From b9490350f793126ac2bc1a434a43b5e199bb92dc Mon Sep 17 00:00:00 2001 From: inter Date: Sun, 21 Sep 2025 20:18:51 +0800 Subject: [PATCH] Add File --- .../backbones_3d/spconv_backbone_focal.py | 269 ++++++++++++++++++ 1 file changed, 269 insertions(+) create mode 100644 pcdet/models/backbones_3d/spconv_backbone_focal.py diff --git a/pcdet/models/backbones_3d/spconv_backbone_focal.py b/pcdet/models/backbones_3d/spconv_backbone_focal.py new file mode 100644 index 0000000..5869b8b --- /dev/null +++ b/pcdet/models/backbones_3d/spconv_backbone_focal.py @@ -0,0 +1,269 @@ +from functools import partial + +import torch +from pcdet.utils.spconv_utils import spconv +import torch.nn as nn + +from .focal_sparse_conv.focal_sparse_conv import FocalSparseConv +from .focal_sparse_conv.SemanticSeg.pyramid_ffn import PyramidFeat2D + + +class objDict: + @staticmethod + def to_object(obj: object, **data): + obj.__dict__.update(data) + +class ConfigDict: + def __init__(self, name): + self.name = name + def __getitem__(self, item): + return getattr(self, item) + + +class SparseSequentialBatchdict(spconv.SparseSequential): + def __init__(self, *args, **kwargs): + super(SparseSequentialBatchdict, self).__init__(*args, **kwargs) + + def forward(self, input, batch_dict=None): + loss = 0 + for k, module in self._modules.items(): + if module is None: + continue + if isinstance(module, (FocalSparseConv,)): + input, batch_dict, _loss = module(input, batch_dict) + loss += _loss + else: + input = module(input) + return input, batch_dict, loss + + +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(True), + ) + + return m + + +class SparseBasicBlock(spconv.SparseModule): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None): + super(SparseBasicBlock, self).__init__() + + assert norm_fn is not 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(True) + 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 = out.replace_feature(self.bn1(out.features)) + out = out.replace_feature(self.relu(out.features)) + + out = self.conv2(out) + out = out.replace_feature(self.bn2(out.features)) + + if self.downsample is not None: + identity = self.downsample(x) + + out = out.replace_feature(out.features + identity.features) + out = out.replace_feature(self.relu(out.features)) + + return out + + +class VoxelBackBone8xFocal(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(True), + ) + + block = post_act_block + + use_img = model_cfg.get('USE_IMG', False) + topk = model_cfg.get('TOPK', True) + threshold = model_cfg.get('THRESHOLD', 0.5) + kernel_size = model_cfg.get('KERNEL_SIZE', 3) + mask_multi = model_cfg.get('MASK_MULTI', False) + skip_mask_kernel = model_cfg.get('SKIP_MASK_KERNEL', False) + skip_mask_kernel_image = model_cfg.get('SKIP_MASK_KERNEL_IMG', False) + enlarge_voxel_channels = model_cfg.get('ENLARGE_VOXEL_CHANNELS', -1) + img_pretrain = model_cfg.get('IMG_PRETRAIN', "../checkpoints/deeplabv3_resnet50_coco-cd0a2569.pth") + + if use_img: + model_cfg_seg=dict( + name='SemDeepLabV3', + backbone='ResNet50', + num_class=21, # pretrained on COCO + args={"feat_extract_layer": ["layer1"], + "pretrained_path": img_pretrain}, + channel_reduce={ + "in_channels": [256], + "out_channels": [16], + "kernel_size": [1], + "stride": [1], + "bias": [False] + } + ) + cfg_dict = ConfigDict('SemDeepLabV3') + objDict.to_object(cfg_dict, **model_cfg_seg) + self.semseg = PyramidFeat2D(optimize=True, model_cfg=cfg_dict) + + self.conv_focal_multimodal = FocalSparseConv(16, 16, image_channel=model_cfg_seg['channel_reduce']['out_channels'][0], + topk=topk, threshold=threshold, use_img=True, skip_mask_kernel=skip_mask_kernel_image, + voxel_stride=1, norm_fn=norm_fn, indice_key='spconv_focal_multimodal') + + special_spconv_fn = partial(FocalSparseConv, mask_multi=mask_multi, enlarge_voxel_channels=enlarge_voxel_channels, + topk=topk, threshold=threshold, kernel_size=kernel_size, padding=kernel_size//2, + skip_mask_kernel=skip_mask_kernel) + self.use_img = use_img + + self.conv1 = SparseSequentialBatchdict( + block(16, 16, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'), + special_spconv_fn(16, 16, voxel_stride=1, norm_fn=norm_fn, indice_key='focal1'), + ) + + self.conv2 =SparseSequentialBatchdict( + # [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'), + special_spconv_fn(32, 32, voxel_stride=2, norm_fn=norm_fn, indice_key='focal2'), + ) + + self.conv3 = SparseSequentialBatchdict( + # [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'), + special_spconv_fn(64, 64, voxel_stride=4, norm_fn=norm_fn, indice_key='focal3'), + ) + + self.conv4 = SparseSequentialBatchdict( + # [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(True), + ) + self.num_point_features = 128 + self.backbone_channels = { + 'x_conv1': 16, + 'x_conv2': 32, + 'x_conv3': 64, + 'x_conv4': 64 + } + + self.forward_ret_dict = {} + + def get_loss(self, tb_dict=None): + loss = self.forward_ret_dict['loss_box_of_pts'] + if tb_dict is None: + tb_dict = {} + tb_dict['loss_box_of_pts'] = loss.item() + return loss, tb_dict + + 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 + ) + + loss_img = 0 + + x = self.conv_input(input_sp_tensor) + x_conv1, batch_dict, loss1 = self.conv1(x, batch_dict) + + if self.use_img: + x_image = self.semseg(batch_dict['images'])['layer1_feat2d'] + x_conv1, batch_dict, loss_img = self.conv_focal_multimodal(x_conv1, batch_dict, x_image) + + x_conv2, batch_dict, loss2 = self.conv2(x_conv1, batch_dict) + x_conv3, batch_dict, loss3 = self.conv3(x_conv2, batch_dict) + x_conv4, batch_dict, loss4 = self.conv4(x_conv3, batch_dict) + + self.forward_ret_dict['loss_box_of_pts'] = loss1 + loss2 + loss3 + loss4 + loss_img + # 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