def forward(self, x, label):
x = self.truck(x)
# your code here
batchsize = x.size(0)
#-- Head architecture
self.head_s2 = nn.Sequential(
nn.Linear(4096, 84),
nn.ReLU(inplace=True),
#nn.Dropout(),
nn.Linear(84, self.nr_cate * 3), # 252=3*3
)
self.head_s1 = nn.Sequential(
nn.Linear(4096, 84),
nn.ReLU(inplace=True),
#nn.Dropout(),
nn.Linear(84, self.nr_cate * 2),
)
# self.maskout = Maskout(nr_cate=nr_cate)
# Note: s1(x,y) is on a circle and (x^2+y^2=1)
x_s2 = self.maskout(
self.head_s2(x).view(batchsize, self.nr_cate, 3), label)
x_s1 = self.maskout(
self.head_s1(x).view(batchsize, self.nr_cate, 2), label)
#-- Normalize coordinate to a unit
x_s2 = norm2unit(x_s2) #, nr_cate=self.nr_cate)
x_s1 = norm2unit(x_s1) #, nr_cate=self.nr_cate)
Pred = edict(s2=x_s2, s1=x_s1)
return Pred
def forward(self, x, label):
# forward truck
x = self.truck(x)
# your code here
batchsize = x.size(0)
x = x.view(batchsize, -1) # viewed as (batchsize, 2048)
# Note: s1(x,y) is on a circle and (x^2+y^2=1)
x_s2 = self.maskout(
self.head_s2(x).view(batchsize, self.nr_cate, 3), label)
x_s1 = self.maskout(
self.head_s1(x).view(batchsize, self.nr_cate, 2), label)
#-- Normalize coordinate to a unit
x_s2 = norm2unit(x_s2) #, nr_cate=self.nr_cate)
x_s1 = norm2unit(x_s1) #, nr_cate=self.nr_cate)
Pred = edict(s2=x_s2, s1=x_s1)
return Pred
def compute_pred(Prob):
x_quat = Prob['quat']
#-- Normalize coordinate to a unit
x_quat = norm2unit(
x_quat
) # Note: here we do l2 normalization. Just to make predicted quaternion a unit norm.
#
batchsize = x_quat.size(0)
# Get cpu data.
batch_data = x_quat.data.cpu().numpy().copy()
assert batch_data.shape == (batchsize, 4), batch_data.shape
Pred = edict(quat=batch_data)
return Pred
def forward(self, x):
"""label shape (batchsize, ) """
x = self.trunk(x) # Forward Conv and Fc6,Fc7
#
batchsize = x.size(0) # x of shape (40, 3, 240, 320)
#-- Normalize coordinate to a unit
x_norm = norm2unit(x, dim=1)
Prob = edict(norm=x_norm.permute(
0, 2, 3,
1).double()) # transpose prediction from BxCxHxW to BxHxWxC order.
return Prob
def forward(self, x, label):
"""label shape (batchsize, ) """
x = self.trunk(x)
#
batchsize = x.size(0)
# Note: quat(a,b,c,d) is on a 4d sphere and (x^2+y^2=1)
x_quat = self.maskout(
self.head_quat(x).view(batchsize, self.nr_cate, self.reg_n_D),
label)
#-- Normalize coordinate to a unit
x_quat = norm2unit(x_quat) #, nr_cate=self.nr_cate)
Prob = edict(quat=x_quat)
return Prob