mindspore.mint.nn.functional.adaptive_avg_pool3d

mindspore.mint.nn.functional.adaptive_avg_pool3d(input, output_size)

Performs 3D adaptive average pooling on a multi-plane input signal. That is, for any input size, the size of the specified output is $(D,H,W)$. The number of output features is equal to the number of input planes.

Suppose the last 3 dimension size of x is $(D_{in},H_{in},W_{in})$, the last 3 dimension size of output is $(D_{out},H_{out},W_{out})$.

$$\begin{array}{r}\begin{array}{l}\\ \mathrm{\forall }od\in [0,D_{out}-1],oh\in [0,H_{out}-1],ow\in [0,W_{out}-1]\\ output[od,oh,ow]=\\ mean(x[D_{istart}:D_{iend}+1,H_{istart}:H_{iend}+1,W_{istart}:W_{iend}+1])\\ where,\\ D_{istart}=\lceil \frac{od\ast D_{in}}{D_{out}}\rceil \\ D_{iend}=\lfloor \frac{(od+1)\ast D_{in}}{D_{out}}\rfloor \\ H_{istart}=\lceil \frac{oh\ast H_{in}}{H_{out}}\rceil \\ H_{iend}=\lfloor \frac{(oh+1)\ast H_{in}}{H_{out}}\rfloor \\ W_{istart}=\lceil \frac{ow\ast W_{in}}{W_{out}}\rceil \\ W_{iend}=\lfloor \frac{(ow+1)\ast W_{in}}{W_{out}}\rfloor \end{array}\end{array}$$

Warning

For Ascend, it is only supported on Atlas A2 Training Series Products. This is an experimental optimizer API that is subject to change or deletion.

Parameters

• input (Tensor) – The input of adaptive_avg_pool3d, which is a 4D or 5D Tensor.

• output_size (Union[int, tuple]) – The target output size. output_size can be a tuple $(D,H,W)$, or an int D for $(D,D,D)$. $D$, $H$ and $W$ can be int or None which means the output size is the same as that of the input.

Returns

Tensor, with the same type as the input.

Raises

• TypeError – If input is not a Tensor.

• ValueError – If the dimension of input is not 4D or 5D.

• ValueError – If output_size value is not positive.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> # case 1: output_size=(3, 3, 4)
>>> output_size=(3, 3, 4)
>>> input_val = np.random.randn(4, 3, 5, 6, 7)
>>> input = Tensor(input_val, mindspore.float32)
>>> output = mint.nn.functional.adaptive_avg_pool3d(input, output_size)
>>> print(output.shape)
(4, 3, 3, 3, 4)
>>> # case 2: output_size=4
>>> output_size=5
>>> input_val = np.random.randn(2, 3, 8, 6, 12)
>>> input = Tensor(input_val, mindspore.float32)
>>> output = mint.nn.functional.adaptive_avg_pool3d(input, output_size)
>>> print(output.shape)
(2, 3, 5, 5, 5)
>>> # case 3: output_size=(None, 4, 5)
>>> output_size=(None, 4, 5)
>>> input_val = np.random.randn(4, 1, 9, 10, 8)
>>> input = Tensor(input_val, mindspore.float32)
>>> output = mint.nn.functional.adaptive_avg_pool3d(input, output_size)
>>> print(output.shape)
(4, 1, 9, 4, 5)