Differences with torch.nn.BatchNorm3d

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torch.nn.BatchNorm3d

class torch.nn.BatchNorm3d(
    num_features,
    eps=1e-05,
    momentum=0.1,
    affine=True,
    track_running_stats=True
)(input) -> Tensor

For more information, see torch.nn.BatchNorm3d.

mindspore.nn.BatchNorm3d

class mindspore.nn.BatchNorm3d(
    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
)(x) -> Tensor

For more information, see mindspore.nn.BatchNorm3d.

Differences

PyTorch: Apply batch normalization on five-dimensional inputs (three-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 MindSpore and PyTorch - BatchNorm.

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 Typical differences with PyTorch -nn.BatchNorm

Parameter 6

-

gamma_init

The initialization method of the γ parameter, default value: “ones”.

Parameter 7

-

beta_init

The initialization method of the β parameter, default value: “zeros”.

Parameter 8

-

moving_mean_init

Initialization method of dynamic average, default value: “zeros”.

Parameter 9

-

moving_var_init

Initialization method of dynamic variance, default value: “ones”.

Input

Single input

input

x

Interface input, same function, only 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.BatchNorm3d(num_features=2, momentum=0.1)
input_x = tensor(np.array([[[[[0.1, 0.2], [0.3, 0.4]]],
                             [[[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 ]]]
#
#
#   [[[-1.3411034  -0.44703388]
#     [ 0.44703573  1.3411053 ]]]]]

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

m = nn.BatchNorm3d(num_features=2, momentum=0.9)
m.set_train()
# BatchNorm3d<
#      (bn2d): BatchNorm2d<num_features=2, eps=1e-05, momentum=0.9, gamma=Parameter (name=bn2d.gamma, shape=(2,), dtype=Float32, requires_grad=True), beta=Parameter (name=bn2d.beta, shape=(2,), dtype=Float32, requires_grad=True), moving_mean=Parameter (name=bn2d.moving_mean, shape=(2,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=bn2d.moving_variance, shape=(2,), dtype=Float32, requires_grad=False)>
#      >
input_x = Tensor(np.array([[[[[0.1, 0.2], [0.3, 0.4]]],
                             [[[0.9, 1], [1.1, 1.2]]]]]).astype(np.float32))
output = m(input_x)
print(output)
# [[[[[-1.3411044  -0.44703478]
#     [ 0.4470349   1.3411044 ]]]
#
#
#   [[[-1.3411039  -0.44703427]
#     [ 0.44703534  1.341105  ]]]]]