Differences with torch.nn.GroupNorm

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torch.nn.GroupNorm

class torch.nn.GroupNorm(
    num_groups,
    num_channels,
    eps=1e-05,
    affine=True
)(input) -> Tensor

For more information, see torch.nn.GroupNorm.

mindspore.nn.GroupNorm

class mindspore.nn.GroupNorm(
    num_groups,
    num_channels,
    eps=1e-05,
    affine=True,
    gamma_init='ones',
    beta_init='zeros'
)(x) -> Tensor

For more information, see mindspore.nn.GroupNorm.

Differences

PyTorch: Group normalization is performed on the mini-batch input by dividing the channels into groups and then calculating the mean and variance within each group for normalization.

MindSpore: MindSpore API implements basically the same function as PyTorch. MindSpore can also perform additional initialization of the radiating parameters that need to be learned.

Categories

Subcategories

PyTorch

MindSpore

Difference

Parameters

Parameter 1

num_groups

num_groups

-

Parameter 2

num_channels

num_channels

-

Parameter 3

eps

eps

-

Parameter 4

affine

affine

-

Parameter 5

-

gamma_init

Initialize the radial transform parameter gamma used for learning in the formula. The default is ‘ones’, while PyTorch cannot be set additionally, can only be ‘ones’.

Parameter 6

-

beta_init

Initialize the radial transform parameter beta used for learning in the formula. The default is ‘ones’, while PyTorch cannot be set additionally, can only be ‘ones’.

Input

Single input

input

x

Interface input, same function, different parameter names

Code Example 1

MindSpore API basically implements the same function as TensorFlow, and MindSpore can also perform additional initialization of the two learning parameters.

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

x = tensor(np.ones([1, 2, 4, 4], np.float32))
net = nn.GroupNorm(2, 2)
output = net(x).detach().numpy()
print(output)
# [[[[0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]]
#
#   [[0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]]]]

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

x = Tensor(np.ones([1, 2, 4, 4], np.float32))
net = nn.GroupNorm(2, 2)
output = net(x)
print(output)
# [[[[0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]]
#
#   [[0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]
#    [0. 0. 0. 0.]]]]