def verts_pair_func(neighbor_feats, att_vec, B, max_o_grid, max_p_grid, N, C, att_full = False, pt_mlp_lst= None, scope="pair_relation", bn_decay=0.9):
'''
:param centers: B * max_o_grid * 3
:param neighbor_feats: B * max_o_grid * max_p_grid * C
:param neighbor_locs: B * max_o_grid * max_p_grid * 3
:param C:
:param max_p_grid:
:param scope:
:return: geo_feat: B * max_o_grid * max_p_grid * C
'''
if pt_mlp_lst is not None and len(pt_mlp_lst) > 0:
C = pt_mlp_lst[-1]
print("pt_mlp_lst:", pt_mlp_lst)
if BN:
neighbor_feats = mlp2d_c(neighbor_feats, pt_mlp_lst, use_bn=BN, bn_decay=bn_decay, scope=scope)
else:
neighbor_feats = fully_connected_mlp(neighbor_feats, pt_mlp_lst, dim = 4, bn_decay=bn_decay, dropout_ratio=0, flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope + '/cov2fc', flip=False)
# centers=centers.reshape((B, max_o_grid, 3), name=scope+"/centerreshape")
if configs["attfdim"] > 0:
if BN:
att_vec = mlp2d_c(att_vec, [C if att_full else C//4, C], use_bn=BN, bn_decay=bn_decay, attr=None, scope=scope+"/update_att_mlp2d")
else:
att_vec = fully_connected_mlp(att_vec, [C if att_full else C//4, C], dim = 4, bn_decay=bn_decay, dropout_ratio=0, flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope + '/update_att_mlp2dfc', flip=False)
pair_feats = att_vec * neighbor_feats
else:
pair_feats = neighbor_feats
return pair_feats
def verts_pair_func(ori_neighbor_feats, att_vec, B, max_o_grid, max_p_grid, N, C, att_full = "off", pt_mlp_lst= None, att_ele_lst = None, contextvec = None, scope="pair_relation", bn_decay=0.9):
'''
:param centers: B * max_o_grid * 3
:param neighbor_feats: B * max_o_grid * max_p_grid * C
:param neighbor_locs: B * max_o_grid * max_p_grid * 3
:param C:
:param max_p_grid:
:param scope:
:return: geo_feat: B * max_o_grid * max_p_grid * C
'''
if len(att_ele_lst)>0:
C = att_ele_lst[-1] + configs["localfdim"]
if pt_mlp_lst is not None and len(pt_mlp_lst) > 0:
C = pt_mlp_lst[-1]
print("pt_mlp_lst:", pt_mlp_lst)
if BN:
neighbor_feats = mlp2d_c(ori_neighbor_feats, pt_mlp_lst, use_bn=BN, bn_decay=bn_decay, scope=scope+ '/cov2fc')
else:
neighbor_feats = fully_connected_mlp(ori_neighbor_feats, pt_mlp_lst, dim = 4, bn_decay=bn_decay, dropout_ratio=0, flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope + '/cov2fc', flip=False) # centers=centers.reshape((B, max_o_grid, 3), name=scope+"/centerreshape")
if configs["attfdim"] > 0 and len(att_ele_lst)>0:
att_ele_lst[-1] = C
if BN:
att_vec = mlp2d_c(att_vec, [att_ele_lst[0]], use_bn=BN, bn_decay=bn_decay, attr=None,
scope=scope + "/update_att_mlp2d_frst")
if att_full == "last":
att_vec = mx.sym.concat(att_vec, ori_neighbor_feats, dim=C_dim, name=scope + "/att_full_concat")
print("has attfull last")
elif att_full == "next":
att_vec = mx.sym.concat(att_vec, neighbor_feats, dim=C_dim, name=scope + "/att_full_concat")
print("has attfull next")
if contextvec is not None:
att_vec = mx.sym.concat(att_vec, contextvec, dim=C_dim, name=scope + "/att_contextvec_concat")
print("att_vec has contextvec")
att_vec = mlp2d_c(att_vec, [att_ele_lst[1]] if len(att_ele_lst)==2 else att_ele_lst[1:], use_bn=BN, bn_decay=bn_decay, attr=None, scope=scope + "/update_att_mlp2d_scnd")
else:
att_vec = fully_connected_mlp(att_vec, [att_ele_lst[0]], dim=4, bn_decay=bn_decay, dropout_ratio=0, flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope + '/update_att_mlp2dfc_frst', flip=False)
if att_full == "last":
att_vec = mx.sym.concat(att_vec, ori_neighbor_feats, dim=C_dim, name=scope + "/att_full_concat")
print("has attfull last")
elif att_full == "next":
att_vec = mx.sym.concat(att_vec, neighbor_feats, dim=C_dim, name=scope + "/att_full_concat")
print("has attfull next")
if contextvec is not None:
att_vec = mx.sym.concat(att_vec, contextvec, dim=C_dim, name=scope + "/att_contextvec_concat")
print("att_vec has contextvec")
att_vec = fully_connected_mlp(att_vec, [att_ele_lst[1]] if len(att_ele_lst)==2 else att_ele_lst[1:], dim=4, bn_decay=bn_decay, dropout_ratio=0, flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope + '/update_att_mlp2dfc_scnd', flip=False)
pair_feats = att_vec * neighbor_feats
else:
pair_feats = neighbor_feats
return pair_feats
def mlp_mask_and_pool(data, mlp_list, max_p_grid, pool_type="max", use_bn=BN, bn_decay=0.9, mask=None, scope=''):
"""
mlp and max-pooling
Input:
data: (B, fps_npoints, rs_npoints, C), Symbol
mlp: list of int32, output size of MLP
bn_decay: decay parameter in batch normalization
Output:
pooled_data: (B, fps_npoints, mlp[-1]), Symbol
"""
# mlp
if BN:
data = mlp2d_c(data, mlp_list, use_bn=use_bn, bn_decay=bn_decay, scope=scope)
else:
data = fully_connected_mlp(data, mlp_list, dim = 4, bn_decay=configs["bn_decay"], dropout_ratio=0,
flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope, flip=False)
# pooling
if mask is not None and pool_type!="max" and pool_type!="max_pooling":
data = mx.sym.broadcast_mul(data, mask, axis=1, name="pairmask") # B * C * max_o_grid * max_p_grid
# data = mx.symbol.max(data, axis=2, name=scope+'/pool')
if pool_type == "max_pooling":
data = mx.sym.Pooling(name=scope + "/max_pooling", data=data,
kernel=(1, max_p_grid), pool_type="max")
return mx.sym.squeeze(data, axis=3)
elif pool_type == "max":
data = mx.symbol.max(data, axis=3, name=scope + '/max')
return data
else:
raise NotImplementedError("agg %s is no implemented" % pool_type)
def contextvec_func(cntxt, max_p_grid, cntxt_mlp = [64,64], bn_decay=0.9, scope=""):
print("cntxt_mlp:", cntxt_mlp)
if len(cntxt_mlp)>0:
if BN:
cntxt = mlp2d_c(cntxt, cntxt_mlp, use_bn=BN, bn_decay=bn_decay, scope=scope+"/cnt")
else:
cntxt = fully_connected_mlp(cntxt, cntxt_mlp, dim = 4, bn_decay=bn_decay, dropout_ratio=0, flatten=False, use_bn=BN, use_relu=True, bn_axis=1, attr=None, scope=scope + '/cnt/cov2fc', flip=False)
aggregated_cntxt = mx.sym.Pooling(name=scope + "/cnt/max_pooling", data=cntxt,
kernel=(1, max_p_grid) if BN else (max_p_grid, 1), pool_type="max", layout="NCHW")
aggregated_cntxt = mx.sym.tile(aggregated_cntxt, reps=(1, 1, 1, max_p_grid) if BN else (1, 1, max_p_grid, 1))
return aggregated_cntxt
def sub_g_update(centers_xyz,
center_den,
neighbors,
has_feats,
center_masks,
neighbor_masks,
attfdim,
center_ori_feats=None,
pt_mlp_lst=None,
outDim=[64, 256],
cntxt_mlp=None,
shape=[64, 800000, 100, 6],
scope='layer',
aggtype="gcn",
pool_type="max_pooling",
att_full="",
center_dim=[],
recalden=False,
bn_decay=0.9):
'''
RELU( MAX_POOLING ((geoRelation() * f_neib)) )
:param centers: B * max_o_grid * 3
:param neighbor_feats: B * max_o_grid * max_p_grid * 4+C
:param center_masks: B * max_o_grid
:param neighbor_masks: B * max_o_grid * max_p_grid
:param scope:
:return:
'''
#
B, max_o_grid, max_p_grid, N, C = shape
if neighbor_masks is not None:
neighbor_masks_expand = mx.sym.expand_dims(neighbor_masks,
axis=C_dim,
name=scope + "/concatmask")
neighbor_feats = mx.sym.slice_axis(
neighbors, axis=C_dim, begin=4, end=None) if has_feats else None
# todo expand centers in Gridify
centers_expand_xyz = mx.sym.tile(
mx.sym.expand_dims(centers_xyz,
axis=P_dim,
name=scope + "/centerxyzexpand"),
reps=(1, 1, 1, max_p_grid) if BN else (1, 1, max_p_grid, 1))
neighbor_locs_xyz = mx.sym.slice_axis(neighbors,
axis=C_dim,
begin=0,
end=3)
geo_vec = neighbor_locs_xyz - centers_expand_xyz # B * 3 * max_o_grid * max_p_grid
geo_dist = mx.sym.sqrt(
mx.sym.sum(mx.sym.square(geo_vec), axis=C_dim,
keepdims=True)) # B * max_o_grid * max_p_grid
if max(attfdim, configs["localfdim"]) in [5, 11, 12]:
geo_num = mx.sym.slice_axis(neighbors, axis=C_dim, begin=3,
end=4) # B * 1 * O * P or B * O * P * 1
num_points = mx.sym.ones_like(
geo_num, name=scope + "/oneslike") * configs["num_points"]
geo_global_density = mx.sym.elemwise_div(geo_num,
num_points,
name=scope +
"/den_glo_ele_div")
if max(attfdim, configs["localfdim"]) in [5, 12]:
if recalden:
geo_num_masked = geo_num
if neighbor_masks is not None:
geo_num_masked = mx.sym.elemwise_mul(
neighbor_masks_expand, geo_num)
geo_den_sum = mx.sym.sum(
geo_num_masked, axis=P_dim,
keepdims=True) # B * 1 * O * 1 or B * O * 1 * 1
geo_den_sum = mx.sym.clip(
geo_den_sum, 1, 1000000) # make sure it's bigger than zero
else:
geo_den_sum = mx.sym.tile(mx.sym.expand_dims(
center_den, axis=P_dim, name=scope + "/centerdenexpand"),
reps=(1, 1, 1, max_p_grid) if BN else
(1, 1, max_p_grid, 1))
geo_density = mx.sym.broadcast_div(geo_num,
geo_den_sum,
name=scope + "/den_ele_div")
if attfdim <= 3:
att_vec = geo_vec
elif attfdim == 4:
att_vec = mx.sym.concat(geo_dist,
geo_vec,
dim=C_dim,
name=scope + "/attconcat")
elif attfdim == 5:
att_vec = mx.sym.concat(geo_dist,
geo_vec,
geo_global_density,
dim=C_dim,
name=scope + "/attconcat")
elif attfdim == 10:
att_vec = mx.sym.concat(geo_dist,
geo_vec,
centers_expand_xyz,
neighbor_locs_xyz,
dim=C_dim,
name=scope + "/attconcat")
elif attfdim == 11:
att_vec = mx.sym.concat(geo_dist,
geo_vec,
centers_expand_xyz,
neighbor_locs_xyz,
geo_global_density,
dim=C_dim,
name=scope + "/attconcat")
elif attfdim == 12:
att_vec = mx.sym.concat(geo_dist,
geo_vec,
centers_expand_xyz,
neighbor_locs_xyz,
geo_density,
geo_global_density,
dim=C_dim,
name=scope + "/attconcat")
else:
raise NotImplementedError
#
if configs["localfdim"] <= 3:
geo_feats = geo_vec
elif configs["localfdim"] == 4:
geo_feats = mx.sym.concat(geo_dist,
geo_vec,
dim=C_dim,
name=scope + "/geo_concat")
elif configs["localfdim"] == 5:
geo_feats = mx.sym.concat(geo_dist,
geo_vec,
geo_global_density,
dim=C_dim,
name=scope + "/geo_concat")
elif configs["localfdim"] == 10:
geo_feats = mx.sym.concat(geo_dist,
geo_vec,
centers_expand_xyz,
neighbor_locs_xyz,
dim=C_dim,
name=scope + "/geo_concat")
elif configs["localfdim"] == 11:
geo_feats = mx.sym.concat(geo_dist,
geo_vec,
centers_expand_xyz,
neighbor_locs_xyz,
geo_global_density,
dim=C_dim,
name=scope + "/geo_concat")
elif configs["localfdim"] == 12:
geo_feats = mx.sym.concat(geo_dist,
geo_vec,
centers_expand_xyz,
neighbor_locs_xyz,
geo_density,
geo_global_density,
dim=C_dim,
name=scope + "/geo_concat")
else:
raise NotImplementedError
if neighbor_feats is None:
neighbor_feats = geo_feats
if len(configs["elevation"]) > 0:
if BN:
neighbor_feats = mlp2d_c(neighbor_feats,
configs["elevation"],
use_bn=BN,
bn_decay=bn_decay,
scope=scope + "/elevation")
else:
neighbor_feats = fully_connected_mlp(neighbor_feats,
configs["elevation"],
dim=4,
bn_decay=bn_decay,
dropout_ratio=0,
flatten=False,
use_bn=BN,
use_relu=True,
bn_axis=1,
attr=None,
scope=scope +
'/elevation/cov2fc',
flip=False)
elif configs["localfdim"] != 0:
neighbor_feats = mx.symbol.concat(geo_feats,
neighbor_feats,
dim=C_dim,
name=scope +
"/neighbor_feats_concat")
contextvec = None
if cntxt_mlp is not None:
contextvec = contextvec_func(neighbor_feats,
max_p_grid,
cntxt_mlp=cntxt_mlp,
bn_decay=bn_decay,
scope=scope)
if aggtype == "gcn":
pair_feats = verts_pair_func(
neighbor_feats,
att_vec,
B,
max_o_grid,
max_p_grid,
N,
C,
contextvec=contextvec,
att_full=att_full,
pt_mlp_lst=pt_mlp_lst,
scope=scope,
bn_decay=bn_decay) # B * max_o_grid * max_p_grid * C
if neighbor_masks is not None and pool_type != "max" and pool_type != "max_pooling":
pair_feats = mx.sym.broadcast_mul(
pair_feats, neighbor_masks_expand,
name="pairmask") # B * max_o_grid * max_p_grid * C
agg_neigh_feats = aggregation_func(
pair_feats,
neighbor_masks,
geo_dist,
max_o_grid,
max_p_grid,
pool_type=pool_type,
scope=scope + "/center_aggregation",
bn_decay=bn_decay) # B * max_o_grid * 1 * outDim[-1]
elif aggtype == "agg_gcn":
agg_neigh_feats = aggregation_func(neighbor_feats,
neighbor_masks,
geo_dist,
max_o_grid,
max_p_grid,
pool_type=pool_type,
mlp_lst=pt_mlp_lst,
scope=scope + "/neigh_aggregation",
bn_decay=bn_decay)
else:
raise NotImplementedError("aggtype %s is not implemented" % aggtype)
if center_ori_feats is not None:
if len(center_dim) > 0:
if BN:
center_feats = mlp1d_c(center_ori_feats,
center_dim,
bn_decay=bn_decay,
use_bn=BN,
attr=None,
scope=scope + '/centerfeats_conv1d')
elif configs["use_bn"] == 'p':
center_feats = fully_connected_mlp_withbn(
center_ori_feats,
center_dim,
dim=3,
bn_decay=bn_decay,
dropout_ratio=0,
flatten=False,
use_bn=BN,
use_relu=True,
bn_axis=1,
attr=None,
scope=scope + '/centerfeats_conv1d1d',
flip=False)
else:
center_feats = fully_connected_mlp(center_ori_feats,
center_dim,
dim=3,
bn_decay=bn_decay,
dropout_ratio=0,
flatten=False,
use_bn=BN,
use_relu=True,
bn_axis=1,
attr=None,
scope=scope +
'/centerfeats_conv1d1d',
flip=False)
else:
center_feats = center_ori_feats
if configs["up_center_inte"] == "add":
agg_neigh_feats = center_feats + agg_neigh_feats
elif configs["up_center_inte"] == "concat":
agg_neigh_feats = mx.symbol.concat(center_feats,
agg_neigh_feats,
dim=1 if BN else 2,
name=scope +
"/agg_feats_concat")
agg_feats = update_func(agg_neigh_feats,
outDim=outDim,
scope=scope + "/center_update",
bn_decay=bn_decay) # B * max_o_grid * outDim[-1]
if center_masks is not None:
agg_feats = mx.sym.broadcast_mul(
agg_feats,
mx.sym.expand_dims(center_masks,
axis=1 if BN else 2,
name=scope + "/maskexpand"),
name=scope + "/centermask") # B * outDim[-1] * max_o_grid
return agg_feats
