def network_initialization()

in downstream/votenet/models/backbone/sparseconv/models_sparseconv/resunet.py [0:0]

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

  def network_initialization(self, in_channels, out_channels, config, D):
    # Setup net_metadata
    dilations = self.DILATIONS
    bn_momentum = config.bn_momentum

    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)

    # Output of the first conv concated to conv6
    self.inplanes = self.INIT_DIM
    self.conv1p1s1 = conv(
        in_channels,
        self.inplanes,
        kernel_size=space_n_time_m(config.conv1_kernel_size, 1),
        stride=1,
        dilation=1,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)

    self.bn1 = get_norm(self.NORM_TYPE, self.PLANES[0], D, bn_momentum=bn_momentum)
    self.block1 = self._make_layer(
        self.BLOCK,
        self.PLANES[0],
        self.LAYERS[0],
        dilation=dilations[0],
        norm_type=self.NORM_TYPE,
        bn_momentum=bn_momentum)

    self.conv2p1s2 = conv(
        self.inplanes,
        self.inplanes,
        kernel_size=space_n_time_m(2, 1),
        stride=space_n_time_m(2, 1),
        dilation=1,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)
    self.bn2 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
    self.block2 = self._make_layer(
        self.BLOCK,
        self.PLANES[1],
        self.LAYERS[1],
        dilation=dilations[1],
        norm_type=self.NORM_TYPE,
        bn_momentum=bn_momentum)

    self.conv3p2s2 = conv(
        self.inplanes,
        self.inplanes,
        kernel_size=space_n_time_m(2, 1),
        stride=space_n_time_m(2, 1),
        dilation=1,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)
    self.bn3 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
    self.block3 = self._make_layer(
        self.BLOCK,
        self.PLANES[2],
        self.LAYERS[2],
        dilation=dilations[2],
        norm_type=self.NORM_TYPE,
        bn_momentum=bn_momentum)

    self.conv4p4s2 = conv(
        self.inplanes,
        self.inplanes,
        kernel_size=space_n_time_m(2, 1),
        stride=space_n_time_m(2, 1),
        dilation=1,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)
    self.bn4 = get_norm(self.NORM_TYPE, self.inplanes, D, bn_momentum=bn_momentum)
    self.block4 = self._make_layer(
        self.BLOCK,
        self.PLANES[3],
        self.LAYERS[3],
        dilation=dilations[3],
        norm_type=self.NORM_TYPE,
        bn_momentum=bn_momentum)
    self.convtr4p8s2 = conv_tr(
        self.inplanes,
        self.PLANES[4],
        kernel_size=space_n_time_m(2, 1),
        upsample_stride=space_n_time_m(2, 1),
        dilation=1,
        bias=False,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)
    self.bntr4 = get_norm(self.NORM_TYPE, self.PLANES[4], D, bn_momentum=bn_momentum)

    self.inplanes = self.PLANES[4] + self.PLANES[2] * self.BLOCK.expansion
    self.block5 = self._make_layer(
        self.BLOCK,
        self.PLANES[4],
        self.LAYERS[4],
        dilation=dilations[4],
        norm_type=self.NORM_TYPE,
        bn_momentum=bn_momentum)
    self.convtr5p4s2 = conv_tr(
        self.inplanes,
        self.PLANES[5],
        kernel_size=space_n_time_m(2, 1),
        upsample_stride=space_n_time_m(2, 1),
        dilation=1,
        bias=False,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)
    self.bntr5 = get_norm(self.NORM_TYPE, self.PLANES[5], D, bn_momentum=bn_momentum)

    self.inplanes = self.PLANES[5] + self.PLANES[1] * self.BLOCK.expansion
    self.block6 = self._make_layer(
        self.BLOCK,
        self.PLANES[5],
        self.LAYERS[5],
        dilation=dilations[5],
        norm_type=self.NORM_TYPE,
        bn_momentum=bn_momentum)
    self.convtr6p2s2 = conv_tr(
        self.inplanes,
        self.PLANES[6],
        kernel_size=space_n_time_m(2, 1),
        upsample_stride=space_n_time_m(2, 1),
        dilation=1,
        bias=False,
        conv_type=self.NON_BLOCK_CONV_TYPE,
        D=D)
    self.bntr6 = get_norm(self.NORM_TYPE, self.PLANES[6], D, bn_momentum=bn_momentum)
    self.relu = MinkowskiReLU(inplace=True)

    self.final = nn.Sequential(
        conv(
            self.PLANES[6] + self.PLANES[0] * self.BLOCK.expansion,
            512,
            kernel_size=1,
            stride=1,
            dilation=1,
            bias=False,
            D=D), ME.MinkowskiBatchNorm(512), ME.MinkowskiReLU(),
        conv(512, out_channels, kernel_size=1, stride=1, dilation=1, bias=True, D=D))