LPPool2D¶

class paddle.nn. LPPool2D ( norm_type: float, kernel_size: Size2, stride: Size2 | None = None, padding: _PaddingSizeMode | Size2 | Size4 = 0, ceil_mode: bool = False, data_format: DataLayout2D = 'NCHW', name: str | None = None ) [source]

Performing 2D power-average pooling over input features based on the input, and kernel_size, stride, padding parameters. Input(X) and Output(Out) are in NCHW format, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature.

Example

Input:

X shape: \((N, C, H_{in}, W_{in})\)

Attr:

kernel_size: kernel_size
norm_type: norm_type

Output:

Out shape: \((N, C, H_{out}, W_{out})\)

\[Output(N_i, C_j, h, w) = (\sum_{m=0}^{ksize[0]-1} \sum_{n=0}^{ksize[1]-1} Input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)^{norm\_type})^{1 / norm\_type}\]

Parameters

norm_type (int|float) – The number the power operation.
kernel_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain two integers, (pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be a square of an int.
stride (int|list|tuple, optional) – The pool stride size. If pool stride size is a tuple or list, it must contain two integers, (pool_stride_Height, pool_stride_Width). Otherwise, the pool stride size will be a square of an int. Default None, then stride will be equal to the kernel_size.
padding (str|int|list|tuple, optional) – The padding size. Padding could be in one of the following forms. 1. A string in [‘valid’, ‘same’]. 2. An int, which means the feature map is zero padded by size of padding on every sides. 3. A list[int] or tuple(int) whose length is 2, [pad_height, pad_weight] whose value means the padding size of each dimension. 4. A list[int] or tuple(int) whose length is 4. [pad_height_top, pad_height_bottom, pad_width_left, pad_width_right] whose value means the padding size of each side. 5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], …]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0). The default value is 0.
ceil_mode (bool, optional) – When True, it will use ceil instead of floor to compute the output shape. Default: False.
data_format (str, optional) – The data format of the input and output data. An optional string from: “NCHW”, “NHWC”. When it is “NCHW”, the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. Default: “NCHW”.
name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Shape:

x(Tensor): The input tensor of lp pool2d operator, which is a 4-D tensor. The data type can be float32, float64.
output(Tensor): The output tensor of lp pool2d operator, which is a 4-D tensor. The data type is same as input x.

Returns: A callable object of LPPool2D.

Examples

>>> import paddle
>>> import paddle.nn as nn

>>> # lp pool2d
>>> input = paddle.uniform([1, 3, 32, 32], dtype="float32", min=-1, max=1)
>>> LPPool2D = nn.LPPool2D(norm_type=2, kernel_size=2, stride=2, padding=0)
>>> output = LPPool2D(input)
>>> print(output.shape)
[1, 3, 16, 16]

forward ( x: Tensor ) → Tensor forward¶

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments

extra_repr ( ) → str extra_repr¶: Extra representation of this layer, you can have custom implementation of your own layer.

LPPool2D¶

forward¶

extra_repr¶