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from functools import partial | |
import torch | |
import torch.nn as nn | |
import spconv.pytorch as spconv | |
from spconv.core import ConvAlgo | |
def replace_feature(out, new_features): | |
return out.replace_feature(new_features) | |
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, algo=ConvAlgo.Native) | |
elif conv_type == 'spconv': | |
conv = spconv.SparseConv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, | |
bias=False, indice_key=indice_key, algo=ConvAlgo.Native) | |
elif conv_type == 'inverseconv': | |
conv = spconv.SparseInverseConv3d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False, algo=ConvAlgo.Native) | |
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.SubMConv3d( | |
inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key, algo=ConvAlgo.Native | |
) | |
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, algo=ConvAlgo.Native | |
) | |
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 VoxelResBackBone8xVoxelNeXt(nn.Module): | |
def __init__(self, input_channels, grid_size, **kwargs): | |
super().__init__() | |
norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01) | |
spconv_kernel_sizes = [3, 3, 3, 3] | |
channels = [16, 32, 64, 128, 128] | |
out_channel = 128 | |
self.sparse_shape = grid_size[::-1] + [1, 0, 0] | |
self.conv_input = spconv.SparseSequential( | |
spconv.SubMConv3d(input_channels, channels[0], 3, padding=1, bias=False, indice_key='subm1', algo=ConvAlgo.Native), | |
norm_fn(channels[0]), | |
nn.ReLU(), | |
) | |
block = post_act_block | |
self.conv1 = spconv.SparseSequential( | |
SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'), | |
SparseBasicBlock(channels[0], channels[0], norm_fn=norm_fn, indice_key='res1'), | |
) | |
self.conv2 = spconv.SparseSequential( | |
# [1600, 1408, 41] <- [800, 704, 21] | |
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'), | |
SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'), | |
SparseBasicBlock(channels[1], channels[1], norm_fn=norm_fn, indice_key='res2'), | |
) | |
self.conv3 = spconv.SparseSequential( | |
# [800, 704, 21] <- [400, 352, 11] | |
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'), | |
SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'), | |
SparseBasicBlock(channels[2], channels[2], norm_fn=norm_fn, indice_key='res3'), | |
) | |
self.conv4 = spconv.SparseSequential( | |
# [400, 352, 11] <- [200, 176, 6] | |
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'), | |
SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'), | |
SparseBasicBlock(channels[3], channels[3], norm_fn=norm_fn, indice_key='res4'), | |
) | |
self.conv5 = spconv.SparseSequential( | |
# [200, 176, 6] <- [100, 88, 3] | |
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'), | |
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'), | |
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res5'), | |
) | |
self.conv6 = spconv.SparseSequential( | |
# [200, 176, 6] <- [100, 88, 3] | |
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'), | |
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'), | |
SparseBasicBlock(channels[4], channels[4], norm_fn=norm_fn, indice_key='res6'), | |
) | |
self.conv_out = spconv.SparseSequential( | |
# [200, 150, 5] -> [200, 150, 2] | |
spconv.SparseConv2d(channels[3], out_channel, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2', algo=ConvAlgo.Native), | |
norm_fn(out_channel), | |
nn.ReLU(), | |
) | |
self.shared_conv = spconv.SparseSequential( | |
spconv.SubMConv2d(out_channel, out_channel, 3, stride=1, padding=1, bias=True, algo=ConvAlgo.Native), | |
nn.BatchNorm1d(out_channel), | |
nn.ReLU(True), | |
) | |
self.forward_ret_dict = {} | |
self.num_point_features = out_channel | |
self.backbone_channels = { | |
'x_conv1': channels[0], | |
'x_conv2': channels[1], | |
'x_conv3': channels[2], | |
'x_conv4': channels[3] | |
} | |
def bev_out(self, x_conv, index): | |
features_cat = x_conv.features | |
indices_cat = x_conv.indices[:, [0, 2, 3]] | |
spatial_shape = x_conv.spatial_shape[1:] | |
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) | |
perm = torch.arange(_inv.size(0), dtype=_inv.dtype, device=_inv.device) | |
perm = _inv.new_empty(indices_unique.size(0)).scatter_(0, _inv, perm) | |
index_out = index[perm] | |
x_out = spconv.SparseConvTensor( | |
features=features_unique, | |
indices=indices_unique, | |
spatial_shape=spatial_shape, | |
batch_size=x_conv.batch_size | |
) | |
return x_out, index_out | |
def track_voxels_2d(self, x, x_downsample, index, kernel_size=3): | |
_step = int(kernel_size//2) | |
kernel_offsets = [[i, j] for i in range(-_step, _step+1) for j in range(-_step, _step+1)] | |
#kernel_offsets.remove([0, 0]) | |
kernel_offsets = torch.Tensor(kernel_offsets).to(x.indices.device) | |
batch_size = x.batch_size | |
index_batch = [] | |
indices_batch = [] | |
for b in range(batch_size): | |
batch_index = x.indices[:, 0]==b | |
indices_ori = x.indices[batch_index] | |
features_ori = index[batch_index] | |
features_fore = features_ori | |
coords_fore = indices_ori | |
voxel_kerels_imp = kernel_offsets.unsqueeze(0).repeat(features_fore.shape[0],1, 1) | |
indices_fore_kernels = coords_fore[:, 1:].unsqueeze(1).repeat(1, kernel_offsets.shape[0], 1) | |
indices_with_imp = indices_fore_kernels + voxel_kerels_imp | |
features_fore = features_fore.repeat(1, kernel_offsets.shape[0]) | |
selected_indices = indices_with_imp | |
spatial_indices = (selected_indices[:, :, 0] >=0) * (selected_indices[:, :, 1] >=0) * \ | |
(selected_indices[:, :, 0] < x.spatial_shape[0]) * (selected_indices[:, :, 1] < x.spatial_shape[1]) | |
selected_indices = selected_indices[spatial_indices] | |
features_fore = features_fore[spatial_indices].view(-1, 1) | |
selected_indices = torch.cat([torch.ones((selected_indices.shape[0], 1), device=features_fore.device)*b, selected_indices], dim=1) | |
features_fore, coords_fore = features_fore, selected_indices | |
index_batch.append(features_fore) | |
indices_batch.append(coords_fore) | |
index_batch = torch.cat(index_batch) | |
indices_batch = torch.cat(indices_batch) | |
return self.index_from_sparse(index_batch, indices_batch, x_downsample, True) | |
def index_from_sparse(self, feature, indices, x_target, _2d=False): | |
sparse_index = spconv.SparseConvTensor( | |
features=feature, | |
indices=indices.int(), | |
spatial_shape=x_target.spatial_shape, | |
batch_size=x_target.batch_size | |
) | |
dense_index = sparse_index.dense() | |
indices_downsample = x_target.indices.long() | |
if _2d: | |
index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2]] | |
else: | |
index_downsample = dense_index[indices_downsample[:, 0], :, indices_downsample[:, 1], indices_downsample[:, 2], indices_downsample[:, 3]] | |
return index_downsample | |
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) | |
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]) | |
index6_out = torch.arange(x_conv4.indices.shape[0], device=x_conv4.indices.device).unsqueeze(-1) | |
out_bevout, index_bevout = self.bev_out(x_conv4, index6_out) | |
out = self.conv_out(out_bevout) | |
index_out = self.track_voxels_2d(out_bevout, out, index_bevout) | |
out = self.shared_conv(out) | |
batch_dict.update({ | |
'encoded_spconv_tensor': out, | |
'encoded_spconv_tensor_stride': 8, | |
'out_voxels': x_conv4.indices[index_out.squeeze(-1)], | |
}) | |
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 | |