avg_pool2d#

ivy.avg_pool2d(x, kernel, strides, padding, /, *, data_format='NHWC', count_include_pad=False, ceil_mode=False, divisor_override=None, out=None)[source]#

Compute a 2-D average pool given 4-D input x.

Parameters:

x (Union[Array, NativeArray]) – Input image [batch_size,h,w,d_in].
kernel (Union[int, Tuple[int], Tuple[int, int]]) – Size of the kernel i.e., the sliding window for each dimension of input. [h,w].
strides (Union[int, Tuple[int], Tuple[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: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.
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.
out (Optional[Array], default: None) – optional output array, for writing the result 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(12.).reshape((2, 1, 3, 2))
>>> print(ivy.avg_pool2d(x, (2, 2), (1, 1), 'SAME'))
ivy.array([[[[ 1.,  2.],
         [ 3.,  4.],
         [ 4.,  5.]]],

[[[ 7., 8.],
[ 9., 10.], [10., 11.]]]])

>>> x = ivy.arange(48.).reshape((2, 4, 3, 2))
>>> print(ivy.avg_pool2d(x, 3, 1, 'VALID'))
ivy.array([[[[ 8.,  9.]],

[[14., 15.]]],

[[[32., 33.]],

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

Array.avg_pool2d(self, kernel, strides, padding, /, *, data_format='NHWC', count_include_pad=False, ceil_mode=False, divisor_override=None, out=None)[source]#

ivy.Array instance method variant of ivy.avg_pool2d. This method simply wraps the function, and so the docstring for ivy.avg_pool2d also applies to this method with minimal changes.

Parameters:

x – Input image [batch_size,h,w,d_in].
kernel (Union[int, Tuple[int], Tuple[int, int]]) – The size of the window for each dimension of the input tensor.
strides (Union[int, Tuple[int], Tuple[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: 'NHWC') – “NHWC” or “NCHW”. Defaults to “NHWC”.
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 given, it will be used as the 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 max pooling operation.

Examples

>>> x = ivy.arange(12.).reshape((2, 1, 3, 2))
>>> print(x.max_pool2d((2, 2), (1, 1), 'SAME'))
ivy.array([[[[ 2,  3],
[ 4,  5],
[ 4,  5]]],
[[[ 8,  9],
[10, 11],
[10, 11]]]])

>>> x = ivy.arange(48.).reshape((2, 4, 3, 2))
>>> print(x.max_pool2d(3, 1, 'VALID'))
ivy.array([[[[16, 17]],
[[22, 23]]],
[[[40, 41]],
[[46, 47]]]])

Container.avg_pool2d(self, kernel, strides, padding, /, *, data_format='NHWC', 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 instance method variant of ivy.avg_pool2d. This method simply wraps the function, and so the docstring for ivy.avg_pool2d also applies to this method with minimal changes.

Parameters:

x – Input image [batch_size,h,w,d_in].
kernel (Union[int, Tuple[int], Tuple[int, int], Container]) – The size of the window to take a max over.
strides (Union[int, Tuple[int], Tuple[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: 'NHWC') – “NHWC” or “NCHW”. Defaults to “NHWC”.
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 divisor, otherwise kernel_size will be used.
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(12).reshape((2, 1, 3, 2))
>>> b = ivy.arange(48).reshape((2, 4, 3, 2))
>>> x = ivy.Container({'a': a, 'b': b})
>>> y = x.avg_pool2d(2, 1, "SAME")
>>> print(y)
{
    a: (<class ivy.data_classes.array.array.Array> shape=[2, 1, 3, 2]),
    b: (<class ivy.data_classes.array.array.Array> shape=[2, 4, 3, 2])
}