mindspore.ops.adaptive_max_pool3d
- mindspore.ops.adaptive_max_pool3d(x, output_size, return_indices=False)[source]
Applies a 3D adaptive max pooling over an input signal composed of several input planes.
The output is of size
, for any input size. The number of output features is equal to the number of input planes.- Parameters
x (Tensor) – Tensor, with shape
or , which support int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32 or float64 data type.output_size (Union[int, tuple]) – The target output size. ouput_size can be a tuple
, or an int D for . , and can be int or None which means the output size is the same as that of the input.return_indices (bool, optional) – If return_indices is True, the indices of max value would be output, else would not be output. Default: False.
- Returns
y (Tensor) - Tensor, with the same number of dims and data type as the x.
argmax (Tensor) - Tensor, the indices of max value, which has the same shape as the y and it’s data type is int32. It will output only when return_indices is True.
- Raises
TypeError – If x is not a Tensor.
ValueError – If the dimensions number of x is not 4 or 5.
TypeError – If dtype of x is not int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32 or float64.
ValueError – If output_size is neither an int nor a tuple with shape (3,).
- Supported Platforms:
GPU
CPU
Examples
>>> x = Tensor(np.arange(0,36).reshape((1, 3, 3, 4)).astype(np.float32)) >>> output_size = (1, 1, 2) >>> output = ops.adaptive_max_pool3d(x, output_size, True) >>> print(output[0].asnumpy()) [[[[33. 35.]]]] >>> print(output[1].asnumpy()) [[[[33 35]]]]