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pointnet_Ref.py
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pointnet_Ref.py
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from __future__ import print_function
import argparse
import os,sys
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
# import torchvision.transforms as transforms
# import torchvision.utils as vutils
from torch.autograd import Variable
# from PIL import Image
import numpy as np
# import matplotlib.pyplot as plt
# import pdb
import torch.nn.functional as F
class STN3d(nn.Module):
def __init__(self):
super(STN3d, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 9)
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.bn4 = nn.BatchNorm1d(512)
self.bn5 = nn.BatchNorm1d(256)
def forward(self, x):
batchsize = x.size()[0]
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
x = torch.max(x, 2, keepdim=True)[0]
x = x.view(-1, 1024)
x = F.relu(self.bn4(self.fc1(x)))
x = F.relu(self.bn5(self.fc2(x)))
x = self.fc3(x)
iden = Variable(torch.from_numpy(np.array([1,0,0,0,1,0,0,0,1]).astype(np.float32))).view(1,9).repeat(batchsize,1)
if x.is_cuda:
iden = iden.cuda()
x = x + iden
x = x.view(-1, 3, 3)
return x
class N_Views_MLP_for_3d(nn.Module):
def __init__(self,views=12):
super(N_Views_MLP_for_3d,self).__init__()
self.views = views
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
def forward(self,x):
x=x.squeeze(2)
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = (self.bn3(self.conv3(x)))
return x
class PointNetfeat_for_3d(nn.Module):
def __init__(self, global_feat = True,views=12):
super(PointNetfeat_for_3d, self).__init__()
self.views=views
self.stn = STN3d()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 1024, 1)
self.convto64=torch.nn.Conv1d(1024*views,64,1)
self.bn1 = nn.BatchNorm1d(64)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(1024)
self.bn_64=nn.BatchNorm1d(64)
self.global_feat = global_feat
self.r_mlps=list()
for r in range(views):
self.r_mlps.append(N_Views_MLP_for_3d(self.views).cuda())
self.r_mlps = nn.ModuleList(self.r_mlps)
# self.r_mlps = nn.ModuleList(self.r_mlps)
def forward(self, x):
#x is in B*R*N*6
x = x[:,:,:,:3]-x[:,:,:,3:]
r_mlp_result_glo=list()
batchsize = x.size()[0]
n_pts = x.size()[2]
r_vews= x.size()[1]
# x to R*B*6*N
x=x.transpose(1,3).transpose(2,3)
# send R B*6*N tensors to R different mlps
for r in range(r_vews):
r_mlp_result_glo.append(self.r_mlps[r](x[:,:,r,:]))
# concate n_views B*64*N to B*(64*view)*N
x=torch.cat(tuple(r_mlp_result_glo),1)
# B*(64*view)*N =>B*64*N
x=F.relu(self.bn_64(self.convto64(x)))
pointfeat = x
# mlp: B*64*N=>B*128*N=>B*1024*N
x = F.relu(self.bn2(self.conv2(x)))
x = self.bn3(self.conv3(x))
x = torch.max(x, 2, keepdim=True)[0]
x = x.view(-1, 1024)
if self.global_feat:
return x
else:
x = x.view(-1, 1024, 1).repeat(1, 1, n_pts)
return torch.cat([x, pointfeat], 1)
class PointNetRef3d_for_DenseCls(nn.Module):
def __init__(self, k = 2,views=12):
super(PointNetRef3d_for_DenseCls, self).__init__()
self.views=views
self.k = k
self.feat = PointNetfeat_for_3d(global_feat=False,views=self.views)
self.conv1 = torch.nn.Conv1d(1088, 512, 1)
self.conv2 = torch.nn.Conv1d(512, 256, 1)
self.conv3 = torch.nn.Conv1d(256, 128, 1)
self.conv4 = torch.nn.Conv1d(128, self.k, 1)
self.bn1 = nn.BatchNorm1d(512)
self.bn2 = nn.BatchNorm1d(256)
self.bn3 = nn.BatchNorm1d(128)
def forward(self, x):
batchsize = x.size()[0]
n_pts = x.size()[2]
x = self.feat(x)
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
x = self.conv4(x)
x = x.transpose(2,1).contiguous()
x = F.log_softmax(x.view(-1,self.k), dim=-1)
x = x.view(batchsize, n_pts, self.k)
return x
if __name__ == '__main__':
sim_data = Variable(torch.rand(1,12,2500,6))
PointNetfeat_for_3d = PointNetfeat_for_3d(global_feat=True,views = 12)
out = PointNetfeat_for_3d(sim_data)
print(out.shape)
sys.exit(0)
# pointfeat = PointNetfeat(global_feat=True)
# out, _ = pointfeat(sim_data)
# print('global feat', out.size())
# pointfeat = PointNetfeat(global_feat=False)
# out, _ = pointfeat(sim_data)
# print('point feat', out.size())
# cls = PointNetCls(k = 5)
# out, _ = cls(sim_data)
# print('class', out.size())
# seg = PointNetDenseCls(k = 3)
# out, _ = seg(sim_data)
# print('seg', out.size())