# 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.
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# http://www.apache.org/licenses/LICENSE-2.0
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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.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 = P.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 apply to loss. The optional values are "mean", "sum", "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, 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)
"""
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 create a criterion to measures 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 apply to loss. The optional values are "mean", "sum", "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}
0.5 (x_i - y_i)^2, & \text{if } |x_i - y_i| < \text{sigma}; \\
|x_i - y_i| - 0.5, & \text{otherwise. }
\end{cases}
Here :math:`\text{sigma}` 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:
sigma (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, sigma=1.0):
super(SmoothL1Loss, self).__init__()
self.sigma = sigma
self.smooth_l1_loss = P.SmoothL1Loss(self.sigma)
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 be exclusive. All that is required is that the predicted probability distribution
of entry is a valid one.
Args:
is_grad (bool): Specifies whether calculate grad only. Default: True.
sparse (bool): Specifies whether labels use sparse format or not. Default: False.
reduction (Union[str, None]): Type of reduction to apply to loss. Support 'sum' or 'mean' If None,
do not reduction. Default: None.
smooth_factor (float): Label smoothing factor. It is a optional input. Default: 0.
num_classes (int): The number of classes in the task. It is a optional input Default: 2.
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 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,
is_grad=True,
sparse=False,
reduction=None,
smooth_factor=0,
num_classes=2):
super(SoftmaxCrossEntropyWithLogits, self).__init__(reduction)
self.is_grad = is_grad
self.sparse = sparse
validator.check_integer("num_classes", num_classes, 1, Rel.GT, self.cls_name)
validator.check_number_range("smooth_factor", smooth_factor, 0, 1, Rel.INC_BOTH, self.cls_name)
self.smooth_factor = smooth_factor
self.num_classes = num_classes
self.softmax_cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0 - self.smooth_factor, mstype.float32)
self.off_value = Tensor(1.0 * self.smooth_factor / (self.num_classes - 1), mstype.float32)
self.is_cpugpu = context.get_context('device_target') in ["CPU", "GPU"]
if self.is_cpugpu:
self.sparse_softmax_cross_entropy = P.SparseSoftmaxCrossEntropyWithLogits(is_grad=self.is_grad)
def construct(self, logits, labels):
if self.is_cpugpu and self.sparse:
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)
[docs]class SoftmaxCrossEntropyExpand(Cell):
r"""
Computes softmax cross entropy between logits and labels. Implemented by expanded formula.
This is a wrapper of several functions.
.. math::
\ell(x_i, t_i) = -log\left(\frac{\exp(x_{t_i})}{\sum_j \exp(x_j)}\right),
where :math:`x_i` is a 1D score Tensor, :math:`t_i` is the target class.
Note:
When argument sparse is set to True, the format of label is the index
range from :math:`0` to :math:`C - 1` instead of one-hot vectors.
Args:
sparse(bool): Specifies whether labels use sparse format or not. Default: False.
Inputs:
- **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
- **label** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`.
Outputs:
Tensor, a scalar tensor including the mean loss.
Examples:
>>> loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
>>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32)
>>> label = Tensor(np.ones([64]), dtype=mindspore.int32)
>>> loss(input_data, label)
"""
def __init__(self, sparse=False):
super(SoftmaxCrossEntropyExpand, self).__init__()
self.exp = P.Exp()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.onehot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.div = P.Div()
self.log = P.Log()
self.sum_cross_entropy = P.ReduceSum(keep_dims=False)
self.mul = P.Mul()
self.mul2 = P.Mul()
self.cast = P.Cast()
self.reduce_mean = P.ReduceMean(keep_dims=False)
self.sparse = sparse
self.reduce_max = P.ReduceMax(keep_dims=True)
self.sub = P.Sub()
def construct(self, logit, label):
logit_max = self.reduce_max(logit, -1)
exp = self.exp(self.sub(logit, logit_max))
exp_sum = self.reduce_sum(exp, -1)
softmax_result = self.div(exp, exp_sum)
if self.sparse:
label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
softmax_result_log = self.log(softmax_result)
loss = self.sum_cross_entropy((self.mul(softmax_result_log, label)), -1)
loss = self.mul2(F.scalar_to_array(-1.0), loss)
loss = self.reduce_mean(loss, -1)
return loss