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eval_mvp_rg.py
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eval_mvp_rg.py
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import argparse
import copy
import glob
import numpy as np
import os
import pdb
import torch
import open3d as o3d
from easydict import EasyDict as edict
from tqdm import tqdm
from data import MVP_RG, get_dataloader
from models import architectures, NgeNet, vote
from utils import decode_config, npy2pcd, pcd2npy, execute_global_registration, \
npy2feat, vis_plys, setup_seed, fmat, to_tensor, get_blue, get_yellow
from metrics import Error_R, Error_t, RMSE
CUR = os.path.dirname(os.path.abspath(__file__))
def main(args):
setup_seed(22)
config = decode_config(os.path.join(CUR, 'configs', 'mvp_rg.yaml'))
config = edict(config)
config.architecture = architectures[config.dataset]
config.num_workers = 4
test_dataset = MVP_RG(root=args.data_root,
split='val',
rot_mag=config.rot_mag,
trans_mag=config.trans_mag,
overlap_radius=config.overlap_radius)
test_dataloader, neighborhood_limits = get_dataloader(config=config,
dataset=test_dataset,
batch_size=config.batch_size,
num_workers=config.num_workers,
shuffle=False,
neighborhood_limits=None)
print(neighborhood_limits)
model = NgeNet(config)
use_cuda = not args.no_cuda
if use_cuda:
model = model.cuda()
model.load_state_dict(torch.load(args.checkpoint))
else:
model.load_state_dict(
torch.load(args.checkpoint, map_location=torch.device('cpu')))
model.eval()
Ts, gt_Ts, srcs = [], [], []
with torch.no_grad():
for pair_ind, inputs in enumerate(tqdm(test_dataloader)):
if use_cuda:
for k, v in inputs.items():
if isinstance(v, list):
for i in range(len(v)):
inputs[k][i] = inputs[k][i].cuda()
else:
inputs[k] = inputs[k].cuda()
batched_feats_h, batched_feats_m, batched_feats_l = model(inputs)
stack_points = inputs['points']
stack_lengths = inputs['stacked_lengths']
coords_src = stack_points[0][:stack_lengths[0][0]]
coords_tgt = stack_points[0][stack_lengths[0][0]:]
feats_src_h = batched_feats_h[:stack_lengths[0][0]]
feats_tgt_h = batched_feats_h[stack_lengths[0][0]:]
feats_src_m = batched_feats_m[:stack_lengths[0][0]]
feats_tgt_m = batched_feats_m[stack_lengths[0][0]:]
feats_src_l = batched_feats_l[:stack_lengths[0][0]]
feats_tgt_l = batched_feats_l[stack_lengths[0][0]:]
coors = inputs['coors'][0] # list, [coors1, coors2, ..], preparation for batchsize > 1
transf = inputs['transf'][0] # (1, 4, 4), preparation for batchsize > 1
coors = coors.detach().cpu().numpy()
T = transf.detach().cpu().numpy()
source_npy = coords_src.detach().cpu().numpy()
target_npy = coords_tgt.detach().cpu().numpy()
srcs.append(source_npy)
source_npy_raw = copy.deepcopy(source_npy)
target_npy_raw = copy.deepcopy(target_npy)
source_feats_h = feats_src_h[:, :-2].detach().cpu().numpy()
target_feats_h = feats_tgt_h[:, :-2].detach().cpu().numpy()
source_feats_m = feats_src_m.detach().cpu().numpy()
target_feats_m = feats_tgt_m.detach().cpu().numpy()
source_feats_l = feats_src_l.detach().cpu().numpy()
target_feats_l = feats_tgt_l.detach().cpu().numpy()
source_overlap_scores = feats_src_h[:, -2].detach().cpu().numpy()
target_overlap_scores = feats_tgt_h[:, -2].detach().cpu().numpy()
source_saliency_scores = feats_src_h[:, -1].detach().cpu().numpy()
target_saliency_scores = feats_tgt_h[:, -1].detach().cpu().numpy()
source_scores = source_overlap_scores * source_saliency_scores
target_scores = target_overlap_scores * target_saliency_scores
npoints = args.npts
if source_npy.shape[0] > npoints:
p = source_scores / np.sum(source_scores)
idx = np.random.choice(len(source_npy), size=npoints, replace=False, p=p)
source_npy = source_npy[idx]
source_feats_h = source_feats_h[idx]
source_feats_m = source_feats_m[idx]
source_feats_l = source_feats_l[idx]
# if target_npy.shape[0] > npoints:
# p = target_scores / np.sum(target_scores)
# idx = np.random.choice(len(target_npy), size=npoints, replace=False, p=p)
# target_npy = target_npy[idx]
# target_feats_h = target_feats_h[idx]
# target_feats_m = target_feats_m[idx]
# target_feats_l = target_feats_l[idx]
after_vote = vote(source_npy=source_npy,
target_npy=target_npy,
source_feats=[source_feats_h, source_feats_m, source_feats_l],
target_feats=[target_feats_h, target_feats_m, target_feats_l],
voxel_size=config.first_subsampling_dl,
use_cuda=use_cuda)
source_npy, target_npy, source_feats_npy, target_feats_npy = after_vote
M = torch.cdist(to_tensor(source_feats_npy, use_cuda), to_tensor(target_feats_npy, use_cuda))
row_max_inds = torch.min(M, dim=-1)[1].cpu().numpy()
col_max_inds = torch.min(M, dim=0)[1].cpu().numpy()
source, target = npy2pcd(source_npy), npy2pcd(target_npy)
source_feats, target_feats = npy2feat(source_feats_npy), npy2feat(target_feats_npy)
pred_T, estimate = execute_global_registration(source=source,
target=target,
source_feats=source_feats,
target_feats=target_feats,
voxel_size=0.02)
Ts.append(pred_T)
gt_Ts.append(T)
if args.vis:
source_ply = npy2pcd(source_npy_raw)
source_ply.paint_uniform_color(get_yellow())
estimate_ply = copy.deepcopy(source_ply).transform(pred_T)
target_ply = npy2pcd(target_npy_raw)
target_ply.paint_uniform_color(get_blue())
vis_plys([target_ply, estimate_ply], need_color=False)
Ts, gt_Ts, srcs = np.array(Ts), np.array(gt_Ts), np.array(srcs)
rot_error = Error_R(Ts[:, :3, :3], gt_Ts[:, :3, :3])
trans_error = Error_t(Ts[:, :3, 3], gt_Ts[:, :3, 3])
rmse = RMSE(srcs, Ts[:, :3, :3], gt_Ts[:, :3, :3], Ts[:, :3, 3], gt_Ts[:, :3, 3])
re = np.mean(rot_error)
te = np.mean(trans_error)
rmse = np.mean(rmse)
print('Rotation error: ', fmat(re))
print('translation error: ', fmat(te))
print('RMSE: ', fmat(rmse))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Configuration Parameters')
parser.add_argument('--npts', type=int, default=768,
help='the number of sampled points for registration')
parser.add_argument('--data_root', required=True, help='data root')
parser.add_argument('--checkpoint', required=True, help='checkpoint path')
parser.add_argument('--vis', action='store_true',
help='whether for visualization')
parser.add_argument('--no_cuda', action='store_true',
help='whether to use cuda')
args = parser.parse_args()
main(args)