mindspore.ops.adaptive_avg_pool3d

mindspore.ops.adaptive_avg_pool3d(input_x, 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

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

  • output_size (Union[int, tuple]) – The 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.

Returns

Tensor, with the same type as the input_x.

Raises

  • TypeError – If input_x is not a Tensor.

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

  • ValueError – If the dimension of input_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)
>>> output = ops.adaptive_avg_pool3d(input_x, output_size)
>>> 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)
>>> output = ops.adaptive_avg_pool3d(input_x, output_size)
>>> 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)
>>> output = ops.adaptive_avg_pool3d(input_x, output_size)
>>> print(output.shape)
(4, 1, 9, 4, 5)