220 lines
8.4 KiB
Python
220 lines
8.4 KiB
Python
from functools import partial
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import torch
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import torch.nn as nn
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from ...utils.spconv_utils import replace_feature, spconv
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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.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
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elif conv_type == 'spconv':
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conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
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bias=False, indice_key=indice_key)
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elif conv_type == 'inverseconv':
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conv = spconv.SparseInverseConv2d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
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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.SubMConv2d(
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inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
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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.SubMConv2d(
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planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
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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 VoxelResBackBone8xVoxelNeXt2D(nn.Module):
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def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
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super().__init__()
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self.model_cfg = model_cfg
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norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
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self.sparse_shape = grid_size[[1, 0]]
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block = post_act_block
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spconv_kernel_sizes = model_cfg.get('SPCONV_KERNEL_SIZES', [3, 3, 3, 3])
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self.conv1 = spconv.SparseSequential(
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SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
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SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
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SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
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)
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self.conv2 = spconv.SparseSequential(
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# [1600, 1408] <- [800, 704]
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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'),
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SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
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SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
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SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
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SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
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)
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self.conv3 = spconv.SparseSequential(
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# [800, 704] <- [400, 352]
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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'),
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SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
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SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
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SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
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SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
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SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
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SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
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)
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self.conv4 = spconv.SparseSequential(
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# [400, 352] <- [200, 176]
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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'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
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)
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self.conv5 = spconv.SparseSequential(
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# [400, 352] <- [200, 176]
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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'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res5'),
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)
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self.conv6 = spconv.SparseSequential(
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# [400, 352] <- [200, 176]
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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'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
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SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res6'),
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)
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self.conv_out = spconv.SparseSequential(
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# [200, 150, 5] -> [200, 150, 2]
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spconv.SparseConv2d(256, 256, 3, stride=1, padding=1, bias=False, indice_key='spconv_down2'),
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norm_fn(256),
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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(256, 256, 3, stride=1, padding=1, bias=True),
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nn.BatchNorm1d(256),
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nn.ReLU(True),
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)
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self.num_point_features = 256
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self.backbone_channels = {
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'x_conv1': 32,
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'x_conv2': 64,
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'x_conv3': 128,
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'x_conv4': 256,
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'x_conv5': 256
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}
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self.forward_ret_dict = {}
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def bev_out(self, x_conv):
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features_cat = x_conv.features
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indices_cat = x_conv.indices
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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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x_out = spconv.SparseConvTensor(
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features=features_unique,
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indices=indices_unique,
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spatial_shape=x_conv.spatial_shape,
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batch_size=x_conv.batch_size
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)
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return x_out
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def forward(self, batch_dict):
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pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_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=pillar_features,
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indices=pillar_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_conv1 = self.conv1(input_sp_tensor)
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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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out = self.bev_out(x_conv4)
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out = self.conv_out(out)
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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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})
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batch_dict.update({
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'multi_scale_2d_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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'x_conv5': x_conv5,
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}
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})
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batch_dict.update({
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'multi_scale_2d_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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'x_conv5': 16,
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}
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})
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return batch_dict
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