Function Differences with torch.nn.BCEWithLogitsLoss

torch.nn.BCEWithLogitsLoss(
    weight=None,
    size_average=None,
    reduce=None,
    reduction='mean',
    pos_weight=None
)(input, target) -> Tensor

class mindspore.nn.BCEWithLogitsLoss(
    reduction='mean',
    weight=None,
    pos_weight=None
)(logits, labels) -> Tensor

# PyTorch
import torch
from torch import Tensor
import numpy as np

np.random.seed(1)
input = Tensor(np.random.rand(1, 2, 3).astype(np.float32))
print(input.numpy())
# [[[4.17021990e-01 7.20324516e-01 1.14374816e-04]
#   [3.02332580e-01 1.46755889e-01 9.23385918e-02]]]
target = Tensor(np.random.randint(2,size=(1, 2, 3)).astype(np.float32))
print(target.numpy())
# [[[0. 1. 1.]
#   [0. 0. 1.]]]
torch_BCEWithLogitsLoss = torch.nn.BCEWithLogitsLoss()
torch_output = torch_BCEWithLogitsLoss(input, target)
torch_output_np = torch_output.numpy()
print(torch_output_np)
# 0.7142954

# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np

np.random.seed(1)
logits = Tensor(np.random.rand(1, 2, 3).astype(np.float32))
print(logits.asnumpy())
# [[[4.17021990e-01 7.20324516e-01 1.14374816e-04]
#   [3.02332580e-01 1.46755889e-01 9.23385918e-02]]]
labels = Tensor(np.random.randint(2,size=(1, 2, 3)).astype(np.float32))
print(labels.asnumpy())
# [[[0. 1. 1.]
#   [0. 0. 1.]]]
ms_BCEWithLogitsLoss = mindspore.nn.BCEWithLogitsLoss()
ms_output = ms_BCEWithLogitsLoss(logits, labels)
ms_output_np = ms_output.asnumpy()
print(ms_output_np)
# 0.71429545