lp_pool1d

paddle.nn.functional. lp_pool1d ( x: Tensor, norm_type: float, kernel_size: Size1, stride: Size1 | None = None, padding: _PaddingSizeMode | Size1 | Size2 = 0, ceil_mode: bool = False, data_format: DataLayout1D = 'NCL', name: str | None = None ) → Tensor [source]

This API implements power-average pooling 1d operation. See more details in LPPool1D .

Parameters

  • x (Tensor) – The input tensor of pooling operator which is a 3-D tensor with shape [N, C, L]. where N is batch size, C is the number of channels, L is the length of the feature. The data type is float16, float32 or float64.

  • norm_type (int|float) – The number the power operation.

  • kernel_size (int|list|tuple) – The pool kernel size. If it is a tuple or list, it must contain two integers, (kernel_size_Height, kernel_size_Width). Otherwise, the pool kernel size will be a square of an int.

  • stride (int|list|tuple) – The stride size. If it is a tuple or list, it must contain two integers, (stride_Height, stride_Width). Otherwise, the stride size will be a square of an int.

  • padding (string|int|list|tuple) – 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: “NCL”, “NLC”. When it is “NCL”, the data is stored in the order of: [batch_size, input_channels, input_length]. Default:”NCL”.

  • name (str|None, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Returns

The output tensor of pooling result. The data type is same as input tensor.

Return type

Tensor

Examples

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

>>> data = paddle.uniform([1, 3, 32], paddle.float32)
>>> LPPool1D = nn.LPPool1D(norm_type=3, kernel_size=2, stride=2, padding=0)
>>> pool_out = LPPool1D(data)
>>> print(pool_out.shape)
[1, 3, 16]