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
)(input) -> Tensor

For more information, see torch.nn.BatchNorm2d.

mindspore.nn.BatchNorm2d

class mindspore.nn.BatchNorm2d(
    num_features,
    eps=1e-5,
    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'
)(x) -> Tensor

For more information, see mindspore.nn.BatchNorm2d.

Differences

PyTorch: Apply batch normalization on four-dimensional inputs (two-dimensional input with additional mini-batch and channel channels) to avoid internal covariate bias.

MindSpore: The function of this API is basically the same as that of PyTorch, with two typical differences. The default value of the momentum parameter in MindSpore is 0.9, and the momentum conversion relationship with PyTorch is 1-momentum. The behavior of the default value is the same as that of PyTorch. The parameter update strategy during training and inference is different from that of PyTorch. For details, please refer to Differences between PyTorch and MindSpore - nn.BatchNorm2d.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	num_features	num_features	-
	Parameter 2	eps	eps	-
	Parameter 3	momentum	momentum	The function is the same, but the default value in PyTorch is 0.1, and in MindSpore is 0.9, the conversion relationship with PyTorch’s momentum is 1-momentum, and the default value behavior is the same as PyTorch
	Parameter 4	affine	affine	-
	Parameter 5	track_running_stats	use_batch_statistics	The function is the same, and different values correspond to different default methods. For details, please refer to Differences between PyTorch and MindSpore - BatchNorm
	Parameter 6	-	gamma_init	The initialization method of the γ parameter, default value: “ones”. PyTorch does not have this parameter.
	Parameter 7	-	beta_init	The initialization method of the β parameter, default value: “zeros”. PyTorch does not have this parameter.
	Parameter 8	-	moving_mean_init	Initialization method of dynamic average, default value: “zeros”. PyTorch does not have this parameter.
	Parameter 9	-	moving_var_init	Initialization method of dynamic variance, default value: “ones”. PyTorch does not have this parameter.
	Parameter 10	-	data_format	MindSpore can specify the input data format as “NHWC” or “NCHW”, default value: “NCHW”. PyTorch does not have this parameter
Input	Single input	input	x	Same function, different parameter names

Code Example

In PyTorch, the value after 1-momentum is equal to the momentum of MindSpore, both trained by using mini-batch data and learning parameters.

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

m = nn.BatchNorm2d(num_features=3, momentum=0.1)
input_py = 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_py)
print(output.detach().numpy())
# [[[[-1.3411044  -0.44703478]
#    [ 0.4470349   1.3411044 ]]
#
#   [[-1.3411043  -0.44703442]
#    [ 0.44703496  1.3411049 ]]
#
#   [[-1.3411039  -0.44703427]
#    [ 0.44703534  1.341105  ]]]]

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

m = nn.BatchNorm2d(num_features=3, momentum=0.9)
m.set_train()
#  BatchNorm2d<num_features=3, eps=1e-05, momentum=0.9, gamma=Parameter (name=gamma, shape=(3,), dtype=Float32, requires_grad=True), beta=Parameter (name=beta, shape=(3,), dtype=Float32, requires_grad=True), moving_mean=Parameter (name=mean, shape=(3,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=variance, shape=(3,), dtype=Float32, requires_grad=False)>

input_ms = 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_ms)
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 ]]]]