YouTipPytorch Torch Argwhere
# PyTorch torch.argwhere Function
* * *
[ values in the input tensor.
### Function Definition
torch.argwhere(input)
**Parameters**:
* `input` (Tensor): Input tensor.
**Return Value**:
* `torch.Tensor`: Returns a 2D tensor where each row is an index of an element that satisfies the condition.
* * *
## Usage Examples
## Example
import torch
# Create a tensor
x = torch.tensor([[1,0,2],
[0,3,0],
[4,5,0]])
print("Original tensor:")
print(x)
# Return indices of non-zero elements
indices = torch.argwhere(x)
print("nIndices of non-zero elements:")
print(indices)
Output:
Original tensor: tensor([[1, 0, 2], [0, 3, 0], [4, 5, 0]])Indices of non-zero elements: tensor([[0, 0], [0, 2], [1, 1], [2, 0], [2, 1]])
## Example
import torch
# Boolean condition
x = torch.tensor([[True,False,True],
[False,True,False],
[True,True,False]])
print("Boolean tensor:")
print(x)
indices = torch.argwhere(x)
print("nIndices of True values:")
print(indices)
Output:
Boolean tensor: tensor([[True, False, True], [False, True, False], [True, True, False]])Indices of True values: tensor([[0, 0], [0, 2], [1, 1], [2, 0], [2, 1]])
## Example
import torch
# Find elements greater than a certain value
x = torch.randn(3,4)
threshold =0
print("Original tensor:")
print(x)
# Find indices of elements greater than threshold
indices = torch.argwhere(x > threshold)
print(f"nIndices of elements greater than {threshold}:")
print(indices)
# Can also use nonzero function with same effect
indices2 = torch.nonzero(x > threshold)
print("nResult using nonzero:")
print(indices2)
Output:
Original tensor: tensor([[-1.2345, 0.5678, -0.8901, 1.2345], [ 0.3456, -0.6789, 0.9012, -0.1234], [-0.5678, 1.2345, -0.3456, 0.7890]])Indices of elements greater than 0: tensor([[0, 1], [0, 3], [1, 0], [1, 2], [2, 1], [2, 3]])Result using nonzero: tensor([[0, 1], [0, 3], [1, 0], [1, 2], [2, 1], [2, 3]])
## Example
import torch
# 1D tensor
x = torch.tensor([1,0,0,4,0,5,0])
indices = torch.
```