From 6c87bbd0dcd84093b2f3a45f6f18b1b38324f0bf Mon Sep 17 00:00:00 2001 From: inter Date: Sun, 21 Sep 2025 20:18:55 +0800 Subject: [PATCH] Add File --- pcdet/models/backbones_3d/spconv_unet.py | 212 +++++++++++++++++++++++ 1 file changed, 212 insertions(+) create mode 100644 pcdet/models/backbones_3d/spconv_unet.py diff --git a/pcdet/models/backbones_3d/spconv_unet.py b/pcdet/models/backbones_3d/spconv_unet.py new file mode 100644 index 0000000..a5e7c4b --- /dev/null +++ b/pcdet/models/backbones_3d/spconv_unet.py @@ -0,0 +1,212 @@ +from functools import partial + +import torch +import torch.nn as nn + +from ...utils.spconv_utils import replace_feature, spconv +from ...utils import common_utils +from .spconv_backbone import post_act_block + + +class SparseBasicBlock(spconv.SparseModule): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, indice_key=None, norm_fn=None): + super(SparseBasicBlock, self).__init__() + self.conv1 = spconv.SubMConv3d( + inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False, indice_key=indice_key + ) + self.bn1 = norm_fn(planes) + self.relu = nn.ReLU() + self.conv2 = spconv.SubMConv3d( + planes, planes, kernel_size=3, stride=1, padding=1, bias=False, indice_key=indice_key + ) + self.bn2 = norm_fn(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x.features + + assert x.features.dim() == 2, 'x.features.dim()=%d' % x.features.dim() + + 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) + out = replace_feature(out, self.relu(out.features)) + + return out + + +class UNetV2(nn.Module): + """ + Sparse Convolution based UNet for point-wise feature learning. + Reference Paper: https://arxiv.org/abs/1907.03670 (Shaoshuai Shi, et. al) + From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network + """ + + def __init__(self, model_cfg, input_channels, grid_size, voxel_size, point_cloud_range, **kwargs): + super().__init__() + self.model_cfg = model_cfg + 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'), + ) + + if self.model_cfg.get('RETURN_ENCODED_TENSOR', True): + last_pad = self.model_cfg.get('last_pad', 0) + + self.conv_out = spconv.SparseSequential( + # [200, 150, 5] -> [200, 150, 2] + spconv.SparseConv3d(64, 128, (3, 1, 1), stride=(2, 1, 1), padding=last_pad, + bias=False, indice_key='spconv_down2'), + norm_fn(128), + nn.ReLU(), + ) + else: + 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 + + def UR_block_forward(self, x_lateral, x_bottom, conv_t, conv_m, conv_inv): + x_trans = conv_t(x_lateral) + x = x_trans + x = replace_feature(x, torch.cat((x_bottom.features, x_trans.features), dim=1)) + x_m = conv_m(x) + x = self.channel_reduction(x, x_m.features.shape[1]) + x = replace_feature(x, x_m.features + x.features) + x = conv_inv(x) + return x + + @staticmethod + def channel_reduction(x, out_channels): + """ + Args: + x: x.features (N, C1) + out_channels: C2 + + Returns: + + """ + features = x.features + n, in_channels = features.shape + assert (in_channels % out_channels == 0) and (in_channels >= out_channels) + + x = replace_feature(x, features.view(n, out_channels, -1).sum(dim=2)) + return x + + 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 + point_features: (N, C) + """ + 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) + + if self.conv_out is not None: + # for detection head + # [200, 176, 5] -> [200, 176, 2] + out = self.conv_out(x_conv4) + batch_dict['encoded_spconv_tensor'] = out + batch_dict['encoded_spconv_tensor_stride'] = 8 + + # for segmentation head + # [400, 352, 11] <- [200, 176, 5] + x_up4 = self.UR_block_forward(x_conv4, x_conv4, self.conv_up_t4, self.conv_up_m4, self.inv_conv4) + # [800, 704, 21] <- [400, 352, 11] + x_up3 = self.UR_block_forward(x_conv3, x_up4, self.conv_up_t3, self.conv_up_m3, self.inv_conv3) + # [1600, 1408, 41] <- [800, 704, 21] + x_up2 = self.UR_block_forward(x_conv2, x_up3, self.conv_up_t2, self.conv_up_m2, self.inv_conv2) + # [1600, 1408, 41] <- [1600, 1408, 41] + x_up1 = self.UR_block_forward(x_conv1, x_up2, self.conv_up_t1, self.conv_up_m1, self.conv5) + + batch_dict['point_features'] = x_up1.features + point_coords = common_utils.get_voxel_centers( + x_up1.indices[:, 1:], downsample_times=1, voxel_size=self.voxel_size, + point_cloud_range=self.point_cloud_range + ) + batch_dict['point_coords'] = torch.cat((x_up1.indices[:, 0:1].float(), point_coords), dim=1) + return batch_dict