def __init_

def init()

in models/trunks/spconv_unet.py [0:0]
58 lines of code
2 McCabe index (conditional complexity)

    def __init__(self, use_mlp=False, mlp_dim=None):
        super().__init__()

        input_channels = 4
        voxel_size = [0.1, 0.1, 0.2]
        point_cloud_range = np.array([  0. , -75. ,  -3. ,  75.0,  75. ,   3. ], dtype=np.float32)

        grid_size = (point_cloud_range[3:6] - point_cloud_range[0:3]) / np.array(voxel_size)
        grid_size = np.round(grid_size).astype(np.int64)
        model_cfg = {'NAME': 'UNetV2', 'RETURN_ENCODED_TENSOR': False}
        
        self.sparse_shape = grid_size[::-1] + [1, 0, 0]
        self.voxel_size = voxel_size
        self.point_cloud_range = point_cloud_range

        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)

        self.conv_input = spconv.SparseSequential(
            spconv.SubMConv3d(input_channels, 16, 3, padding=1, bias=False, indice_key='subm1'),
            norm_fn(16),
            nn.ReLU(),
        )
        block = post_act_block

        self.conv1 = spconv.SparseSequential(
            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
        )

        self.conv2 = spconv.SparseSequential(
            # [1600, 1408, 41] <- [800, 704, 21]
            block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
        )

        self.conv3 = spconv.SparseSequential(
            # [800, 704, 21] <- [400, 352, 11]
            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
        )

        self.conv4 = spconv.SparseSequential(
            # [400, 352, 11] <- [200, 176, 5]
            block(64, 64, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'),
            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
        )

        self.conv_out = None

        # decoder
        # [400, 352, 11] <- [200, 176, 5]
        self.conv_up_t4 = SparseBasicBlock(64, 64, indice_key='subm4', norm_fn=norm_fn)
        self.conv_up_m4 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm4')
        self.inv_conv4 = block(64, 64, 3, norm_fn=norm_fn, indice_key='spconv4', conv_type='inverseconv')
        
        # [800, 704, 21] <- [400, 352, 11]
        self.conv_up_t3 = SparseBasicBlock(64, 64, indice_key='subm3', norm_fn=norm_fn)
        self.conv_up_m3 = block(128, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm3')
        self.inv_conv3 = block(64, 32, 3, norm_fn=norm_fn, indice_key='spconv3', conv_type='inverseconv')

        # [1600, 1408, 41] <- [800, 704, 21]
        self.conv_up_t2 = SparseBasicBlock(32, 32, indice_key='subm2', norm_fn=norm_fn)
        self.conv_up_m2 = block(64, 32, 3, norm_fn=norm_fn, indice_key='subm2')
        self.inv_conv2 = block(32, 16, 3, norm_fn=norm_fn, indice_key='spconv2', conv_type='inverseconv')

        # [1600, 1408, 41] <- [1600, 1408, 41]
        self.conv_up_t1 = SparseBasicBlock(16, 16, indice_key='subm1', norm_fn=norm_fn)
        self.conv_up_m1 = block(32, 16, 3, norm_fn=norm_fn, indice_key='subm1')

        self.conv5 = spconv.SparseSequential(
            block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1')
        )
        self.num_point_features = 16
        
        self.all_feat_names = [
            "conv4",
        ]
    
        if use_mlp:
            self.use_mlp = True
            self.head = MLP(mlp_dim)
def __init__()

def init()
