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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
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