mindspore.ops.MaxPool3D

class mindspore.ops.MaxPool3D(*args, **kwargs)[source]

3D max pooling operation.

Applies a 3D max pooling over an input Tensor which can be regarded as a composition of 3D planes.

Typically the input is of shape \((N_{in}, C_{in}, D_{in}, H_{in}, W_{in})\), MaxPool outputs regional maximum in the \((D_{in}, H_{in}, W_{in})\)-dimension. Given kernel size \(ks = (d_{ker}, h_{ker}, w_{ker})\) and stride \(s = (s_0, s_1, s_2)\), the operation is as follows.

\[\text{output}(N_i, C_j, d, h, w) = \max_{l=0, \ldots, d_{ker}-1} \max_{m=0, \ldots, h_{ker}-1} \max_{n=0, \ldots, w_{ker}-1} \text{input}(N_i, C_j, s_0 \times d + l, s_1 \times h + m, s_2 \times w + n)\]
Parameters

  • kernel_size (Union[int, tuple[int]]) – The size of kernel used to take the maximum value, is an int number that represents height and width are both kernel_size, or a tuple of three int numbers that represent depth, height and width respectively. Default: 1.

  • strides (Union[int, tuple[int]]) – The distance of kernel moving, an int number that represents the depth, height and width of movement are both strides, or a tuple of three int numbers that represent depth, height and width of movement respectively. Default: 1.

  • pad_mode (str) –

    The optional value for pad mode, is “same” or “valid”, not case sensitive. Default: “valid”.

    • same: Adopts the way of completion. The height and width of the output will be the same as the input. The total number of padding will be calculated in horizontal and vertical directions and evenly distributed to top and bottom, left and right if possible. Otherwise, the last extra padding will be done from the bottom and the right side.

    • valid: Adopts the way of discarding. The possible largest height and width of output will be returned without padding. Extra pixels will be discarded.

  • data_format (str) – The optional value for data format. Currently only support ‘NCDHW’. Default: ‘NCDHW’.

Inputs:

  • input (Tensor) - Tensor of shape \((N, C, D_{in}, H_{in}, W_{in})\). Data type must be float16.

Outputs:

Tensor, with shape \((N, C, D_{out}, H_{out}, W_{out})\). Has the data type with input.

Raises

  • TypeError – If kernel_size or strides is neither an int not a tuple.

  • TypeError – If pad_mode or data_format is not a string.

  • ValueError – If numbers in kernel_size or strides are not positive.

  • ValueError – If pad_mode is not one of ‘same’, ‘valid’.

  • ValueError – If kernel_size or strides is a tuple whose length is not equal to 3.

  • ValueError – If data_format is not ‘NCDHW’.

Supported Platforms:

Ascend

Examples

>>> input_tensor = Tensor(np.arange(1 * 2 * 2 * 2 * 3).reshape((1, 2, 2, 2, 3)), mindspore.float32)
>>> max_pool3d = ops.MaxPool3D(kernel_size=2, strides=1, pad_mode="valid")
>>> output = max_pool3d(input_tensor)
>>> print(output)
[[[[[10. 11.]]]
  [[[22. 23.]]]]]