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未验证 提交 b9207054 编写于 作者: A Ayuan 提交者： GitHub
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InstanceNorm1D、InstanceNorm2D、InstanceNorm3D (#48940) 

* modified:   python/paddle/nn/layer/norm.py

* modified:   python/paddle/nn/layer/norm.py

* modified:   python/paddle/nn/layer/norm.py

* modified:   python/paddle/nn/layer/norm.py

* modified:   python/paddle/nn/layer/norm.py

* modified:   python/paddle/nn/layer/norm.py

* test=docs_preview

* InstanceNorm2D中文档格式修改

* test=docs_preview

* modified:   python/paddle/nn/functional/loss.py
	modified:   python/paddle/nn/functional/norm.py
	modified:   python/paddle/nn/layer/loss.py
	modified:   python/paddle/nn/layer/norm.py

* test=docs_preview

* test=docs_preview
上级 f6915d42
隐藏空白更改
内联 并排
Showing with 120 addition and 52 deletion
python/paddle/nn/functional/loss.py
浏览文件 @ b9207054
......@@ -1546,33 +1546,27 @@ def kl_div(input, label, reduction='mean', name=None):
$$l(x, y) = y * (\log(y) - x)$$
While :math:`x` is input and :math:`y` is label.
Here :math:`x` is input and :math:`y` is label.
While :attr:`reduction` is :attr:`none`, output loss is in
the same shape as input, loss in each point is calculated
separately and no reduction is applied.
If `reduction` is ``'none'``, the output loss is the same shape as the input, and the loss at each point is calculated separately. There is no reduction to the result.
While :attr:`reduction` is :attr:`mean`, output loss is in
shape of [1] and loss value is the mean value of all losses.
If `reduction` is ``'mean'``, the output loss is the shape of [1], and the output is the average of all losses.
While :attr:`reduction` is :attr:`sum`, output loss is in
shape of [1] and loss value is the sum value of all losses.
If `reduction` is ``'sum'``, the output loss is the shape of [1], and the output is the sum of all losses.
While :attr:`reduction` is :attr:`batchmean`, output loss is
in shape of [1] and loss value is the sum value of all losses
divided by batch size.
If `reduction` is ``'batchmean'``, the output loss is the shape of [N], N is the batch size, and the output is the sum of all losses divided by the batch size.
Args:
input (Tensor): The input tensor. The shapes is [N, *], where N is batch size and `*` means
any number of additional dimensions. It's data type should be float32, float64.
any number of additional dimensions. It's data type should be float32, float64.
label (Tensor): label. The shapes is [N, *], same shape as ``input`` . It's data type should be float32, float64.
reduction (Tensor): Indicate how to average the loss,
the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``.
If `reduction` is ``'mean'``, the reduced mean loss is returned;
If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned;
if `reduction` is ``'sum'``, the reduced sum loss is returned;
if `reduction` is ``'none'``, no reduction will be apllied.
Default is ``'mean'``.
reduction (str, optional): Indicate how to average the loss,
the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``.
If `reduction` is ``'mean'``, the reduced mean loss is returned;
If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned;
if `reduction` is ``'sum'``, the reduced sum loss is returned;
if `reduction` is ``'none'``, no reduction will be apllied.
Default is ``'mean'``.
name(str, optional): Name for the operation (optional, default is None). For more information,
please refer to :ref:`api_guide_Name`.
......
python/paddle/nn/functional/norm.py
浏览文件 @ b9207054
......@@ -327,7 +327,8 @@ def layer_norm(
x, normalized_shape, weight=None, bias=None, epsilon=1e-05, name=None
):
"""
see more detail in paddle.nn.LayerNorm
nn.LayerNorm is recommended.
For more information, please refer to :ref:`api_paddle_nn_LayerNorm` .
Parameters:
x(Tensor): Input Tensor. It's data type should be float32, float64.
......@@ -335,11 +336,11 @@ def layer_norm(
size :math:`[*, normalized_shape[0], normalized_shape[1], ..., normalized_shape[-1]]`.
If it is a single integer, this module will normalize over the last dimension
which is expected to be of that specific size.
epsilon(float, optional): The small value added to the variance to prevent
division by zero. Default: 1e-05.
weight(Tensor, optional): The weight tensor of batch_norm. Default: None.
bias(Tensor, optional): The bias tensor of batch_norm. Default: None.
name(str, optional): Name for the LayerNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
epsilon(float, optional): The small value added to the variance to prevent
division by zero. Default: 1e-05.
name(str, optional): Name for the LayerNorm, default is None. For more information, please refer to :ref:`api_guide_Name` .
Returns:
None
......@@ -448,7 +449,7 @@ def instance_norm(
name=None,
):
"""
See more detail in nn.layer.InstanceNorm2D.
It is recommended to use :ref:`api_paddle_nn_InstanceNorm1D` , :ref:`api_paddle_nn_InstanceNorm2D` , :ref:`api_paddle_nn_InstanceNorm3D` to call this method internally.
Parameters:
x(Tensor): Input Tensor. It's data type should be float32, float64.
......
python/paddle/nn/layer/loss.py
浏览文件 @ b9207054
......@@ -891,19 +891,32 @@ class KLDivLoss(Layer):
$$l(x, y) = y * (\log(y) - x)$$
Here :math:`x` is input and :math:`y` is label.
If `reduction` is ``'none'``, the output loss is the same shape as the input, and the loss at each point is calculated separately. There is no reduction to the result.
If `reduction` is ``'mean'``, the output loss is the shape of [1], and the output is the average of all losses.
If `reduction` is ``'sum'``, the output loss is the shape of [1], and the output is the sum of all losses.
If `reduction` is ``'batchmean'``, the output loss is the shape of [N], N is the batch size, and the output is the sum of all losses divided by the batch size.
Parameters:
reduction (Tensor): Indicate how to average the loss,
the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``.
If `reduction` is ``'mean'``, the reduced mean loss is returned;
If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned;
if `reduction` is ``'sum'``, the reduced sum loss is returned;
if `reduction` is ``'none'``, no reduction will be apllied.
Default is ``'mean'``.
reduction (str, optional): Indicate how to average the loss,
the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``.
If `reduction` is ``'mean'``, the reduced mean loss is returned;
If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned;
if `reduction` is ``'sum'``, the reduced sum loss is returned;
if `reduction` is ``'none'``, no reduction will be apllied.
Default is ``'mean'``.
Shape:
- input (Tensor): ``(N, *)``, where ``*`` means, any number of additional dimensions.
- label (Tensor): ``(N, *)``, same shape as input.
- output (Tensor): tensor with shape: [1] by default.
input (Tensor): ``(N, *)``, where ``*`` means, any number of additional dimensions.
label (Tensor): ``(N, *)``, same shape as input.
output (Tensor): tensor with shape: [1] by default.
Examples:
.. code-block:: python
......
python/paddle/nn/layer/norm.py
浏览文件 @ b9207054
......@@ -132,25 +132,25 @@ class InstanceNorm1D(_InstanceNormBase):
\sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\
y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift
Where `H` means height of feature map, `W` means width of feature map.
Where `H` means height of feature map, `W` means width of feature map.
Parameters:
num_features(int): Indicate the number of channels of the input ``Tensor``.
epsilon(float, optional): A value added to the denominator for
numerical stability. Default is 1e-5.
momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9.
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale`
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale` of instance_norm.
If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
If the Initializer of the weight_attr is not set, the parameter is initialized
one. If it is set to False, will not create weight_attr. Default: None.
one. If it is set to False, will not create weight_attr. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm.
If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
If it is set to False, will not create bias_attr. Default: None.
If it is set to False, will not create bias_attr. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
data_format(str, optional): Specify the input data format, may be "NC", "NCL". Default "NCL".
name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name` .
Shape:
......@@ -175,6 +175,26 @@ Where `H` means height of feature map, `W` means width of feature map.
"""
def __init__(
self,
num_features,
epsilon=0.00001,
momentum=0.9,
weight_attr=None,
bias_attr=None,
data_format="NCL",
name=None,
):
super().__init__(
num_features,
epsilon,
momentum,
weight_attr,
bias_attr,
data_format,
name,
)
def _check_input_dim(self, input):
if len(input.shape) != 2 and len(input.shape) != 3:
raise ValueError(
......@@ -203,7 +223,7 @@ class InstanceNorm2D(_InstanceNormBase):
\sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\
y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift
Where `H` means height of feature map, `W` means width of feature map.
Where `H` means height of feature map, `W` means width of feature map.
Parameters:
num_features(int): Indicate the number of channels of the input ``Tensor``.
......@@ -214,14 +234,14 @@ Where `H` means height of feature map, `W` means width of feature map.
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
If the Initializer of the weight_attr is not set, the parameter is initialized
one. If it is set to False, will not create weight_attr. Default: None.
one. If it is set to False, will not create weight_attr. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm.
If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
` If it is set to False, will not create bias_attr. Default: None.
If it is set to False, will not create bias_attr. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
data_format(str, optional): Specify the input data format, could be "NCHW". Default: NCHW.
name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name` .
Shape:
- x: 4-D tensor with shape: (batch, num_features, height, weight).
......@@ -244,6 +264,26 @@ Where `H` means height of feature map, `W` means width of feature map.
print(instance_norm_out)
"""
def __init__(
self,
num_features,
epsilon=0.00001,
momentum=0.9,
weight_attr=None,
bias_attr=None,
data_format="NCHW",
name=None,
):
super().__init__(
num_features,
epsilon,
momentum,
weight_attr,
bias_attr,
data_format,
name,
)
def _check_input_dim(self, input):
if len(input.shape) != 4:
raise ValueError(
......@@ -255,7 +295,7 @@ class InstanceNorm3D(_InstanceNormBase):
r"""
Create a callable object of `InstanceNorm3D`. Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization .
DataLayout: NCHW `[batch, in_channels, D, in_height, in_width]`
DataLayout: NCDHW `[batch, in_channels, D, in_height, in_width]`
:math:`input` is the input features over a mini-batch.
......@@ -270,7 +310,7 @@ class InstanceNorm3D(_InstanceNormBase):
\sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\
y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift
Where `H` means height of feature map, `W` means width of feature map.
Where `H` means height of feature map, `W` means width of feature map.
Parameters:
num_features(int): Indicate the number of channels of the input ``Tensor``.
......@@ -281,14 +321,14 @@ Where `H` means height of feature map, `W` means width of feature map.
of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr.
If the Initializer of the weight_attr is not set, the parameter is initialized
one. If it is set to False, will not create weight_attr. Default: None.
one. If it is set to False, will not create weight_attr. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm.
If it is set to None or one attribute of ParamAttr, instance_norm
will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr.
If the Initializer of the bias_attr is not set, the bias is initialized zero.
If it is set to False, will not create bias_attr. Default: None.
If it is set to False, will not create bias_attr. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
data_format(str, optional): Specify the input data format, could be "NCDHW". Default: NCDHW.
name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name` .
Shape:
- x: 5-D tensor with shape: (batch, num_features, dims, height, weight).
......@@ -311,6 +351,26 @@ Where `H` means height of feature map, `W` means width of feature map.
print(instance_norm_out.numpy)
"""
def __init__(
self,
num_features,
epsilon=0.00001,
momentum=0.9,
weight_attr=None,
bias_attr=None,
data_format="NCDHW",
name=None,
):
super().__init__(
num_features,
epsilon,
momentum,
weight_attr,
bias_attr,
data_format,
name,
)
def _check_input_dim(self, input):
if len(input.shape) != 5:
raise ValueError(
......@@ -508,11 +568,11 @@ class LayerNorm(Layer):
division by zero. Default: 1e-05.
weight_attr(ParamAttr|bool, optional): The parameter attribute for the learnable
gain :math:`g`. If False, weight is None. If is None, a default :code:`ParamAttr` would be added as scale. The
:attr:`param_attr` is initialized as 1 if it is added. Default: None.
:attr:`param_attr` is initialized as 1 if it is added. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
bias_attr(ParamAttr|bool, optional): The parameter attribute for the learnable
bias :math:`b`. If is False, bias is None. If is None, a default :code:`ParamAttr` would be added as bias. The
:attr:`bias_attr` is initialized as 0 if it is added. Default: None.
name(str, optional): Name for the LayerNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
:attr:`bias_attr` is initialized as 0 if it is added. Default: None. For more information, please refer to :ref:`api_paddle_ParamAttr` .
name(str, optional): Name for the LayerNorm, default is None. For more information, please refer to :ref:`api_guide_Name` .
Shape:
- x: 2-D, 3-D, 4-D or 5-D tensor.
......
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