LpPool

LpPool - 11

Version

• name: LpPool (GitHub)

• domain: main

• since_version: 11

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 11.

Summary

LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.

Attributes

• auto_pad: auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that output_shape[i] = ceil(input_shape[i] / strides[i]) for each axis i. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER. Default value is 'NOTSET'.

• kernel_shape (required): The size of the kernel along each axis.

• p: p value of the Lp norm used to pool over the input data. Default value is 2.

• pads: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. pads format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.

• strides: Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.

Inputs

• X (heterogeneous) - T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.

Outputs

• Y (heterogeneous) - T: Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Examples

Differences

0	0	LpPool consumes an input tensor X and applies Lp pooling across	LpPool consumes an input tensor X and applies Lp pooling across
1	1	the tensor according to kernel sizes, stride sizes, and pad lengths.	the tensor according to kernel sizes, stride sizes, and pad lengths.
2	2	Lp pooling consisting of computing the Lp norm on all values of a subset	Lp pooling consisting of computing the Lp norm on all values of a subset
3	3	of the input tensor according to the kernel size and downsampling the	of the input tensor according to the kernel size and downsampling the
4	4	data into the output tensor Y for further processing.	data into the output tensor Y for further processing.
5	5
6	6	**Attributes**	**Attributes**
7	7
8	8	* **auto_pad**:	* **auto_pad**:
9	9	auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.	auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
10	10	Where default value is NOTSET, which means explicit padding is used.	Where default value is NOTSET, which means explicit padding is used.
11	11	SAME_UPPER or SAME_LOWER mean pad the input so that the output	SAME_UPPER or SAME_LOWER mean pad the input so that output_shape[i]
12		spatial size match the input.In case of odd number add the extra
	12		= ceil(input_shape[i] / strides[i]) for each axis i. The padding
	13		is split between the two sides equally or almost equally (depending
	14		on whether it is even or odd). In case the padding is an odd number,
13	15	padding at the end for SAME_UPPER and at the beginning for	the extra padding is added at the end for SAME_UPPER and at the
14	16	SAME_LOWER. VALID mean no padding. Default value is 'NOTSET'.	beginning for SAME_LOWER. Default value is 'NOTSET'.
15	17	* **kernel_shape** (required):	* **kernel_shape** (required):
16	18	The size of the kernel along each axis.	The size of the kernel along each axis.
17	19	* **p**:	* **p**:
18	20	p value of the Lp norm used to pool over the input data. Default value is 2.	p value of the Lp norm used to pool over the input data. Default value is 2.
19	21	* **pads**:	* **pads**:
20	22	Padding for the beginning and ending along each spatial axis, it can	Padding for the beginning and ending along each spatial axis, it can
21	23	take any value greater than or equal to 0. The value represent the	take any value greater than or equal to 0. The value represent the
22	24	number of pixels added to the beginning and end part of the	number of pixels added to the beginning and end part of the
23	25	corresponding axis. pads format should be as follow [x1_begin,	corresponding axis. pads format should be as follow [x1_begin,
24	26	x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels	x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels
25	27	added at the beginning of axis i and xi_end, the number of pixels	added at the beginning of axis i and xi_end, the number of pixels
26	28	added at the end of axis i. This attribute cannot be used	added at the end of axis i. This attribute cannot be used
27	29	simultaneously with auto_pad attribute. If not present, the padding	simultaneously with auto_pad attribute. If not present, the padding
28	30	defaults to 0 along start and end of each spatial axis.	defaults to 0 along start and end of each spatial axis.
29	31	* **strides**:	* **strides**:
30	32	Stride along each spatial axis.	Stride along each spatial axis. If not present, the stride defaults
	33		to 1 along each spatial axis.
31	34
32	35	**Inputs**	**Inputs**
33	36
34	37	* **X** (heterogeneous) - **T**:	* **X** (heterogeneous) - **T**:
35	38	Input data tensor from the previous operator; dimensions for image	Input data tensor from the previous operator; dimensions for image
36	39	case are (N x C x H x W), where N is the batch size, C is the number	case are (N x C x H x W), where N is the batch size, C is the number
37	40	of channels, and H and W are the height and the width of the data.	of channels, and H and W are the height and the width of the data.
38	41	For non image case, the dimensions are in the form of (N x C x D1 x	For non image case, the dimensions are in the form of (N x C x D1 x
39	42	D2 ... Dn), where N is the batch size.	D2 ... Dn), where N is the batch size.
40	43
41	44	**Outputs**	**Outputs**
42	45
43	46	* **Y** (heterogeneous) - **T**:	* **Y** (heterogeneous) - **T**:
44	47	Output data tensor from Lp pooling across the input tensor.	Output data tensor from Lp pooling across the input tensor.
45	48	Dimensions will vary based on various kernel, stride, and pad sizes.	Dimensions will vary based on various kernel, stride, and pad sizes.
46	49
47	50	**Type Constraints**	**Type Constraints**
48	51
49	52	* **T** in (	* **T** in (
50	53	tensor(double),	tensor(double),
51	54	tensor(float),	tensor(float),
52	55	tensor(float16)	tensor(float16)
53	56	):	):
54	57	Constrain input and output types to float tensors.	Constrain input and output types to float tensors.

LpPool - 2

Version

• name: LpPool (GitHub)

• domain: main

• since_version: 2

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 2.

Summary

LpPool consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.

Attributes

• auto_pad: auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. Default value is 'NOTSET'.

• kernel_shape (required): The size of the kernel along each axis.

• p: p value of the Lp norm used to pool over the input data. Default value is 2.

• pads: Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. pads format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.

• strides: Stride along each spatial axis.

Inputs

• X (heterogeneous) - T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.

Outputs

• Y (heterogeneous) - T: Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

Differences

0	0	LpPool consumes an input tensor X and applies Lp pooling across the	LpPool consumes an input tensor X and applies Lp pooling across
1	1	the tensor according to kernel sizes, stride sizes, and pad lengths.	the tensor according to kernel sizes, stride sizes, and pad lengths.
2	2	Lp pooling consisting of computing the Lp norm on all values of a subset	Lp pooling consisting of computing the Lp norm on all values of a subset
3	3	of the input tensor according to the kernel size and downsampling the	of the input tensor according to the kernel size and downsampling the
4	4	data into the output tensor Y for further processing.	data into the output tensor Y for further processing.
5	5
6	6	**Attributes**	**Attributes**
7	7
8	8	* **auto_pad**:	* **auto_pad**:
9	9	auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.	auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
10	10	Where default value is NOTSET, which means explicit padding is used.	Where default value is NOTSET, which means explicit padding is used.
11	11	SAME_UPPER or SAME_LOWER mean pad the input so that the output size	SAME_UPPER or SAME_LOWER mean pad the input so that the output
12	12	match the input.In case of odd number add the extra padding at the	spatial size match the input.In case of odd number add the extra
13	13	end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean	padding at the end for SAME_UPPER and at the beginning for
	14		SAME_LOWER. VALID mean no padding. Default value is 'NOTSET'.
14	15	no padding. DEPRECATION NOTE: auto_pad is only intended to support	* **kernel_shape** (required):
15		legacy uses, and for framework authors, one is explicitly encouraged
16		to use explicit padding specified in the pads attribute. Default value is 'NOTSET'.
17		* **kernel_shape**:
18	16	The size of the kernel along each axis.	The size of the kernel along each axis.
19	17	* **p**:	* **p**:
20	18	p value of the Lp norm used to pool over the input data, default is	p value of the Lp norm used to pool over the input data. Default value is 2.
21		2.0. Default value is 2.0.
22	19	* **pads**:	* **pads**:
23	20	Padding for the beginning and ending along each axis, it can take	Padding for the beginning and ending along each spatial axis, it can
24	21	any value greater than or equal to 0. The value represent the number	take any value greater than or equal to 0. The value represent the
25	22	of pixels added to the beginning and end part of the corresponding	number of pixels added to the beginning and end part of the
26	23	axis. pads format should be as follow [x1_begin,	corresponding axis. pads format should be as follow [x1_begin,
27	24	x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels	x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels
28	25	added at the beginning of axis i and xi_end, the number of pixels	added at the beginning of axis i and xi_end, the number of pixels
29	26	added at the end of axis i. This attribute cannot be used	added at the end of axis i. This attribute cannot be used
30	27	simultaneously with auto_pad attribute.	simultaneously with auto_pad attribute. If not present, the padding
	28		defaults to 0 along start and end of each spatial axis.
31	29	* **strides**:	* **strides**:
32	30	Stride along each axis.	Stride along each spatial axis.
33	31
34	32	**Inputs**	**Inputs**
35	33
36	34	* **X** (heterogeneous) - **T**:	* **X** (heterogeneous) - **T**:
37	35	Input data tensor from the previous operator; dimensions for image	Input data tensor from the previous operator; dimensions for image
38	36	case are (N x C x H x W), where N is the batch size, C is the number	case are (N x C x H x W), where N is the batch size, C is the number
39	37	of channels, and H and W are the height and the width of the data.	of channels, and H and W are the height and the width of the data.
40	38	For non image case, the dimension are in the form of (N x C x D1 x	For non image case, the dimensions are in the form of (N x C x D1 x
41	39	D2 ... Dn), where N is the batch size.	D2 ... Dn), where N is the batch size.
42	40
43	41	**Outputs**	**Outputs**
44	42
45	43	* **Y** (heterogeneous) - **T**:	* **Y** (heterogeneous) - **T**:
46	44	Output data tensor from Lp pooling across the input tensor.	Output data tensor from Lp pooling across the input tensor.
47	45	Dimensions will vary based on various kernel, stride, and pad sizes.	Dimensions will vary based on various kernel, stride, and pad sizes.
48	46
49	47	**Type Constraints**	**Type Constraints**
50	48
51	49	* **T** in (	* **T** in (
52	50	tensor(double),	tensor(double),
53	51	tensor(float),	tensor(float),
54	52	tensor(float16)	tensor(float16)
55	53	):	):
56	54	Constrain input and output types to float tensors.	Constrain input and output types to float tensors.

LpPool - 1

Version

• name: LpPool (GitHub)

• domain: main

• since_version: 1

• function: False

• support_level: SupportType.COMMON

• shape inference: False

This version of the operator has been available since version 1.

Summary

LpPool consumes an input tensor X and applies Lp pooling across the the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.

Attributes

• auto_pad: auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute. Default value is 'NOTSET'.

• kernel_shape: The size of the kernel along each axis.

• p: p value of the Lp norm used to pool over the input data, default is 2.0. Default value is 2.0.

• pads: Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. pads format should be as follow [x1_begin, x2_begin…x1_end, x2_end,…], where xi_begin the number of pixels added at the beginning of axis i and xi_end, the number of pixels added at the end of axis i. This attribute cannot be used simultaneously with auto_pad attribute.

• strides: Stride along each axis.

Inputs

• X (heterogeneous) - T: Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 … Dn), where N is the batch size.

Outputs

• Y (heterogeneous) - T: Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.

Type Constraints

• T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.

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