Differences with torch.nn.BatchNorm1d

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torch.nn.BatchNorm1d

class torch.nn.BatchNorm1d(
    num_features,
    eps=1e-05,
    momentum=0.1,
    affine=True,
    track_running_stats=True
)(input) -> Tensor

For more information, see torch.nn.BatchNorm1d.

mindspore.nn.BatchNorm1d

class mindspore.nn.BatchNorm1d(
    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.BatchNorm1d.

Differences

PyTorch:Batch normalization of the input 2D or 3D data.

MindSpore:The implementation function of the API in MindSpore is basically the same as that of PyTorch. 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.

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.

Parameter 6

-

gamma_init

PyTorch does not have this parameter, while MindSpore can initialize the value of the parameter gamma

Parameter 7

-

beta_init

PyTorch does not have this parameter, while MindSpore can initialize the value of the parameter beta

Parameter 8

-

moving_mean_init

PyTorch does not have this parameter, while MindSpore can initialize the value of the parameter moving_mean

Parameter 9

-

moving_var_init

PyTorch does not have this parameter, while MindSpore can initialize the value of the parameter moving_var

Parameter 10

-

data_format

PyTorch does not have this parameter

Input

Single input

input

x

Same function, different parameter names

The detailed differences are as follows: BatchNorm is a special regularization method in the CV field. It has different computation processes during training and inference and is usually controlled by operator attributes. BatchNorm of MindSpore and PyTorch uses two different parameter groups at this point.

  • Difference 1

    torch.nn.BatchNorm1d status under different parameters

    training

    track_running_stats

    Status

    True

    True

    Expected training status. running_mean and running_var trace the statistical features of the batch in the entire training process. Each group of input data is normalized based on the mean and var statistical features of the current batch, and then running_mean and running_var are updated.

    True

    False

    Each group of input data is normalized based on the statistics feature of the current batch, but the running_mean and running_var parameters do not exist.

    False

    True

    Expected inference status. The BN uses running_mean and running_var for normalization and does not update them.

    False

    False

    The effect is the same as that of the second status. The only difference is that this is the inference status and does not learn the weight and bias parameters. Generally, this status is not used.

    mindspore.nn.BatchNorm1d status under different parameters

    use_batch_statistics

    Status

    True

    Expected training status. moving_mean and moving_var trace the statistical features of the batch in the entire training process. Each group of input data is normalized based on the mean and var statistical features of the current batch, and then moving_mean and moving_var are updated.

    Fasle

    Expected inference status. The BN uses moving_mean and moving_var for normalization and does not update them.

    None

    use_batch_statistics is automatically set. For training, set use_batch_statistics to True. For inference, set use_batch_statistics to False.

    Compared with torch.nn.BatchNorm1d, mindspore.nn.BatchNorm1d does not have two redundant states and retains only the most commonly used training and inference states.

  • Difference 2

    In PyTorch, the network is in training mode by default, while in MindSpore, it is in inference mode by default (is_training is False). You need to use the net.set_train() method in MindSpore to switch the network to training mode. In this case, the parameters mean and variance are calculated during the training. Otherwise, in inference mode, the parameters are loaded from the checkpoint.

  • Difference 3

    The meaning of the momentum parameter of the BatchNorm series operators in MindSpore is opposite to that in PyTorch. The relationship is as follows:

    \[momentum_{pytorch} = 1 - momentum_{mindspore}\]

Code Example

The two APIs achieve the same function and have the same usage.

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

net = nn.BatchNorm1d(4, affine=False, momentum=0.1)
x = tensor(np.array([[0.7, 0.5, 0.5, 0.6], [0.5, 0.4, 0.6, 0.9]]).astype(np.float32))
output = net(x)
print(output.detach().numpy())
# [[ 0.9995001   0.9980063  -0.998006   -0.99977785]
#  [-0.9995007  -0.9980057   0.998006    0.99977785]]

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

net = nn.BatchNorm1d(num_features=4, affine=False, momentum=0.9)
net.set_train()
# BatchNorm1d<num_features=4, eps=1e-05, momentum=0.9, gamma=Parameter (name=gamma, shape=(4,), dtype=Float32, requires_grad=False), beta=Parameter (name=beta, shape=(4,), dtype=Float32, requires_grad=False), moving_mean=Parameter (name=mean, shape=(4,), dtype=Float32, requires_grad=False), moving_variance=Parameter (name=variance, shape=(4,), dtype=Float32, requires_grad=False)>

x = Tensor(np.array([[0.7, 0.5, 0.5, 0.6], [0.5, 0.4, 0.6, 0.9]]).astype(np.float32))
output = net(x)
print(output.asnumpy())
# [[ 0.9995001  0.9980063 -0.998006  -0.9997778]
#  [-0.9995007 -0.9980057  0.998006   0.9997778]]