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.

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.BatchNorm2d 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.BatchNorm2d 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.BatchNorm2d, mindspore.nn.BatchNorm2d 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

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 ]]]]