mindspore.ops.MaxPool3DWithArgmax

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class mindspore.ops.MaxPool3DWithArgmax(ksize, strides, pads, dilation=(1, 1, 1), ceil_mode=False, data_format='NCDHW', argmax_type=mstype.int64)[source]

Performs a 3D max pooling on the input Tensor and returns both max values and indices.

Typically the input is a Tensor with shape \((N_{in}, C_{in}, D_{in}, H_{in}, W_{in})\), outputs regional maximum in the \((D_{in}, H_{in}, W_{in})\)-dimension. Given ksize \(ks = (d_{ker}, h_{ker}, w_{ker})\) and strides \(s = (s_0, s_1, s_2)\), the operation is as follows.

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

The output is a Tensor with shape \((N_{out}, C_{out}, D_{out}, H_{out}, W_{out})\) and its depth, height and width are:

\[\begin{split}\begin{array}{ll} \\ D_{out} = \frac{D_{in} + 2 \times \text{pads}[0] - \text{dilation}[0] \times (\text{ksize}[0] - 1) - 1} {\text{stride}[0]} + 1 \\ H_{out} = \frac{H_{in} + 2 \times \text{pads}[1] - \text{dilation}[1] \times (\text{ksize}[1] - 1) - 1} {\text{stride}[1]} + 1 \\ W_{out} = \frac{W_{in} + 2 \times \text{pads}[2] - \text{dilation}[2] \times (\text{ksize}[2] - 1) - 1} {\text{stride}[2]} + 1 \\ \end{array}\end{split}\]

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • ksize (Union[int, tuple[int]]) – The size of kernel used to take the maximum value and arg 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.

  • 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.

  • pads (Union[int, tuple[int]]) – 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.

  • dilation (Union[int, tuple[int]]) – Default: (1, 1, 1) .

  • ceil_mode (bool) – Whether to use ceil instead of floor to calculate output shape. Default: False .

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

  • argmax_type (mindspore.dtype) – The dtype for argmax. Default: mstype.int64 .

Inputs:

  • x (Tensor) - Tensor of shape \((N_{in}, C_{in}, D_{in}, H_{in}, W_{in})\) with data type of int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32 or float64.

Outputs:

Tuple of 2 Tensors, representing the maxpool result and where the max values are generated.

  • output (Tensor) - Maxpooling result, with shape \((N_{out}, C_{out}, D_{out}, H_{out}, W_{out})\). It has the same data type as x.

  • argmax (Tensor) - Index corresponding to the maximum value. Data type is int32 or int64.

Raises

  • TypeError – If x is not a Tensor.

  • ValueError – If length of shape of x is not equal to 5.

  • TypeError – If ksize , strides , pads or dilation is not int or tuple.

  • ValueError – If ksize or strides is less than 1.

  • ValueError – If pads is less than 0.

  • ValueError – If data_format is not 'NCDHW'.

  • ValueError – If argmax_type is not mindspore.int64 or mindspore.int32.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.arange(2 * 1 * 2 * 2 * 2).reshape((2, 1, 2, 2, 2)), mindspore.float32)
>>> max_pool3d_with_arg_op = ops.MaxPool3DWithArgmax(ksize=2, strides=1, pads=1)
>>> output_tensor, argmax = max_pool3d_with_arg_op(x)
>>> print(output_tensor.shape)
(2, 1, 3, 3, 3)
>>> print(argmax.shape)
(2, 1, 3, 3, 3)