比较与torch.nn.InstanceNorm2d的差异

class torch.nn.InstanceNorm2d(
    num_features,
    eps=1e-05,
    momentum=0.1,
    affine=False,
    track_running_stats=False
)(input) -> Tensor

class mindspore.nn.InstanceNorm2d(
    num_features,
    eps=1e-5,
    momentum=0.1,
    affine=True,
    gamma_init='ones',
    beta_init='zeros'
)(x) -> Tensor

# PyTorch
from torch import nn, tensor
import numpy as np

m = nn.InstanceNorm2d(num_features=3)
input_x = tensor(np.array([[[[0.1, 0.2], [0.3, 0.4]],
                          [[0.5, 0.6], [0.7, 0.8]],
                          [[0.9, 1], [1.1, 1.2]]]]).astype(np.float32))
output = m(input_x)
print(output.detach().numpy())
# [[[[-1.3411044  -0.44703478]
#    [ 0.4470349   1.3411044 ]]
#
#   [[-1.3411045  -0.44703463]
#    [ 0.44703472  1.3411046 ]]
#
#   [[-1.3411034  -0.44703388]
#    [ 0.44703573  1.3411053 ]]]]

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

m = nn.InstanceNorm2d(num_features=3, affine=False)
m.set_train()
input_x = Tensor(np.array([[[[0.1, 0.2], [0.3, 0.4]],
                          [[0.5, 0.6], [0.7, 0.8]],
                          [[0.9, 1], [1.1, 1.2]]]]).astype(np.float32))
output = m(input_x)
print(output)
# [[[[-1.3411045  -0.4470348 ]
#    [ 0.44703496  1.3411045 ]]
#
#   [[-1.341105   -0.4470351 ]
#    [ 0.44703424  1.3411041 ]]
#
#   [[-1.3411034  -0.44703388]
#    [ 0.44703573  1.3411053 ]]]]