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 as ms
import mindspore.ops as ops
import torch
import numpy as np
# Output tuple(index of min, min).
input_x = ms.Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), ms.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.)