Differences with torch.nn.TripletMarginLoss
The following mapping relationships can be found in this file.
PyTorch APIs |
MindSpore APIs |
---|---|
torch.nn.TripletMarginLoss |
mindspore.nn.TripletMarginLoss |
torch.functional.triplet_margin_loss |
mindspore.ops.triplet_margin_loss |
torch.nn.TripletMarginLoss
torch.nn.TripletMarginLoss(
margin=1.0,
p=2.0,
eps=1e-06,
swap=False,
size_average=None,
reduce=None,
reduction='mean'
)(anchor, positive, negative) -> Tensor/Scalar
For more information, see torch.nn.TripletMarginLoss.
mindspore.nn.TripletMarginLoss
mindspore.nn.TripletMarginLoss(
p=2,
swap=False,
eps=1e-06,
reduction='mean'
)(margin, x, positive, negative) -> Tensor/Scalar
For more information, see mindspore.nn.TripletMarginLoss.
Differences
API function of MindSpore is consistent with that of PyTorch.
PyTorch:
PyTorch has two initialization parameters
size_average
andreduce
, which are deprecated and replaced byreduction
.margin
is an initialization parameter, not an input parameter. The data type ofmargin
is float.
MindSpore:
MindSpore doesn't have initialization parameters
size_average
andreduce
.margin
is not an initialization parameter, but an input parameter. The data type ofmargin
can be tensor or float.The input parameter
x
of MindSpore corresponds to the input parameteranchor
of PyTorch.MindSpore's initialization parameters
swap
andeps
are positioned in a different order than PyTorch.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
margin |
- |
Different position, same function. Data type is float. |
Parameter 2 |
p |
p |
- |
|
Parameter 3 |
eps |
swap |
Different position, same function. |
|
Parameter 4 |
swap |
eps |
Different position, same function. |
|
Parameter 5 |
size_average |
- |
PyTorch has deprecated this parameter, while MindSpore does not have this parameter. |
|
Parameter 6 |
reduce |
- |
PyTorch has deprecated this parameter, while MindSpore does not have this parameter. |
|
Parameter 7 |
reduction |
reduction |
- |
|
Input |
Input 1 |
- |
margin |
Different position, same function. Data type can be tensor or float. |
Input 2 |
anchor |
x |
Same function, different parameter names. |
|
Input 3 |
positive |
positive |
- |
|
Input 4 |
negative |
negative |
- |
Code Example
# PyTorch
import torch
import torch.nn as nn
import numpy as np
p = 2
swap = False
eps = 1e-06
reduction = 'mean'
margin = 1.0
triplet_margin_loss = nn.TripletMarginLoss(margin=margin, p=p, eps=eps, swap=swap, reduction=reduction)
x = torch.tensor(np.array([[0.3, 0.7], [0.5, 0.5]]), dtype=torch.float32)
positive = torch.tensor(np.array([[0.4, 0.6], [0.4, 0.6]]), dtype=torch.float32)
negative = torch.tensor(np.array([[0.2, 0.9], [0.3, 0.7]]), dtype=torch.float32)
output = triplet_margin_loss(x, positive, negative)
print(output)
# tensor(0.8882)
# MindSpore
import mindspore as ms
import mindspore.nn as nn
import numpy as np
p = 2
swap = False
eps = 1e-06
reduction = 'mean'
triplet_margin_loss = nn.TripletMarginLoss(p=p, swap=swap, eps=eps, reduction=reduction)
x = ms.Tensor(np.array([[0.3, 0.7], [0.5, 0.5]]), dtype=ms.float32)
positive = ms.Tensor(np.array([[0.4, 0.6], [0.4, 0.6]]), dtype=ms.float32)
negative = ms.Tensor(np.array([[0.2, 0.9], [0.3, 0.7]]), dtype=ms.float32)
margin = ms.Tensor(1.0, ms.float32)
output = triplet_margin_loss(x, positive, negative, margin)
print(output)
# 0.8881968