def __init__(self, params, norm=None):
super(RelSO3OutBlockR, self).__init__()
c_in = params['dim_in']
mlp = params['mlp']
na = params['kanchor']
self.pointnet = sptk.PointnetSO3Conv(c_in, c_in, na)
c_in = c_in * 2
self.linear = nn.ModuleList()
self.temperature = params['temperature']
rp = params['representation']
if rp == 'quat':
self.out_channel = 4
elif rp == 'ortho6d':
self.out_channel = 6
else:
raise KeyError("Unrecognized representation of rotation: %s" % rp)
self.attention_layer = nn.Conv2d(mlp[-1], 1, (1, 1))
# out channel equals 4 for quaternion representation, 6 for ortho representation
self.regressor_layer = nn.Conv2d(mlp[-1], self.out_channel, (1, 1))
# ------------------ uniary conv ----------------
for c in mlp:
self.linear.append(nn.Conv2d(c_in, c, (1, 1)))
c_in = c
def __init__(self, params, norm=None, debug=False):
super(ClsOutBlockPointnet, self).__init__()
c_in = params['dim_in']
mlp = params['mlp']
fc = params['fc']
k = params['k']
na = params['kanchor']
self.outDim = k
self.linear = nn.ModuleList()
self.norm = nn.ModuleList()
# ------------------ uniary conv ----------------
for c in mlp:
self.linear.append(nn.Conv2d(c_in, c, 1))
self.norm.append(nn.BatchNorm2d(c))
c_in = c
# -----------------------------------------------
# ----------------- pooling ---------------------
if 'pooling' not in params.keys():
self.pooling_method = 'max'
else:
self.pooling_method = params['pooling']
# BxCxA -> Bx1xA or BxCxA attention weights
if self.pooling_method == 'attention':
self.temperature = params['temperature']
self.attention_layer = nn.Conv1d(c_in, 1, 1)
# ------------------------------------------------
# self.fc1 = nn.ModuleList()
# for c in fc:
# self.fc1.append(nn.Linear(c_in, c))
# # self.norm.append(nn.BatchNorm1d(c))
# c_in = c
self.pointnet = sptk.PointnetSO3Conv(c_in, c_in, na)
self.norm.append(nn.BatchNorm1d(c_in))
self.fc2 = nn.Linear(c_in, self.outDim)
self.debug = debug
def __init__(self, params, norm=None):
super(InvOutBlockMVD, self).__init__()
c_in = params['dim_in']
mlp = params['mlp']
c_out = mlp[-1]
na = params['kanchor']
# Attention layer
self.temperature = params['temperature']
self.attention_layer = nn.Sequential(nn.Conv2d(c_in, c_in, 1), \
nn.ReLU(inplace=True), \
nn.Conv2d(c_in,c_in,1))
if 'pooling' not in params.keys():
self.pooling_method = 'max'
else:
self.pooling_method = params['pooling']
self.pointnet = sptk.PointnetSO3Conv(c_in, c_out, na)
def __init__(self, params, norm=None):
super(InvOutBlockPointnet, self).__init__()
c_in = params['dim_in']
mlp = params['mlp']
c_out = mlp[-1]
na = params['kanchor']
if 'pooling' not in params.keys():
self.pooling_method = 'max'
else:
self.pooling_method = params['pooling']
self.pointnet = sptk.PointnetSO3Conv(c_in, c_out, na)
# Attention layer
if self.pooling_method == 'attention':
self.temperature = params['temperature']
self.attention_layer = nn.Conv1d(c_out, 1, 1)