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

$\begin{split}hstart &= floor(i * H_{in} / H_{out}) \\ hend &= ceil((i + 1) * H_{in} / H_{out}) \\ wstart &= floor(j * W_{in} / W_{out}) \\ wend &= ceil((j + 1) * W_{in} / W_{out}) \\ Output(i ,j) &= \frac{\sum Input[hstart:hend, wstart:wend]}{(hend - hstart) * (wend - wstart)}\end{split}$
Parameters
• x (Tensor) – The input tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type can be float32 or float64.

• 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.

Returns

Tensor, The output tensor of avg adaptive pool2d result. The data type is same as input tensor.

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])
>>> #