max_pool2d#
- ivy.max_pool2d(x, kernel, strides, padding, /, *, data_format='NHWC', dilation=1, ceil_mode=False, out=None)[source]#
Compute a 2-D max 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
,Tuple
[int
],Tuple
[int
,int
]]) – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.data_format (
str
) – NHWC” or “NCHW”. Defaults to “NHWC”. (default:'NHWC'
)out (
Optional
[Array
]) – optional output array, for writing the result to. (default:None
)
- Return type:
- 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 ofthe arguments.
Examples
>>> x = ivy.arange(12.).reshape((2, 1, 3, 2)) >>> print(ivy.max_pool2d(x, (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(ivy.max_pool2d(x, 3, 1, 'VALID')) ivy.array([[[[16, 17]],
[[22, 23]]],
[[[40, 41]],
[[46, 47]]]])
- Array.max_pool2d(self, kernel, strides, padding, /, *, data_format='NHWC', dilation=1, ceil_mode=False, out=None)#
ivy.Array instance method variant of ivy.max_pool2d. This method simply wraps the function, and so the docstring for ivy.max_pool2d also applies to this method with minimal changes.
- Parameters:
self (
Array
) – 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
) – “NHWC” or “NCHW”. Defaults to “NHWC”. (default:'NHWC'
)out (
Optional
[Array
]) – optional output array, for writing the result to. It must have a shape that (default:None
) 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.max_pool2d(self, kernel, strides, padding, /, *, data_format='NHWC', dilation=1, ceil_mode=False, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#
ivy.Container instance method variant of ivy.max_pool2d. This method simply wraps the function, and so the docstring for ivy.max_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 to take a max over.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
) – “NHWC” or “NCHW”. Defaults to “NHWC”. (default:'NHWC'
)dilations – The dilation factor for each dimension of input. (Default value = 1)
out (
Optional
[Container
]) – optional output array, for writing the result to. It must have a shape (default:None
) 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}) >>> print(x.max_pool2d((2, 2), (1, 1), "SAME")) { a: (<class ivy.array.array.Array> shape=[2, 1, 3, 2]), b: (<class ivy.array.array.Array> shape=[2, 4, 3, 2]) }