def validate(model, loss_fn, metric, dataloader, log_period=-1):
logger = logging.getLogger('shaper.validate')
meters = MetricLogger(delimiter=' ')
# reset metrics
metric.reset()
meters.bind(metric)
# set evaluate mode
model.eval()
loss_fn.eval()
metric.eval()
end = time.time()
with torch.no_grad():
for iteration, data_batch in enumerate(dataloader):
data_time = time.time() - end
data_batch = {
k: v.cuda(non_blocking=True)
for k, v in data_batch.items()
}
preds = model(data_batch)
loss_dict = loss_fn(preds, data_batch)
total_loss = sum(loss_dict.values())
# update metrics and meters
meters.update(loss=total_loss, **loss_dict)
metric.update_dict(preds, data_batch)
batch_time = time.time() - end
meters.update(time=batch_time, data=data_time)
end = time.time()
if log_period > 0 and iteration % log_period == 0:
logger.info(
meters.delimiter.join([
'iter: {iter:4d}',
'{meters}',
]).format(
iter=iteration,
meters=str(meters),
))
return meters
def test(cfg, output_dir='', output_dir_merge='', output_dir_save=''):
logger = logging.getLogger('shaper.test')
# build model
model, loss_fn, _, val_metric = build_model(cfg)
model = nn.DataParallel(model).cuda()
model_merge = nn.DataParallel(PointNetCls(in_channels=3,
out_channels=128)).cuda()
# build checkpointer
checkpointer = Checkpointer(model, save_dir=output_dir, logger=logger)
checkpointer_merge = Checkpointer(model_merge,
save_dir=output_dir_merge,
logger=logger)
if cfg.TEST.WEIGHT:
# load weight if specified
weight_path = cfg.TEST.WEIGHT.replace('@', output_dir)
checkpointer.load(weight_path, resume=False)
else:
# load last checkpoint
checkpointer.load(None, resume=True)
checkpointer_merge.load(None, resume=True)
#checkpointer_refine.load(None, resume=True)
# build data loader
test_dataloader = build_dataloader(cfg, mode='test')
test_dataset = test_dataloader.dataset
assert cfg.TEST.BATCH_SIZE == 1, '{} != 1'.format(cfg.TEST.BATCH_SIZE)
save_fig_dir = osp.join(output_dir_save, 'test_fig')
os.makedirs(save_fig_dir, exist_ok=True)
save_fig_dir_size = osp.join(save_fig_dir, 'size')
os.makedirs(save_fig_dir_size, exist_ok=True)
save_fig_dir_gt = osp.join(save_fig_dir, 'gt')
os.makedirs(save_fig_dir_gt, exist_ok=True)
# ---------------------------------------------------------------------------- #
# Test
# ---------------------------------------------------------------------------- #
model.eval()
model_merge.eval()
loss_fn.eval()
softmax = nn.Softmax()
set_random_seed(cfg.RNG_SEED)
NUM_POINT = 10000
n_shape = len(test_dataloader)
NUM_INS = 200
out_mask = np.zeros((n_shape, NUM_INS, NUM_POINT), dtype=np.bool)
out_valid = np.zeros((n_shape, NUM_INS), dtype=np.bool)
out_conf = np.ones((n_shape, NUM_INS), dtype=np.float32)
meters = MetricLogger(delimiter=' ')
meters.bind(val_metric)
tot_purity_error_list = list()
tot_purity_error_small_list = list()
tot_purity_error_large_list = list()
tot_pred_acc = list()
tot_pred_small_acc = list()
tot_pred_large_acc = list()
tot_mean_rela_size_list = list()
tot_mean_policy_label0 = list()
tot_mean_label_policy0 = list()
tot_mean_policy_label0_large = list()
tot_mean_policy_label0_small = list()
tot_mean_label_policy0_large = list()
tot_mean_label_policy0_small = list()
with torch.no_grad():
start_time = time.time()
end = start_time
for iteration, data_batch in enumerate(test_dataloader):
print(iteration)
data_time = time.time() - end
iter_start_time = time.time()
data_batch = {
k: v.cuda(non_blocking=True)
for k, v in data_batch.items()
}
preds = model(data_batch)
loss_dict = loss_fn(preds, data_batch)
meters.update(**loss_dict)
val_metric.update_dict(preds, data_batch)
#extraction box features
batch_size, _, num_centroids, num_neighbours = data_batch[
'neighbour_xyz'].shape
num_points = data_batch['points'].shape[-1]
#batch_size, num_centroid, num_neighbor
_, p = torch.max(preds['ins_logit'], 1)
box_index_expand = torch.zeros(
(batch_size * num_centroids, num_points)).cuda()
box_index_expand = box_index_expand.scatter_(
dim=1,
index=data_batch['neighbour_index'].reshape(
[-1, num_neighbours]),
src=p.reshape([-1, num_neighbours]).float())
#centroid_label = data_batch['centroid_label'].reshape(-1)
minimum_box_pc_num = 16
minimum_overlap_pc_num = 16 #1/16 * num_neighbour
gtmin_mask = (torch.sum(box_index_expand, dim=-1) >
minimum_box_pc_num)
#remove purity < 0.8
box_label_expand = torch.zeros(
(batch_size * num_centroids, 200)).cuda()
purity_pred = torch.zeros([0]).type(torch.LongTensor).cuda()
purity_pred_float = torch.zeros([0]).type(torch.FloatTensor).cuda()
for i in range(batch_size):
cur_xyz_pool, xyz_mean = mask_to_xyz(
data_batch['points'][i],
box_index_expand.view(batch_size, num_centroids,
num_points)[i],
sample_num=512)
cur_xyz_pool -= xyz_mean
cur_xyz_pool /= (cur_xyz_pool + 1e-6).norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
logits_purity = model_merge(cur_xyz_pool, 'purity')
p = (logits_purity > 0.8).long().squeeze()
purity_pred = torch.cat([purity_pred, p])
purity_pred_float = torch.cat(
[purity_pred_float,
logits_purity.squeeze()])
p_thresh = 0.8
purity_pred = purity_pred_float > p_thresh
#in case remove too much
while (torch.sum(purity_pred) < 48):
p_thresh = p_thresh - 0.01
purity_pred = purity_pred_float > p_thresh
valid_mask = gtmin_mask.long() * purity_pred.long()
box_index_expand = torch.index_select(
box_index_expand, dim=0, index=valid_mask.nonzero().squeeze())
box_num = torch.sum(valid_mask.reshape(batch_size, num_centroids),
1)
cumsum_box_num = torch.cumsum(box_num, dim=0)
cumsum_box_num = torch.cat([
torch.from_numpy(np.array(0)).cuda().unsqueeze(0),
cumsum_box_num
],
dim=0)
with torch.no_grad():
pc_all = data_batch['points']
xyz_pool1 = torch.zeros([0, 3, 1024]).float().cuda()
xyz_pool2 = torch.zeros([0, 3, 1024]).float().cuda()
label_pool = torch.zeros([0]).float().cuda()
for i in range(pc_all.shape[0]):
bs = 1
pc = pc_all[i].clone()
cur_mask_pool = box_index_expand[
cumsum_box_num[i]:cumsum_box_num[i + 1]].clone()
cover_ratio = torch.unique(
cur_mask_pool.nonzero()[:, 1]).shape[0] / num_points
#print(iteration, cover_ratio)
cur_xyz_pool, xyz_mean = mask_to_xyz(pc, cur_mask_pool)
subpart_pool = cur_xyz_pool.clone()
subpart_mask_pool = cur_mask_pool.clone()
init_pool_size = cur_xyz_pool.shape[0]
meters.update(cover_ratio=cover_ratio,
init_pool_size=init_pool_size)
negative_num = 0
positive_num = 0
#remove I
inter_matrix = torch.matmul(cur_mask_pool,
cur_mask_pool.transpose(0, 1))
inter_matrix_full = inter_matrix.clone(
) > minimum_overlap_pc_num
inter_matrix[torch.eye(inter_matrix.shape[0]).byte()] = 0
pair_idx = (inter_matrix.triu() >
minimum_overlap_pc_num).nonzero()
zero_pair = torch.ones([0, 2]).long()
purity_matrix = torch.zeros(inter_matrix.shape).cuda()
policy_matrix = torch.zeros(inter_matrix.shape).cuda()
bsp = 64
idx = torch.arange(pair_idx.shape[0]).cuda()
#calculate initial policy score matrix
purity_pool = torch.zeros([0]).float().cuda()
policy_pool = torch.zeros([0]).float().cuda()
for k in range(int(np.ceil(idx.shape[0] / bsp))):
sub_part_idx = torch.index_select(
pair_idx, dim=0, index=idx[k * bsp:(k + 1) * bsp])
part_xyz1 = torch.index_select(cur_xyz_pool,
dim=0,
index=sub_part_idx[:,
0])
part_xyz2 = torch.index_select(cur_xyz_pool,
dim=0,
index=sub_part_idx[:,
1])
part_xyz = torch.cat([part_xyz1, part_xyz2], -1)
part_xyz -= torch.mean(part_xyz, -1).unsqueeze(-1)
part_norm = part_xyz.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_norm
logits_purity = model_merge(part_xyz,
'purity').squeeze()
if len(logits_purity.shape) == 0:
logits_purity = logits_purity.unsqueeze(0)
purity_pool = torch.cat([purity_pool, logits_purity],
dim=0)
part_xyz11 = part_xyz1 - torch.mean(part_xyz1,
-1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(part_xyz2,
-1).unsqueeze(-1)
part_xyz11 /= part_norm
part_xyz22 /= part_norm
logits11 = model_merge(part_xyz11, 'policy')
logits22 = model_merge(part_xyz22, 'policy')
policy_scores = model_merge(
torch.cat([logits11, logits22], dim=-1),
'policy_head').squeeze()
if len(policy_scores.shape) == 0:
policy_scores = policy_scores.unsqueeze(0)
policy_pool = torch.cat([policy_pool, policy_scores],
dim=0)
purity_matrix[pair_idx[:, 0], pair_idx[:, 1]] = purity_pool
policy_matrix[pair_idx[:, 0], pair_idx[:, 1]] = policy_pool
score_matrix = torch.zeros(purity_matrix.shape).cuda()
score_matrix[pair_idx[:, 0], pair_idx[:, 1]] = softmax(
purity_pool * policy_pool)
meters.update(initial_pair_num=pair_idx.shape[0])
iteration_num = 0
remote_flag = False
#info
policy_list = []
purity_list = []
gt_purity_list = []
gt_label_list = []
pred_label_list = []
size_list = []
relative_size_list = []
while (pair_idx.shape[0] > 0) or (remote_flag == False):
if pair_idx.shape[0] == 0:
remote_flag = True
inter_matrix = 20 * torch.ones([
cur_mask_pool.shape[0], cur_mask_pool.shape[0]
]).cuda()
inter_matrix[zero_pair[:, 0], zero_pair[:, 1]] = 0
inter_matrix[torch.eye(
inter_matrix.shape[0]).byte()] = 0
pair_idx = (inter_matrix.triu() >
minimum_overlap_pc_num).nonzero()
if pair_idx.shape[0] == 0:
break
purity_matrix = torch.zeros(
inter_matrix.shape).cuda()
policy_matrix = torch.zeros(
inter_matrix.shape).cuda()
bsp = 64
idx = torch.arange(pair_idx.shape[0]).cuda()
purity_pool = torch.zeros([0]).float().cuda()
policy_pool = torch.zeros([0]).float().cuda()
for k in range(int(np.ceil(idx.shape[0] / bsp))):
sub_part_idx = torch.index_select(
pair_idx,
dim=0,
index=idx[k * bsp:(k + 1) * bsp])
part_xyz1 = torch.index_select(
cur_xyz_pool,
dim=0,
index=sub_part_idx[:, 0])
part_xyz2 = torch.index_select(
cur_xyz_pool,
dim=0,
index=sub_part_idx[:, 1])
part_xyz = torch.cat([part_xyz1, part_xyz2],
-1)
part_xyz -= torch.mean(part_xyz,
-1).unsqueeze(-1)
part_norm = part_xyz.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_norm
logits_purity = model_merge(
part_xyz, 'purity').squeeze()
if len(logits_purity.shape) == 0:
logits_purity = logits_purity.unsqueeze(0)
purity_pool = torch.cat(
[purity_pool, logits_purity], dim=0)
part_xyz11 = part_xyz1 - torch.mean(
part_xyz1, -1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(
part_xyz2, -1).unsqueeze(-1)
part_xyz11 /= part_norm
part_xyz22 /= part_norm
logits11 = model_merge(part_xyz11, 'policy')
logits22 = model_merge(part_xyz22, 'policy')
policy_scores = model_merge(
torch.cat([logits11, logits22], dim=-1),
'policy_head').squeeze()
if len(policy_scores.shape) == 0:
policy_scores = policy_scores.unsqueeze(0)
policy_pool = torch.cat(
[policy_pool, policy_scores], dim=0)
purity_matrix[pair_idx[:, 0],
pair_idx[:, 1]] = purity_pool
policy_matrix[pair_idx[:, 0],
pair_idx[:, 1]] = policy_pool
score_matrix = torch.zeros(
purity_matrix.shape).cuda()
score_matrix[pair_idx[:, 0],
pair_idx[:, 1]] = softmax(
purity_pool * policy_pool)
iteration_num += 1
#everytime select the pair with highest score
score_arr = score_matrix[pair_idx[:, 0], pair_idx[:,
1]]
highest_score, rank_idx = torch.topk(score_arr,
1,
largest=True,
sorted=False)
perm_idx = rank_idx
assert highest_score == score_matrix[pair_idx[rank_idx,
0],
pair_idx[rank_idx,
1]]
sub_part_idx = torch.index_select(pair_idx,
dim=0,
index=perm_idx[:bs])
purity_score = purity_matrix[sub_part_idx[:, 0],
sub_part_idx[:, 1]]
policy_score = policy_matrix[sub_part_idx[:, 0],
sub_part_idx[:, 1]]
#info
policy_list.append(policy_score.cpu().data.numpy()[0])
purity_list.append(purity_score.cpu().data.numpy()[0])
part_xyz1 = torch.index_select(cur_xyz_pool,
dim=0,
index=sub_part_idx[:,
0])
part_xyz2 = torch.index_select(cur_xyz_pool,
dim=0,
index=sub_part_idx[:,
1])
part_xyz = torch.cat([part_xyz1, part_xyz2], -1)
part_xyz -= torch.mean(part_xyz, -1).unsqueeze(-1)
part_xyz1 -= torch.mean(part_xyz1, -1).unsqueeze(-1)
part_xyz2 -= torch.mean(part_xyz2, -1).unsqueeze(-1)
part_xyz1 /= part_xyz1.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz2 /= part_xyz2.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_xyz.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_mask11 = torch.index_select(cur_mask_pool,
dim=0,
index=sub_part_idx[:,
0])
part_mask22 = torch.index_select(cur_mask_pool,
dim=0,
index=sub_part_idx[:,
1])
context_idx1 = torch.index_select(
inter_matrix_full, dim=0, index=sub_part_idx[:, 0])
context_idx2 = torch.index_select(
inter_matrix_full, dim=0, index=sub_part_idx[:, 1])
context_mask1 = (torch.matmul(
context_idx1.float(), cur_mask_pool) > 0).float()
context_mask2 = (torch.matmul(
context_idx2.float(), cur_mask_pool) > 0).float()
context_mask = ((context_mask1 + context_mask2) >
0).float()
context_xyz, xyz_mean = mask_to_xyz(pc,
context_mask,
sample_num=2048)
context_xyz = context_xyz - xyz_mean
context_xyz /= context_xyz.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
if cfg.DATASET.PartNetInsSeg.TEST.shape not in [
'Chair', 'Lamp', 'StorageFurniture'
]:
logits1 = model_merge(part_xyz1, 'backbone')
logits2 = model_merge(part_xyz2, 'backbone')
merge_logits = model_merge(
torch.cat([
part_xyz,
torch.cat([
logits1.unsqueeze(-1).expand(
-1, -1, part_xyz1.shape[-1]),
logits2.unsqueeze(-1).expand(
-1, -1, part_xyz2.shape[-1])
],
dim=-1)
],
dim=1), 'head')
else:
if (cur_xyz_pool.shape[0] >= 32):
logits1 = model_merge(part_xyz1, 'backbone')
logits2 = model_merge(part_xyz2, 'backbone')
merge_logits = model_merge(
torch.cat([
part_xyz,
torch.cat([
logits1.unsqueeze(-1).expand(
-1, -1, part_xyz1.shape[-1]),
logits2.unsqueeze(-1).expand(
-1, -1, part_xyz2.shape[-1])
],
dim=-1)
],
dim=1), 'head')
else:
logits1 = model_merge(part_xyz1, 'backbone')
logits2 = model_merge(part_xyz2, 'backbone')
context_logits = model_merge(
context_xyz, 'backbone2')
merge_logits = model_merge(
torch.cat([
part_xyz,
torch.cat([
logits1.unsqueeze(-1).expand(
-1, -1, part_xyz1.shape[-1]),
logits2.unsqueeze(-1).expand(
-1, -1, part_xyz2.shape[-1])
],
dim=-1),
torch.cat([
context_logits.unsqueeze(-1).
expand(-1, -1, part_xyz.shape[-1])
],
dim=-1)
],
dim=1), 'head2')
_, p = torch.max(merge_logits, 1)
if not remote_flag:
siamese_label = p * (
(purity_score > p_thresh).long())
else:
siamese_label = p
siamese_label = p * ((purity_score > p_thresh).long())
negative_num += torch.sum(siamese_label == 0)
positive_num += torch.sum(siamese_label == 1)
pred_label_list.append(
siamese_label.cpu().data.numpy())
#info
new_part_mask = 1 - (1 - part_mask11) * (1 -
part_mask22)
size_list.append(
torch.sum(new_part_mask).cpu().data.numpy())
size1 = torch.sum(part_mask11).cpu().data.numpy()
size2 = torch.sum(part_mask22).cpu().data.numpy()
relative_size_list.append(size1 / size2 +
size2 / size1)
#update info
merge_idx1 = torch.index_select(
sub_part_idx[:, 0],
dim=0,
index=siamese_label.nonzero().squeeze())
merge_idx2 = torch.index_select(
sub_part_idx[:, 1],
dim=0,
index=siamese_label.nonzero().squeeze())
merge_idx = torch.unique(
torch.cat([merge_idx1, merge_idx2], dim=0))
nonmerge_idx1 = torch.index_select(
sub_part_idx[:, 0],
dim=0,
index=(1 - siamese_label).nonzero().squeeze())
nonmerge_idx2 = torch.index_select(
sub_part_idx[:, 1],
dim=0,
index=(1 - siamese_label).nonzero().squeeze())
part_mask1 = torch.index_select(cur_mask_pool,
dim=0,
index=merge_idx1)
part_mask2 = torch.index_select(cur_mask_pool,
dim=0,
index=merge_idx2)
new_part_mask = 1 - (1 - part_mask1) * (1 - part_mask2)
equal_matrix = torch.matmul(
new_part_mask,
1 - new_part_mask.transpose(0, 1)) + torch.matmul(
1 - new_part_mask, new_part_mask.transpose(
0, 1))
equal_matrix[torch.eye(
equal_matrix.shape[0]).byte()] = 1
fid = (equal_matrix == 0).nonzero()
if fid.shape[0] > 0:
flag = torch.ones(merge_idx1.shape[0])
for k in range(flag.shape[0]):
if flag[k] != 0:
flag[fid[:, 1][fid[:, 0] == k]] = 0
new_part_mask = torch.index_select(
new_part_mask,
dim=0,
index=flag.nonzero().squeeze().cuda())
new_part_xyz, xyz_mean = mask_to_xyz(pc, new_part_mask)
#update purity and score, policy score matrix
if new_part_mask.shape[0] > 0:
overlap_idx = (
torch.matmul(cur_mask_pool,
new_part_mask.transpose(0, 1)) >
minimum_overlap_pc_num).nonzero().squeeze()
if overlap_idx.shape[0] > 0:
if len(overlap_idx.shape) == 1:
overlap_idx = overlap_idx.unsqueeze(0)
part_xyz1 = torch.index_select(
cur_xyz_pool,
dim=0,
index=overlap_idx[:, 0])
part_xyz2 = tile(new_part_xyz, 0,
overlap_idx.shape[0])
part_xyz = torch.cat([part_xyz1, part_xyz2],
-1)
part_xyz -= torch.mean(part_xyz,
-1).unsqueeze(-1)
part_norm = part_xyz.norm(dim=1).max(
dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_norm
overlap_purity_scores = model_merge(
part_xyz, 'purity').squeeze()
part_xyz11 = part_xyz1 - torch.mean(
part_xyz1, -1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(
part_xyz2, -1).unsqueeze(-1)
part_xyz11 /= part_norm
part_xyz22 /= part_norm
logits11 = model_merge(part_xyz11, 'policy')
logits22 = model_merge(part_xyz22, 'policy')
overlap_policy_scores = model_merge(
torch.cat([logits11, logits22], dim=-1),
'policy_head').squeeze()
tmp_purity_arr = torch.zeros(
[purity_matrix.shape[0]]).cuda()
tmp_policy_arr = torch.zeros(
[policy_matrix.shape[0]]).cuda()
tmp_purity_arr[
overlap_idx[:, 0]] = overlap_purity_scores
tmp_policy_arr[
overlap_idx[:, 0]] = overlap_policy_scores
purity_matrix = torch.cat([
purity_matrix,
tmp_purity_arr.unsqueeze(1)
],
dim=1)
policy_matrix = torch.cat([
policy_matrix,
tmp_policy_arr.unsqueeze(1)
],
dim=1)
purity_matrix = torch.cat([
purity_matrix,
torch.zeros(purity_matrix.shape[1]).cuda().
unsqueeze(0)
])
policy_matrix = torch.cat([
policy_matrix,
torch.zeros(policy_matrix.shape[1]).cuda().
unsqueeze(0)
])
else:
purity_matrix = torch.cat([
purity_matrix,
torch.zeros(purity_matrix.shape[0]).cuda().
unsqueeze(1)
],
dim=1)
policy_matrix = torch.cat([
policy_matrix,
torch.zeros(policy_matrix.shape[0]).cuda().
unsqueeze(1)
],
dim=1)
purity_matrix = torch.cat([
purity_matrix,
torch.zeros(purity_matrix.shape[1]).cuda().
unsqueeze(0)
])
policy_matrix = torch.cat([
policy_matrix,
torch.zeros(policy_matrix.shape[1]).cuda().
unsqueeze(0)
])
cur_mask_pool = torch.cat(
[cur_mask_pool, new_part_mask], dim=0)
subpart_mask_pool = torch.cat(
[subpart_mask_pool, new_part_mask], dim=0)
cur_xyz_pool = torch.cat([cur_xyz_pool, new_part_xyz],
dim=0)
subpart_pool = torch.cat([subpart_pool, new_part_xyz],
dim=0)
cur_pool_size = cur_mask_pool.shape[0]
new_mask = torch.ones([cur_pool_size])
new_mask[merge_idx] = 0
new_idx = new_mask.nonzero().squeeze().cuda()
cur_xyz_pool = torch.index_select(cur_xyz_pool,
dim=0,
index=new_idx)
cur_mask_pool = torch.index_select(cur_mask_pool,
dim=0,
index=new_idx)
inter_matrix = torch.matmul(
cur_mask_pool, cur_mask_pool.transpose(0, 1))
inter_matrix_full = inter_matrix.clone(
) > minimum_overlap_pc_num
if remote_flag:
inter_matrix = 20 * torch.ones([
cur_mask_pool.shape[0], cur_mask_pool.shape[0]
]).cuda()
#update zero_matrix
zero_matrix = torch.zeros(
[cur_pool_size, cur_pool_size])
zero_matrix[zero_pair[:, 0], zero_pair[:, 1]] = 1
zero_matrix[nonmerge_idx1, nonmerge_idx2] = 1
zero_matrix[nonmerge_idx2, nonmerge_idx1] = 1
zero_matrix = torch.index_select(zero_matrix,
dim=0,
index=new_idx.cpu())
zero_matrix = torch.index_select(zero_matrix,
dim=1,
index=new_idx.cpu())
zero_pair = zero_matrix.nonzero()
inter_matrix[zero_pair[:, 0], zero_pair[:, 1]] = 0
inter_matrix[torch.eye(
inter_matrix.shape[0]).byte()] = 0
pair_idx = (inter_matrix.triu() >
minimum_overlap_pc_num).nonzero()
purity_matrix = torch.index_select(purity_matrix,
dim=0,
index=new_idx)
purity_matrix = torch.index_select(purity_matrix,
dim=1,
index=new_idx)
policy_matrix = torch.index_select(policy_matrix,
dim=0,
index=new_idx)
policy_matrix = torch.index_select(policy_matrix,
dim=1,
index=new_idx)
score_matrix = torch.zeros(purity_matrix.shape).cuda()
score_idx = pair_idx
score_matrix[score_idx[:, 0],
score_idx[:, 1]] = softmax(
purity_matrix[score_idx[:, 0],
score_idx[:, 1]] *
policy_matrix[score_idx[:, 0],
score_idx[:, 1]])
final_pool_size = subpart_pool.shape[0]
meters.update(final_pool_size=final_pool_size,
negative_num=negative_num,
positive_num=positive_num)
meters.update(iteration_num=iteration_num)
meters.update(iteration_time=time.time() - iter_start_time)
t1 = torch.matmul(cur_mask_pool, 1 - cur_mask_pool.transpose(0, 1))
t1[torch.eye(t1.shape[0]).byte()] = 1
t1_id = (t1 == 0).nonzero()
final_idx = torch.ones(t1.shape[0])
final_idx[t1_id[:, 0]] = 0
cur_mask_pool = torch.index_select(
cur_mask_pool,
dim=0,
index=final_idx.nonzero().squeeze().cuda())
pred_ins_label = torch.zeros(num_points).cuda()
for k in range(cur_mask_pool.shape[0]):
pred_ins_label[cur_mask_pool[k].byte()] = k + 1
valid_idx = torch.sum(cur_mask_pool, 0) > 0
if torch.sum(1 - valid_idx) != 0:
valid_points = pc[:, valid_idx]
invalid_points = pc[:, 1 - valid_idx]
#perform knn to cover all points
knn_index, _ = _F.knn_distance(invalid_points.unsqueeze(0),
valid_points.unsqueeze(0), 5,
False)
invalid_pred, _ = pred_ins_label[valid_idx][
knn_index.squeeze()].mode()
pred_ins_label[1 - valid_idx] = invalid_pred
cur_mask_pool_new = torch.zeros([0, num_points]).cuda()
for k in range(cur_mask_pool.shape[0]):
if torch.sum(pred_ins_label == (k + 1)) != 0:
cur_mask_pool_new = torch.cat([
cur_mask_pool_new,
((pred_ins_label == (k + 1)).float()).unsqueeze(0)
],
dim=0)
out_mask[iteration, :cur_mask_pool_new.shape[0]] = copy.deepcopy(
cur_mask_pool_new.cpu().data.numpy().astype(np.bool))
out_valid[iteration, :cur_mask_pool_new.shape[0]] = np.sum(
cur_mask_pool_new.cpu().data.numpy()) > 10
test_time = time.time() - start_time
logger.info('Test {} test time: {:.2f}s'.format(meters.summary_str,
test_time))
for i in range(int(out_mask.shape[0] / 1024) + 1):
save_h5(os.path.join(output_dir_save, 'test-%02d.h5' % (i)),
out_mask[i * 1024:(i + 1) * 1024],
out_valid[i * 1024:(i + 1) * 1024],
out_conf[i * 1024:(i + 1) * 1024])
def train_one_epoch(model,
model_merge,
loss_fn,
metric,
dataloader,
cur_epoch,
optimizer,
optimizer_embed,
checkpointer_embed,
output_dir_merge,
max_grad_norm=0.0,
freezer=None,
log_period=-1):
global xyz_pool1
global xyz_pool2
global context_xyz_pool1
global context_xyz_pool2
global context_context_xyz_pool
global context_label_pool
global context_purity_pool
global label_pool
global purity_purity_pool
global purity_xyz_pool
global policy_purity_pool
global policy_reward_pool
global policy_xyz_pool1
global policy_xyz_pool2
global old_iteration
logger = logging.getLogger('shaper.train')
meters = MetricLogger(delimiter=' ')
metric.reset()
meters.bind(metric)
model.eval()
loss_fn.eval()
model_merge.eval()
softmax = nn.Softmax()
policy_total_bs = 8
rnum = 1 if policy_total_bs-cur_epoch < 1 else policy_total_bs-cur_epoch
end = time.time()
buffer_txt = os.path.join(output_dir_merge, 'last_buffer')
checkpoint_txt = os.path.join(output_dir_merge, 'last_checkpoint')
if os.path.exists(checkpoint_txt):
checkpoint_f = open(checkpoint_txt,'r')
cur_checkpoint = checkpoint_f.read()
checkpoint_f.close()
else:
cur_checkpoint = 'no_checkpoint'
checkpointer_embed.load(None, resume=True)
print('load checkpoint from %s'%cur_checkpoint)
model_merge.eval()
for iteration, data_batch in enumerate(dataloader):
print('epoch: %d, iteration: %d, size of binary: %d, size of context: %d'%(cur_epoch, iteration, len(xyz_pool1), len(context_xyz_pool1)))
sys.stdout.flush()
#add conditions
if os.path.exists(checkpoint_txt):
checkpoint_f = open(checkpoint_txt,'r')
new_checkpoint = checkpoint_f.read()
checkpoint_f.close()
if cur_checkpoint != new_checkpoint:
checkpointer_embed.load(None, resume=True)
cur_checkpoint = new_checkpoint
print('load checkpoint from %s'%cur_checkpoint)
model_merge.eval()
data_time = time.time() - end
data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()}
#predict box's coords
with torch.no_grad():
preds = model(data_batch)
loss_dict = loss_fn(preds, data_batch)
total_loss = sum(loss_dict.values())
meters.update(loss=total_loss, **loss_dict)
with torch.no_grad():
# TODO add loss_dict hack
metric.update_dict(preds, data_batch)
#extraction box features
batch_size, _, num_centroids, num_neighbours = data_batch['neighbour_xyz'].shape
num_points = data_batch['points'].shape[-1]
#batch_size, num_centroid, num_neighbor
_, p = torch.max(preds['ins_logit'], 1)
box_index_expand = torch.zeros((batch_size*num_centroids, num_points)).cuda()
box_index_expand = box_index_expand.scatter_(dim=1, index=data_batch['neighbour_index'].reshape([-1, num_neighbours]), src=p.reshape([-1, num_neighbours]).float())
centroid_label = data_batch['centroid_label'].reshape(-1)
#remove proposal < minimum_num
minimum_box_pc_num = 8
minimum_overlap_pc_num = 8 #1/32 * num_neighbour
gtmin_mask = (torch.sum(box_index_expand, dim=-1) > minimum_box_pc_num)
#remove proposal whose purity score < 0.8
box_label_expand = torch.zeros((batch_size*num_centroids, 200)).cuda()
box_idx_expand = tile(data_batch['ins_id'],0,num_centroids).cuda()
box_label_expand = box_label_expand.scatter_add_(dim=1, index=box_idx_expand, src=box_index_expand).float()
maximum_label_num, maximum_label = torch.max(box_label_expand, 1)
centroid_label = maximum_label
total_num = torch.sum(box_label_expand, 1)
box_purity = maximum_label_num / (total_num+1e-6)
box_purity_mask = box_purity > 0.8
box_purity_valid_mask = 1 - (box_purity < 0.8)*(box_purity > 0.6)
box_purity_valid_mask *= gtmin_mask
box_purity_valid_mask_l2 = gtmin_mask.long()#*data_batch['centroid_valid_mask'].reshape(-1).long()
meters.update(purity_ratio = torch.sum(box_purity_mask).float()/box_purity_mask.shape[0], purity_valid_ratio=torch.sum(box_purity_valid_mask).float()/box_purity_mask.shape[0])
meters.update(purity_pos_num = torch.sum(box_purity_mask), purity_neg_num = torch.sum(1-box_purity_mask), purity_neg_valid_num=torch.sum(box_purity<0.6))
centroid_valid_mask = data_batch['centroid_valid_mask'].reshape(-1).long()
meters.update(centroid_valid_purity_ratio = torch.sum(torch.index_select(box_purity_mask, dim=0, index=centroid_valid_mask.nonzero().squeeze())).float()/torch.sum(centroid_valid_mask),centroid_nonvalid_purity_ratio = torch.sum(torch.index_select(box_purity_mask, dim=0, index=(1-centroid_valid_mask).nonzero().squeeze())).float()/torch.sum(1-centroid_valid_mask))
purity_pred = torch.zeros([0]).type(torch.FloatTensor).cuda()
#update pool by valid_mask
valid_mask = gtmin_mask.long() * box_purity_mask.long() * (centroid_label!=0).long()
centroid_label = torch.index_select(centroid_label, dim=0, index=valid_mask.nonzero().squeeze())
box_num = torch.sum(valid_mask.reshape(batch_size, num_centroids),1)
cumsum_box_num = torch.cumsum(box_num, dim=0)
cumsum_box_num = torch.cat([torch.from_numpy(np.array(0)).cuda().unsqueeze(0),cumsum_box_num],dim=0)
#initialization
pc_all = data_batch['points']
centroid_label_all = centroid_label.clone()
sub_xyz_pool1 = torch.zeros([0,3,1024]).float().cuda()
sub_xyz_pool2 = torch.zeros([0,3,1024]).float().cuda()
sub_context_xyz_pool1 = torch.zeros([0,3,1024]).float().cuda()
sub_context_xyz_pool2 = torch.zeros([0,3,1024]).float().cuda()
sub_context_context_xyz_pool = torch.zeros([0,3,2048]).float().cuda()
sub_context_label_pool = torch.zeros([0]).float().cuda()
sub_context_purity_pool = torch.zeros([0]).float().cuda()
sub_label_pool = torch.zeros([0]).float().cuda()
sub_purity_pool = torch.zeros([0]).float().cuda()
sub_purity_xyz_pool = torch.zeros([0,3,1024]).float().cuda()
sub_policy_purity_pool = torch.zeros([0,policy_update_bs]).float().cuda()
sub_policy_reward_pool = torch.zeros([0,policy_update_bs]).float().cuda()
sub_policy_xyz_pool1 = torch.zeros([0,policy_update_bs,3,1024]).float().cuda()
sub_policy_xyz_pool2 = torch.zeros([0,policy_update_bs,3,1024]).float().cuda()
for i in range(pc_all.shape[0]):
bs = policy_total_bs
BS = policy_update_bs
pc = pc_all[i].clone()
cur_mask_pool = box_index_expand[cumsum_box_num[i]:cumsum_box_num[i+1]].clone()
centroid_label = centroid_label_all[cumsum_box_num[i]:cumsum_box_num[i+1]].clone()
cover_ratio = torch.unique(cur_mask_pool.nonzero()[:,1]).shape[0]/num_points
cur_xyz_pool, xyz_mean = mask_to_xyz(pc, cur_mask_pool)
init_pool_size = cur_xyz_pool.shape[0]
meters.update(cover_ratio=cover_ratio, init_pool_size=init_pool_size)
negative_num = 0
positive_num = 0
#intial adjacent matrix
inter_matrix = torch.matmul(cur_mask_pool, cur_mask_pool.transpose(0, 1))
inter_matrix_full = inter_matrix.clone()>minimum_overlap_pc_num
inter_matrix[torch.eye(inter_matrix.shape[0]).byte()] = 0
pair_idx = (inter_matrix.triu()>minimum_overlap_pc_num).nonzero()
zero_pair = torch.ones([0,2]).long()
small_flag = False
model_merge.eval()
with torch.no_grad():
while pair_idx.shape[0] > 0:
#when there are too few pairs, we calculate the policy score matrix on all pairs
if pair_idx.shape[0] <= BS and small_flag == False:
small_flag = True
purity_matrix = torch.zeros(inter_matrix.shape).cuda()
policy_matrix = torch.zeros(inter_matrix.shape).cuda()
bsp = 64
idx = torch.arange(pair_idx.shape[0]).cuda()
purity_pool = torch.zeros([0]).float().cuda()
policy_pool = torch.zeros([0]).float().cuda()
for k in range(int(np.ceil(idx.shape[0]/bsp))):
sub_part_idx = torch.index_select(pair_idx, dim=0, index=idx[k*bsp:(k+1)*bsp])
part_xyz1 = torch.index_select(cur_xyz_pool, dim=0, index=sub_part_idx[:,0])
part_xyz2 = torch.index_select(cur_xyz_pool, dim=0, index=sub_part_idx[:,1])
part_xyz = torch.cat([part_xyz1,part_xyz2],-1)
part_xyz -= torch.mean(part_xyz,-1).unsqueeze(-1)
part_norm = part_xyz.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_norm
logits_purity = model_merge(part_xyz, 'purity').squeeze()
if len(logits_purity.shape) == 0:
logits_purity = logits_purity.unsqueeze(0)
purity_pool = torch.cat([purity_pool, logits_purity], dim=0)
part_xyz11 = part_xyz1 - torch.mean(part_xyz1,-1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(part_xyz2,-1).unsqueeze(-1)
part_xyz11 /= part_norm
part_xyz22 /= part_norm
logits11 = model_merge(part_xyz11, 'policy')
logits22 = model_merge(part_xyz22, 'policy')
policy_scores = model_merge(torch.cat([logits11, logits22],dim=-1), 'policy_head').squeeze()
if len(policy_scores.shape) == 0:
policy_scores = policy_scores.unsqueeze(0)
policy_pool = torch.cat([policy_pool, policy_scores], dim=0)
purity_matrix[pair_idx[:,0],pair_idx[:,1]] = purity_pool
policy_matrix[pair_idx[:,0],pair_idx[:,1]] = policy_pool
score_matrix = torch.zeros(purity_matrix.shape).cuda()
score_matrix[pair_idx[:,0],pair_idx[:,1]] = softmax(purity_pool*policy_pool)
#if there are many pairs, we randomly sample a small batch of pairs and then compute the policy score matrix thereon to select pairs into the next stage
#else, we select a pair with highest policy score
if pair_idx.shape[0] > BS and small_flag != True:
perm_idx = torch.randperm(pair_idx.shape[0]).cuda()
perm_idx_rnd = perm_idx[:bs]
sub_part_idx = torch.index_select(pair_idx, dim=0, index=perm_idx[:int(BS)])
part_xyz1 = torch.index_select(cur_xyz_pool, dim=0, index=sub_part_idx[:,0])
part_xyz2 = torch.index_select(cur_xyz_pool, dim=0, index=sub_part_idx[:,1])
part_xyz = torch.cat([part_xyz1,part_xyz2],-1)
part_xyz -= torch.mean(part_xyz,-1).unsqueeze(-1)
part_norm = part_xyz.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_norm
logits_purity = model_merge(part_xyz, 'purity').squeeze()
sub_policy_purity_pool = torch.cat([sub_policy_purity_pool, logits_purity.detach().unsqueeze(0).clone()],dim=0)
part_xyz11 = part_xyz1 - torch.mean(part_xyz1,-1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(part_xyz2,-1).unsqueeze(-1)
part_xyz11 /= part_norm
part_xyz22 /= part_norm
logits11 = model_merge(part_xyz11, 'policy')
logits22 = model_merge(part_xyz22, 'policy')
policy_scores = model_merge(torch.cat([logits11, logits22],dim=-1), 'policy_head').squeeze()
sub_policy_xyz_pool1 = torch.cat([sub_policy_xyz_pool1, part_xyz11.unsqueeze(0).clone()], dim=0)
sub_policy_xyz_pool2 = torch.cat([sub_policy_xyz_pool2, part_xyz22.unsqueeze(0).clone()], dim=0)
if sub_policy_xyz_pool1.shape[0] > 64:
policy_xyz_pool1 = torch.cat([policy_xyz_pool1, sub_policy_xyz_pool1.cpu().clone()], dim=0)
policy_xyz_pool2 = torch.cat([policy_xyz_pool2, sub_policy_xyz_pool2.cpu().clone()], dim=0)
sub_policy_xyz_pool1 = torch.zeros([0,policy_update_bs,3,1024]).float().cuda()
sub_policy_xyz_pool2 = torch.zeros([0,policy_update_bs,3,1024]).float().cuda()
score = softmax(logits_purity*policy_scores)
part_label1 = torch.index_select(centroid_label, dim=0, index=sub_part_idx[:,0])
part_label2 = torch.index_select(centroid_label, dim=0, index=sub_part_idx[:,1])
siamese_label_gt = (part_label1 == part_label2)*(1 - (part_label1 == -1))*(1 - (part_label2 == -1))*(logits_purity>0.8)
sub_policy_reward_pool = torch.cat([sub_policy_reward_pool, siamese_label_gt.unsqueeze(0).float().clone()], dim=0)
loss_policy = -torch.sum(score*(siamese_label_gt.float()))
meters.update(loss_policy =loss_policy)
#we also introduce certain random samples to encourage exploration
_, rank_idx = torch.topk(score,bs,largest=True,sorted=False)
perm_idx = perm_idx[rank_idx]
perm_idx = torch.cat([perm_idx[:policy_total_bs-rnum], perm_idx_rnd[:rnum]], dim=0)
if cur_epoch == 1 and iteration < 128:
perm_idx = torch.randperm(pair_idx.shape[0]).cuda()
perm_idx = perm_idx[:policy_total_bs]
else:
score = score_matrix[pair_idx[:,0],pair_idx[:,1]]
_, rank_idx = torch.topk(score,1,largest=True,sorted=False)
perm_idx = rank_idx
if len(perm_idx.shape) == 0:
perm_idx = perm_idx.unsqueeze(0)
if cur_epoch == 1 and iteration < 128:
perm_idx = torch.randperm(pair_idx.shape[0]).cuda()
perm_idx = perm_idx[:1]
#send the selected pairs into verification network
sub_part_idx = torch.index_select(pair_idx, dim=0, index=perm_idx[:bs])
part_xyz1 = torch.index_select(cur_xyz_pool, dim=0, index=sub_part_idx[:,0])
part_xyz2 = torch.index_select(cur_xyz_pool, dim=0, index=sub_part_idx[:,1])
part_mask11 = torch.index_select(cur_mask_pool, dim=0, index=sub_part_idx[:,0])
part_mask22 = torch.index_select(cur_mask_pool, dim=0, index=sub_part_idx[:,1])
part_label1 = torch.index_select(centroid_label, dim=0, index=sub_part_idx[:,0])
part_label2 = torch.index_select(centroid_label, dim=0, index=sub_part_idx[:,1])
new_part_mask = 1-(1-part_mask11)*(1-part_mask22)
box_label_expand = torch.zeros((new_part_mask.shape[0], 200)).cuda()
box_idx_expand = tile(data_batch['ins_id'][i].unsqueeze(0),0,new_part_mask.shape[0]).cuda()
box_label_expand = box_label_expand.scatter_add_(dim=1, index=box_idx_expand, src=new_part_mask).float()
maximum_label_num, maximum_label = torch.max(box_label_expand, 1)
total_num = torch.sum(box_label_expand, 1)
box_purity = maximum_label_num / (total_num+1e-6)
sub_purity_pool = torch.cat([sub_purity_pool, box_purity.clone()], dim=0)
purity_xyz, xyz_mean = mask_to_xyz(pc, new_part_mask)
purity_xyz -= xyz_mean
purity_xyz /=(purity_xyz+1e-6).norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
sub_purity_xyz_pool = torch.cat([sub_purity_xyz_pool, purity_xyz.clone()],dim=0)
siamese_label_gt = (part_label1 == part_label2)*(1 - (part_label1 == -1))*(1 - (part_label2 == -1))*(box_purity > 0.8)
negative_num += torch.sum(siamese_label_gt == 0)
positive_num += torch.sum(siamese_label_gt == 1)
#save data
sub_xyz_pool1 = torch.cat([sub_xyz_pool1, part_xyz1.clone()], dim=0)
sub_xyz_pool2 = torch.cat([sub_xyz_pool2, part_xyz2.clone()], dim=0)
sub_label_pool = torch.cat([sub_label_pool, siamese_label_gt.clone().float()], dim=0)
#renorm
part_xyz = torch.cat([part_xyz1,part_xyz2],-1)
part_xyz -= torch.mean(part_xyz,-1).unsqueeze(-1)
part_xyz11 = part_xyz1 - torch.mean(part_xyz1,-1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(part_xyz2,-1).unsqueeze(-1)
part_xyz11 /=part_xyz11.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz22 /=part_xyz22.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /=part_xyz.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
#save data
if cur_xyz_pool.shape[0] <= 32:
context_idx1 = torch.index_select(inter_matrix_full,dim=0,index=sub_part_idx[:,0])
context_idx2 = torch.index_select(inter_matrix_full,dim=0,index=sub_part_idx[:,1])
context_mask1 = (torch.matmul(context_idx1.float(), cur_mask_pool)>0).float()
context_mask2 = (torch.matmul(context_idx2.float(), cur_mask_pool)>0).float()
context_mask = ((context_mask1+context_mask2)>0).float()
context_xyz, xyz_mean = mask_to_xyz(pc, context_mask, sample_num=2048)
context_xyz = context_xyz - xyz_mean
context_xyz /= context_xyz.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
sub_context_context_xyz_pool = torch.cat([sub_context_context_xyz_pool, context_xyz.clone()], dim=0)
sub_context_xyz_pool1 = torch.cat([sub_context_xyz_pool1, part_xyz1.clone()], dim=0)
sub_context_xyz_pool2 = torch.cat([sub_context_xyz_pool2, part_xyz2.clone()], dim=0)
sub_context_label_pool = torch.cat([sub_context_label_pool, siamese_label_gt.clone().float()], dim=0)
sub_context_purity_pool = torch.cat([sub_context_purity_pool, box_purity.clone()], dim=0)
#at the very beginning, we group pairs according to ground-truth
if (cur_epoch == 1 and iteration < 128) or (cur_checkpoint == 'no_checkpoint'):
siamese_label = (part_label1 == part_label2)
#if we have many sub-parts in the pool, we use the binary branch to predict
elif cur_xyz_pool.shape[0] > 32:
logits1 = model_merge(part_xyz11,'backbone')
logits2 = model_merge(part_xyz22,'backbone')
merge_logits = model_merge(torch.cat([part_xyz, torch.cat([logits1.unsqueeze(-1).expand(-1,-1,part_xyz1.shape[-1]), logits2.unsqueeze(-1).expand(-1,-1,part_xyz2.shape[-1])], dim=-1)], dim=1), 'head')
_, p = torch.max(merge_logits, 1)
siamese_label = p
#if there are too few sub-parts in the pool, we use the context branch to predict
else:
logits1 = model_merge(part_xyz11,'backbone')
logits2 = model_merge(part_xyz22,'backbone')
context_logits = model_merge(context_xyz,'backbone2')
merge_logits = model_merge(torch.cat([part_xyz, torch.cat([logits1.unsqueeze(-1).expand(-1,-1,part_xyz1.shape[-1]), logits2.unsqueeze(-1).expand(-1,-1,part_xyz2.shape[-1])], dim=-1), torch.cat([context_logits.unsqueeze(-1).expand(-1,-1,part_xyz.shape[-1])], dim=-1)], dim=1), 'head2')
_, p = torch.max(merge_logits, 1)
siamese_label = p
#group sub-parts according to the prediction
merge_idx1 = torch.index_select(sub_part_idx[:,0], dim=0, index=siamese_label.nonzero().squeeze())
merge_idx2 = torch.index_select(sub_part_idx[:,1], dim=0, index=siamese_label.nonzero().squeeze())
merge_idx = torch.unique(torch.cat([merge_idx1, merge_idx2], dim=0))
nonmerge_idx1 = torch.index_select(sub_part_idx[:,0], dim=0, index=(1-siamese_label).nonzero().squeeze())
nonmerge_idx2 = torch.index_select(sub_part_idx[:,1], dim=0, index=(1-siamese_label).nonzero().squeeze())
part_mask1 = torch.index_select(cur_mask_pool, dim=0, index=merge_idx1)
part_mask2 = torch.index_select(cur_mask_pool, dim=0, index=merge_idx2)
new_part_mask = 1-(1-part_mask1)*(1-part_mask2)
new_part_label = torch.index_select(part_label1, dim=0, index=siamese_label.nonzero().squeeze()).long()
new_part_label_invalid = torch.index_select(siamese_label_gt, dim=0, index=siamese_label.nonzero().squeeze()).long()
new_part_label = new_part_label*new_part_label_invalid + -1*(1-new_part_label_invalid)
#sometimes, we may obtain several identical sub-parts
#for those, we only keep one
equal_matrix = torch.matmul(new_part_mask,1-new_part_mask.transpose(0,1))+torch.matmul(1-new_part_mask,new_part_mask.transpose(0,1))
equal_matrix[torch.eye(equal_matrix.shape[0]).byte()]=1
fid = (equal_matrix==0).nonzero()
if fid.shape[0] > 0:
flag = torch.ones(equal_matrix.shape[0])
for k in range(flag.shape[0]):
if flag[k] != 0:
flag[fid[:,1][fid[:,0]==k]] = 0
new_part_mask = torch.index_select(new_part_mask, dim=0, index=flag.nonzero().squeeze().cuda())
new_part_label = torch.index_select(new_part_label, dim=0, index=flag.nonzero().squeeze().cuda())
new_part_xyz, xyz_mean = mask_to_xyz(pc, new_part_mask)
#when there are too few pairs, update the policy score matrix so that we do not need to calculate the whole matrix everytime
if small_flag and (new_part_mask.shape[0] > 0):
overlap_idx = (torch.matmul(cur_mask_pool, new_part_mask.transpose(0,1))>minimum_overlap_pc_num).nonzero().squeeze()
if overlap_idx.shape[0] > 0:
if len(overlap_idx.shape) == 1:
overlap_idx = overlap_idx.unsqueeze(0)
part_xyz1 = torch.index_select(cur_xyz_pool, dim=0, index=overlap_idx[:,0])
part_xyz2 = tile(new_part_xyz, 0, overlap_idx.shape[0])
part_xyz = torch.cat([part_xyz1,part_xyz2],-1)
part_xyz -= torch.mean(part_xyz,-1).unsqueeze(-1)
part_norm = part_xyz.norm(dim=1).max(dim=-1)[0].unsqueeze(-1).unsqueeze(-1)
part_xyz /= part_norm
overlap_purity_scores = model_merge(part_xyz, 'purity').squeeze()
part_xyz11 = part_xyz1 - torch.mean(part_xyz1,-1).unsqueeze(-1)
part_xyz22 = part_xyz2 - torch.mean(part_xyz2,-1).unsqueeze(-1)
part_xyz11 /= part_norm
part_xyz22 /= part_norm
logits11 = model_merge(part_xyz11, 'policy')
logits22 = model_merge(part_xyz22, 'policy')
overlap_policy_scores = model_merge(torch.cat([logits11, logits22],dim=-1), 'policy_head').squeeze()
tmp_purity_arr = torch.zeros([purity_matrix.shape[0]]).cuda()
tmp_policy_arr = torch.zeros([policy_matrix.shape[0]]).cuda()
tmp_purity_arr[overlap_idx[:,0]] = overlap_purity_scores
tmp_policy_arr[overlap_idx[:,0]] = overlap_policy_scores
purity_matrix = torch.cat([purity_matrix,tmp_purity_arr.unsqueeze(1)],dim=1)
policy_matrix = torch.cat([policy_matrix,tmp_policy_arr.unsqueeze(1)],dim=1)
purity_matrix = torch.cat([purity_matrix,torch.zeros(purity_matrix.shape[1]).cuda().unsqueeze(0)])
policy_matrix = torch.cat([policy_matrix,torch.zeros(policy_matrix.shape[1]).cuda().unsqueeze(0)])
else:
purity_matrix = torch.cat([purity_matrix,torch.zeros(purity_matrix.shape[0]).cuda().unsqueeze(1)],dim=1)
policy_matrix = torch.cat([policy_matrix,torch.zeros(policy_matrix.shape[0]).cuda().unsqueeze(1)],dim=1)
purity_matrix = torch.cat([purity_matrix,torch.zeros(purity_matrix.shape[1]).cuda().unsqueeze(0)])
policy_matrix = torch.cat([policy_matrix,torch.zeros(policy_matrix.shape[1]).cuda().unsqueeze(0)])
#update cur_pool, add new parts, pick out merged input pairs
cur_mask_pool = torch.cat([cur_mask_pool, new_part_mask], dim=0)
cur_xyz_pool = torch.cat([cur_xyz_pool, new_part_xyz], dim=0)
centroid_label = torch.cat([centroid_label, new_part_label], dim=0)
cur_pool_size = cur_mask_pool.shape[0]
new_mask = torch.ones([cur_pool_size])
new_mask[merge_idx] = 0
new_idx = new_mask.nonzero().squeeze().cuda()
cur_xyz_pool = torch.index_select(cur_xyz_pool, dim=0, index=new_idx)
cur_mask_pool = torch.index_select(cur_mask_pool, dim=0, index=new_idx)
centroid_label = torch.index_select(centroid_label, dim=0, index=new_idx)
inter_matrix = torch.matmul(cur_mask_pool, cur_mask_pool.transpose(0, 1))
inter_matrix_full = inter_matrix.clone()>minimum_overlap_pc_num
#update zero_matrix
zero_matrix = torch.zeros([cur_pool_size, cur_pool_size])
zero_matrix[zero_pair[:,0], zero_pair[:,1]] = 1
zero_matrix[nonmerge_idx1, nonmerge_idx2] = 1
zero_matrix[nonmerge_idx2, nonmerge_idx1] = 1
zero_matrix = torch.index_select(zero_matrix, dim=0, index=new_idx.cpu())
zero_matrix = torch.index_select(zero_matrix, dim=1, index=new_idx.cpu())
zero_pair = zero_matrix.nonzero()
inter_matrix[zero_pair[:,0], zero_pair[:,1]] = 0
inter_matrix[torch.eye(inter_matrix.shape[0]).byte()] = 0
pair_idx = (inter_matrix.triu()>minimum_overlap_pc_num).nonzero()
if small_flag == True:
purity_matrix = torch.index_select(purity_matrix, dim=0, index=new_idx)
purity_matrix = torch.index_select(purity_matrix, dim=1, index=new_idx)
policy_matrix = torch.index_select(policy_matrix, dim=0, index=new_idx)
policy_matrix = torch.index_select(policy_matrix, dim=1, index=new_idx)
score_matrix = torch.zeros(purity_matrix.shape).cuda()
score_idx = pair_idx
score_matrix[score_idx[:,0], score_idx[:,1]] = softmax(purity_matrix[score_idx[:,0], score_idx[:,1]] * policy_matrix[score_idx[:,0], score_idx[:,1]])
final_pool_size = negative_num + positive_num
meters.update(final_pool_size=final_pool_size,negative_num=negative_num, positive_num=positive_num)
xyz_pool1 = torch.cat([xyz_pool1, sub_xyz_pool1.cpu().clone()],dim=0)
xyz_pool2 = torch.cat([xyz_pool2, sub_xyz_pool2.cpu().clone()],dim=0)
label_pool = torch.cat([label_pool, sub_label_pool.cpu().clone()], dim=0)
context_context_xyz_pool = torch.cat([context_context_xyz_pool, sub_context_context_xyz_pool.cpu().clone()],dim=0)
context_xyz_pool1 = torch.cat([context_xyz_pool1, sub_context_xyz_pool1.cpu().clone()],dim=0)
context_xyz_pool2 = torch.cat([context_xyz_pool2, sub_context_xyz_pool2.cpu().clone()],dim=0)
context_label_pool = torch.cat([context_label_pool, sub_context_label_pool.cpu().clone()], dim=0)
context_purity_pool = torch.cat([context_purity_pool, sub_context_purity_pool.cpu().clone()], dim=0)
purity_purity_pool = torch.cat([purity_purity_pool, sub_purity_pool.cpu().clone()], dim=0)
purity_xyz_pool = torch.cat([purity_xyz_pool, sub_purity_xyz_pool.cpu().clone()], dim=0)
policy_purity_pool = torch.cat([policy_purity_pool, sub_policy_purity_pool.cpu().clone()], dim=0)
policy_reward_pool = torch.cat([policy_reward_pool, sub_policy_reward_pool.cpu().clone()], dim=0)
policy_xyz_pool1 = torch.cat([policy_xyz_pool1, sub_policy_xyz_pool1.cpu().clone()], dim=0)
policy_xyz_pool2 = torch.cat([policy_xyz_pool2, sub_policy_xyz_pool2.cpu().clone()], dim=0)
produce_time = time.time() - end
#condition
if context_xyz_pool1.shape[0] > 10000:
rbuffer = dict()
rbuffer['xyz_pool1'] = xyz_pool1
rbuffer['xyz_pool2'] = xyz_pool2
rbuffer['context_xyz_pool1'] = context_xyz_pool1
rbuffer['context_xyz_pool2'] = context_xyz_pool2
rbuffer['context_context_xyz_pool'] = context_context_xyz_pool
rbuffer['context_label_pool'] = context_label_pool
rbuffer['context_purity_pool'] = context_purity_pool
rbuffer['label_pool'] = label_pool
rbuffer['purity_purity_pool'] = purity_purity_pool
rbuffer['purity_xyz_pool'] = purity_xyz_pool
rbuffer['policy_purity_pool'] = policy_purity_pool
rbuffer['policy_reward_pool'] = policy_reward_pool
rbuffer['policy_xyz_pool1'] = policy_xyz_pool1
rbuffer['policy_xyz_pool2'] = policy_xyz_pool2
torch.save(rbuffer, os.path.join(output_dir_merge, 'buffer', '%d_%d.pt'%(cur_epoch, iteration)))
buffer_f = open(buffer_txt, 'w')
buffer_f.write('%d_%d'%(cur_epoch, iteration))
buffer_f.close()
p = Popen('rm -rf %s'%(os.path.join(output_dir_merge, 'buffer', '%d_%d.pt'%(cur_epoch, old_iteration))), shell=True)
old_iteration = iteration
p = Popen('rm -rf %s_*'%(os.path.join(output_dir_merge, 'buffer', '%d'%(cur_epoch-1))), shell=True)
xyz_pool1 = torch.zeros([0,3,1024]).float()
xyz_pool2 = torch.zeros([0,3,1024]).float()
context_xyz_pool1 = torch.zeros([0,3,1024]).float()
context_xyz_pool2 = torch.zeros([0,3,1024]).float()
context_context_xyz_pool = torch.zeros([0,3,2048]).float()
context_label_pool = torch.zeros([0]).float()
context_purity_pool = torch.zeros([0]).float()
label_pool = torch.zeros([0]).float()
purity_purity_pool = torch.zeros([0]).float()
purity_xyz_pool = torch.zeros([0,3,1024]).float()
policy_purity_pool = torch.zeros([0,policy_update_bs]).float()
policy_reward_pool = torch.zeros([0,policy_update_bs]).float()
policy_xyz_pool1 = torch.zeros([0,policy_update_bs,3,1024]).float()
policy_xyz_pool2 = torch.zeros([0,policy_update_bs,3,1024]).float()
batch_time = time.time() - end
end = time.time()
meters.update(data_time=data_time, produce_time=produce_time)
if log_period > 0 and iteration % log_period == 0:
logger.info(
meters.delimiter.join(
[
'iter: {iter:4d}',
'{meters}',
'max mem: {memory:.0f}',
]
).format(
iter=iteration,
meters=str(meters),
memory=torch.cuda.max_memory_allocated() / (1024.0 ** 2),
)
)
return meters
def train_one_epoch(model,
loss_fn,
metric,
dataloader,
optimizer,
max_grad_norm=0.0,
freezer=None,
log_period=-1):
logger = logging.getLogger('shaper.train')
meters = MetricLogger(delimiter=' ')
# reset metrics
metric.reset()
meters.bind(metric)
# set training mode
model.train()
if freezer is not None:
freezer.freeze()
loss_fn.train()
metric.train()
end = time.time()
for iteration, data_batch in enumerate(dataloader):
data_time = time.time() - end
data_batch = {
k: v.cuda(non_blocking=True)
for k, v in data_batch.items()
}
preds = model(data_batch)
# backward
optimizer.zero_grad()
loss_dict = loss_fn(preds, data_batch)
total_loss = sum(loss_dict.values())
# It is slightly faster to update metrics and meters before backward
meters.update(loss=total_loss, **loss_dict)
with torch.no_grad():
metric.update_dict(preds, data_batch)
total_loss.backward()
if max_grad_norm > 0:
# CAUTION: built-in clip_grad_norm_ clips the total norm.
nn.utils.clip_grad_norm_(model.parameters(),
max_norm=max_grad_norm)
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
if log_period > 0 and iteration % log_period == 0:
logger.info(
meters.delimiter.join([
'iter: {iter:4d}',
'{meters}',
'lr: {lr:.2e}',
'max mem: {memory:.0f}',
]).format(
iter=iteration,
meters=str(meters),
lr=optimizer.param_groups[0]['lr'],
memory=torch.cuda.max_memory_allocated() / (1024.0**2),
))
return meters
def train_one_epoch(model,
loss_fn,
metric,
dataloader,
optimizer,
max_grad_norm=0.0,
freezer=None,
log_period=-1):
logger = logging.getLogger('shaper.train')
meters = MetricLogger(delimiter=' ')
metric.reset()
meters.bind(metric)
model.train()
if freezer is not None:
freezer.freeze()
loss_fn.train()
end = time.time()
for iteration, data_batch in enumerate(dataloader):
data_time = time.time() - end
data_batch = {
k: v.cuda(non_blocking=True)
for k, v in data_batch.items()
}
preds = model(data_batch)
optimizer.zero_grad()
loss_dict = loss_fn(preds, data_batch)
total_loss = sum(loss_dict.values())
meters.update(loss=total_loss, **loss_dict)
meters.update(node_acc=preds['node_acc'],
node_pos_acc=preds['node_pos_acc'],
node_neg_acc=preds['node_neg_acc'],
center_valid_ratio=preds['center_valid_ratio'])
with torch.no_grad():
# TODO add loss_dict hack
metric.update_dict(preds, data_batch)
total_loss.backward()
if max_grad_norm > 0:
nn.utils.clip_grad_norm_(model.parameters(),
max_norm=max_grad_norm)
optimizer.step()
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
if log_period > 0 and iteration % log_period == 0:
logger.info(
meters.delimiter.join([
'iter: {iter:4d}',
'{meters}',
'lr: {lr:.2e}',
'max mem: {memory:.0f}',
]).format(
iter=iteration,
meters=str(meters),
lr=optimizer.param_groups[0]['lr'],
memory=torch.cuda.max_memory_allocated() / (1024.0**2),
))
return meters
def test(cfg, output_dir=''):
logger = logging.getLogger('shaper.test')
# build model
model, loss_fn, metric = build_model(cfg)
model = nn.DataParallel(model).cuda()
# model = model.cuda()
# build checkpointer
checkpointer = Checkpointer(model, save_dir=output_dir, logger=logger)
if cfg.TEST.WEIGHT:
# load weight if specified
weight_path = cfg.TEST.WEIGHT.replace('@', output_dir)
checkpointer.load(weight_path, resume=False)
else:
# load last checkpoint
checkpointer.load(None, resume=True)
# build data loader
test_dataloader = build_dataloader(cfg, mode='test')
test_dataset = test_dataloader.dataset
# ---------------------------------------------------------------------------- #
# Test
# ---------------------------------------------------------------------------- #
model.eval()
loss_fn.eval()
metric.eval()
set_random_seed(cfg.RNG_SEED)
evaluator = Evaluator(test_dataset.class_names)
if cfg.TEST.VOTE.NUM_VOTE > 1:
# remove old transform
test_dataset.transform = None
if cfg.TEST.VOTE.TYPE == 'AUGMENTATION':
tmp_cfg = cfg.clone()
tmp_cfg.defrost()
tmp_cfg.TEST.AUGMENTATION = tmp_cfg.TEST.VOTE.AUGMENTATION
transform = T.Compose([T.ToTensor()] +
parse_augmentations(tmp_cfg, False) +
[T.Transpose()])
transform_list = [transform] * cfg.TEST.VOTE.NUM_VOTE
elif cfg.TEST.VOTE.TYPE == 'MULTI_VIEW':
# build new transform
transform_list = []
for view_ind in range(cfg.TEST.VOTE.NUM_VOTE):
aug_type = T.RotateByAngleWithNormal if cfg.INPUT.USE_NORMAL else T.RotateByAngle
rotate_by_angle = aug_type(
cfg.TEST.VOTE.MULTI_VIEW.AXIS,
2 * np.pi * view_ind / cfg.TEST.VOTE.NUM_VOTE)
t = [T.ToTensor(), rotate_by_angle, T.Transpose()]
if cfg.TEST.VOTE.MULTI_VIEW.SHUFFLE:
# Some non-deterministic algorithms, like PointNet++, benefit from shuffle.
t.insert(-1, T.Shuffle())
transform_list.append(T.Compose(t))
else:
raise NotImplementedError('Unsupported voting method.')
with torch.no_grad():
tmp_dataloader = DataLoader(test_dataset,
num_workers=1,
collate_fn=lambda x: x[0])
start_time = time.time()
end = start_time
for ind, data in enumerate(tmp_dataloader):
data_time = time.time() - end
points = data['points']
# convert points into tensor
points_batch = [t(points.copy()) for t in transform_list]
points_batch = torch.stack(points_batch, dim=0)
points_batch = points_batch.cuda(non_blocking=True)
preds = model({'points': points_batch})
cls_logit_batch = preds['cls_logit'].cpu().numpy(
) # (batch_size, num_classes)
cls_logit_ensemble = np.mean(cls_logit_batch, axis=0)
pred_label = np.argmax(cls_logit_ensemble)
evaluator.update(pred_label, data['cls_label'])
batch_time = time.time() - end
end = time.time()
if cfg.TEST.LOG_PERIOD > 0 and ind % cfg.TEST.LOG_PERIOD == 0:
logger.info('iter: {:4d} time:{:.4f} data:{:.4f}'.format(
ind, batch_time, data_time))
test_time = time.time() - start_time
logger.info('Test total time: {:.2f}s'.format(test_time))
else:
test_meters = MetricLogger(delimiter=' ')
test_meters.bind(metric)
with torch.no_grad():
start_time = time.time()
end = start_time
for iteration, data_batch in enumerate(test_dataloader):
data_time = time.time() - end
cls_label_batch = data_batch['cls_label'].numpy()
data_batch = {
k: v.cuda(non_blocking=True)
for k, v in data_batch.items()
}
preds = model(data_batch)
loss_dict = loss_fn(preds, data_batch)
total_loss = sum(loss_dict.values())
test_meters.update(loss=total_loss, **loss_dict)
metric.update_dict(preds, data_batch)
cls_logit_batch = preds['cls_logit'].cpu().numpy(
) # (batch_size, num_classes)
pred_label_batch = np.argmax(cls_logit_batch, axis=1)
evaluator.batch_update(pred_label_batch, cls_label_batch)
batch_time = time.time() - end
end = time.time()
test_meters.update(time=batch_time, data=data_time)
if cfg.TEST.LOG_PERIOD > 0 and iteration % cfg.TEST.LOG_PERIOD == 0:
logger.info(
test_meters.delimiter.join([
'iter: {iter:4d}',
'{meters}',
]).format(
iter=iteration,
meters=str(test_meters),
))
test_time = time.time() - start_time
logger.info('Test {} total time: {:.2f}s'.format(
test_meters.summary_str, test_time))
# evaluate
logger.info('overall accuracy={:.2f}%'.format(100.0 *
evaluator.overall_accuracy))
logger.info('average class accuracy={:.2f}%.\n{}'.format(
100.0 * np.nanmean(evaluator.class_accuracy), evaluator.print_table()))
evaluator.save_table(osp.join(output_dir, 'eval.cls.tsv'))