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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | 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 DeepGMR
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 rotation_error(R, R_gt):
cos_theta = (torch.einsum('bij,bij->b', R, R_gt) - 1) / 2
cos_theta = torch.clamp(cos_theta, -1, 1)
return torch.acos(cos_theta) * 180 / math.pi
def translation_error(t, t_gt):
return torch.norm(t - t_gt, dim=1)
def rmse(pts, T, T_gt):
pts_pred = pts @ T[:, :3, :3].transpose(1, 2) + T[:, :3, 3].unsqueeze(1)
pts_gt = pts @ T_gt[:, :3, :3].transpose(1, 2) + T_gt[:, :3, 3].unsqueeze(1)
return torch.norm(pts_pred - pts_gt, dim=2).mean(dim=1)
def test_one_epoch(device, model, test_loader):
model.eval()
test_loss = 0.0
pred = 0.0
count = 0
rotation_errors, translation_errors, rmses = [], [], []
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, :, :3], source.detach().cpu().numpy()[0, :, :3], output['transformed_source'].detach().cpu().numpy()[0])
eye = torch.eye(4).expand_as(igt).to(igt.device)
mse1 = F.mse_loss(output['est_T_inverse'] @ torch.inverse(igt), eye)
mse2 = F.mse_loss(output['est_T'] @ igt, eye)
loss = mse1 + mse2
r_err = rotation_error(est_T_inverse[:, :3, :3], igt[:, :3, :3])
t_err = translation_error(est_T_inverse[:, :3, 3], igt[:, :3, 3])
rmse_val = rmse(template[:, :100], est_T_inverse, igt)
rotation_errors.append(r_err)
translation_errors.append(t_err)
rmses.append(rmse_val)
test_loss += loss_val.item()
count += 1
test_loss = float(test_loss)/count
print("Mean rotation error: {}, Mean translation error: {} and Mean RMSE: {}".format(np.mean(rotation_errors), np.mean(translation_errors), np.mean(rmses)))
return test_loss
def test(args, model, test_loader):
test_loss = 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_deepgmr', 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)')
parser.add_argument('--nearest_neighbors', default=20, type=int,
metavar='K', help='No of nearest neighbors to be estimated.')
parser.add_argument('--use_rri', default=True, type=bool,
help='Find nearest neighbors to estimate features from PointNet.')
# 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=2, type=int,
metavar='N', help='mini-batch size (default: 32)')
parser.add_argument('--pretrained', default='learning3d/pretrained/exp_deepgmr/models/best_model.pth', 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()
torch.backends.cudnn.deterministic = True
trainset = RegistrationData('DeepGMR', ModelNet40Data(train=True))
testset = RegistrationData('DeepGMR', ModelNet40Data(train=False))
train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=args.workers)
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)
model = DeepGMR(use_rri=args.use_rri, nearest_neighbors=args.nearest_neighbors)
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'), strict=False)
model.to(args.device)
test(args, model, test_loader)
if __name__ == '__main__':
main() |