I am trying to convert the output from a layer in my network whose size is [batch, 3] using a function which I wrote:
def W300_EulerAngles2Vectors(self,x):
'''
rx: pitch
ry: yaw
rz: roll
'''
b,_ = x.shape
rx=x[:,0]* (3.14 / 180.0)
ry=x[:,1]* (3.14 / 180.0)
rz=x[:,2]* (3.14 / 180.0)
ry= ry * (-1)
'''
R_x = torch.tensor([[1.0, 0.0, 0.0],
[0.0, rx.cos(), -rx.sin()],
[0.0, rx.sin(), rx.cos()]], requires_grad=True)
R_y =torch.tensor([[np.cos(ry), 0.0, np.sin(ry)],
[0.0, 1.0, 0.0],
[-np.sin(ry), 0.0, np.cos(ry)]], requires_grad=True)
R_z = torch.tensor([[np.cos(rz), -np.sin(rz), 0.0],
[np.sin(rz), np.cos(rz), 0.0],
[0.0, 0.0, 1.0]], requires_grad=True)
'''
tensor_0 = torch.zeros(b)
tensor_1 = torch.ones(b)
print('rx',x[1][0].size(),rx.size())
R_x = torch.stack([
torch.stack([tensor_1, tensor_0, tensor_0]),
torch.stack([tensor_0, torch.cos(rx), -torch.sin(rx)]),
torch.stack([tensor_0, torch.sin(rx), torch.cos(rx)])]).reshape(b,3,3)
R_y = torch.stack([
torch.stack([torch.cos(ry), tensor_0, torch.sin(ry)]),
torch.stack([tensor_0, tensor_1, tensor_0]),
torch.stack([-torch.sin(ry), tensor_0, torch.cos(ry)])]).reshape(b,3,3)
R_z = torch.stack([
torch.stack([torch.cos(rz), -torch.sin(rz), tensor_0]),
torch.stack([torch.sin(rz), torch.cos(rz), tensor_0]),
torch.stack([tensor_0, tensor_0, tensor_1])]).reshape(b,3,3)
R = torch.matmul(R_x,R_y)
R = torch.matmul(R,R_z)
l_vec = torch.matmul(R, torch.t(torch.tensor([1, 0, 0])))
b_vec = torch.matmul(R, torch.t(torch.tensor([0, 1, 0])))
f_vec = torch.matmul(R, torch.t(torch.tensor([0, 0, 1]))) # R @ np.array([0, 0, 1]).T
return [l_vec, b_vec, f_vec]
But, unfortunately, if I use the definitions of R_x, R_y, R_z in strings (comments), then it gives me error:
‘‘Only one element tensors can be converted to Python scalars’’
If I use define R_x, R_y, R_z using stacks, then it gives me error:
‘‘RuntimeError: All input tensors must be on the same device. Received cpu and cuda:0’’
I want to use the output this function in my loss function. So, I want to backpropagate.
Please help. I will be very thankful for it 
@ptrblck