adaptive_avg_pool3d

paddle.nn.functional. adaptive_avg_pool3d ( x: Tensor, output_size: Size3, data_format: DataLayout3D = 'NCDHW', name: str | None = None ) → Tensor [source]

This operation applies 3D 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 pool3d:

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

Parameters

• x (Tensor) – The input tensor of adaptive avg pool3d operator, which is a 5-D tensor. The data type can be float32, float64.

• output_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, 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: “NCDHW”, “NDHWC”. The default is “NCDHW”. When it is “NCDHW”, the data is stored in the order of: [batch_size, input_channels, input_depth, 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

Tensor, The output tensor of avg adaptive pool3d result. The data type is same as input tensor.

Examples

>>> # adaptive avg pool3d
>>> # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
>>> # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
>>> # of input data into l * m * n grids averagely and performs poolings in each
>>> # grid to get output.
>>> # adaptive avg pool performs calculations as follow:
>>> #
>>> #     for i in range(l):
>>> #         for j in range(m):
>>> #             for k in range(n):
>>> #                 dstart = floor(i * D / l)
>>> #                 dend = ceil((i + 1) * D / l)
>>> #                 hstart = floor(j * H / m)
>>> #                 hend = ceil((j + 1) * H / m)
>>> #                 wstart = floor(k * W / n)
>>> #                 wend = ceil((k + 1) * W / n)
>>> #                 output[:, :, i, j, k] =
>>> #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
>>> import paddle

>>> input_data = paddle.randn(shape=(2, 3, 8, 32, 32))
>>> out = paddle.nn.functional.adaptive_avg_pool3d(x = input_data,
...                                                output_size=[3, 3, 3])
>>> print(out.shape)
[2, 3, 3, 3, 3]