avg_pool3d#

ivy.avg_pool3d(x, kernel, strides, padding, /, *, data_format='NDHWC', count_include_pad=False, ceil_mode=False, divisor_override=None, out=None)[source]#

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

Parameters:

x (Union[Array, NativeArray]) – Input volume [batch_size,d,h,w,d_in].
kernel (Union[int, Tuple[int], Tuple[int, int, int]]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int], Tuple[int, int, int]]) – The stride of the sliding window for each dimension of input.
padding (str) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.
data_format (str) – NDHWC” or “NCDHW”. Defaults to “NDHWC”. (default: 'NDHWC')
count_include_pad (bool) – Whether to include padding in the averaging calculation. (default: False)
ceil_mode (bool) – Whether to use ceil or floor for creating the output shape. (default: False)
divisor_override (Optional[int]) – If specified, it will be used as divisor, otherwise kernel_size will be used. (default: None)
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that the (default: None) 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.avg_pool3d(x,2,2,'VALID'))
ivy.array([[[[[ 7.,  8.]]]],

[[[[31., 32.]]]]])

>>> print(ivy.avg_pool3d(x,2,2,'SAME'))
ivy.array([[[[[ 7.,  8.]]],

[[[19., 20.]]]],

[[[[31., 32.]]],

[[[43., 44.]]]]])

Array.avg_pool3d(self, kernel, strides, padding, /, *, data_format='NDHWC', count_include_pad=False, ceil_mode=False, divisor_override=None, out=None)#

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], Tuple[int, int, int]]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int], Tuple[int, int, int]]) – The stride of the sliding window for each dimension of input.
padding (str) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.
data_format (str) – NDHWC” or “NCDHW”. Defaults to “NDHWC”. (default: 'NDHWC')
count_include_pad (bool) – Whether to include padding in the averaging calculation. (default: False)
ceil_mode (bool) – Whether to use ceil or floor for creating the output shape. (default: False)
divisor_override (Optional[int]) – If specified, it will be used as divisor, (default: None) otherwise kernel_size will be used.
out (Optional[Array]) – optional output array, for writing the result to. It must have (default: None) 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.avg_pool3d(2, 2, 'VALID'))
ivy.array([[[[[ 7.,  8.]]]],
       [[[[31., 32.]]]]])
>>> print(x.avg_pool3d(2, 2, 'SAME'))
ivy.array([[[[[ 7.,  8.]]],
        [[[19., 20.]]]],
       [[[[31., 32.]]],
        [[[43., 44.]]]]])

Container.avg_pool3d(self, kernel, strides, padding, /, *, data_format='NDHWC', count_include_pad=False, ceil_mode=False, divisor_override=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#

ivy.Container static method variant of ivy.avg_pool3d. This method simply wraps the function, and so the docstring for ivy.avg_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], Tuple[int, int, int]]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int], Tuple[int, int, int]]) – The stride of the sliding window for each dimension of input.
padding (str) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.
data_format (str) – NDHWC” or “NCDHW”. Defaults to “NDHWC”. (default: 'NDHWC')
count_include_pad (bool) – Whether to include padding in the averaging calculation. (default: False)
ceil_mode (bool) – Whether to use ceil or floor for creating the output shape. (default: False)
divisor_override (Optional[int]) – If specified, it will be used as the divisor, otherwise (default: None)
out (Optional[Container]) – optional output array, for writing the result to. It must (default: None) have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – The result of the pooling operation.

Examples

>>> a = ivy.arange(12).reshape((1, 2, 1, 3, 2))
>>> b = ivy.arange(48).reshape((2, 2, 2, 3, 2))
>>> x = ivy.Container({'a': a, 'b': b})
>>> print(x.max_pool3d(2, 1, "VALID"))
{
    a: ivy.array([], shape=(1, 1, 0, 2, 2)),
    b: ivy.array([[[[[20, 21],
                     [22, 23]]]],
               [[[[44, 45],
                     [46, 47]]]]])
}