def forward(self, x):
# nb, np, 3 -> [nb, 3, np] x [nb, 1, np, na]
x = M.preprocess_input(x, self.na_in, False)
# x = M.preprocess_input(x, 1)
for block_i, block in enumerate(self.backbone):
x = block(x)
x = self.outblock(x)
return x
def forward(self, x, rlabel=None):
# nb, np, 3 -> [nb, 3, np] x [nb, 1, np, na]
input_x = x
x = M.preprocess_input(x, self.na_in, False)
for block_i, block in enumerate(self.backbone):
x = block(x)
# x = self.outblock(x.feats, rlabel)
x = self.outblock(x, rlabel)
return x
def forward(self, x):
# nb, 2, np, 3 -> [nb, 3, np] x [nb, 1, np, na]
x = torch.cat((x[:,0], x[:,1]),dim=0)
x = M.preprocess_input(x, self.na_in, False)
# x = M.preprocess_input(x, 1, False)
for block_i, block in enumerate(self.backbone):
x = block(x)
f1, f2 = torch.chunk(x.feats,2,dim=0)
x1, x2 = torch.chunk(x.xyz,2,dim=0)
# confidence, quats = self.outblock(x.feats)
confidence, quats = self.outblock(f1, f2, x1, x2)
return confidence, quats