mindspore.nn.AdaptiveAvgPool3d

class mindspore.nn.AdaptiveAvgPool3d(output_size)[source]

3D adaptive average pooling for temporal data.

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 x is \((inD, inH, inW)\), the last 3 dimension size of output is \((outD, outH, outW)\).

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

output_size (Union[int, tuple]) – he target output size. ouput_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:

  • x (Tensor) - The input of AdaptiveAvgPool3d, which is a 5D or 4D Tensor.

Outputs:

Tensor, with the same type as the x.

Raises

  • TypeError – If x is not a Tensor.

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

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

  • ValueError – If output_size value is not positive.

Supported Platforms:

GPU

Examples

>>> # 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 = Tensor(input_x_val, mindspore.float32)
>>> net = 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 = Tensor(input_x_val, mindspore.float32)
>>> net = 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 = Tensor(input_x_val, mindspore.float32)
>>> net = nn.AdaptiveAvgPool3d(output_size)
>>> output = net(input_x)
>>> print(output.shape)
(4, 1, 9, 4, 5)