File size: 4,366 Bytes
97aa5af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import open3d as o3d
import argparse
import os
import sys
import logging
import numpy
import numpy as np
import torch
import torch.utils.data
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
# Only if the files are in example folder.
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
if BASE_DIR[-8:] == 'examples':
sys.path.append(os.path.join(BASE_DIR, os.pardir))
os.chdir(os.path.join(BASE_DIR, os.pardir))
from learning3d.models import PointNet, iPCRNet
from learning3d.losses import ChamferDistanceLoss
from learning3d.data_utils import RegistrationData, ModelNet40Data
def display_open3d(template, source, transformed_source):
template_ = o3d.geometry.PointCloud()
source_ = o3d.geometry.PointCloud()
transformed_source_ = o3d.geometry.PointCloud()
template_.points = o3d.utility.Vector3dVector(template)
source_.points = o3d.utility.Vector3dVector(source + np.array([0,0,0]))
transformed_source_.points = o3d.utility.Vector3dVector(transformed_source)
template_.paint_uniform_color([1, 0, 0])
source_.paint_uniform_color([0, 1, 0])
transformed_source_.paint_uniform_color([0, 0, 1])
o3d.visualization.draw_geometries([template_, source_, transformed_source_])
def test_one_epoch(device, model, test_loader):
model.eval()
test_loss = 0.0
pred = 0.0
count = 0
for i, data in enumerate(tqdm(test_loader)):
template, source, igt = data
template = template.to(device)
source = source.to(device)
igt = igt.to(device)
output = model(template, source)
display_open3d(template.detach().cpu().numpy()[0], source.detach().cpu().numpy()[0], output['transformed_source'].detach().cpu().numpy()[0])
loss_val = ChamferDistanceLoss()(template, output['transformed_source'])
test_loss += loss_val.item()
count += 1
test_loss = float(test_loss)/count
return test_loss
def test(args, model, test_loader):
test_loss, test_accuracy = test_one_epoch(args.device, model, test_loader)
def options():
parser = argparse.ArgumentParser(description='Point Cloud Registration')
parser.add_argument('--exp_name', type=str, default='exp_ipcrnet', metavar='N',
help='Name of the experiment')
parser.add_argument('--dataset_path', type=str, default='ModelNet40',
metavar='PATH', help='path to the input dataset') # like '/path/to/ModelNet40'
parser.add_argument('--eval', type=bool, default=False, help='Train or Evaluate the network.')
# settings for input data
parser.add_argument('--dataset_type', default='modelnet', choices=['modelnet', 'shapenet2'],
metavar='DATASET', help='dataset type (default: modelnet)')
parser.add_argument('--num_points', default=1024, type=int,
metavar='N', help='points in point-cloud (default: 1024)')
# settings for PointNet
parser.add_argument('--emb_dims', default=1024, type=int,
metavar='K', help='dim. of the feature vector (default: 1024)')
parser.add_argument('--symfn', default='max', choices=['max', 'avg'],
help='symmetric function (default: max)')
# settings for on training
parser.add_argument('-j', '--workers', default=4, type=int,
metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('-b', '--batch_size', default=20, type=int,
metavar='N', help='mini-batch size (default: 32)')
parser.add_argument('--pretrained', default='learning3d/pretrained/exp_ipcrnet/models/best_model.t7', type=str,
metavar='PATH', help='path to pretrained model file (default: null (no-use))')
parser.add_argument('--device', default='cuda:0', type=str,
metavar='DEVICE', help='use CUDA if available')
args = parser.parse_args()
return args
def main():
args = options()
testset = RegistrationData('PCRNet', ModelNet40Data(train=False))
test_loader = DataLoader(testset, batch_size=args.batch_size, shuffle=False, drop_last=False, num_workers=args.workers)
if not torch.cuda.is_available():
args.device = 'cpu'
args.device = torch.device(args.device)
# Create PointNet Model.
ptnet = PointNet(emb_dims=args.emb_dims)
model = iPCRNet(feature_model=ptnet)
model = model.to(args.device)
if args.pretrained:
assert os.path.isfile(args.pretrained)
model.load_state_dict(torch.load(args.pretrained, map_location='cpu'))
model.to(args.device)
test(args, model, test_loader)
if __name__ == '__main__':
main() |