From e367dc6d464d6eeccd7b677953c8ede9adc94e62 Mon Sep 17 00:00:00 2001 From: inter Date: Sun, 21 Sep 2025 20:18:51 +0800 Subject: [PATCH] Add File --- pcdet/models/backbones_3d/spconv_backbone.py | 295 +++++++++++++++++++ 1 file changed, 295 insertions(+) create mode 100644 pcdet/models/backbones_3d/spconv_backbone.py diff --git a/pcdet/models/backbones_3d/spconv_backbone.py b/pcdet/models/backbones_3d/spconv_backbone.py new file mode 100644 index 0000000..f0c231a --- /dev/null +++ b/pcdet/models/backbones_3d/spconv_backbone.py @@ -0,0 +1,295 @@ +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 VoxelBackBone8x(nn.Module): + def __init__(self, model_cfg, input_channels, grid_size, **kwargs): + super().__init__() + self.model_cfg = model_cfg + 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( + 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'), + ) + + last_pad = 0 + last_pad = self.model_cfg.get('last_pad', last_pad) + 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(), + ) + self.num_point_features = 128 + self.backbone_channels = { + 'x_conv1': 16, + 'x_conv2': 32, + 'x_conv3': 64, + 'x_conv4': 64 + } + + + + 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) + + # for detection head + # [200, 176, 5] -> [200, 176, 2] + 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 + + +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( + # [1600, 1408, 41] <- [800, 704, 21] + 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( + # [800, 704, 21] <- [400, 352, 11] + 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( + # [400, 352, 11] <- [200, 176, 5] + 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( + # [200, 150, 5] -> [200, 150, 2] + 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) + + # for detection head + # [200, 176, 5] -> [200, 176, 2] + 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