Differences with torch.nn.SmoothL1Loss

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torch.nn.SmoothL1Loss

class torch.nn.SmoothL1Loss(size_average=None, reduce=None, reduction='mean', beta=1.0)(input, target) -> Tensor

For more information, see torch.nn.SmoothL1Loss.

mindspore.nn.SmoothL1Loss

class mindspore.nn.SmoothL1Loss(beta=1.0, reduction='none')(logits, labels) -> Tensor

For more information, see mindspore.nn.SmoothL1Loss.

Differences

PyTorch: SmoothL1Loss loss function. If the element-wise absolute error between the predicted and target values is less than a set threshold beta, use a squared term, otherwise with an absolute error term.

MindSpore: There are no functional differences except for two parameters that have been deprecated in PyTorch.

Categories

Subcategories

PyTorch

MindSpore

Difference

Parameters

Parameter 1

size_average

-

Deprecated, replaced by reduction. MindSpore does not have this parameter

Parameter 2

reduce

-

Deprecated, replaced by reduction. MindSpore does not have this parameter

Parameter 3

reduction

reduction

Same function, different parameter names

Parameter 4

beta

beta

-

Input

Input 1

input

logits

Same function, different parameter names

Input 2

target

labels

Same function, different parameter names

Code Example

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

# PyTorch
import torch
import torch.nn as nn

beta = 1
loss = nn.SmoothL1Loss(reduction="none", beta=beta)
logits = torch.FloatTensor([1, 2, 3])
labels = torch.FloatTensor([1, 2, 2])
output = loss(logits, labels)
print(output.numpy())
# [0.  0.  0.5]

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

loss = mindspore.nn.SmoothL1Loss()
logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
labels = Tensor(np.array([1, 2, 2]), mindspore.float32)
output = loss(logits, labels)
print(output)
# [0.  0.  0.5]