1. (input, other, out=None)
Description: Compare elements for equality, the second argument can be a number, or a tensor of the same type and shape as the first argument.
Parameters:
input(Tensor) ---- tensor to be compared
other(Tenosr or float) ---- Compare tensor or number
out(Tensor, optional) ---- Output tensor
Return Value: A tensor containing the comparison results for each position (1 for equal, 0 for unequal)
>>> a = ([[1, 2], [3, 4]]) >>> b = ([[1, 1], [4, 4]]) >>> (a, b) tensor([[1, 0], [0, 1]], dtype=torch.uint8)
2. (tensor1, tensor2, out=None)
Description: Returns true if the two tensors have the same shape and element values, otherwise False.
Parameters:
tensor1(Tenosr) ---- Compare tensor1
tensor2(Tensor) ---- Compare tensor2
out(Tensor, optional) ---- Output tensor
>>> a = ([1, 2]) >>> b = ([1, 2]) >>> (a, b) True
3. (input, other, out=None)
Description: Compare input and other on an element-by-element basis, i.e. whether input >= other.
Parameters:
input(Tensor) ---- Tensor to be compared
other(Tensor or float) ---- Contrasting tensor or float value
out(Tensor, optional) ---- outputs the tensor.
>>> a = ([[1, 2], [3, 4]]) >>> b = ([[1, 1], [4, 4]]) >>> (a, b) tensor([[1, 1], [0, 1]], dtype=torch.uint8)
4. (input, other, out=None)
Description: Compare input and other element by element, i.e. whether input > other.
Parameters:
input(Tensor) ---- tensor to compare
other(Tensor or float) ---- Tensor or float values to compare
out(Tensor, optional) ---- Output tensor
>>> a = ([[1, 2], [3, 4]]) >>> b = ([[1, 1], [4, 4]]) >>> (a, b) tensor([[0, 1], [0, 0]], dtype=torch.uint8)
5. (input, k, dim=None, out=None)
Description: Takes the kth smallest value on the dimension specified by the input tensor input. If dim is not specified, it defaults to the last dimension. Returns a tuple (value, indices), where indices is the subscript of the kth smallest value along the dim dimension of the original input tensor.
Parameters:
input(Tensor) ---- tensor to compare
k(int) ---- kth smallest value
dim(int, optional) ---- Sort along this dimension
out(tuple, optional) ---- output tuple
>>> x = (1, 6) >>> x tensor([1, 2, 3, 4, 5]) >>> (x, 4) torch.return_types.kthvalue( values=tensor(4), indices=tensor(3)) >>> (x, 1) torch.return_types.kthvalue( values=tensor(1), indices=tensor(0))
6. (input, other, out=None)
Description: Compare input and other element by element, i.e. whether input <= other.
Parameters:
input(Tenosr) ---- Tensor to be compared
other(Tensor or float) ---- Comparative tensor or float value
out(Tensor, optional) ---- Output tensor
>>> a = ([[1, 2], [3, 4]]) >>> b = ([[1, 1], [4, 4]]) >>> (a, b) tensor([[1, 0], [1, 1]], dtype=torch.uint8)
7. (input, other, out=None)
Description: Compare input and other element by element, i.e., whether input < other
Parameters:
input(Tensor) ---- tensor to compare
other(Tensor or float) ---- Comparative tensor or float value
out(Tensor, optional) ---- Output tensor
>>> a = ([[1, 2], [3, 4]]) >>> b = ([[1, 1], [4, 4]]) >>> (a, b) tensor([[0, 0], [1, 0]], dtype=torch.uint8)
8. (input)
Description: Returns the maximum value of all elements of the input tensor.
Parameters:
input(Tensor) ---- input tensor
>>> a = (1, 3) >>> a tensor([[ 0.1553, -0.4140, 1.8393]]) >>> (a) tensor(1.8393)
9. (input, dim, max=None, max_indices=None)
Description: Returns the maximum value of each row of the given dimension of the input tensor, along with the position index of each maximum value.
Parameters:
input(Tensor) ---- input tensor
dim(int) ---- Specified dimensions
max(Tensor, optional) ---- The result tensor, containing the maximum value in the given dimension
max_indices(LongTensor, optional) ---- The result tensor containing the index of the position of each maximum value on the given dimension.
>>> a = (4, 4) >>> a tensor([[ 0.4067, -0.7722, -0.6560, -0.9621], [-0.8754, 0.0282, -0.7947, -0.1870], [ 0.4300, 0.5444, 0.3180, 1.2647], [ 0.0775, 0.5886, 0.1662, 0.8986]]) >>> (a, 1) torch.return_types.max( values=tensor([0.4067, 0.0282, 1.2647, 0.8986]), indices=tensor([0, 1, 3, 3]))
10. (input, other, out=None)
Description: Returns the maximum value of two elements.
Parameters:
input(Tensor) ---- tensor to be compared
other(Tensor) ---- Compare tensor
out(Tensor, optional) ---- Result tensor
>>> a = (4) >>> a tensor([ 0.5767, -1.0841, -0.0942, -0.9405]) >>> b = (4) >>> b tensor([-0.6375, 1.4165, 0.2738, -0.8996]) >>> (a, b) tensor([ 0.5767, 1.4165, 0.2738, -0.8996])
(input)
Description: Returns the minimum value of all elements of the input tensor.
Parameters:
input(Tensor) ---- input tensor
>>> a = (1, 4) >>> a tensor([[-0.8142, -0.9847, -0.3637, 0.5191]]) >>> (a) tensor(-0.9847)
12. (input, dim, min=None, min_indices=None)
Description: Returns the minimum value of each row of the given dimension of the input tensor, and also returns the position index of each minimum value.
Parameters:
input(Tensor) ---- input tensor
dim(int) ---- Specified dimension
min(Tensor, optional) ---- The result tensor, containing the minimum value on the given dimension
min_indices(LongTensor, optional) ---- The result tensor containing the positional indices of each minimum value in the given dimension.
>>> a = (4, 4) >>> a tensor([[-0.0243, -0.7382, 0.3102, 0.9720], [-0.3805, -0.7999, -1.2856, 0.2657], [-1.0284, -0.1638, -0.8840, 1.2679], [-1.0347, -2.3428, 0.3107, 1.0575]]) >>> (a, 1) torch.return_types.min( values=tensor([-0.7382, -1.2856, -1.0284, -2.3428]), indices=tensor([1, 2, 0, 1]))
13. (input, other, out=None)
Description: compares input and other element by element, i.e. if input is not equal to other. the second argument can be a number or a tensor of the same shape and type as the first argument.
Parameters:
input(Tensor) ---- Tensor to be compared
other(Tensor or float) ---- Comparative tensor or float value
out(Tensor, optional) ---- Output Tensor
** Return Value:** A tensor containing the comparison results for each position, if tensor and other are not equal True, return 1.
>>> import torch >>> a = ([[1, 2], [3, 4]]) >>> b = ([[1, 1], [4, 4]]) >>> (a, b) tensor([[0, 1], [1, 0]], dtype=torch.uint8)
14. (input, dim=None, descending=False, out=None)
Description: Sorts the input tensor input in ascending order along the specified dimensions, defaulting to the last dimension of the input if dim is not given. If the parameter descending is given as True, the input is sorted in descending order.
Parameters:
input(Tensor) ---- Tensor to be sorted
dim(int, optional) ---- Sorting along this dimension
descending(bool, optional) ---- Boolean value to control ascending sorting
out(tuple, optional) ---- Output tensor
Return value: of type ByteTensor or the same type as tensor, a tuple (sorted_tensor,sorted_indices), sorted_indices is the subscript in the original input
>>> x = (3, 4) >>> x tensor([[-0.3613, -0.2583, -0.4276, -1.3106], [-1.1577, -0.7505, 1.7217, -0.6247], [-0.1338, 0.4423, 0.0280, -1.4796]]) >>> sorted, indices = (x) >>> sorted tensor([[-1.3106, -0.4276, -0.3613, -0.2583], [-1.1577, -0.7505, -0.6247, 1.7217], [-1.4796, -0.1338, 0.0280, 0.4423]]) >>> indices tensor([[3, 2, 0, 1], [0, 1, 3, 2], [3, 0, 2, 1]])
15. (input, dim=None, largest=True, sorted=True, out=None)
Description: Returns the k largest values in the input tensor input along the specified dim dimension. If dim is not specified, it defaults to the last dimension of the input, and if largest is False, the smallest k values are returned.
Parameters:
input(Tensor) ---- input tensor
k(int) ---- Value of k in "top-k"
dim(int, optional) ---- Dimension of sorting
largest(bool, optional) ---- boolean value, control to return the maximum or minimum value
sorted(bool, optional) ---- boolean, control whether the return value is sorted or not
out(tuple, optional) ---- Optional output tensor
Return Value: Returns a tuple (values, indices), where indices are the subscripts of the sorted elements in the original input tensor input. Setting the boolean sorted to True will ensure that the k values returned are sorted.
>>> x = (1, 6) >>> x tensor([1, 2, 3, 4, 5]) >>> (x, 3) torch.return_types.topk( values=tensor([5, 4, 3]), indices=tensor([4, 3, 2])) >>> (x, 3, 0, largest=False) torch.return_types.topk( values=tensor([1, 2, 3]), indices=tensor([0, 1, 2]))
Above this Pytorch learning of torch usage ---- Comparison Ops (Comparison Ops) is all I have to share with you, I hope to be able to give you a reference, and I hope you will support me more.