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from functools import partial |
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import torch |
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import torch.nn as nn |
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import spconv.pytorch as spconv |
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from spconv.core import ConvAlgo |
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def replace_feature(out, new_features): |
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return out.replace_feature(new_features) |
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def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0, |
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conv_type='subm', norm_fn=None): |
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if conv_type == 'subm': |
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conv = spconv.SubMConv3d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key, algo=ConvAlgo.Native) |
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elif conv_type == 'spconv': |
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conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, |
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bias=False, indice_key=indice_key, algo=ConvAlgo.Native) |
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elif conv_type == 'inverseconv': |
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conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False, algo=ConvAlgo.Native) |
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else: |
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raise NotImplementedError |
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m = spconv.SparseSequential( |
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conv, |
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norm_fn(out_channels), |
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nn.ReLU(), |
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) |
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return m |
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class SparseBasicBlock(spconv.SparseModule): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None): |
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super(SparseBasicBlock, self).__init__() |
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assert norm_fn is not None |
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bias = norm_fn is not None |
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self.conv1 = spconv.SubMConv3d( |
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inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native |
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) |
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self.bn1 = norm_fn(planes) |
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self.relu = nn.ReLU() |
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self.conv2 = spconv.SubMConv3d( |
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planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native |
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) |
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self.bn2 = norm_fn(planes) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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identity = x |
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out = self.conv1(x) |
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out = replace_feature(out, self.bn1(out.features)) |
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out = replace_feature(out, self.relu(out.features)) |
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out = self.conv2(out) |
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out = replace_feature(out, self.bn2(out.features)) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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out = replace_feature(out, out.features + identity.features) |
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out = replace_feature(out, self.relu(out.features)) |
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return out |
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class VoxelResBackBone8xVoxelNeXt(nn.Module): |
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def __init__(self, input_channels, grid_size, **kwargs): |
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super().__init__() |
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norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01) |
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spconv_kernel_sizes = [3, 3, 3, 3] |
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channels = [16, 32, 64, 128, 128] |
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out_channel = 128 |
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self.sparse_shape = grid_size[::-1] + [1, 0, 0] |
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self.conv_input = spconv.SparseSequential( |
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spconv.SubMConv3d(input_channels, channels[0], 3, padding=1, bias=False, indice_key='subm1', algo=ConvAlgo.Native), |
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norm_fn(channels[0]), |
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nn.ReLU(), |
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) |
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block = post_act_block |
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self.conv1 = spconv.SparseSequential( |
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SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'), |
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SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'), |
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) |
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self.conv2 = spconv.SparseSequential( |
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block(channels[0], channels[1], spconv_kernel_sizes[0], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[0]//2), indice_key='spconv2', conv_type='spconv'), |
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SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'), |
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SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'), |
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) |
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self.conv3 = spconv.SparseSequential( |
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block(channels[1], channels[2], spconv_kernel_sizes[1], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[1]//2), indice_key='spconv3', conv_type='spconv'), |
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SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'), |
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SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'), |
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) |
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self.conv4 = spconv.SparseSequential( |
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block(channels[2], channels[3], spconv_kernel_sizes[2], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[2]//2), indice_key='spconv4', conv_type='spconv'), |
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SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'), |
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SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'), |
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) |
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self.conv5 = spconv.SparseSequential( |
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block(channels[3], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv5', conv_type='spconv'), |
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SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'), |
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SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'), |
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) |
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self.conv6 = spconv.SparseSequential( |
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block(channels[4], channels[4], spconv_kernel_sizes[3], norm_fn=norm_fn, stride=2, padding=int(spconv_kernel_sizes[3]//2), indice_key='spconv6', conv_type='spconv'), |
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SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'), |
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SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'), |
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) |
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self.conv_out = spconv.SparseSequential( |
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spconv.SparseConv2d(channels[3], out_channel, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2', algo=ConvAlgo.Native), |
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norm_fn(out_channel), |
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nn.ReLU(), |
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) |
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self.shared_conv = spconv.SparseSequential( |
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spconv.SubMConv2d(out_channel, out_channel, 3, stride=1, padding=1, bias=True, algo=ConvAlgo.Native), |
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nn.BatchNorm1d(out_channel), |
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nn.ReLU(True), |
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) |
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self.forward_ret_dict = {} |
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self.num_point_features = out_channel |
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self.backbone_channels = { |
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'x_conv1': channels[0], |
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'x_conv2': channels[1], |
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'x_conv3': channels[2], |
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'x_conv4': channels[3] |
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} |
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def bev_out(self, x_conv, index): |
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features_cat = x_conv.features |
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indices_cat = x_conv.indices[:, [0, 2, 3]] |
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spatial_shape = x_conv.spatial_shape[1:] |
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indices_unique, _inv = torch.unique(indices_cat, dim=0, return_inverse=True) |
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features_unique = features_cat.new_zeros((indices_unique.shape[0], features_cat.shape[1])) |
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features_unique.index_add_(0, _inv, features_cat) |
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perm = torch.arange(_inv.size(0), dtype=_inv.dtype, device=_inv.device) |
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perm = _inv.new_empty(indices_unique.size(0)).scatter_(0, _inv, perm) |
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index_out = index[perm] |
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x_out = spconv.SparseConvTensor( |
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features=features_unique, |
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indices=indices_unique, |
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spatial_shape=spatial_shape, |
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batch_size=x_conv.batch_size |
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) |
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return x_out, index_out |
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def track_voxels_2d(self, x, x_downsample, index, kernel_size=3): |
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_step = int(kernel_size//2) |
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kernel_offsets = [[i, j] for i in range(-_step, _step+1) for j in range(-_step, _step+1)] |
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kernel_offsets = torch.Tensor(kernel_offsets).to(x.indices.device) |
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batch_size = x.batch_size |
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index_batch = [] |
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indices_batch = [] |
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for b in range(batch_size): |
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batch_index = x.indices[:, 0]==b |
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indices_ori = x.indices[batch_index] |
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features_ori = index[batch_index] |
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features_fore = features_ori |
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coords_fore = indices_ori |
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voxel_kerels_imp = kernel_offsets.unsqueeze(0).repeat(features_fore.shape[0],1, 1) |
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indices_fore_kernels = coords_fore[:, 1:].unsqueeze(1).repeat(1, kernel_offsets.shape[0], 1) |
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indices_with_imp = indices_fore_kernels + voxel_kerels_imp |
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features_fore = features_fore.repeat(1, kernel_offsets.shape[0]) |
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selected_indices = indices_with_imp |
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spatial_indices = (selected_indices[:, :, 0] >=0) * (selected_indices[:, :, 1] >=0) * \ |
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(selected_indices[:, :, 0] < x.spatial_shape[0]) * (selected_indices[:, :, 1] < x.spatial_shape[1]) |
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selected_indices = selected_indices[spatial_indices] |
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features_fore = features_fore[spatial_indices].view(-1, 1) |
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selected_indices = torch.cat([torch.ones((selected_indices.shape[0], 1), device=features_fore.device)*b, selected_indices], dim=1) |
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features_fore, coords_fore = features_fore, selected_indices |
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index_batch.append(features_fore) |
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indices_batch.append(coords_fore) |
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index_batch = torch.cat(index_batch) |
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indices_batch = torch.cat(indices_batch) |
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return self.index_from_sparse(index_batch, indices_batch, x_downsample, True) |
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def index_from_sparse(self, feature, indices, x_target, _2d=False): |
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sparse_index = spconv.SparseConvTensor( |
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features=feature, |
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indices=indices.int(), |
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spatial_shape=x_target.spatial_shape, |
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batch_size=x_target.batch_size |
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) |
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dense_index = sparse_index.dense() |
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indices_downsample = x_target.indices.long() |
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if _2d: |
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index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2]] |
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else: |
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index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2], indices_downsample[:, 3]] |
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return index_downsample |
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def forward(self, batch_dict): |
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""" |
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Args: |
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batch_dict: |
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batch_size: int |
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vfe_features: (num_voxels, C) |
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voxel_coords: (num_voxels, 4), [batch_idx, z_idx, y_idx, x_idx] |
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Returns: |
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batch_dict: |
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encoded_spconv_tensor: sparse tensor |
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""" |
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voxel_features, voxel_coords = batch_dict['voxel_features'], batch_dict['voxel_coords'] |
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batch_size = batch_dict['batch_size'] |
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input_sp_tensor = spconv.SparseConvTensor( |
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features=voxel_features, |
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indices=voxel_coords.int(), |
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spatial_shape=self.sparse_shape, |
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batch_size=batch_size |
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) |
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x = self.conv_input(input_sp_tensor) |
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x_conv1 = self.conv1(x) |
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x_conv2 = self.conv2(x_conv1) |
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x_conv3 = self.conv3(x_conv2) |
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x_conv4 = self.conv4(x_conv3) |
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x_conv5 = self.conv5(x_conv4) |
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x_conv6 = self.conv6(x_conv5) |
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x_conv5.indices[:, 1:] *= 2 |
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x_conv6.indices[:, 1:] *= 4 |
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x_conv4 = x_conv4.replace_feature(torch.cat([x_conv4.features, x_conv5.features, x_conv6.features])) |
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x_conv4.indices = torch.cat([x_conv4.indices, x_conv5.indices, x_conv6.indices]) |
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index6_out = torch.arange(x_conv4.indices.shape[0], device=x_conv4.indices.device).unsqueeze(-1) |
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out_bevout, index_bevout = self.bev_out(x_conv4, index6_out) |
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out = self.conv_out(out_bevout) |
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index_out = self.track_voxels_2d(out_bevout, out, index_bevout) |
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out = self.shared_conv(out) |
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batch_dict.update({ |
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'encoded_spconv_tensor': out, |
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'encoded_spconv_tensor_stride': 8, |
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'out_voxels': x_conv4.indices[index_out.squeeze(-1)], |
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}) |
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batch_dict.update({ |
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'multi_scale_3d_features': { |
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'x_conv1': x_conv1, |
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'x_conv2': x_conv2, |
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'x_conv3': x_conv3, |
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'x_conv4': x_conv4, |
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} |
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}) |
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batch_dict.update({ |
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'multi_scale_3d_strides': { |
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'x_conv1': 1, |
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'x_conv2': 2, |
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'x_conv3': 4, |
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'x_conv4': 8, |
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} |
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}) |
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return batch_dict |
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