| from functools import partial |
|
|
| import torch.nn as nn |
|
|
| from ...utils.spconv_utils import replace_feature, spconv |
|
|
|
|
| def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0, |
| conv_type='subm', norm_fn=None): |
|
|
| if conv_type == 'subm': |
| conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key) |
| elif conv_type == 'spconv': |
| conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, |
| bias=False, indice_key=indice_key) |
| elif conv_type == 'inverseconv': |
| conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False) |
| else: |
| raise NotImplementedError |
|
|
| m = spconv.SparseSequential( |
| conv, |
| norm_fn(out_channels), |
| nn.ReLU(), |
| ) |
|
|
| return m |
|
|
|
|
| class SparseBasicBlock(spconv.SparseModule): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, bias=None, norm_fn=None, downsample=None, indice_key=None): |
| super(SparseBasicBlock, self).__init__() |
|
|
| assert norm_fn is not None |
| if bias is None: |
| bias = norm_fn is not None |
| self.conv1 = spconv.SubMConv3d( |
| inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key |
| ) |
| self.bn1 = norm_fn(planes) |
| self.relu = nn.ReLU() |
| self.conv2 = spconv.SubMConv3d( |
| planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key |
| ) |
| self.bn2 = norm_fn(planes) |
| self.downsample = downsample |
| self.stride = stride |
|
|
| def forward(self, x): |
| identity = x |
|
|
| out = self.conv1(x) |
| out = replace_feature(out, self.bn1(out.features)) |
| out = replace_feature(out, self.relu(out.features)) |
|
|
| out = self.conv2(out) |
| out = replace_feature(out, self.bn2(out.features)) |
|
|
| if self.downsample is not None: |
| identity = self.downsample(x) |
|
|
| out = replace_feature(out, out.features + identity.features) |
| out = replace_feature(out, self.relu(out.features)) |
|
|
| return out |
|
|
|
|
| class VoxelResBackBone8x(nn.Module): |
| def __init__(self, model_cfg, input_channels, grid_size, **kwargs): |
| super().__init__() |
| self.model_cfg = model_cfg |
| use_bias = self.model_cfg.get('USE_BIAS', None) |
| norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01) |
|
|
| self.sparse_shape = grid_size[::-1] + [1, 0, 0] |
|
|
| 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( |
| SparseBasicBlock(16, 16, bias=use_bias, norm_fn=norm_fn, indice_key='res1'), |
| SparseBasicBlock(16, 16, bias=use_bias, norm_fn=norm_fn, indice_key='res1'), |
| ) |
|
|
| self.conv2 = spconv.SparseSequential( |
| |
| block(16, 32, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'), |
| SparseBasicBlock(32, 32, bias=use_bias, norm_fn=norm_fn, indice_key='res2'), |
| SparseBasicBlock(32, 32, bias=use_bias, norm_fn=norm_fn, indice_key='res2'), |
| ) |
|
|
| self.conv3 = spconv.SparseSequential( |
| |
| block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'), |
| SparseBasicBlock(64, 64, bias=use_bias, norm_fn=norm_fn, indice_key='res3'), |
| SparseBasicBlock(64, 64, bias=use_bias, norm_fn=norm_fn, indice_key='res3'), |
| ) |
|
|
| self.conv4 = spconv.SparseSequential( |
| |
| block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=(0, 1, 1), indice_key='spconv4', conv_type='spconv'), |
| SparseBasicBlock(128, 128, bias=use_bias, norm_fn=norm_fn, indice_key='res4'), |
| SparseBasicBlock(128, 128, bias=use_bias, norm_fn=norm_fn, indice_key='res4'), |
| ) |
|
|
| last_pad = 0 |
| last_pad = self.model_cfg.get('last_pad', last_pad) |
| self.conv_out = spconv.SparseSequential( |
| |
| spconv.SparseConv3d(128, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad, |
| bias=False, indice_key='spconv_down2'), |
| norm_fn(128), |
| nn.ReLU(), |
| ) |
| self.num_point_features = 128 |
| self.backbone_channels = { |
| 'x_conv1': 16, |
| 'x_conv2': 32, |
| 'x_conv3': 64, |
| 'x_conv4': 128 |
| } |
|
|
| def forward(self, batch_dict): |
| """ |
| Args: |
| batch_dict: |
| batch_size: int |
| vfe_features: (num_voxels, C) |
| voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx] |
| Returns: |
| batch_dict: |
| encoded_spconv_tensor: sparse tensor |
| """ |
| voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords'] |
| batch_size = batch_dict['batch_size'] |
| input_sp_tensor = spconv.SparseConvTensor( |
| features=voxel_features, |
| indices=voxel_coords.int(), |
| spatial_shape=self.sparse_shape, |
| batch_size=batch_size |
| ) |
| x = self.conv_input(input_sp_tensor) |
|
|
| x_conv1 = self.conv1(x) |
| x_conv2 = self.conv2(x_conv1) |
| x_conv3 = self.conv3(x_conv2) |
| x_conv4 = self.conv4(x_conv3) |
|
|
| |
| |
| out = self.conv_out(x_conv4) |
|
|
| batch_dict.update({ |
| 'encoded_spconv_tensor': out, |
| 'encoded_spconv_tensor_stride': 8 |
| }) |
| batch_dict.update({ |
| 'multi_scale_3d_features': { |
| 'x_conv1': x_conv1, |
| 'x_conv2': x_conv2, |
| 'x_conv3': x_conv3, |
| 'x_conv4': x_conv4, |
| } |
| }) |
|
|
| batch_dict.update({ |
| 'multi_scale_3d_strides': { |
| 'x_conv1': 1, |
| 'x_conv2': 2, |
| 'x_conv3': 4, |
| 'x_conv4': 8, |
| } |
| }) |
| |
| return batch_dict |
|
|
