mindspore.mint.nn.AdaptiveAvgPool3d

class mindspore.mint.nn.AdaptiveAvgPool3d(output_size)

Apply a 3-D 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 \((outD, outH, outW)\).

\[\begin{split}\begin{array}{ll} \\ \forall \quad od \in [0,outD-1], oh \in [0,outH-1], ow \in [0,outW-1]\\ output[od,oh,ow] = \\ \qquad mean(input[istartD:iendD+1,istartH:iendH+1,istartW:iendW+1])\\ where,\\ \qquad istartD= \left\lceil \frac{od * inD}{outD} \right\rceil \\ \qquad iendD=\left\lfloor \frac{(od+1)* inD}{outD} \right\rfloor \\ \qquad istartH=\left\lceil \frac{oh * inH}{outH} \right\rceil \\ \qquad iendH=\left\lfloor \frac{(oh+1) * inH}{outH} \right\rfloor \\ \qquad istartW=\left\lceil \frac{ow * inW}{outW} \right\rceil \\ \qquad iendW=\left\lfloor \frac{(ow+1) * inW}{outW} \right\rfloor \end{array}\end{split}\]

Warning

For Ascend, it is only supported on Atlas A2 Training Series Products.

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 tensor with shape \((N, C, D, H, W)\) or \((C, D, H, W)\).

Outputs:

Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> # case 1: output_size=(3, 3, 4)
>>> output_size=(3, 3, 4)
>>> input_x = mindspore.mint.randn((4, 3, 5, 6, 7), mindspore.float32)
>>> net = mindspore.mint.nn.AdaptiveAvgPool3d(output_size)
>>> output = net(input_x)
>>> print(output.shape)
(4, 3, 3, 3, 4)
>>> # case 2: output_size=5
>>> output_size=5
>>> input_x = mindspore.mint.randn((2, 3, 8, 6, 12), mindspore.float32)
>>> net = mindspore.mint.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 = mindspore.mint.randn((4, 1, 9, 10, 8), mindspore.float32)
>>> net = mindspore.mint.nn.AdaptiveAvgPool3d(output_size)
>>> output = net(input_x)
>>> print(output.shape)
(4, 1, 9, 4, 5)