return output
if __name__ == "__main__":
conversion_factor = 1440 # Conversion factor for Tinker units to Mv/cm.
###########################################################################
#
# Handle user arguments
#
###########################################################################
start = time.time()
parser = create_parser()
args = parser.parse_args()
nengines = args.nengines
equil = args.equil
stride = args.stride
# Process args for MDI
mdi.MDI_Init(args.mdi, mpi_world)
if use_mpi4py:
mpi_world = mdi.MDI_Get_Intra_Code_MPI_Comm()
world_rank = mpi_world.Get_rank()
snapshot_filename = args.snap
probes = [int(x) for x in args.probes.split()]
import cv2
import math
import argparse
import os
import random
import torch
import torch.optim as optim
import ngransac
from network import CNNet
from dataset import SparseDataset
import util
parser = util.create_parser(
'NG-RANSAC demo for a user defined image pair. Fits an essential matrix (default) or fundamental matrix (-fmat) using OpenCV RANSAC vs. NG-RANSAC.'
)
parser.add_argument('--image1',
'-img1',
default='images/demo1.jpg',
help='path to image 1')
parser.add_argument('--image2',
'-img2',
default='images/demo2.jpg',
help='path to image 2')
parser.add_argument(
'--outimg',
'-out',
import numpy as np
import cv2
import random
import torch
import torch.optim as optim
import ngransac
from network import CNNet
from dataset import SparseDataset
import util
# parse command line arguments
parser = util.create_parser(
description="Train a neural guidance network end-to-end using a task loss."
)
parser.add_argument(
'--datasets',
'-ds',
default=
'brown_bm_3---brown_bm_3-maxpairs-10000-random---skip-10-dilate-25,st_peters_square',
help='which datasets to use, separate multiple datasets by comma')
parser.add_argument('--variant',
'-v',
default='train',
help='subfolder of the dataset to use')
parser.add_argument(
'--hyps',
import numpy as np
import cv2
import random
import os
import torch
import ngransac
import time
from network import CNNet
from dataset import SparseDataset
import util
parser = util.create_parser(
description="Test NG-RANSAC on pre-calculated correspondences.")
parser.add_argument('--dataset',
'-ds',
default='reichstag',
help='which dataset to use')
parser.add_argument('--batchmode',
'-bm',
action='store_true',
help='loop over all test datasets defined in util.py')
parser.add_argument('--variant',
'-v',
default='test',
help='subfolder of the dataset to use')
import numpy as np
import math
import torch
import torch.optim as optim
import ngransac
from network import CNNet
from dataset import SparseDataset
import util
# parse command line arguments
parser = util.create_parser(
description=
"Train a neural guidance network using correspondence distance to a ground truth model to calculate target probabilities."
)
parser.add_argument(
'--datasets',
'-ds',
default=
'brown_bm_3---brown_bm_3-maxpairs-10000-random---skip-10-dilate-25,st_peters_square',
help='which datasets to use, separate multiple datasets by comma')
parser.add_argument('--variant',
'-v',
default='train',
help='subfolder of the dataset to use')
parser.add_argument('--learningrate',
'-lr',
