max_pool1d

paddle.nn.functional. max_pool1d ( x: Tensor, kernel_size: Size1, stride: Size1 | None = None, padding: _PaddingSizeMode | Size1 | Size2 = 0, return_mask: bool = False, ceil_mode: bool = False, name: str | None = None ) Tensor [source]

This API implements max pooling 1d operation. See more details in MaxPool1D .

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 if float32 or float64.

  • kernel_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain an integer.

  • stride (int|list|tuple) – The pool stride size. If pool stride size is a tuple or list, it must contain an integer.

  • 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 integer, 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 1, which means the feature map is zero padded by the size of padding[0] on every sides. 4. A list[int] or tuple(int) whose length is 2. It has the form [pad_before, pad_after]. 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.

  • return_mask (bool) – Whether return the max indices along with the outputs. default is False.

  • ceil_mode (bool) – Whether to use the ceil function to calculate output height and width. False is the default. If it is set to False, the floor function will be used. Default False.

  • 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.functional as F

>>> data = paddle.uniform([1, 3, 32], paddle.float32)
>>> pool_out = F.max_pool1d(data, kernel_size=2, stride=2, padding=0)
>>> print(pool_out.shape)
[1, 3, 16]
>>> pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_mask=True)
>>> print(pool_out.shape)
[1, 3, 16]
>>> print(indices.shape)
[1, 3, 16]