Given two tensors t1
and t2
:
t1=torch.tensor([[1,2],[3,4],[5,6]])
t2=torch.tensor([[1,2],[5,6]])
If the row elements of t1
is exist in t2
, return True
, otherwise return False
. The ideal result is
[Ture, False, True]
.
I tried torch.isin(t1, t2)
, but its return the results by elements not by rows. By the way, if they are numpy arrays, it can be completed by
np.in1d(t1.view('i,i').reshape(-1), t2.view('i,i').reshape(-1))
I wonder how to get the similar result in tensor?
CodePudding user response:
def rowwise_in(a,b):
"""
a - tensor of size a0,c
b - tensor of size b0,c
returns - tensor of size a1 with 1 for each row of a in b, 0 otherwise
"""
# dimensions
a0 = a.shape[0]
b0 = b.shape[0]
c = a.shape[1]
assert c == b.shape[1] , "Tensors must have same number of columns"
a_expand = a.unsqueeze(1).expand(a0,b0,c)
b_expand = b.unsqueeze(0).expand(a0,b0,c)
# element-wise equality
equal = a_expand == b_expand
# sum along dim 2 (all elements along this dimension must be true for the summed dimension to be True)
row_equal = torch.prod(equal,dim = 2)
row_in_b = torch.max(row_equal, dim = 1)[0]
return row_in_b