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import torch
import torch.nn as nn
try:
import torchsparse
import torchsparse.nn as spnn
from ..ts import basic_blocks
except ImportError:
raise Exception('Required ts lib. Reference: https://github.com/mit-han-lab/torchsparse/tree/v1.4.0')
class Model(nn.Module):
def __init__(self, config):
super().__init__()
cr = config.model_params.cr
cs = config.model_params.layer_num
cs = [int(cr * x) for x in cs]
self.pres = self.vres = config.model_params.voxel_size
self.num_classes = config.model_params.num_class
self.stem = nn.Sequential(
spnn.Conv3d(config.model_params.input_dims, cs[0], kernel_size=3, stride=1),
spnn.BatchNorm(cs[0]), spnn.ReLU(True),
spnn.Conv3d(cs[0], cs[0], kernel_size=3, stride=1),
spnn.BatchNorm(cs[0]), spnn.ReLU(True))
self.stage1 = nn.Sequential(
basic_blocks.BasicConvolutionBlock(cs[0], cs[0], ks=2, stride=2, dilation=1),
basic_blocks.ResidualBlock(cs[0], cs[1], ks=3, stride=1, dilation=1),
basic_blocks.ResidualBlock(cs[1], cs[1], ks=3, stride=1, dilation=1),
)
self.stage2 = nn.Sequential(
basic_blocks.BasicConvolutionBlock(cs[1], cs[1], ks=2, stride=2, dilation=1),
basic_blocks.ResidualBlock(cs[1], cs[2], ks=3, stride=1, dilation=1),
basic_blocks.ResidualBlock(cs[2], cs[2], ks=3, stride=1, dilation=1),
)
self.stage3 = nn.Sequential(
basic_blocks.BasicConvolutionBlock(cs[2], cs[2], ks=2, stride=2, dilation=1),
basic_blocks.ResidualBlock(cs[2], cs[3], ks=3, stride=1, dilation=1),
basic_blocks.ResidualBlock(cs[3], cs[3], ks=3, stride=1, dilation=1),
)
self.stage4 = nn.Sequential(
basic_blocks.BasicConvolutionBlock(cs[3], cs[3], ks=2, stride=2, dilation=1),
basic_blocks.ResidualBlock(cs[3], cs[4], ks=3, stride=1, dilation=1),
basic_blocks.ResidualBlock(cs[4], cs[4], ks=3, stride=1, dilation=1),
)
self.up1 = nn.ModuleList([
basic_blocks.BasicDeconvolutionBlock(cs[4], cs[5], ks=2, stride=2),
nn.Sequential(
basic_blocks.ResidualBlock(cs[5] + cs[3], cs[5], ks=3, stride=1,
dilation=1),
basic_blocks.ResidualBlock(cs[5], cs[5], ks=3, stride=1, dilation=1),
)
])
self.up2 = nn.ModuleList([
basic_blocks.BasicDeconvolutionBlock(cs[5], cs[6], ks=2, stride=2),
nn.Sequential(
basic_blocks.ResidualBlock(cs[6] + cs[2], cs[6], ks=3, stride=1,
dilation=1),
basic_blocks.ResidualBlock(cs[6], cs[6], ks=3, stride=1, dilation=1),
)
])
self.up3 = nn.ModuleList([
basic_blocks.BasicDeconvolutionBlock(cs[6], cs[7], ks=2, stride=2),
nn.Sequential(
basic_blocks.ResidualBlock(cs[7] + cs[1], cs[7], ks=3, stride=1,
dilation=1),
basic_blocks.ResidualBlock(cs[7], cs[7], ks=3, stride=1, dilation=1),
)
])
self.up4 = nn.ModuleList([
basic_blocks.BasicDeconvolutionBlock(cs[7], cs[8], ks=2, stride=2),
nn.Sequential(
basic_blocks.ResidualBlock(cs[8] + cs[0], cs[8], ks=3, stride=1,
dilation=1),
basic_blocks.ResidualBlock(cs[8], cs[8], ks=3, stride=1, dilation=1),
)
])
self.classifier = nn.Sequential(nn.Linear(cs[8], self.num_classes))
self.weight_initialization()
self.dropout = nn.Dropout(0.3, True)
def weight_initialization(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, data_dict, return_logits=False, return_final_logits=False):
x = data_dict['lidar']
x.C = x.C.int()
x0 = self.stem(x)
x1 = self.stage1(x0)
x2 = self.stage2(x1)
x3 = self.stage3(x2)
x4 = self.stage4(x3)
if return_logits:
output_dict = dict()
output_dict['logits'] = x4.F
output_dict['batch_indices'] = x4.C[:, -1]
return output_dict
y1 = self.up1[0](x4)
y1 = torchsparse.cat([y1, x3])
y1 = self.up1[1](y1)
y2 = self.up2[0](y1)
y2 = torchsparse.cat([y2, x2])
y2 = self.up2[1](y2)
y3 = self.up3[0](y2)
y3 = torchsparse.cat([y3, x1])
y3 = self.up3[1](y3)
y4 = self.up4[0](y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.up4[1](y4)
if return_final_logits:
output_dict = dict()
output_dict['logits'] = y4.F
output_dict['coords'] = y4.C[:, :3]
output_dict['batch_indices'] = y4.C[:, -1]
return output_dict
output = self.classifier(y4.F)
data_dict['output'] = output.F
return data_dict
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