def main(args):
model = Pointnet2StructurePointNet(
num_structure_points=args.num_structure_points,
input_channels=0,
use_xyz=True)
model.cuda()
checkpoint_util.load_checkpoint(model_3d=model, filename=args.model_fname)
if os.path.exists(args.output_dir) is False:
os.makedirs(args.output_dir)
fnames = glob.glob(os.path.join(args.data_dir, '*.off'))
for fname in fnames:
fname = os.path.basename(fname)
pts = point_cloud_utils.read_points_off(
os.path.join(args.data_dir, fname))
batch_pts = torch.from_numpy(pts)[None, :, :].cuda()
structure_points = model(batch_pts)
structure_points = structure_points[0].cpu().detach().numpy()
outfname = os.path.join(args.output_dir, fname[:-4] + '_stpts.off')
point_cloud_utils.write_points_off(
outfname, structure_points,
COLOR_LIST[:structure_points.shape[0], :])
print('output saved to {0}'.format(args.output_dir))
def main(args):
test_set = bhcp_dataloader.bhcp_dataloader(
args.data_dir, args.category, is_pts_aligned=args.test_on_aligned)
test_loader = DataLoader(
test_set,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False,
)
model = Pointnet2StructurePointNet(
num_structure_points=args.num_structure_points,
input_channels=0,
use_xyz=True)
model.cuda()
checkpoint_util.load_checkpoint(model_3d=model, filename=args.model_fname)
dis_ratios, dis_thresholds = bhcp_evaluate_one_epoch(
model, test_loader, args.test_on_aligned)
print('distance threshold|radio:')
for i in range(dis_ratios.shape[0]):
print(' {0}|{1}'.format(dis_thresholds[i], ("%.6f" % dis_ratios[i])),
end='')
print('\n')
def main(args):
model = Pointnet2StructurePointNet(
num_structure_points=args.num_structure_points,
input_channels=0,
use_xyz=True)
model.cuda()
checkpoint_util.load_checkpoint(model_3d=model, filename=args.model_fname)
src_shape_fname = args.src_shape_fname
query_pts_fname = args.query_pts_fname
tgt_shape_fname = args.tgt_shape_fname
out_corres_pts_fname = args.out_corres_pts_fname
src_pts = point_cloud_utils.read_points_off(src_shape_fname)
tgt_pts = point_cloud_utils.read_points_off(tgt_shape_fname)
query_pts, query_pts_colors = point_cloud_utils.read_points_off(
query_pts_fname, read_color=True)
batch_src_pts = torch.from_numpy(src_pts)[None, :, :].cuda()
batch_tgt_pts = torch.from_numpy(tgt_pts)[None, :, :].cuda()
query_pts = torch.from_numpy(query_pts)[:, :]
if args.test_on_aligned is False:
batch_src_pts, src_rot_mats, _ = d_utils.AddPCATransformsToBatchPoints(
batch_src_pts, num_of_trans=1)
batch_src_pts = batch_src_pts.squeeze(dim=0)
src_rot_mat = src_rot_mats.squeeze(dim=0)[0]
batch_tgt_pts, tgt_rot_mats, _ = d_utils.AddPCATransformsToBatchPoints(
batch_tgt_pts, num_of_trans=1)
batch_tgt_pts = batch_tgt_pts.squeeze(dim=0)
tgt_rot_mat = tgt_rot_mats.squeeze(dim=0)[0]
structure_points_src = model(batch_src_pts)[0]
structure_points_tgt = model(batch_tgt_pts)[0]
structure_points_src = torch.mm(src_rot_mat.transpose(0, 1),
structure_points_src.transpose(
0, 1)).transpose(0, 1)
structure_points_tgt = torch.mm(tgt_rot_mat.transpose(0, 1),
structure_points_tgt.transpose(
0, 1)).transpose(0, 1)
corres_pts_in_tgt = compute_correspondence(structure_points_src, query_pts,
structure_points_tgt)
point_cloud_utils.write_points_off(out_corres_pts_fname, corres_pts_in_tgt,
query_pts_colors)
print('output saved to {0}'.format(out_corres_pts_fname))
def main(args):
model = Pointnet2StructurePointNet(
num_structure_points=args.num_structure_points,
input_channels=0,
use_xyz=True)
model.cuda()
checkpoint_util.load_checkpoint(model_3d=model, filename=args.model_fname)
model.eval()
if os.path.exists(args.output_dir) is False:
os.makedirs(args.output_dir)
fnames = glob.glob(os.path.join(args.data_dir, '*.off'))
for fname in fnames:
fname = os.path.basename(fname)
pts = point_cloud_utils.read_points_off(
os.path.join(args.data_dir, fname))
batch_pts = torch.from_numpy(pts)[None, :, :].cuda()
if args.test_on_aligned is False:
batch_pts, rot_mats, _ = d_utils.AddPCATransformsToBatchPoints(
batch_pts, num_of_trans=1)
batch_pts = batch_pts.squeeze(dim=0).contiguous()
rot_mats = rot_mats.squeeze(dim=0)
structure_points = model(batch_pts)
if args.test_on_aligned is False:
inv_rot_mats = rot_mats.transpose(1, 2)
structure_points = torch.matmul(inv_rot_mats,
structure_points.transpose(
1, 2)).transpose(1, 2)
structure_points = structure_points[0].cpu().detach().numpy()
outfname = os.path.join(args.output_dir, fname[:-4] + '_stpts.off')
point_cloud_utils.write_points_off(
outfname, structure_points,
COLOR_LIST[:structure_points.shape[0], :])
print('output saved to {0}'.format(args.output_dir))
def train(cmd_args):
if os.path.exists(cmd_args.log_dir) is not True:
os.makedirs(cmd_args.log_dir)
checkpoints_dir = os.path.join(cmd_args.log_dir, "checkpoints")
if os.path.exists(checkpoints_dir) is not True:
os.mkdir(checkpoints_dir)
lr_clip = 1e-5
bnm_clip = 1e-2
trans = transforms.Compose([
d_utils.PointcloudToTensor(),
#d_utils.PointcloudRandomPermutation(),
d_utils.PointcloudJitter()
])
train_set = bhcp_dataloader.bhcp_dataloader(cmd_args.data_dir,
cmd_args.category,
transforms=trans,
is_pts_aligned=False)
train_loader = DataLoader(
train_set,
batch_size=cmd_args.batch_size,
shuffle=True,
num_workers=5,
pin_memory=False,
)
model = Pointnet2StructurePointNet(
num_structure_points=cmd_args.num_structure_points,
input_channels=0,
use_xyz=True)
model.cuda()
optimizer = optim.Adam(model.parameters(),
lr=cmd_args.lr,
weight_decay=cmd_args.weight_decay)
lr_lbmd = lambda it: max(
cmd_args.lr_decay**
(int(it * cmd_args.batch_size / cmd_args.decay_step)),
lr_clip / cmd_args.lr,
)
bn_lbmd = lambda it: max(
cmd_args.bn_momentum * cmd_args.bnm_decay**
(int(it * cmd_args.batch_size / cmd_args.decay_step)),
bnm_clip,
)
iters = -1
criterions = {'ComputeLoss3d': ComputeLoss3d()}
# load status from checkpoint
log_open_mode = 'w'
start_epoch = 0
if cmd_args.checkpoint is not None:
fname = os.path.join(checkpoints_dir, cmd_args.checkpoint)
start_epoch, iters = checkpoint_util.load_checkpoint(
model_3d=model, optimizer=optimizer, filename=fname)
start_epoch += 1
log_open_mode = 'a'
log = LogUtils(os.path.join(cmd_args.log_dir, 'logfile'), log_open_mode)
log.write('train unsupervised structure points for bhcp\n')
log.write_args(cmd_args)
lr_scheduler = lr_sched.LambdaLR(optimizer,
lr_lambda=lr_lbmd,
last_epoch=iters)
bnm_scheduler = pt_utils.BNMomentumScheduler(model,
bn_lambda=bn_lbmd,
last_epoch=iters)
iters = max(iters, 0)
for epoch_i in range(start_epoch, cmd_args.max_epochs):
log.write('\nepoch: {0} ###########################'.format(epoch_i))
train_loss, train_cd_loss, train_consistent_loss, iters = train_one_epoch(
model,
optimizer,
train_loader,
lr_scheduler,
bnm_scheduler,
iters,
criterions,
num_of_trans=cmd_args.num_of_transform,
num_inpts=cmd_args.num_inpts)
log.write('\nave_train_loss:{0}, cd_loss:{1}, consis_loss:{2}'.format(
("%.8f" % train_loss), ("%.8f" % train_cd_loss),
("%.8f" % train_consistent_loss)))
if cmd_args.checkpoint_save_step != -1 and (
epoch_i + 1) % cmd_args.checkpoint_save_step is 0:
fname = os.path.join(checkpoints_dir,
'checkpoint_{}'.format(epoch_i))
checkpoint_util.save_checkpoint(filename=fname,
model_3d=model,
optimizer=optimizer,
iters=iters,
epoch=epoch_i)
fname = os.path.join(checkpoints_dir, 'model')
checkpoint_util.save_checkpoint(filename=fname,
model_3d=model,
optimizer=optimizer,
iters=iters,
epoch=epoch_i)
# Load the model
input_channel = 1
model = ftp_psp3(input_channel).cuda()
model = nn.DataParallel(model) # For using multiple GPUs
# Define the optimizer
optimizer = optim.Adam(model.parameters(), lr=opt.lr)
#Load status from checkpoint
log_open_mode = 'w'
start_epoch = 0
if opt.checkpoint is not None:
fname = os.path.join(checkpoints_dir, opt.checkpoint)
start_epoch, iters = checkpoint_util.load_checkpoint(model_3d=model,
optimizer=optimizer,
filename=fname)
start_epoch += 1
log_open_mode = 'a'
log = LogUtils(os.path.join(opt.log_dir, 'logfile'), log_open_mode)
log.write('Supervised learning for phase map enhancement - Training\n')
log.write_args(opt)
lr_scheduler = lr_scd.StepLR(optimizer,
step_size=opt.decay_step,
gamma=opt.lr_decay)
iters = max(iters, 0)
reg = 1e-7
# Train the network on the training dataset
for epoch_num in range(start_epoch, opt.num_epochs):
trainData = iter(trainLoader)
if os.path.exists(opt.log_dir) is not True:
os.makedirs(opt.log_dir)
checkpoints_dir = os.path.join(opt.log_dir, "checkpoints")
if os.path.exists(checkpoints_dir) is not True:
os.mkdir(checkpoints_dir)
# Load the model
input_channel = 1
model = ftp_psp5(input_channel).cuda()
# model = nn.DataParallel(model) # For using multiple GPUs
#Load status from checkpoint
log_open_mode = 'w'
checkpoint_util.load_checkpoint(model_3d=model, filename=opt.checkpoint)
log = LogUtils(os.path.join(opt.log_dir, 'logfile'), log_open_mode)
log.write('Supervised learning for phase map enhancement - Testing\n')
log.write_args(opt)
if opt.checkpoint is None:
print('Checkpoint is missing! Load the checkpoint to start the testing')
sys.exit()
# Test the network using the trained model
testData = iter(testLoader)
ave_loss = 0
count = 0
for data in iter(testLoader):
inp_PM, gt_PM, mask_PM, filename_PM = next(testData)
