*Memos:
div() can do division with two of the 0D or more D tensors of zero or more elements or scalars or the 0D or more D tensor of zero or more elements and a scalar, getting the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
div()can be used with torch or a tensor. - The 1st argument(
input) withtorch(Type:tensororscalarofint,float,complexorbool) or using a tensor(Type:tensorofint,float,complexorbool)(Required). - The 2nd argument with
torchor the 1st argument with a tensor isother(Required-Type:tensororscalarofint,float,complexorbool). - There is
rounding_modeargument withtorchor a tensor(Optional-Default:None-Type:str): *Memos:-
None,"trunc"or"floor"can be set. -
rounding_mode=must be used. -
Noneperforms no rounding and, if both input and other are integer types, promotes the inputs to the default scalar type. *Equivalent to true division in Python (the/operator) and NumPy’s np.true_divide(). -
"trunc"rounds the results of the division towards zero. *Equivalent to C-style integer division. -
"floor"rounds the results of the division down. *Equivalent to floor division in Python (the//operator) and NumPy’s np.floor_divide(). - The tensor or scalar of
complexorboolcannot be used with"trunc"or"floor". - Setting
0(int) toothergetsZeroDivisionErrorwith"trunc"or"floor".
-
- There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. -
My post explains
outargument.
-
-
divide() is the alias of
div(). -
true_divide() is the alias of
div()withrounding_mode=None. -
floor_divide() is the same as
div()withrounding_mode="trunc"as long as I experimented.
import torch
tensor1 = torch.tensor([9.75, 7.08, 6.26])
tensor2 = torch.tensor([[4.26, -4.54, 3.37], [-2.16, 5.43, -5.98]])
torch.div(input=tensor1, other=tensor2)
tensor1.div(other=tensor2)
# tensor([[2.2887, -1.5595, 1.8576], [-4.5139, 1.3039, -1.0468]])
torch.div(input=9.75, other=tensor2)
# tensor([[2.2887, -2.1476, 2.8932], [-4.5139, 1.7956, -1.6304]])
torch.div(input=tensor1, other=4.26)
# tensor([2.2887, 1.6620, 1.4695])
torch.div(input=9.75, other=4.26)
# tensor(2.2887)
torch.div(input=tensor1, other=tensor2, rounding_mode="trunc")
# tensor([[2., -1., 1.], [-4., 1., -1.]])
torch.div(input=9.75, other=tensor2, rounding_mode="trunc")
# tensor([[2., -2., 2.], [-4., 1., -1.]])
torch.div(input=tensor1, other=4.26, rounding_mode="trunc")
# tensor([2., 1., 1.])
torch.div(input=9.75, other=4.26, rounding_mode="trunc")
# tensor(2.)
torch.div(input=tensor1, other=tensor2, rounding_mode="floor")
# tensor([[2., -2., 1.], [-5., 1., -2.]])
torch.div(input=9.75, other=tensor2, rounding_mode="floor")
# tensor([[2., -3., 2.], [-5., 1., -2.]])
torch.div(input=tensor1, other=4.26, rounding_mode="floor")
# tensor([2., 1., 1.])
torch.div(input=9.75, other=4.26, rounding_mode="floor")
# tensor(2.)
tensor1 = torch.tensor([9, 7, 6])
tensor2 = torch.tensor([[4, -4, 3], [-2, 5, -5]])
torch.div(input=tensor1, other=tensor2)
# tensor([[2.2500, -1.7500, 2.0000], [-4.5000, 1.4000, -1.2000]])
torch.div(input=9, other=tensor2)
# tensor([[2.2500, -2.2500, 3.0000], [-4.5000, 1.8000, -1.8000]])
torch.div(input=tensor1, other=4)
# tensor([2.2500, 1.7500, 1.5000])
torch.div(input=9, other=4)
# tensor(2.2500)
tensor1 = torch.tensor([9.+0.j, 7.+0.j, 6.+0.j])
tensor2 = torch.tensor([[4.+0.j, -4.+0.j, 3.+0.j],
[-2.+0.j, 5.+0.j, -5.+0.j]])
torch.div(input=tensor1, other=tensor2)
# tensor([[2.2500+0.j, -1.7500-0.j, 2.0000+0.j],
# [-4.5000-0.j, 1.4000+0.j, -1.2000-0.j]])
torch.div(input=9.+0.j, other=tensor2)
# tensor([[2.2500+0.j, -2.2500-0.j, 3.0000+0.j],
# [-4.5000-0.j, 1.8000+0.j, -1.8000-0.j]])
torch.div(input=tensor1, other=4.+0.j)
# tensor([2.2500+0.j, 1.7500+0.j, 1.5000+0.j])
torch.div(input=9.+0.j, other=4.+0.j)
# tensor(2.2500+0.j)
tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[False, True, False], [True, False, True]])
torch.div(input=tensor1, other=tensor2)
# tensor([[inf, 0., inf], [1., nan, 1.]])
torch.div(input=True, other=tensor2)
# tensor([[inf, 1., inf], [1., inf, 1.]])
torch.div(input=tensor1, other=False)
# tensor([inf, nan, inf])
torch.div(input=True, other=False)
# tensor(inf)
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