Function create_pointnet2_fp_modules

model/tiger.py:133–171

(fp_blocks, in_channels, sa_in_channels, embed_dim=64, use_att=False,
                                dropout=0.1,
                                with_se=False, normalize=True, eps=0,
                                width_multiplier=1, voxel_resolution_multiplier=1)

def create_pointnet2_fp_modules(fp_blocks, in_channels, sa_in_channels, embed_dim=64, use_att=False,
                                dropout=0.1,
                                with_se=False, normalize=True, eps=0,
                                width_multiplier=1, voxel_resolution_multiplier=1):
    r, vr = width_multiplier, voxel_resolution_multiplier

    fp_layers = []
    c = 0
    for fp_idx, (fp_configs, conv_configs) in enumerate(fp_blocks):
        fp_blocks = []
        out_channels = tuple(int(r * oc) for oc in fp_configs)
        fp_blocks.append(
            PointNetFPModule(in_channels=in_channels + sa_in_channels[-1 - fp_idx] + embed_dim, out_channels=out_channels)
        )
        in_channels = out_channels[-1]

        if conv_configs is not None:
            out_channels, num_blocks, voxel_resolution = conv_configs
            out_channels = int(r * out_channels)
            for p in range(num_blocks):
                attention = (c+1) % 2 == 0 and c < len(fp_blocks) - 1 and use_att and p == 0
                if voxel_resolution is None:
                    block = SharedMLP
                else:
                    block = functools.partial(PVConv, kernel_size=3, resolution=int(vr * voxel_resolution), attention=attention,
                                              dropout=dropout,
                                              with_se=with_se, with_se_relu=True,
                                              normalize=normalize, eps=eps)

                fp_blocks.append(block(in_channels, out_channels))
                in_channels = out_channels
        if len(fp_blocks) == 1:
            fp_layers.append(fp_blocks[0])
        else:
            fp_layers.append(nn.Sequential(*fp_blocks))

        c += 1

    return fp_layers, in_channels