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 (Union[str, 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”.
count_include_pad (bool, default: False) – Whether to include padding in the averaging calculation.
ceil_mode (bool, default: False) – Whether to use ceil or floor for creating the output shape.
divisor_override (Optional[int], default: None) – If specified, it will be used as divisor, otherwise kernel_size will be used.
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.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)[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], 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, default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.
count_include_pad (bool, default: False) – Whether to include padding in the averaging calculation.
ceil_mode (bool, default: False) – Whether to use ceil or floor for creating the output shape.
divisor_override (Optional[int], default: None) – If specified, it will be used as divisor, otherwise kernel_size will be used.
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.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)[source]#

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], Container]) – Convolution filters [d,h,w].
strides (Union[int, Tuple[int], Tuple[int, int, int], Container]) – The stride of the sliding window for each dimension of input.
padding (Union[str, 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”.
count_include_pad (Union[bool, Container], default: False) – Whether to include padding in the averaging calculation.
ceil_mode (Union[bool, Container], default: False) – Whether to use ceil or floor for creating the output shape.
divisor_override (Optional[Union[int, Container]], default: None) – If specified, it will be used as the divisor, otherwise
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.avg_pool3d(2, 1, "VALID"))
{
    a: ivy.array([[[[[8.5],
                     [9.5],
                     [10.5]],
                    [[12.5],
                     [13.5],
                     [14.5]]]]]),
    b: (<class ivy.data_classes.array.array.Array> shape=[2, 3, 2, 1, 1])
}