from functools import partial import torch 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.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key) elif conv_type == 'spconv': conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=False, indice_key=indice_key) elif conv_type == 'inverseconv': conv = spconv.SparseInverseConv2d(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, 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.SubMConv2d( 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.SubMConv2d( 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 VoxelResBackBone8xVoxelNeXt2D(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, 0]] block = post_act_block spconv_kernel_sizes = model_cfg.get('SPCONV_KERNEL_SIZES', [3, 3, 3, 3]) self.conv1 = spconv.SparseSequential( SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'), SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'), SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'), ) self.conv2 = spconv.SparseSequential( # [1600, 1408] <- [800, 704] block(32, 64, spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'), SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'), SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'), SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'), SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'), ) self.conv3 = spconv.SparseSequential( # [800, 704] <- [400, 352] block(64, 128, spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'), SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'), SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'), SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'), SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'), SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'), SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'), ) self.conv4 = spconv.SparseSequential( # [400, 352] <- [200, 176] block(128, 256, spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'), ) self.conv5 = spconv.SparseSequential( # [400, 352] <- [200, 176] block(256, 256, spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'), ) self.conv6 = spconv.SparseSequential( # [400, 352] <- [200, 176] block(256, 256, spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'), SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'), ) self.conv_out = spconv.SparseSequential( # [200, 150, 5] -> [200, 150, 2] spconv.SparseConv2d(256, 256, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2'), norm_fn(256), nn.ReLU(), ) self.shared_conv = spconv.SparseSequential( spconv.SubMConv2d(256, 256, 3, stride=1, padding=1, bias=True), nn.BatchNorm1d(256), nn.ReLU(True), ) self.num_point_features = 256 self.backbone_channels = { 'x_conv1': 32, 'x_conv2': 64, 'x_conv3': 128, 'x_conv4': 256, 'x_conv5': 256 } self.forward_ret_dict = {} def bev_out(self, x_conv): features_cat = x_conv.features indices_cat = x_conv.indices indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True) features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1])) features_unique.index_add_(0, _inv, features_cat) x_out = spconv.SparseConvTensor( features=features_unique, indices=indices_unique, spatial_shape=x_conv.spatial_shape, batch_size=x_conv.batch_size ) return x_out def forward(self, batch_dict): pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords'] batch_size = batch_dict['batch_size'] input_sp_tensor = spconv.SparseConvTensor( features=pillar_features, indices=pillar_coords.int(), spatial_shape=self.sparse_shape, batch_size=batch_size ) x_conv1 = self.conv1(input_sp_tensor) x_conv2 = self.conv2(x_conv1) x_conv3 = self.conv3(x_conv2) x_conv4 = self.conv4(x_conv3) x_conv5 = self.conv5(x_conv4) x_conv6 = self.conv6(x_conv5) x_conv5.indices[:, 1:] *= 2 x_conv6.indices[:, 1:] *= 4 x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features])) x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices]) out = self.bev_out(x_conv4) out = self.conv_out(out) out = self.shared_conv(out) batch_dict.update({ 'encoded_spconv_tensor': out, 'encoded_spconv_tensor_stride': 8 }) batch_dict.update({ 'multi_scale_2d_features': { 'x_conv1': x_conv1, 'x_conv2': x_conv2, 'x_conv3': x_conv3, 'x_conv4': x_conv4, 'x_conv5': x_conv5, } }) batch_dict.update({ 'multi_scale_2d_strides': { 'x_conv1': 1, 'x_conv2': 2, 'x_conv3': 4, 'x_conv4': 8, 'x_conv5': 16, } }) return batch_dict