Function Differences with torch.min

torch.min

torch.min(
    input,
    dim,
    keepdim=False,
    out=None
)

For more information, see torch.min.

mindspore.ops.ArgMinWithValue

class mindspore.ops.ArgMinWithValue(
    axis=0,
    keep_dims=False
)(input_x)

For more information, see mindspore.ops.ArgMinWithValue.

Differences

PyTorch: Output tuple(min, index of min).

MindSpore: Output tuple(index of min, min).

Code Example

import mindspore
from mindspore import Tensor
import mindspore.ops as ops
import torch
import numpy as np

# Output tuple(index of min, min).
input_x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
argmin = ops.ArgMinWithValue()
index, output = argmin(input_x)
print(index)
print(output)
# Out：
# 0
# 0.0

# Output tuple(min, index of min).
input_x = torch.tensor([0.0, 0.4, 0.6, 0.7, 0.1])
output, index = torch.min(input_x, 0)
print(index)
print(output)
# Out：
# tensor(0)
# tensor(0.)