mindspore.ops.MaxPool3D

class mindspore.ops.MaxPool3D(kernel_size=1, strides=1, pad_mode='VALID', pad_list=0, ceil_mode=None, data_format='NCDHW')[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 depth, height and width of the kernel, 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 not only the depth, height of movement but also the width of movement,, or a tuple of three int numbers that represent depth, height and width of movement respectively. Default: 1.

• pad_mode (str) –

The optional value of pad mode is “same” or “valid”. 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, 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.

• pad: Implicit paddings on both sides of the input in depth, height and width. The number of “pad” will be padded to the input Tensor borders. “pad” must be greater than or equal to 0.

• ceil_mode (bool) – Whether to use ceil instead of floor to calculate output shape. Only effective in “pad” mode. When “pad_mode” is “pad” and “ceil_mode” is “None”, “ceil_mode” will be set as “False”. Default: None.

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

Inputs:
• x (Tensor) - Tensor of shape $$(N, C, D_{in}, H_{in}, W_{in})$$. Data type must be float16 or float32.

Outputs:

Tensor, with shape $$(N, C, D_{out}, H_{out}, W_{out})$$. Has the data type of x.

Raises
• TypeError – If kernel_size or strides is neither an int nor 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’ or ‘pad’.

• ValueError – If pad_mode is ‘same’ or ‘valid’, ‘ceil_mode’ is not None.

• 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 GPU

Examples

>>> x = 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(x)
>>> print(output)
[[[[[10. 11.]]]
[[[22. 23.]]]]]