def train(self):
curr_iter = self.curr_iter
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
total_loss = 0
total_num = 0.0
while (curr_iter < self.config.opt.max_iter):
curr_iter += 1
epoch = curr_iter / len(self.data_loader)
batch_loss, batch_pos_loss, batch_neg_loss = self._train_iter(
data_loader_iter, [data_meter, data_timer, total_timer])
total_loss += batch_loss
total_num += 1
if curr_iter % self.lr_update_freq == 0 or curr_iter == 1:
lr = self.scheduler.get_last_lr()
self.scheduler.step()
if self.is_master:
logging.info(f" Epoch: {epoch}, LR: {lr}")
self._save_checkpoint(curr_iter,
'checkpoint_' + str(curr_iter))
if curr_iter % self.config.trainer.stat_freq == 0 and self.is_master:
self.writer.add_scalar('train/loss', batch_loss, curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss,
curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss,
curr_iter)
logging.info(
"Train Epoch: {:.3f} [{}/{}], Current Loss: {:.3e}".format(
epoch, curr_iter, len(self.data_loader), batch_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}, LR: {}"
.format(data_meter.avg, total_timer.avg - data_meter.avg,
total_timer.avg, self.scheduler.get_last_lr()))
data_meter.reset()
total_timer.reset()
def extract_features_batch(model, config, source_path, target_path, voxel_size,
device):
folders = get_folder_list(source_path)
assert len(
folders) > 0, f"Could not find 3DMatch folders under {source_path}"
logging.info(folders)
list_file = os.path.join(target_path, "list.txt")
f = open(list_file, "w")
timer, tmeter = Timer(), AverageMeter()
num_feat = 0
model.eval()
for fo in folders:
if 'evaluation' in fo:
continue
files = get_file_list(fo, ".ply")
fo_base = os.path.basename(fo)
f.write("%s %d\n" % (fo_base, len(files)))
for i, fi in enumerate(files):
# Extract features from a file
pcd = o3d.io.read_point_cloud(fi)
save_fn = "%s_%03d" % (fo_base, i)
if i % 100 == 0:
logging.info(f"{i} / {len(files)}: {save_fn}")
timer.tic()
xyz_down, feature = extract_features(model,
xyz=np.array(pcd.points),
rgb=None,
normal=None,
voxel_size=voxel_size,
device=device,
skip_check=True)
t = timer.toc()
if i > 0:
tmeter.update(t)
num_feat += len(xyz_down)
np.savez_compressed(os.path.join(target_path, save_fn),
points=np.array(pcd.points),
xyz=xyz_down,
feature=feature.detach().cpu().numpy())
if i % 20 == 0 and i > 0:
# 最后一项算的是每个点的特征提取时间
logging.info(
f'Average time: {tmeter.avg}, FPS: {num_feat / tmeter.sum}, time / feat: {tmeter.sum / num_feat}, '
)
f.close()
def _train_epoch(self, epoch):
config = self.config
gc.collect()
self.model.train()
# Epoch starts from 1
total_loss = 0
total_num = 0.0
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
iter_size = self.iter_size
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
pos_dist_meter, neg_dist_meter = AverageMeter(), AverageMeter()
start_iter = (epoch - 1) * (len(data_loader) // iter_size)
for curr_iter in range(len(data_loader) // iter_size):
self.optimizer.zero_grad()
batch_loss = 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
data_timer.tic()
input_dict = data_loader_iter.next()
data_time += data_timer.toc(average=False)
# pairs consist of (xyz1 index, xyz0 index)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
pos_pairs = input_dict['correspondences']
loss, pos_dist, neg_dist = self.triplet_loss(
F0,
F1,
pos_pairs,
num_pos=config.triplet_num_pos * config.batch_size,
num_hn_samples=config.triplet_num_hn * config.batch_size,
num_rand_triplet=config.triplet_num_rand * config.batch_size)
loss /= iter_size
loss.backward()
batch_loss += loss.item()
pos_dist_meter.update(pos_dist)
neg_dist_meter.update(neg_dist)
self.optimizer.step()
gc.collect()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter)
logging.info(
"Train Epoch: {} [{}/{}], Current Loss: {:.3e}, Pos dist: {:.3e}, Neg dist: {:.3e}"
.format(epoch, curr_iter,
len(self.data_loader) //
iter_size, batch_loss, pos_dist_meter.avg, neg_dist_meter.avg) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format(
data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg))
pos_dist_meter.reset()
neg_dist_meter.reset()
data_meter.reset()
total_timer.reset()
def _train_epoch(self, epoch):
gc.collect()
self.model.train()
# Epoch starts from 1
total_loss = 0
total_num = 0.0
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
iter_size = self.iter_size
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
start_iter = (epoch - 1) * (len(data_loader) // iter_size)
for curr_iter in range(len(data_loader) // iter_size):
self.optimizer.zero_grad()
batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
data_timer.tic()
input_dict = data_loader_iter.next()
data_time += data_timer.toc(average=False)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
pos_pairs = input_dict['correspondences']
pos_loss, neg_loss = self.contrastive_hardest_negative_loss(
F0,
F1,
pos_pairs,
num_pos=self.config.num_pos_per_batch * self.config.batch_size,
num_hn_samples=self.config.num_hn_samples_per_batch *
self.config.batch_size)
pos_loss /= iter_size
neg_loss /= iter_size
loss = pos_loss + self.neg_weight * neg_loss
loss.backward()
batch_loss += loss.item()
batch_pos_loss += pos_loss.item()
batch_neg_loss += neg_loss.item()
self.optimizer.step()
gc.collect()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss, start_iter + curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss, start_iter + curr_iter)
logging.info(
"Train Epoch: {} [{}/{}], Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}"
.format(epoch, curr_iter,
len(self.data_loader) //
iter_size, batch_loss, batch_pos_loss, batch_neg_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format(
data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg))
data_meter.reset()
total_timer.reset()
def _valid_epoch(self):
# Change the network to evaluation mode
self.model.eval()
self.val_data_loader.dataset.reset_seed(0)
num_data = 0
hit_ratio_meter, feat_match_ratio, loss_meter, rte_meter, rre_meter = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
data_timer, feat_timer, matching_timer = Timer(), Timer(), Timer()
tot_num_data = len(self.val_data_loader.dataset)
if self.val_max_iter > 0:
tot_num_data = min(self.val_max_iter, tot_num_data)
data_loader_iter = self.val_data_loader.__iter__()
for batch_idx in range(tot_num_data):
data_timer.tic()
input_dict = data_loader_iter.next()
data_timer.toc()
# pairs consist of (xyz1 index, xyz0 index)
feat_timer.tic()
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
feat_timer.toc()
matching_timer.tic()
xyz0, xyz1, T_gt = input_dict['pcd0'], input_dict['pcd1'], input_dict['T_gt']
xyz0_corr, xyz1_corr = self.find_corr(xyz0, xyz1, F0, F1, subsample_size=5000)
T_est = te.est_quad_linear_robust(xyz0_corr, xyz1_corr)
loss = corr_dist(T_est, T_gt, xyz0, xyz1, weight=None)
loss_meter.update(loss)
rte = np.linalg.norm(T_est[:3, 3] - T_gt[:3, 3])
rte_meter.update(rte)
rre = np.arccos((np.trace(T_est[:3, :3].t() @ T_gt[:3, :3]) - 1) / 2)
if not np.isnan(rre):
rre_meter.update(rre)
hit_ratio = self.evaluate_hit_ratio(
xyz0_corr, xyz1_corr, T_gt, thresh=self.config.hit_ratio_thresh)
hit_ratio_meter.update(hit_ratio)
feat_match_ratio.update(hit_ratio > 0.05)
matching_timer.toc()
num_data += 1
torch.cuda.empty_cache()
if batch_idx % 100 == 0 and batch_idx > 0:
logging.info(' '.join([
f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f},",
f"Feature Extraction Time: {feat_timer.avg:.3f}, Matching Time: {matching_timer.avg:.3f},",
f"Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
data_timer.reset()
logging.info(' '.join([
f"Final Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
return {
"loss": loss_meter.avg,
"rre": rre_meter.avg,
"rte": rte_meter.avg,
'feat_match_ratio': feat_match_ratio.avg,
'hit_ratio': hit_ratio_meter.avg
}
def _train_epoch(self, epoch):
gc.collect()
self.model.train()
# Epoch starts from 1
total_loss = 0
total_num = 0.0
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
iter_size = self.iter_size
start_iter = (epoch - 1) * (len(data_loader) // iter_size)
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
# Main training
for curr_iter in range(len(data_loader) // iter_size):
self.optimizer.zero_grad()
batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
# Caffe iter size
data_timer.tic()
input_dict = data_loader_iter.next()
data_time += data_timer.toc(average=False)
# pairs consist of (xyz1 index, xyz0 index)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
N0, N1 = len(sinput0), len(sinput1)
pos_pairs = input_dict['correspondences']
neg_pairs = self.generate_rand_negative_pairs(pos_pairs, max(N0, N1), N0, N1)
pos_pairs = pos_pairs.long().to(self.device)
neg_pairs = torch.from_numpy(neg_pairs).long().to(self.device)
neg0 = F0.index_select(0, neg_pairs[:, 0])
neg1 = F1.index_select(0, neg_pairs[:, 1])
pos0 = F0.index_select(0, pos_pairs[:, 0])
pos1 = F1.index_select(0, pos_pairs[:, 1])
# Positive loss
pos_loss = (pos0 - pos1).pow(2).sum(1)
# Negative loss
neg_loss = F.relu(self.neg_thresh -
((neg0 - neg1).pow(2).sum(1) + 1e-4).sqrt()).pow(2)
pos_loss_mean = pos_loss.mean() / iter_size
neg_loss_mean = neg_loss.mean() / iter_size
# Weighted loss
loss = pos_loss_mean + self.neg_weight * neg_loss_mean
loss.backward(
) # To accumulate gradient, zero gradients only at the begining of iter_size
batch_loss += loss.item()
batch_pos_loss += pos_loss_mean.item()
batch_neg_loss += neg_loss_mean.item()
self.optimizer.step()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
# Print logs
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss, start_iter + curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss, start_iter + curr_iter)
logging.info(
"Train Epoch: {} [{}/{}], Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}"
.format(epoch, curr_iter,
len(self.data_loader) //
iter_size, batch_loss, batch_pos_loss, batch_neg_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format(
data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg))
data_meter.reset()
total_timer.reset()
def _valid_epoch(self, data_loader_iter):
# Change the network to evaluation mode
self.model.eval()
num_data = 0
hit_ratio_meter, reciprocity_ratio_meter = AverageMeter(
), AverageMeter()
reciprocity_hit_ratio_meter = AverageMeter()
data_timer, feat_timer = Timer(), Timer()
tot_num_data = len(self.val_data_loader.dataset)
if self.val_max_iter > 0:
tot_num_data = min(self.val_max_iter, tot_num_data)
for curr_iter in range(tot_num_data):
data_timer.tic()
input_dict = self.get_data(data_loader_iter)
data_timer.toc()
# pairs consist of (xyz1 index, xyz0 index)
feat_timer.tic()
with torch.no_grad():
F0 = self.model(input_dict['img0'].to(self.device))
F1 = self.model(input_dict['img1'].to(self.device))
feat_timer.toc()
# Test self.num_pos_per_batch * self.batch_size features only.
_, _, H0, W0 = F0.shape
_, _, H1, W1 = F1.shape
for batch_idx, pair in enumerate(input_dict['pairs']):
N = len(pair)
sel = np.random.choice(N,
min(N, self.config.num_pos_per_batch),
replace=False)
curr_pair = pair[sel]
w0, h0, w1, h1 = torch.floor(curr_pair.t() /
self.out_tensor_stride).long()
feats0 = F0[batch_idx, :, h0, w0]
nn_inds1 = find_nn_gpu(feats0,
F1[batch_idx, :].view(F1.shape[1], -1),
nn_max_n=self.config.nn_max_n,
transposed=True)
# Convert the index to coordinate: BxCxHxW
xs1 = nn_inds1 % W1
ys1 = nn_inds1 // W1
# Test reciprocity
nn_inds0 = find_nn_gpu(F1[batch_idx, :, ys1, xs1],
F0[batch_idx, :].view(F0.shape[1], -1),
nn_max_n=self.config.nn_max_n,
transposed=True)
# Convert the index to coordinate: BxCxHxW
xs0 = nn_inds0 % W0
ys0 = nn_inds0 // W0
dist_sq = (w1 - xs1)**2 + (h1 - ys1)**2
is_correct = dist_sq < (self.config.ucn_inlier_threshold_pixel
/ self.out_tensor_stride)**2
hit_ratio_meter.update(is_correct.sum().item() /
len(is_correct))
# Recipocity test result
dist_sq_nn = (w0 - xs0)**2 + (h0 - ys0)**2
mask = dist_sq_nn < (self.config.ucn_inlier_threshold_pixel /
self.out_tensor_stride)**2
reciprocity_ratio_meter.update(mask.sum().item() /
float(len(mask)))
reciprocity_hit_ratio_meter.update(
is_correct[mask].sum().item() / (mask.sum().item() + eps))
torch.cuda.empty_cache()
# visualize_image_correspondence(input_dict['img0'][batch_idx, 0].numpy() + 0.5,
# input_dict['img1'][batch_idx, 0].numpy() + 0.5,
# F0[batch_idx], F1[batch_idx], curr_iter,
# self.config)
num_data += 1
if num_data % 100 == 0:
logging.info(', '.join([
f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f}",
f"Feature Extraction Time: {feat_timer.avg:.3f}, Hit Ratio: {hit_ratio_meter.avg}",
f"Reciprocity Ratio: {reciprocity_ratio_meter.avg}, Reci Filtered Hit Ratio: {reciprocity_hit_ratio_meter.avg}"
]))
data_timer.reset()
logging.info(', '.join([
f"Validation : Data Loading Time: {data_timer.avg:.3f}",
f"Feature Extraction Time: {feat_timer.avg:.3f}, Hit Ratio: {hit_ratio_meter.avg}",
f"Reciprocity Ratio: {reciprocity_ratio_meter.avg}, Reci Filtered Hit Ratio: {reciprocity_hit_ratio_meter.avg}"
]))
return {
'hit_ratio': hit_ratio_meter.avg,
'reciprocity_ratio': reciprocity_ratio_meter.avg,
'reciprocity_hit_ratio': reciprocity_hit_ratio_meter.avg,
}
def _train_epoch(self, epoch, data_loader_iter):
# Epoch starts from 1
total_loss = 0
total_num = 0.0
iter_size = self.iter_size
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
for curr_iter in range(self.train_max_iter):
self.optimizer.zero_grad()
batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
data_timer.tic()
input_dict = self.get_data(data_loader_iter)
data_time += data_timer.toc(average=False)
F0 = self.model(input_dict['img0'].to(self.device))
F1 = self.model(input_dict['img1'].to(self.device))
pos_loss, neg_loss = self.contrastive_loss(
input_dict['img0'].numpy() + 0.5,
input_dict['img1'].numpy() + 0.5,
F0,
F1,
input_dict['pairs'],
num_pos=self.config.num_pos_per_batch,
num_hn_samples=self.config.num_hn_samples_per_batch)
pos_loss /= iter_size
neg_loss /= iter_size
loss = pos_loss + self.neg_weight * neg_loss
loss.backward()
batch_loss += loss.item()
batch_pos_loss += pos_loss.item()
batch_neg_loss += neg_loss.item()
self.optimizer.step()
gc.collect()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
torch.cuda.empty_cache()
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss,
curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss,
curr_iter)
logging.info(
"Train epoch {}, iter {}, Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}"
.format(epoch, curr_iter, batch_loss, batch_pos_loss,
batch_neg_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}"
.format(data_meter.avg, total_timer.avg -
data_meter.avg, total_timer.avg))
data_meter.reset()
total_timer.reset()
def stats_meter(self):
meters = dict()
stats = self.stats_dict()
for key, _ in stats.items():
meters[key] = AverageMeter()
return meters
def main(config):
test_loader = make_data_loader(
config, config.test_phase, 1, num_threads=config.test_num_workers, shuffle=True)
num_feats = 1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
Model = load_model(config.model)
model = Model(
num_feats,
config.model_n_out,
bn_momentum=config.bn_momentum,
conv1_kernel_size=config.conv1_kernel_size,
normalize_feature=config.normalize_feature)
checkpoint = torch.load(config.save_dir + '/checkpoint.pth')
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
model.eval()
success_meter, rte_meter, rre_meter = AverageMeter(), AverageMeter(), AverageMeter()
data_timer, feat_timer, reg_timer = Timer(), Timer(), Timer()
test_iter = test_loader.__iter__()
N = len(test_iter)
n_gpu_failures = 0
# downsample_voxel_size = 2 * config.voxel_size
for i in range(len(test_iter)):
data_timer.tic()
try:
data_dict = test_iter.next()
except ValueError:
n_gpu_failures += 1
logging.info(f"# Erroneous GPU Pair {n_gpu_failures}")
continue
data_timer.toc()
xyz0, xyz1 = data_dict['pcd0'], data_dict['pcd1']
T_gth = data_dict['T_gt']
xyz0np, xyz1np = xyz0.numpy(), xyz1.numpy()
pcd0 = make_open3d_point_cloud(xyz0np)
pcd1 = make_open3d_point_cloud(xyz1np)
with torch.no_grad():
feat_timer.tic()
sinput0 = ME.SparseTensor(
data_dict['sinput0_F'].to(device), coordinates=data_dict['sinput0_C'].to(device))
F0 = model(sinput0).F.detach()
sinput1 = ME.SparseTensor(
data_dict['sinput1_F'].to(device), coordinates=data_dict['sinput1_C'].to(device))
F1 = model(sinput1).F.detach()
feat_timer.toc()
feat0 = make_open3d_feature(F0, 32, F0.shape[0])
feat1 = make_open3d_feature(F1, 32, F1.shape[0])
reg_timer.tic()
distance_threshold = config.voxel_size * 1.0
ransac_result = o3d.registration.registration_ransac_based_on_feature_matching(
pcd0, pcd1, feat0, feat1, distance_threshold,
o3d.registration.TransformationEstimationPointToPoint(False), 4, [
o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3d.registration.CorrespondenceCheckerBasedOnDistance(
distance_threshold)
], o3d.registration.RANSACConvergenceCriteria(4000000, 10000))
T_ransac = torch.from_numpy(
ransac_result.transformation.astype(np.float32))
reg_timer.toc()
# Translation error
rte = np.linalg.norm(T_ransac[:3, 3] - T_gth[:3, 3])
rre = np.arccos((np.trace(T_ransac[:3, :3].t() @ T_gth[:3, :3]) - 1) / 2)
# Check if the ransac was successful. successful if rte < 2m and rre < 5◦
# http://openaccess.thecvf.com/content_ECCV_2018/papers/Zi_Jian_Yew_3DFeat-Net_Weakly_Supervised_ECCV_2018_paper.pdf
if rte < 2:
rte_meter.update(rte)
if not np.isnan(rre) and rre < np.pi / 180 * 5:
rre_meter.update(rre)
if rte < 2 and not np.isnan(rre) and rre < np.pi / 180 * 5:
success_meter.update(1)
else:
success_meter.update(0)
logging.info(f"Failed with RTE: {rte}, RRE: {rre}")
if i % 10 == 0:
logging.info(
f"{i} / {N}: Data time: {data_timer.avg}, Feat time: {feat_timer.avg}," +
f" Reg time: {reg_timer.avg}, RTE: {rte_meter.avg}," +
f" RRE: {rre_meter.avg}, Success: {success_meter.sum} / {success_meter.count}"
+ f" ({success_meter.avg * 100} %)")
data_timer.reset()
feat_timer.reset()
reg_timer.reset()
logging.info(
f"RTE: {rte_meter.avg}, var: {rte_meter.var}," +
f" RRE: {rre_meter.avg}, var: {rre_meter.var}, Success: {success_meter.sum} " +
f"/ {success_meter.count} ({success_meter.avg * 100} %)")
def _valid_epoch(self):
# Change the network to evaluation mode
self.model.eval()
self.val_data_loader.dataset.reset_seed(0)
num_data = 0
hit_ratio_meter, feat_match_ratio, loss_meter, rte_meter, rre_meter = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
data_timer, feat_timer, matching_timer = Timer(), Timer(), Timer()
tot_num_data = len(self.val_data_loader.dataset)
if self.val_max_iter > 0:
tot_num_data = min(self.val_max_iter, tot_num_data)
data_loader_iter = self.val_data_loader.__iter__()
for batch_idx in range(tot_num_data):
data_timer.tic()
input_dict = data_loader_iter.next()
data_timer.toc()
# pairs consist of (xyz1 index, xyz0 index)
feat_timer.tic()
coords = input_dict['sinput0_C'].to(self.device)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'].to(self.device),
coordinates=input_dict['sinput0_C'].to(self.device).type(torch.float))
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'].to(self.device),
coordinates=input_dict['sinput1_C'].to(self.device).type(torch.float))
F1 = self.model(sinput1).F
feat_timer.toc()
matching_timer.tic()
xyz0, xyz1, T_gt = input_dict['pcd0'], input_dict['pcd1'], input_dict['T_gt']
xyz0_corr, xyz1_corr = self.find_corr(xyz0, xyz1, F0, F1, subsample_size=5000)
if False:
from sklearn.decomposition import PCA
import open3d as o3d
pc0 = o3d.geometry.PointCloud()
pc0.points = o3d.utility.Vector3dVector(xyz0.numpy())
pca = PCA(n_components=3)
colors = pca.fit_transform(torch.cat((F0, F1), axis=0).cpu().numpy())
colors -= colors.min()
colors /= colors.max()
pc0.colors = o3d.utility.Vector3dVector(colors[0:F0.shape[0]])
o3d.io.write_point_cloud("pc0.ply", pc0)
pc0.transform(T_gt.numpy())
o3d.io.write_point_cloud("pc0_trans.ply", pc0)
pc1 = o3d.geometry.PointCloud()
pc1.points = o3d.utility.Vector3dVector(xyz1.numpy())
pc1.colors = o3d.utility.Vector3dVector(colors[F0.shape[0]:])
o3d.io.write_point_cloud("pc1.ply", pc1)
ind_0 = input_dict['correspondences'][:, 0].type(torch.long)
ind_1 = input_dict['correspondences'][:, 1].type(torch.long)
pc1.points = o3d.utility.Vector3dVector(xyz1[ind_1].numpy())
pc1.colors = o3d.utility.Vector3dVector(
colors[F0.shape[0]:][ind_1])
o3d.io.write_point_cloud("pc1_corr.ply", pc1)
pc0.points = o3d.utility.Vector3dVector(xyz0[ind_0].numpy())
pc0.colors = o3d.utility.Vector3dVector(colors[:F0.shape[0]][ind_0])
pc0.transform(T_gt.numpy())
o3d.io.write_point_cloud("pc0_trans_corr.ply", pc0)
import pdb
pdb.set_trace()
#pc0.points = o3d.utility.Vector3dVector(xyz0_corr.numpy())
# pc0.transform(T_gt.numpy())
#o3d.io.write_point_cloud("xyz0_corr_trans.ply" , pc0)
#
#pc0.points = o3d.utility.Vector3dVector(xyz1_corr.numpy())
#o3d.io.write_point_cloud("xyz1_corr_trans.ply" , pc0)
T_est = te.est_quad_linear_robust(xyz0_corr, xyz1_corr)
loss = corr_dist(T_est, T_gt, xyz0, xyz1, weight=None)
loss_meter.update(loss)
rte = np.linalg.norm(T_est[:3, 3] - T_gt[:3, 3])
rte_meter.update(rte)
rre = np.arccos((np.trace(T_est[:3, :3].t() @ T_gt[:3, :3]) - 1) / 2)
if not np.isnan(rre):
rre_meter.update(rre)
hit_ratio = self.evaluate_hit_ratio(xyz0_corr, xyz1_corr, T_gt, thresh=self.config.hit_ratio_thresh)
hit_ratio_meter.update(hit_ratio)
feat_match_ratio.update(hit_ratio > 0.05)
matching_timer.toc()
num_data += 1
torch.cuda.empty_cache()
if batch_idx % 100 == 0 and batch_idx > 0:
logging.info(' '.join([
f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f},",
f"Feature Extraction Time: {feat_timer.avg:.3f}, Matching Time: {matching_timer.avg:.3f},",
f"Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
data_timer.reset()
logging.info(' '.join([
f"Final Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},",
f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}"
]))
return {
"loss": loss_meter.avg,
"rre": rre_meter.avg,
"rte": rte_meter.avg,
'feat_match_ratio': feat_match_ratio.avg,
'hit_ratio': hit_ratio_meter.avg
}
def main(config):
test_loader = make_data_loader(config,
config.test_phase,
1,
num_threads=config.test_num_thread,
shuffle=False)
num_feats = 1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
Model = load_model(config.model)
model = Model(num_feats,
config.model_n_out,
bn_momentum=config.bn_momentum,
conv1_kernel_size=config.conv1_kernel_size,
normalize_feature=config.normalize_feature)
checkpoint = torch.load(config.save_dir + '/checkpoint.pth')
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
model.eval()
success_meter, rte_meter, rre_meter = AverageMeter(), AverageMeter(
), AverageMeter()
data_timer, feat_timer, reg_timer = Timer(), Timer(), Timer()
test_iter = test_loader.__iter__()
N = len(test_iter)
n_gpu_failures = 0
# downsample_voxel_size = 2 * config.voxel_size
list_results_to_save = []
for i in range(len(test_iter)):
data_timer.tic()
try:
data_dict = test_iter.next()
except ValueError:
n_gpu_failures += 1
logging.info(f"# Erroneous GPU Pair {n_gpu_failures}")
continue
data_timer.toc()
xyz0, xyz1 = data_dict['pcd0'], data_dict['pcd1']
T_gth = data_dict['T_gt']
xyz0np, xyz1np = xyz0.numpy(), xyz1.numpy()
#import pdb
# pdb.set_trace()
pcd0 = make_open3d_point_cloud(xyz0np)
pcd1 = make_open3d_point_cloud(xyz1np)
with torch.no_grad():
feat_timer.tic()
sinput0 = ME.SparseTensor(
data_dict['sinput0_F'].to(device),
coordinates=data_dict['sinput0_C'].to(device))
F0 = model(sinput0).F.detach()
sinput1 = ME.SparseTensor(
data_dict['sinput1_F'].to(device),
coordinates=data_dict['sinput1_C'].to(device))
F1 = model(sinput1).F.detach()
feat_timer.toc()
# saving files to pkl
print(i)
dict_sample = {
"pts_source": xyz0np,
"feat_source": F0.cpu().detach().numpy(),
"pts_target": xyz1np,
"feat_target": F1.cpu().detach().numpy()
}
list_results_to_save.append(dict_sample)
import pickle
path_results_to_save = "fcgf.results.pkl"
print('Saving results to ', path_results_to_save)
pickle.dump(list_results_to_save, open(path_results_to_save, 'wb'))
print('Saved!')
import pdb
pdb.set_trace()
