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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
@manager.LOSSES.add_component
class MSELoss(nn.MSELoss):
r"""
**Mean Square Error Loss**
Computes the mean square error (squared L2 norm) of given input and label.
If :attr:`reduction` is set to ``'none'``, loss is calculated as:
.. math::
Out = (input - label)^2
If :attr:`reduction` is set to ``'mean'``, loss is calculated as:
.. math::
Out = \operatorname{mean}((input - label)^2)
If :attr:`reduction` is set to ``'sum'``, loss is calculated as:
.. math::
Out = \operatorname{sum}((input - label)^2)
where `input` and `label` are `float32` tensors of same shape.
Args:
reduction (string, optional): The reduction method for the output,
could be 'none' | 'mean' | 'sum'.
If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned.
If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
Default is ``'mean'``.
ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Default: 255.
Shape:
input (Tensor): Input tensor, the data type is float32 or float64
label (Tensor): Label tensor, the data type is float32 or float64
output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.
Examples:
.. code-block:: python
import numpy as np
import paddle
input_data = np.array([1.5]).astype("float32")
label_data = np.array([1.7]).astype("float32")
mse_loss = paddle.nn.loss.MSELoss()
input = paddle.to_tensor(input_data)
label = paddle.to_tensor(label_data)
output = mse_loss(input, label)
print(output)
# [0.04000002]
"""
def __init__(self, reduction='mean', ignore_index=255):
super().__init__(reduction=reduction)
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