LpPool - 2 vs 11

Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.

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  1. LpPool2 → LpPool11 +2 -2
LpPool2 → LpPool11 RENAMED
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  LpPool consumes an input tensor X and applies Lp pooling across
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  the tensor according to kernel sizes, stride sizes, and pad lengths.
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  Lp pooling consisting of computing the Lp norm on all values of a subset
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  of the input tensor according to the kernel size and downsampling the
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  data into the output tensor Y for further processing.
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  ### Attributes
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  * **auto_pad - STRING** (default is 'NOTSET'):
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- 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.
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+ 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.
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  * **kernel_shape - INTS** (required) :
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  The size of the kernel along each axis.
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  * **p - INT** (default is '2'):
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  p value of the Lp norm used to pool over the input data.
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  * **pads - INTS** :
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  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.
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  * **strides - INTS** :
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- Stride along each spatial axis.
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+ Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.
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  ### Inputs
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  - **X** (heterogeneous) - **T**:
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  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.
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  ### Outputs
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  - **Y** (heterogeneous) - **T**:
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  Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.
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  ### Type Constraints
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  * **T** in ( tensor(double), tensor(float), tensor(float16) ):
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  Constrain input and output types to float tensors.