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Train_PFNet.py
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import os
import sys
import argparse
import random
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.utils.data
import torchvision.transforms as transforms
from torch.autograd import Variable
import utils
from utils import PointLoss
from utils import distance_squre
import data_utils as d_utils
import ModelNet40Loader
import shapenet_part_loader
from model_PFNet import _netlocalD,_netG
parser = argparse.ArgumentParser()
parser.add_argument('--dataroot', default='dataset/train', help='path to dataset')
parser.add_argument('--workers', type=int,default=2, help='number of data loading workers')
parser.add_argument('--batchSize', type=int, default=24, help='input batch size')
parser.add_argument('--pnum', type=int, default=2048, help='the point number of a sample')
parser.add_argument('--crop_point_num',type=int,default=512,help='0 means do not use else use with this weight')
parser.add_argument('--nc', type=int, default=3)
parser.add_argument('--niter', type=int, default=201, help='number of epochs to train for')
parser.add_argument('--weight_decay', type=float, default=0.001)
parser.add_argument('--learning_rate', default=0.0002, type=float, help='learning rate in training')
parser.add_argument('--beta1', type=float, default=0.9, help='beta1 for adam. default=0.9')
parser.add_argument('--cuda', type = bool, default = False, help='enables cuda')
parser.add_argument('--ngpu', type=int, default=2, help='number of GPUs to use')
parser.add_argument('--D_choose',type=int, default=1, help='0 not use D-net,1 use D-net')
parser.add_argument('--netG', default='', help="path to netG (to continue training)")
parser.add_argument('--netD', default='', help="path to netD (to continue training)")
parser.add_argument('--manualSeed', type=int, help='manual seed')
parser.add_argument('--drop',type=float,default=0.2)
parser.add_argument('--num_scales',type=int,default=3,help='number of scales')
parser.add_argument('--point_scales_list',type=list,default=[2048,1024,512],help='number of points in each scales')
parser.add_argument('--each_scales_size',type=int,default=1,help='each scales size')
parser.add_argument('--wtl2',type=float,default=0.95,help='0 means do not use else use with this weight')
parser.add_argument('--cropmethod', default = 'random_center', help = 'random|center|random_center')
opt = parser.parse_args()
print(opt)
blue = lambda x: '\033[94m' + x + '\033[0m'
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
USE_CUDA = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
point_netG = _netG(opt.num_scales,opt.each_scales_size,opt.point_scales_list,opt.crop_point_num)
point_netD = _netlocalD(opt.crop_point_num)
cudnn.benchmark = True
resume_epoch=0
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv2d") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("Conv1d") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
elif classname.find("BatchNorm1d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
if USE_CUDA:
print("Let's use", torch.cuda.device_count(), "GPUs!")
point_netG = torch.nn.DataParallel(point_netG)
point_netD = torch.nn.DataParallel(point_netD)
point_netG.to(device)
point_netG.apply(weights_init_normal)
point_netD.to(device)
point_netD.apply(weights_init_normal)
if opt.netG != '' :
point_netG.load_state_dict(torch.load(opt.netG,map_location=lambda storage, location: storage)['state_dict'])
resume_epoch = torch.load(opt.netG)['epoch']
if opt.netD != '' :
point_netD.load_state_dict(torch.load(opt.netD,map_location=lambda storage, location: storage)['state_dict'])
resume_epoch = torch.load(opt.netD)['epoch']
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
transforms = transforms.Compose(
[
d_utils.PointcloudToTensor(),
]
)
dset = shapenet_part_loader.PartDataset( root='./dataset/shapenetcore_partanno_segmentation_benchmark_v0/',classification=True, class_choice=None, npoints=opt.pnum, split='train')
assert dset
dataloader = torch.utils.data.DataLoader(dset, batch_size=opt.batchSize,
shuffle=True,num_workers = int(opt.workers))
test_dset = shapenet_part_loader.PartDataset( root='./dataset/shapenetcore_partanno_segmentation_benchmark_v0/',classification=True, class_choice=None, npoints=opt.pnum, split='test')
test_dataloader = torch.utils.data.DataLoader(test_dset, batch_size=opt.batchSize,
shuffle=True,num_workers = int(opt.workers))
#dset = ModelNet40Loader.ModelNet40Cls(opt.pnum, train=True, transforms=transforms, download = False)
#assert dset
#dataloader = torch.utils.data.DataLoader(dset, batch_size=opt.batchSize,
# shuffle=True,num_workers = int(opt.workers))
#
#
#test_dset = ModelNet40Loader.ModelNet40Cls(opt.pnum, train=False, transforms=transforms, download = False)
#test_dataloader = torch.utils.data.DataLoader(test_dset, batch_size=opt.batchSize,
# shuffle=True,num_workers = int(opt.workers))
#pointcls_net.apply(weights_init)
print(point_netG)
print(point_netD)
criterion = torch.nn.BCEWithLogitsLoss().to(device)
criterion_PointLoss = PointLoss().to(device)
# setup optimizer
optimizerD = torch.optim.Adam(point_netD.parameters(), lr=0.0001,betas=(0.9, 0.999),eps=1e-05,weight_decay=opt.weight_decay)
optimizerG = torch.optim.Adam(point_netG.parameters(), lr=0.0001,betas=(0.9, 0.999),eps=1e-05 ,weight_decay=opt.weight_decay)
schedulerD = torch.optim.lr_scheduler.StepLR(optimizerD, step_size=40, gamma=0.2)
schedulerG = torch.optim.lr_scheduler.StepLR(optimizerG, step_size=40, gamma=0.2)
real_label = 1
fake_label = 0
crop_point_num = int(opt.crop_point_num)
input_cropped1 = torch.FloatTensor(opt.batchSize, opt.pnum, 3)
label = torch.FloatTensor(opt.batchSize)
num_batch = len(dset) / opt.batchSize
###########################
# G-NET and T-NET
##########################
if opt.D_choose == 1:
for epoch in range(resume_epoch,opt.niter):
if epoch<30:
alpha1 = 0.01
alpha2 = 0.02
elif epoch<80:
alpha1 = 0.05
alpha2 = 0.1
else:
alpha1 = 0.1
alpha2 = 0.2
for i, data in enumerate(dataloader, 0):
real_point, target = data
batch_size = real_point.size()[0]
real_center = torch.FloatTensor(batch_size, 1, opt.crop_point_num, 3)
input_cropped1 = torch.FloatTensor(batch_size, opt.pnum, 3)
input_cropped1 = input_cropped1.data.copy_(real_point)
real_point = torch.unsqueeze(real_point, 1)
input_cropped1 = torch.unsqueeze(input_cropped1,1)
p_origin = [0,0,0]
if opt.cropmethod == 'random_center':
#Set viewpoints
choice = [torch.Tensor([1,0,0]),torch.Tensor([0,0,1]),torch.Tensor([1,0,1]),torch.Tensor([-1,0,0]),torch.Tensor([-1,1,0])]
for m in range(batch_size):
index = random.sample(choice,1)#Random choose one of the viewpoint
distance_list = []
p_center = index[0]
for n in range(opt.pnum):
distance_list.append(distance_squre(real_point[m,0,n],p_center))
distance_order = sorted(enumerate(distance_list), key = lambda x:x[1])
for sp in range(opt.crop_point_num):
input_cropped1.data[m,0,distance_order[sp][0]] = torch.FloatTensor([0,0,0])
real_center.data[m,0,sp] = real_point[m,0,distance_order[sp][0]]
label.resize_([batch_size,1]).fill_(real_label)
real_point = real_point.to(device)
real_center = real_center.to(device)
input_cropped1 = input_cropped1.to(device)
label = label.to(device)
############################
# (1) data prepare
###########################
real_center = Variable(real_center,requires_grad=True)
real_center = torch.squeeze(real_center,1)
real_center_key1_idx = utils.farthest_point_sample(real_center,64,RAN = False)
real_center_key1 = utils.index_points(real_center,real_center_key1_idx)
real_center_key1 =Variable(real_center_key1,requires_grad=True)
real_center_key2_idx = utils.farthest_point_sample(real_center,128,RAN = True)
real_center_key2 = utils.index_points(real_center,real_center_key2_idx)
real_center_key2 =Variable(real_center_key2,requires_grad=True)
input_cropped1 = torch.squeeze(input_cropped1,1)
input_cropped2_idx = utils.farthest_point_sample(input_cropped1,opt.point_scales_list[1],RAN = True)
input_cropped2 = utils.index_points(input_cropped1,input_cropped2_idx)
input_cropped3_idx = utils.farthest_point_sample(input_cropped1,opt.point_scales_list[2],RAN = False)
input_cropped3 = utils.index_points(input_cropped1,input_cropped3_idx)
input_cropped1 = Variable(input_cropped1,requires_grad=True)
input_cropped2 = Variable(input_cropped2,requires_grad=True)
input_cropped3 = Variable(input_cropped3,requires_grad=True)
input_cropped2 = input_cropped2.to(device)
input_cropped3 = input_cropped3.to(device)
input_cropped = [input_cropped1,input_cropped2,input_cropped3]
point_netG = point_netG.train()
point_netD = point_netD.train()
############################
# (2) Update D network
###########################
point_netD.zero_grad()
real_center = torch.unsqueeze(real_center,1)
output = point_netD(real_center)
errD_real = criterion(output,label)
errD_real.backward()
fake_center1,fake_center2,fake =point_netG(input_cropped)
fake = torch.unsqueeze(fake,1)
label.data.fill_(fake_label)
output = point_netD(fake.detach())
errD_fake = criterion(output, label)
errD_fake.backward()
errD = errD_real + errD_fake
optimizerD.step()
############################
# (3) Update G network: maximize log(D(G(z)))
###########################
point_netG.zero_grad()
label.data.fill_(real_label)
output = point_netD(fake)
errG_D = criterion(output, label)
errG_l2 = 0
CD_LOSS = criterion_PointLoss(torch.squeeze(fake,1),torch.squeeze(real_center,1))
errG_l2 = criterion_PointLoss(torch.squeeze(fake,1),torch.squeeze(real_center,1))\
+alpha1*criterion_PointLoss(fake_center1,real_center_key1)\
+alpha2*criterion_PointLoss(fake_center2,real_center_key2)
errG = (1-opt.wtl2) * errG_D + opt.wtl2 * errG_l2
errG.backward()
optimizerG.step()
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f / %.4f / %.4f/ %.4f'
% (epoch, opt.niter, i, len(dataloader),
errD.data, errG_D.data,errG_l2,errG,CD_LOSS))
f=open('loss_PFNet.txt','a')
f.write('\n'+'[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f / %.4f / %.4f /%.4f'
% (epoch, opt.niter, i, len(dataloader),
errD.data, errG_D.data,errG_l2,errG,CD_LOSS))
if i % 10 ==0:
print('After, ',i,'-th batch')
f.write('\n'+'After, '+str(i)+'-th batch')
for i, data in enumerate(test_dataloader, 0):
real_point, target = data
batch_size = real_point.size()[0]
real_center = torch.FloatTensor(batch_size, 1, opt.crop_point_num, 3)
input_cropped1 = torch.FloatTensor(batch_size, opt.pnum, 3)
input_cropped1 = input_cropped1.data.copy_(real_point)
real_point = torch.unsqueeze(real_point, 1)
input_cropped1 = torch.unsqueeze(input_cropped1,1)
p_origin = [0,0,0]
if opt.cropmethod == 'random_center':
choice = [torch.Tensor([1,0,0]),torch.Tensor([0,0,1]),torch.Tensor([1,0,1]),torch.Tensor([-1,0,0]),torch.Tensor([-1,1,0])]
for m in range(batch_size):
index = random.sample(choice,1)
distance_list = []
p_center = index[0]
for n in range(opt.pnum):
distance_list.append(distance_squre(real_point[m,0,n],p_center))
distance_order = sorted(enumerate(distance_list), key = lambda x:x[1])
for sp in range(opt.crop_point_num):
input_cropped1.data[m,0,distance_order[sp][0]] = torch.FloatTensor([0,0,0])
real_center.data[m,0,sp] = real_point[m,0,distance_order[sp][0]]
real_center = real_center.to(device)
real_center = torch.squeeze(real_center,1)
input_cropped1 = input_cropped1.to(device)
input_cropped1 = torch.squeeze(input_cropped1,1)
input_cropped2_idx = utils.farthest_point_sample(input_cropped1,opt.point_scales_list[1],RAN = True)
input_cropped2 = utils.index_points(input_cropped1,input_cropped2_idx)
input_cropped3_idx = utils.farthest_point_sample(input_cropped1,opt.point_scales_list[2],RAN = False)
input_cropped3 = utils.index_points(input_cropped1,input_cropped3_idx)
input_cropped1 = Variable(input_cropped1,requires_grad=False)
input_cropped2 = Variable(input_cropped2,requires_grad=False)
input_cropped3 = Variable(input_cropped3,requires_grad=False)
input_cropped2 = input_cropped2.to(device)
input_cropped3 = input_cropped3.to(device)
input_cropped = [input_cropped1,input_cropped2,input_cropped3]
point_netG.eval()
fake_center1,fake_center2,fake =point_netG(input_cropped)
CD_loss = criterion_PointLoss(torch.squeeze(fake,1),torch.squeeze(real_center,1))
print('test result:',CD_loss)
f.write('\n'+'test result: %.4f'%(CD_loss))
break
f.close()
schedulerD.step()
schedulerG.step()
if epoch% 10 == 0:
torch.save({'epoch':epoch+1,
'state_dict':point_netG.state_dict()},
'Trained_Model/point_netG'+str(epoch)+'.pth' )
torch.save({'epoch':epoch+1,
'state_dict':point_netD.state_dict()},
'Trained_Model/point_netD'+str(epoch)+'.pth' )
#
#############################
## ONLY G-NET
############################
else:
for epoch in range(resume_epoch,opt.niter):
if epoch<30:
alpha1 = 0.01
alpha2 = 0.02
elif epoch<80:
alpha1 = 0.05
alpha2 = 0.1
else:
alpha1 = 0.1
alpha2 = 0.2
for i, data in enumerate(dataloader, 0):
real_point, target = data
batch_size = real_point.size()[0]
real_center = torch.FloatTensor(batch_size, 1, opt.crop_point_num, 3)
input_cropped1 = torch.FloatTensor(batch_size, opt.pnum, 3)
input_cropped1 = input_cropped1.data.copy_(real_point)
real_point = torch.unsqueeze(real_point, 1)
input_cropped1 = torch.unsqueeze(input_cropped1,1)
p_origin = [0,0,0]
if opt.cropmethod == 'random_center':
choice = [torch.Tensor([1,0,0]),torch.Tensor([0,0,1]),torch.Tensor([1,0,1]),torch.Tensor([-1,0,0]),torch.Tensor([-1,1,0])]
for m in range(batch_size):
index = random.sample(choice,1)
distance_list = []
p_center = index[0]
for n in range(opt.pnum):
distance_list.append(distance_squre(real_point[m,0,n],p_center))
distance_order = sorted(enumerate(distance_list), key = lambda x:x[1])
for sp in range(opt.crop_point_num):
input_cropped1.data[m,0,distance_order[sp][0]] = torch.FloatTensor([0,0,0])
real_center.data[m,0,sp] = real_point[m,0,distance_order[sp][0]]
real_point = real_point.to(device)
real_center = real_center.to(device)
input_cropped1 = input_cropped1.to(device)
############################
# (1) data prepare
###########################
real_center = Variable(real_center,requires_grad=True)
real_center = torch.squeeze(real_center,1)
real_center_key1_idx = utils.farthest_point_sample(real_center,64,RAN = False)
real_center_key1 = utils.index_points(real_center,real_center_key1_idx)
real_center_key1 =Variable(real_center_key1,requires_grad=True)
real_center_key2_idx = utils.farthest_point_sample(real_center,128,RAN = True)
real_center_key2 = utils.index_points(real_center,real_center_key2_idx)
real_center_key2 =Variable(real_center_key2,requires_grad=True)
input_cropped1 = torch.squeeze(input_cropped1,1)
input_cropped2_idx = utils.farthest_point_sample(input_cropped1,opt.point_scales_list[1],RAN = True)
input_cropped2 = utils.index_points(input_cropped1,input_cropped2_idx)
input_cropped3_idx = utils.farthest_point_sample(input_cropped1,opt.point_scales_list[2],RAN = False)
input_cropped3 = utils.index_points(input_cropped1,input_cropped3_idx)
input_cropped1 = Variable(input_cropped1,requires_grad=True)
input_cropped2 = Variable(input_cropped2,requires_grad=True)
input_cropped3 = Variable(input_cropped3,requires_grad=True)
input_cropped2 = input_cropped2.to(device)
input_cropped3 = input_cropped3.to(device)
input_cropped = [input_cropped1,input_cropped2,input_cropped3]
point_netG = point_netG.train()
point_netG.zero_grad()
fake_center1,fake_center2,fake =point_netG(input_cropped)
fake = torch.unsqueeze(fake,1)
############################
# (3) Update G network: maximize log(D(G(z)))
###########################
CD_LOSS = criterion_PointLoss(torch.squeeze(fake,1),torch.squeeze(real_center,1))
errG_l2 = criterion_PointLoss(torch.squeeze(fake,1),torch.squeeze(real_center,1))\
+alpha1*criterion_PointLoss(fake_center1,real_center_key1)\
+alpha2*criterion_PointLoss(fake_center2,real_center_key2)
errG_l2.backward()
optimizerG.step()
print('[%d/%d][%d/%d] Loss_G: %.4f / %.4f '
% (epoch, opt.niter, i, len(dataloader),
errG_l2,CD_LOSS))
f=open('loss_PFNet.txt','a')
f.write('\n'+'[%d/%d][%d/%d] Loss_G: %.4f / %.4f '
% (epoch, opt.niter, i, len(dataloader),
errG_l2,CD_LOSS))
f.close()
schedulerD.step()
schedulerG.step()
if epoch% 10 == 0:
torch.save({'epoch':epoch+1,
'state_dict':point_netG.state_dict()},
'Checkpoint/point_netG'+str(epoch)+'.pth' )