Function Differences with torch.nn.BatchNorm2d
torch.nn.BatchNorm2d
class torch.nn.BatchNorm2d(
num_features,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True
)
For more information, see torch.nn.BatchNorm2d.
mindspore.nn.BatchNorm2d
class mindspore.nn.BatchNorm2d(
num_features,
eps=1e-05,
momentum=0.9,
affine=True,
gamma_init="ones",
beta_init="zeros",
moving_mean_init="zeros",
moving_var_init="ones",
use_batch_statistics=None,
data_format="NCHW")
)
For more information, see mindspore.nn.BatchNorm2d.
Differences
PyTorch:The default value of the momentum parameter used for running_mean and running_var calculation is 0.1.
MindSpore:The default value of the momentum parameter is 0.9, and the momentum relationship with Pytorch is 1-momentum, that is, when Pytorch’s momentum value is 0.2, MindSpore’s momemtum should be 0.8. Parameter beta, gamma, moving_mean and moving_variance correspond to Pytorch’s bias, weight, running_mean and running_var parameters respectively.
Code Example
# The following implements BatchNorm2d with MindSpore.
import numpy as np
import torch
import mindspore.nn as nn
import mindspore as ms
net = nn.BatchNorm2d(num_features=2, momentum=0.8)
x = ms.Tensor(np.array([[[[1, 2], [1, 2]], [[3, 4], [3, 4]]]]).astype(np.float32))
output = net(x)
print(output)
# Out:
# [[[[0.999995 1.99999]
# [0.999995 1.99999]]
#
# [[2.999985 3.99998]
# [2.999985 3.99998]]]]
# The following implements BatchNorm2d with torch.
input_x = torch.tensor(np.array([[[[1, 2], [1, 2]], [[3, 4], [3, 4]]]]).astype(np.float32))
m = torch.nn.BatchNorm2d(2, momentum=0.2)
output = m(input_x)
print(output)
# Out:
# tensor([[[[-1.0000, 1.0000],
# [-1.0000, 1.0000]],
#
# [[-1.0000, 1.0000],
# [-1.0000, 1.0000]]]], grad_fn=<NativeBatchNormBackward>)