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mindspore.nn.AdaptiveAvgPool3d

class mindspore.nn.AdaptiveAvgPool3d(output_size)

This operator applies a 3D adaptive average pooling to an input signal composed of multiple input planes. 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 input is $(inD,inH,inW)$, then the last 3 dimension size of output is $(outD,outH,outW)$.

$$\begin{array}{r}\begin{array}{l}\\ \mathrm{\forall }od\in [0,outD-1],oh\in [0,outH-1],ow\in [0,outW-1]\\ output[od,oh,ow]=\\ mean(input[istartD:iendD+1,istartH:iendH+1,istartW:iendW+1])\\ where,\\ istartD=\lceil \frac{od\ast inD}{outD}\rceil \\ iendD=\lfloor \frac{(od+1)\ast inD}{outD}\rfloor \\ istartH=\lceil \frac{oh\ast inH}{outH}\rceil \\ iendH=\lfloor \frac{(oh+1)\ast inH}{outH}\rfloor \\ istartW=\lceil \frac{ow\ast inW}{outW}\rceil \\ iendW=\lfloor \frac{(ow+1)\ast inW}{outW}\rfloor \end{array}\end{array}$$

Parameters

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.

Inputs:

• input (Tensor) - The input of AdaptiveAvgPool3d, which is a 5D or 4D Tensor, with float16, float32 or float64 data type.

Outputs:

Tensor, with the same type as the input.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If dtype of input is not float16, float32 or float64.

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

• ValueError – If output_size value is not positive.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> # case 1: output_size=(3, 3, 4)
>>> output_size=(3, 3, 4)
>>> input_x_val = np.random.randn(4, 3, 5, 6, 7)
>>> input_x = ms.Tensor(input_x_val, ms.float32)
>>> net = ms.nn.AdaptiveAvgPool3d(output_size)
>>> output = net(input_x)
>>> print(output.shape)
(4, 3, 3, 3, 4)
>>> # case 2: output_size=4
>>> output_size=5
>>> input_x_val = np.random.randn(2, 3, 8, 6, 12)
>>> input_x = ms.Tensor(input_x_val, ms.float32)
>>> net = ms.nn.AdaptiveAvgPool3d(output_size)
>>> output = net(input_x)
>>> print(output.shape)
(2, 3, 5, 5, 5)
>>> # case 3: output_size=(None, 4, 5)
>>> output_size=(None, 4, 5)
>>> input_x_val = np.random.randn(4, 1, 9, 10, 8)
>>> input_x = ms.Tensor(input_x_val, ms.float32)
>>> net = ms.nn.AdaptiveAvgPool3d(output_size)
>>> output = net(input_x)
>>> print(output.shape)
(4, 1, 9, 4, 5)