max_pool3d#

ivy.max_pool3d(x, kernel, strides, padding, /, *, data_format='NDHWC', dilation=1, ceil_mode=False, out=None)[source]#

Compute a 3-D max pool given 5-D input x.

Parameters:

x (Union[Array, NativeArray]) – Input tensor [batch_size,d,h,w,d_in] if data_format is “NDHWC”.
kernel (Union[int, Tuple[int, ...]]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int, ...]]) – The stride of the sliding window for each dimension of input.
padding (Union[str, int, Tuple[int], List[Tuple[int, int]]]) – “SAME” or “VALID” indicating the algorithm; int, or list of tuple indicating the per-dimension paddings. (e.g. 2, [(1, 0), (0, 1), (1, 1)])
data_format (str, default: 'NDHWC') – “NDHWC” or “NCDHW”. Defaults to “NDHWC”.
dilation (Union[int, Tuple[int, ...]], default: 1) – The stride between elements within a sliding window, must be > 0.
ceil_mode (bool, default: False) – If True, ceil is used instead of floor to compute the output shape. This ensures that every element in ‘x’ is covered by a sliding window.
out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The result of the pooling operation.
Both the description and the type hints above assumes an array input
for simplicity, but this function is *nestable, and therefore*
also accepts ivy.Container instances in place of any of
the arguments.

Examples

>>> x = ivy.arange(48.).reshape((2, 3, 2, 2, 2))
>>> print(ivy.max_pool3d(x, 2, 2, 'VALID'))
ivy.array([[[[[14., 15.]]]],

[[[[38., 39.]]]]])

>>> print(ivy.max_pool3d(x, 2, 2, 'SAME'))
ivy.array([[[[[14., 15.]]],

[[[22., 23.]]]],

[[[[38., 39.]]],

[[[46., 47.]]]]])

Array.max_pool3d(self, kernel, strides, padding, /, *, data_format='NDHWC', dilation=1, ceil_mode=False, out=None)[source]#

Compute a 3-D max pool given 5-D input x.

Parameters:

self (Array) – Input volume [batch_size,d,h,w,d_in].
kernel (Union[int, Tuple[int, ...]]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int, ...]]) – The stride of the sliding window for each dimension of input.
padding (Union[str, int, Tuple[int], List[Tuple[int, int]]]) – “SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.
data_format (str, default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.
dilaton – The stride between elements within a sliding window, must be > 0.
ceil_mode (bool, default: False) – If True, ceil is used instead of floor to compute the output shape. This ensures that every element is covered by a sliding window.
out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The result of the pooling operation.

Examples

>>> x = ivy.arange(48.).reshape((2, 3, 2, 2, 2))
>>> print(x.max_pool3d(2, 2, 'VALID'))
ivy.array([[[[[14., 15.]]]],
   [[[[38., 39.]]]]])
>>> print(x.max_pool3d(2, 2, 'SAME'))
ivy.array([[[[[14., 15.]]],
    [[[22., 23.]]]],
   [[[[38., 39.]]],
    [[[46., 47.]]]]])

Container.max_pool3d(self, kernel, strides, padding, /, *, data_format='NDHWC', dilation=1, ceil_mode=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container static method variant of ivy.max_pool3d. This method simply wraps the function, and so the docstring for ivy.max_pool3d also applies to this method with minimal changes.

Parameters:

x – Input volume [batch_size,d,h,w,d_in].
kernel (Union[int, Tuple[int, ...], Container]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int, ...], Container]) – The stride of the sliding window for each dimension of input.
padding (Union[str, int, Tuple[int], List[Tuple[int, int]], Container]) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.
data_format (Union[str, Container], default: 'NDHWC') – “NDHWC” or “NCDHW”. Defaults to “NDHWC”.
dilaton – The stride between elements within a sliding window, must be > 0.
ceil_mode (Union[bool, Container], default: False) – If True, ceil is used instead of floor to compute the output shape. This ensures that every element is covered by a sliding window.
out (Optional[Container], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – The result of the pooling operation.

Examples

>>> a = ivy.arange(24.).reshape((1, 2, 3, 4, 1))
>>> b = ivy.arange(48.).reshape((2, 4, 3, 2, 1))
>>> x = ivy.Container(a=a, b=b)
>>> print(x.max_pool3d(3, 1, "VALID"))
{
    a: ivy.array([], shape=(1, 0, 1, 2, 1)),
    b: ivy.array([], shape=(2, 2, 1, 0, 1))
}