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voxelnet.py
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voxelnet.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.autograd import Variable
from config import config as cfg
# conv2d + bn + relu
class Conv2d(nn.Module):
def __init__(self,in_channels,out_channels,k,s,p, activation=True, batch_norm=True):
super(Conv2d, self).__init__()
self.conv = nn.Conv2d(in_channels,out_channels,kernel_size=k,stride=s,padding=p)
if batch_norm:
self.bn = nn.BatchNorm2d(out_channels)
else:
self.bn = None
self.activation = activation
def forward(self,x):
x = self.conv(x)
if self.bn is not None:
x=self.bn(x)
if self.activation:
return F.relu(x,inplace=True)
else:
return x
# conv3d + bn + relu
class Conv3d(nn.Module):
def __init__(self, in_channels, out_channels, k, s, p, batch_norm=True):
super(Conv3d, self).__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=k, stride=s, padding=p)
if batch_norm:
self.bn = nn.BatchNorm3d(out_channels)
else:
self.bn = None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
return F.relu(x, inplace=True)
# Fully Connected Network
class FCN(nn.Module):
def __init__(self,cin,cout):
super(FCN, self).__init__()
self.cout = cout
self.linear = nn.Linear(cin, cout)
self.bn = nn.BatchNorm1d(cout)
def forward(self,x):
# KK is the stacked k across batch
kk, t, _ = x.shape
x = self.linear(x.view(kk*t,-1))
x = F.relu(self.bn(x))
return x.view(kk,t,-1)
# Voxel Feature Encoding layer
class VFE(nn.Module):
def __init__(self,cin,cout):
super(VFE, self).__init__()
assert cout % 2 == 0
self.units = cout // 2
self.fcn = FCN(cin,self.units)
def forward(self, x, mask):
# point-wise feauture
pwf = self.fcn(x)
#locally aggregated feature
laf = torch.max(pwf,1)[0].unsqueeze(1).repeat(1,cfg.T,1)
# point-wise concat feature
pwcf = torch.cat((pwf,laf),dim=2)
# apply mask
mask = mask.unsqueeze(2).repeat(1, 1, self.units * 2)
pwcf = pwcf * mask.float()
return pwcf
# Stacked Voxel Feature Encoding
class SVFE(nn.Module):
def __init__(self):
super(SVFE, self).__init__()
self.vfe_1 = VFE(7,32)
self.vfe_2 = VFE(32,128)
self.fcn = FCN(128,128)
def forward(self, x):
mask = torch.ne(torch.max(x,2)[0], 0)
x = self.vfe_1(x, mask)
x = self.vfe_2(x, mask)
x = self.fcn(x)
# element-wise max pooling
x = torch.max(x,1)[0]
return x
# Convolutional Middle Layer
class CML(nn.Module):
def __init__(self):
super(CML, self).__init__()
self.conv3d_1 = Conv3d(128, 64, 3, s=(2, 1, 1), p=(1, 1, 1))
self.conv3d_2 = Conv3d(64, 64, 3, s=(1, 1, 1), p=(0, 1, 1))
self.conv3d_3 = Conv3d(64, 64, 3, s=(2, 1, 1), p=(1, 1, 1))
def forward(self, x):
x = self.conv3d_1(x)
x = self.conv3d_2(x)
x = self.conv3d_3(x)
return x
# Region Proposal Network
class RPN(nn.Module):
def __init__(self):
super(RPN, self).__init__()
self.block_1 = [Conv2d(128, 128, 3, 2, 1)]
self.block_1 += [Conv2d(128, 128, 3, 1, 1) for _ in range(3)]
self.block_1 = nn.Sequential(*self.block_1)
self.block_2 = [Conv2d(128, 128, 3, 2, 1)]
self.block_2 += [Conv2d(128, 128, 3, 1, 1) for _ in range(5)]
self.block_2 = nn.Sequential(*self.block_2)
self.block_3 = [Conv2d(128, 256, 3, 2, 1)]
self.block_3 += [nn.Conv2d(256, 256, 3, 1, 1) for _ in range(5)]
self.block_3 = nn.Sequential(*self.block_3)
self.deconv_1 = nn.Sequential(nn.ConvTranspose2d(256, 256, 4, 4, 0),nn.BatchNorm2d(256))
self.deconv_2 = nn.Sequential(nn.ConvTranspose2d(128, 256, 2, 2, 0),nn.BatchNorm2d(256))
self.deconv_3 = nn.Sequential(nn.ConvTranspose2d(128, 256, 1, 1, 0),nn.BatchNorm2d(256))
self.score_head = Conv2d(768, cfg.anchors_per_position, 1, 1, 0, activation=False, batch_norm=False)
self.reg_head = Conv2d(768, 7 * cfg.anchors_per_position, 1, 1, 0, activation=False, batch_norm=False)
def forward(self,x):
x = self.block_1(x)
x_skip_1 = x
x = self.block_2(x)
x_skip_2 = x
x = self.block_3(x)
x_0 = self.deconv_1(x)
x_1 = self.deconv_2(x_skip_2)
x_2 = self.deconv_3(x_skip_1)
x = torch.cat((x_0,x_1,x_2),1)
return self.score_head(x),self.reg_head(x)
class VoxelNet(nn.Module):
def __init__(self):
super(VoxelNet, self).__init__()
self.svfe = SVFE()
self.cml = CML()
self.rpn = RPN()
def voxel_indexing(self, sparse_features, coords):
dim = sparse_features.shape[-1]
dense_feature = Variable(torch.zeros(dim, cfg.N, cfg.D, cfg.H, cfg.W).cuda())
dense_feature[:, coords[:,0], coords[:,1], coords[:,2], coords[:,3]]= sparse_features
return dense_feature.transpose(0, 1)
def forward(self, voxel_features, voxel_coords):
# feature learning network
vwfs = self.svfe(voxel_features)
vwfs = self.voxel_indexing(vwfs,voxel_coords)
# convolutional middle network
cml_out = self.cml(vwfs)
# region proposal network
# merge the depth and feature dim into one, output probability score map and regression map
psm,rm = self.rpn(cml_out.view(cfg.N,-1,cfg.H, cfg.W))
return psm, rm