# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""loss"""
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.primitive import constexpr
from mindspore.ops import _selected_ops
from mindspore.nn.cell import Cell
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from ... import context
class _Loss(Cell):
"""
Base class for other losses.
"""
def __init__(self, reduction='mean'):
super(_Loss, self).__init__()
if reduction is None:
reduction = 'none'
if reduction not in ('mean', 'sum', 'none'):
raise ValueError(f"reduction method for {reduction.lower()} is not supported")
self.average = True
self.reduce = True
if reduction == 'sum':
self.average = False
if reduction == 'none':
self.reduce = False
self.reduce_mean = _selected_ops.ReduceMean()
self.reduce_sum = P.ReduceSum()
def get_axis(self, x):
shape = F.shape(x)
length = F.tuple_len(shape)
perm = F.make_range(0, length)
return perm
def get_loss(self, x):
if self.reduce and self.average:
x = self.reduce_mean(x, self.get_axis(x))
if self.reduce and not self.average:
x = self.reduce_sum(x, self.get_axis(x))
return x
def construct(self, base, target):
raise NotImplementedError
[docs]class L1Loss(_Loss):
r"""
L1Loss creates a criterion to measure the mean absolute error (MAE) between :math:`x` and :math:`y` by element,
where :math:`x` is the input Tensor and :math:`y` is the target Tensor.
For simplicity, let :math:`x` and :math:`y` be 1-dimensional Tensor with length :math:`N`,
the unreduced loss (i.e. with argument reduction set to 'none') of :math:`x` and :math:`y` is given as:
.. math::
L(x, y) = \{l_1,\dots,l_N\}, \quad \text{with } l_n = \left| x_n - y_n \right|
When argument reduction is 'mean', the mean value of :math:`L(x, y)` will be returned.
When argument reduction is 'sum', the sum of :math:`L(x, y)` will be returned. :math:`N` is the batch size.
Args:
reduction (str): Type of reduction to be applied to loss. The optional values are "mean", "sum", and "none".
Default: "mean".
Inputs:
- **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`. The data type must be float16 or
float32.
- **target_data** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`. The data type must be float16 or
float32.
Outputs:
Tensor, loss float tensor.
Examples:
>>> loss = nn.L1Loss()
>>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> loss(input_data, target_data)
0.33333334
"""
def __init__(self, reduction='mean'):
super(L1Loss, self).__init__(reduction)
self.abs = P.Abs()
def construct(self, base, target):
x = self.abs(base - target)
return self.get_loss(x)
[docs]class MSELoss(_Loss):
r"""
MSELoss creates a criterion to measure the mean squared error (squared L2-norm) between :math:`x` and :math:`y`
by element, where :math:`x` is the input and :math:`y` is the target.
For simplicity, let :math:`x` and :math:`y` be 1-dimensional Tensor with length :math:`N`,
the unreduced loss (i.e. with argument reduction set to 'none') of :math:`x` and :math:`y` is given as:
.. math::
L(x, y) = \{l_1,\dots,l_N\}, \quad \text{with} \quad l_n = (x_n - y_n)^2.
When argument reduction is 'mean', the mean value of :math:`L(x, y)` will be returned.
When argument reduction is 'sum', the sum of :math:`L(x, y)` will be returned. :math:`N` is the batch size.
Args:
reduction (str): Type of reduction to be applied to loss. The optional values are "mean", "sum", and "none".
Default: "mean".
Inputs:
- **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
- **target_data** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`.
Outputs:
Tensor, weighted loss float tensor.
Examples:
>>> loss = nn.MSELoss()
>>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> loss(input_data, target_data)
"""
def construct(self, base, target):
x = F.square(base - target)
return self.get_loss(x)
[docs]class SmoothL1Loss(_Loss):
r"""
A loss class for learning region proposals.
SmoothL1Loss can be regarded as modified version of L1Loss or a combination of L1Loss and L2Loss.
L1Loss computes the element-wise absolute difference between two input Tensor while L2Loss computes the
squared difference between two input Tensor. L2Loss often leads to faster convergence but it is less
robust to outliers.
Given two input :math:`x,\ y` of length :math:`N`, the unreduced SmoothL1Loss can be described
as follows:
.. math::
L_{i} =
\begin{cases}
\frac{0.5 (x_i - y_i)^{2}}{\text{beta}}, & \text{if } |x_i - y_i| < \text{beta} \\
|x_i - y_i| - 0.5 \text{beta}, & \text{otherwise. }
\end{cases}
Here :math:`\text{beta}` controls the point where the loss function changes from quadratic to linear.
Its default value is 1.0. :math:`N` is the batch size. This function returns an
unreduced loss Tensor.
Args:
beta (float): A parameter used to control the point where the function will change from
quadratic to linear. Default: 1.0.
Inputs:
- **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
- **target_data** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`.
Outputs:
Tensor, loss float tensor.
Examples:
>>> loss = nn.SmoothL1Loss()
>>> input_data = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> target_data = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> loss(input_data, target_data)
"""
def __init__(self, beta=1.0):
super(SmoothL1Loss, self).__init__()
self.beta = beta
self.smooth_l1_loss = P.SmoothL1Loss(self.beta)
def construct(self, base, target):
return self.smooth_l1_loss(base, target)
[docs]class SoftmaxCrossEntropyWithLogits(_Loss):
r"""
Computes softmax cross entropy between logits and labels.
Measures the distribution error between the probabilities of the input (computed with softmax function) and the
target where the classes are mutually exclusive (only one class is positive) using cross entropy loss.
Typical input into this function is unnormalized scores and target of each class.
Scores Tensor :math:`x` is of shape :math:`(N, C)` and target Tensor :math:`t` is a
Tensor of shape :math:`(N, C)` which contains one-hot labels of length :math:`C`.
For each instance :math:`N_i`, the loss is given as:
.. math::
\ell(x_i, t_i) = - \log\left(\frac{\exp(x_{t_i})}{\sum_j \exp(x_j)}\right)
= -x_{t_i} + \log\left(\sum_j \exp(x_i)\right),
where :math:`x_i` is a 1D score Tensor, :math:`t_i` is a scalar.
Note:
While the target classes are mutually exclusive, i.e., only one class is positive in the target, the predicted
probabilities need not to be exclusive. It is only required that the predicted probability distribution
of entry is a valid one.
Args:
sparse (bool): Specifies whether labels use sparse format or not. Default: False.
reduction (str): Type of reduction to be applied to loss. The optional values are "mean", "sum", and "none".
If "none", do not perform reduction. Default: "none".
Inputs:
- **logits** (Tensor) - Tensor of shape (N, C).
- **labels** (Tensor) - Tensor of shape (N, ). If `sparse` is True, The type of
`labels` is mindspore.int32. If `sparse` is False, the type of `labels` is the same as the type of `logits`.
Outputs:
Tensor, a tensor of the same shape as logits with the component-wise
logistic losses.
Examples:
>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> logits = Tensor(np.random.randint(0, 9, [1, 10]), mindspore.float32)
>>> labels_np = np.ones([1,]).astype(np.int32)
>>> labels = Tensor(labels_np)
>>> loss(logits, labels)
"""
def __init__(self,
sparse=False,
reduction='none'):
super(SoftmaxCrossEntropyWithLogits, self).__init__(reduction)
self.sparse = sparse
self.reduction = reduction
self.softmax_cross_entropy = _selected_ops.SoftmaxCrossEntropyWithLogits()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0., mstype.float32)
self.is_cpugpu = context.get_context('device_target') in ["CPU", "GPU"]
if self.is_cpugpu:
self.sparse_softmax_cross_entropy = P.SparseSoftmaxCrossEntropyWithLogits()
def construct(self, logits, labels):
if self.is_cpugpu and self.sparse and self.reduction == 'mean':
x = self.sparse_softmax_cross_entropy(logits, labels)
return x
if self.sparse:
labels = self.one_hot(labels, F.shape(logits)[-1], self.on_value, self.off_value)
x = self.softmax_cross_entropy(logits, labels)[0]
return self.get_loss(x)
@constexpr
def _check_reduced_shape_valid(ori_shape, reduced_shape, axis, cls_name):
validator.check_reduce_shape(ori_shape, reduced_shape, axis, cls_name)
[docs]class CosineEmbeddingLoss(_Loss):
r"""
Computes the similarity between two tensors using cosine distance.
Given two tensors `x1`, `x2`, and a Tensor label `y` with values 1 or -1:
.. math::
loss(x_1, x_2, y) = \begin{cases}
1-cos(x_1, x_2), & \text{if } y = 1\\
max(0, cos(x_1, x_2)-margin), & \text{if } y = -1\\
\end{cases}
Args:
margin (float): Should be in [-1.0, 1.0]. Default 0.0.
reduction (str): Specifies which reduction to be applied to the output. It must be one of
"none", "mean", and "sum", meaning no reduction, reduce mean and sum on output, respectively. Default "mean".
Inputs:
- **input_x1** (Tensor) - Input tensor.
- **input_x2** (Tensor) - Its shape and data type must be the same as `input_x1`'s shape and data type.
- **y** (Tensor) - Contains value 1 or -1. Suppose the shape of `input_x1` is
:math:`(x_1, x_2, x_3,..., x_R)`, then the shape of `target` must be :math:`(x_1, x_3, x_4, ..., x_R)`.
Outputs:
- **loss** (Tensor) - If `reduction` is "none", its shape is the same as `y`'s shape, otherwise a scalar value
will be returned.
Examples:
>>> x1 = Tensor(np.array([[0.3, 0.8], [0.4, 0.3]]), mindspore.float32)
>>> x2 = Tensor(np.array([[0.4, 1.2], [-0.4, -0.9]]), mindspore.float32)
>>> y = Tensor(np.array([1,-1]), mindspore.int32)
>>> cosine_embedding_loss = P.CosineEmbeddingLoss()
>>> cosine_embedding_loss(x1, x2, y)
[0.0003426671]
"""
def __init__(self, margin=0.0, reduction="mean"):
super(CosineEmbeddingLoss, self).__init__(reduction)
self.reduce_sum = P.ReduceSum()
self.maximum = P.Maximum()
validator.check_value_type("margin", margin, [float], self.cls_name)
self.margin = validator.check_number_range("margin", margin, -1.0, 1.0, Rel.INC_BOTH, self.cls_name)
def construct(self, x1, x2, y):
F.same_type_shape(x1, x2)
_check_reduced_shape_valid(F.shape(x1), F.shape(y), (1,), self.cls_name)
# if target > 0, 1-cosine(x1, x2)
# else, max(0, cosine(x1, x2)-margin)
prod_sum = self.reduce_sum(x1 * x2, (1,))
square1 = self.reduce_sum(F.square(x1), (1,))
square2 = self.reduce_sum(F.square(x2), (1,))
denom = F.sqrt(square1 * square2)
cosine = prod_sum / denom
pos_value = 1.0 - cosine
neg_value = self.maximum(cosine - self.margin, 0.0)
zeros = F.zeros_like(cosine)
pos_part = F.select(y == 1, pos_value, zeros)
neg_part = F.select(y == -1, neg_value, zeros)
output_unreduced = pos_part + neg_part
return self.get_loss(output_unreduced)