AdaptiveAvgPool2D¶

class paddle.nn. AdaptiveAvgPool2D ( output_size, data_format='NCHW', name=None ) [source]

This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size.

For avg adaptive pool2d:

\[ \begin{align}\begin{aligned}hstart &= floor(i * H_{in} / H_{out})\\hend &= ceil((i + 1) * H_{in} / H_{out})\\wstart &= floor(j * W_{in} / W_{out})\\wend &= ceil((j + 1) * W_{in} / W_{out})\\Output(i ,j) &= \frac{\sum Input[hstart:hend, wstart:wend]}{(hend - hstart) * (wend - wstart)}\end{aligned}\end{align} \]

Parameters

output_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.
data_format (str, optional) – 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, 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 adaptive avg pool2d operator, which is a 4-D tensor. The data type can be float32, float64.
output(Tensor): The output tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type is same as input x.

Returns: A callable object of AdaptiveAvgPool2D.

Examples

           >>> # adaptive avg pool2d
>>> # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
>>> # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
>>> # of input data into m * n grids averagely and performs poolings in each
>>> # grid to get output.
>>> # adaptive avg pool performs calculations as follow:
>>> #
>>> #     for i in range(m):
>>> #         for j in range(n):
>>> #             hstart = floor(i * H / m)
>>> #             hend = ceil((i + 1) * H / m)
>>> #             wstart = floor(i * W / n)
>>> #             wend = ceil((i + 1) * W / n)
>>> #             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
>>> #
>>> import paddle

>>> x = paddle.rand([2, 3, 32, 32])

>>> adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=3)
>>> pool_out = adaptive_avg_pool(x = x)
>>> print(pool_out.shape)
[2, 3, 3, 3]

          

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

AdaptiveAvgPool2D¶

forward¶

extra_repr¶