space_n_time_m(dilations[3], 1))

    self.final = conv(
        self.PLANES[3] * self.BLOCK.expansion, out_channels, kernel_size=1, bias=True, D=D)