oneflow.nn.TripletMarginLoss

class oneflow.nn.TripletMarginLoss(margin: float = 1.0, p: float = 2.0, eps: float = 1e-06, swap: bool = False, size_average=None, reduce=None, reduction: str = 'mean')

Creates a criterion that measures the triplet loss given an input tensors \(x1\), \(x2\), \(x3\) and a margin with a value greater than \(0\). This is used for measuring a relative similarity between samples. A triplet is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be \((N, D)\).

The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.

The loss function for each sample in the mini-batch is:

\[L(a, p, n) = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\}\]

where

\[d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_p\]
Parameters
  • margin (float, optional) – Default: \(1\).

  • p (float, optional) – The norm degree for pairwise distance. Default: \(2.0\).

  • swap (bool, optional) – The distance swap is described in detail in the paper Learning shallow convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al. Default: False.

  • reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

Shape:
  • Input: \((N, D)\) where \(D\) is the vector dimension.

  • Output: A Tensor of shape \((N)\) if reduction is 'none', or a scalar otherwise.

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> triplet_loss = flow.nn.TripletMarginLoss(margin=1.0, p=2)
>>> anchor = np.array([[1, -1, 1],[-1, 1, -1], [1, 1, 1]])
>>> positive = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> negative = np.array([[2, 2, 2], [2, 2, 2], [2, 2, 2]])
>>> output = triplet_loss(flow.Tensor(anchor), flow.Tensor(positive), flow.Tensor(negative))
>>> output
tensor(6.2971, dtype=oneflow.float32)
__init__(margin: float = 1.0, p: float = 2.0, eps: float = 1e-06, swap: bool = False, size_average=None, reduce=None, reduction: str = 'mean')None

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__(*args, **kwargs)

Call self as a function.

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattr__(name)

__getattribute__(name, /)

Return getattr(self, name).

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__([margin, p, eps, swap, …])

Initialize self.

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(**kwargs)

Create and return a new object.

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

_apply(fn[, applied_dict])

_get_name()

_load_from_state_dict(state_dict, prefix, …)

_named_members(get_members_fn[, prefix, recurse])

_save_to_state_dict(destination, prefix, …)

_shallow_repr()

add_module(name, module)

Adds a child module to the current module.

apply(fn)

Applies fn recursively to every submodule (as returned by .children()) as well as self.

buffers([recurse])

Returns an iterator over module buffers.

children()

Returns an iterator over immediate children modules.

cpu()

Moves all model parameters and buffers to the CPU.

cuda([device])

Moves all model parameters and buffers to the GPU.

double()

Casts all floating point parameters and buffers to double datatype.

eval()

Sets the module in evaluation mode.

extra_repr()

Set the extra representation of the module

float()

Casts all floating point parameters and buffers to float datatype.

forward(anchor, positive, negative)

half()

Casts all floating point parameters and buffers to half datatype.

load_state_dict(state_dict[, strict])

Copies parameters and buffers from state_dict into this module and its descendants.

modules()

Returns an iterator over all modules in the network.

named_buffers([prefix, recurse])

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix])

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse])

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

parameters([recurse])

Returns an iterator over module parameters.

register_buffer(name, tensor[, persistent])

Adds a buffer to the module.

register_forward_hook(hook)

Registers a forward hook on the module.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the module.

register_parameter(name, param)

Adds a parameter to the module.

state_dict([destination, prefix, keep_vars])

Returns a dictionary containing a whole state of the module.

to([device])

Moves the parameters and buffers.

to_consistent(*args, **kwargs)

This interface is no longer available, please use oneflow.nn.Module.to_global() instead.

to_global([placement, sbp])

Convert the parameters and buffers to global.

train([mode])

Sets the module in training mode.

zero_grad([set_to_none])

Sets gradients of all model parameters to zero.