class ChamfersDistance(nn.Module):
'''
Use NNDModule as a member.
'''
def __init__(self):
super(ChamfersDistance, self).__init__()
self.nnd = NNDModule()
def forward(self, input1, input2): # BxNxK, BxMxK
dist0, dist1 = self.nnd.forward(input1, input2) # BxN, BxM
loss = torch.mean(torch.sqrt(dist0), 1) + torch.mean(
torch.sqrt(dist1), 1) # B
loss = torch.mean(loss) # 1
# loss = torch.rand(1,requires_grad=True)
return loss
def get_chamfer_dist(get_slow=False):
try:
if get_slow: raise ValueError
import sys
sys.path.insert(0, './nndistance')
from modules.nnd import NNDModule
dist = NNDModule()
except:
dist = chamfer_quadratic
def loss(a, b):
if a.dim() == 4:
if a.size(1) == 2:
a = from_polar(a)
assert a.size(1) == 3
a = a.permute(0, 2, 3, 1).contiguous().reshape(a.size(0), -1, 3)
if b.dim() == 4:
if b.size(1) == 2:
b = from_polar(b)
assert b.size(1) == 3
b = b.permute(0, 2, 3, 1).contiguous().reshape(b.size(0), -1, 3)
assert a.dim() == b.dim() == 3
if a.size(-1) != 3:
assert a.size(-2) == 3
a = a.transpose(-2, -1).contiguous()
if b.size(-1) != 3:
assert b.size(-2) == 3
b = a.transpose(-2, -1).contiguous()
dist_a, dist_b = dist(a, b)
return dist_a.sum(dim=-1) + dist_b.sum(dim=-1)
return loss
from ply import *
import torch.nn.functional as F
import sys
from tqdm import tqdm
import os
import json
import time, datetime
import subprocess
import pandas as pd
try:
from script.normalize_obj import *
except:
print('couldnt load normalize obj')
sys.path.append("./nndistance/")
from modules.nnd import NNDModule
distChamfer = NNDModule()
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=6)
parser.add_argument('--nepoch', type=int, default=50000, help='number of epochs to train for')
parser.add_argument('--model', type=str, default = 'trained_models/ae_baseline.pth', help='yuor path to the trained model')
parser.add_argument('--num_points', type=int, default = 2500, help='number of points fed to poitnet')
opt = parser.parse_args()
print (opt)
blue = lambda x:'\033[94m' + x + '\033[0m'
opt.manualSeed = random.randint(1, 10000) # fix seed
import torch
import torch.nn as nn
from torch.autograd import Variable
from modules.nnd import NNDModule
dist = NNDModule()
p1 = torch.rand(1, 3, 3) * 20
p2 = torch.rand(1, 3, 3) * 20
# p1 = p1.int()
# p1.random_(0,2)
# p1 = p1.float()
# p2 = p2.int()
# p2.random_(0,2)
p2 = p2.float()
print(p1)
print(p2)
print('cpu')
points1 = Variable(p1, requires_grad=True)
points2 = Variable(p2)
idx1, idx2 = dist(points1, points2)
print(idx1, idx1)
print('gpu')
points1_cuda = Variable(p1.cuda(), requires_grad=True)
points2_cuda = Variable(p2.cuda())
idx1, idx2 = dist(points1_cuda, points2_cuda)
print(idx1, idx2)
def __init__(self):
super(ChamfersDistance, self).__init__()
self.nnd = NNDModule()
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
ae.apply(weights_init)
ae.cuda()
optimizer = optim.Adagrad(ae.parameters(), lr=0.001)
nnd = NNDModule()
num_batch = len(dataset) / opt.batchSize
for epoch in range(opt.nepoch):
for i, data in enumerate(dataloader, 0):
optimizer.zero_grad()
points, _ = data
points = Variable(points)
bs = points.size()[0]
points = points.transpose(2, 1)
points = points.cuda()
gen = ae(points)
def __init__(self, n_samples=1024, cuda_opt=True):
super(ChamferLoss, self).__init__()
self.n_samples = n_samples
self.dist = NNDModule()
self.cuda_opt = cuda_opt
