avg_pool2d

paddle.nn.functional. avg_pool2d ( x: Tensor, kernel_size: Size2, stride: Size2 | None = None, padding: _PaddingSizeMode | Size2 | Size4 = 0, ceil_mode: bool = False, exclusive: bool = True, divisor_override: float | None = None, data_format: DataLayout2D = 'NCHW', name: str | None = None ) → Tensor [source]

This API implements average pooling 2d operation. See more details in AvgPool2D .

Parameters

  • x (Tensor) – The input tensor of pooling operator which is a 4-D tensor with shape [N, C, H, W]. The format of input tensor is “NCHW” or “NHWC”, 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. The data type if float32 or float64.

  • 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) – when True, will use ceil instead of floor to compute the output shape

  • exclusive (bool) – Whether to exclude padding points in average pooling mode, default is true.

  • divisor_override (float) – if specified, it will be used as divisor, otherwise kernel_size will be used. Default None.

  • data_format (string) – The data format of the input and output data. An optional string from: “NCHW”, “NHWC”. The default is “NCHW”. When it is “NCHW”, the data is stored in the order of: [batch_size, input_channels, input_height, input_width].

  • name (str|None, optional) – For detailed information, please refer to api_guide_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

>>> # avg pool2d
>>> x = paddle.uniform([1, 3, 32, 32], paddle.float32)
>>> out = F.avg_pool2d(x,
...                    kernel_size=2,
...                    stride=2, padding=0)
>>> print(out.shape)
[1, 3, 16, 16]