MaxPool - 8 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Next section compares an older to a newer version of the same operator after both definition are converted into markdown text.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. MaxPool8 → MaxPool11 +4 -19
MaxPool8 → MaxPool11 RENAMED
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  MaxPool consumes an input tensor X and applies max pooling across
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  the tensor according to kernel sizes, stride sizes, and pad lengths.
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  max pooling consisting of computing the max on all values of a
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  subset 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. The output spatial shape will be following:
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  ::
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- output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
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+ output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1)
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-
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- or
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- ::
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-
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- output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i] + 1)
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-
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- if ceil_mode is enabled
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-
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- ::
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  * pad_shape[i] is sum of pads along axis i
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  auto_pad is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following:
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  ::
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- VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])
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+ VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i])
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  SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])
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  And pad shape will be following if SAME_UPPER or SAME_LOWER:
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  ::
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- pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]
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+ pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i]
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  The output of each pooling window is maximum number of elements exclude pad.
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  **Attributes**
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  * **auto_pad**:
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  auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
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  Where default value is NOTSET, which means explicit padding is used.
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  SAME_UPPER or SAME_LOWER mean pad the input so that the output
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  spatial size match the input.In case of odd number add the extra
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  padding at the end for SAME_UPPER and at the beginning for
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  SAME_LOWER. VALID mean no padding.
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- * **ceil_mode**:
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- Whether to use ceil or floor (default) to compute the output shape.
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- * **dilations**:
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- Dilation value along each spatial axis of filter. If not present,
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- the dilation defaults to 1 along each spatial axis.
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  * **kernel_shape** (required):
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  The size of the kernel along each axis.
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  * **pads**:
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  Padding for the beginning and ending along each spatial axis, it can
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  take any value greater than or equal to 0. The value represent the
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  number of pixels added to the beginning and end part of the
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  corresponding axis. pads format should be as follow [x1_begin,
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  x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels
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  added at the beginning of axis i and xi_end, the number of pixels
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  added at the end of axis i. This attribute cannot be used
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  simultaneously with auto_pad attribute. If not present, the padding
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  defaults to 0 along start and end of each spatial axis.
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  * **storage_order**:
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  The storage order of the tensor. 0 is row major, and 1 is column
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  major.
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  * **strides**:
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- Stride along each spatial axis. If not present, the stride defaults
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- to 1 along each spatial axis.
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+ Stride 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
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  case are (N x C x H x W), where N is the batch size, C is the number
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  of channels, and H and W are the height and the width of the data.
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  For non image case, the dimensions are in the form of (N x C x D1 x
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  D2 ... Dn), where N is the batch size. Optionally, if dimension
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  denotation is in effect, the operation expects the input data tensor
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  to arrive with the dimension denotation of [DATA_BATCH,
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  DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
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  **Outputs**
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  Between 1 and 2 outputs.
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  * **Y** (heterogeneous) - **T**:
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  Output data tensor from average or max pooling across the input
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  tensor. Dimensions will vary based on various kernel, stride, and
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  pad sizes. Floor value of the dimension is used
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  * **Indices** (optional, heterogeneous) - **I**:
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  Indices tensor from max pooling across the input tensor. The
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  dimensions of indices are the same as output tensor. The values in
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  indices of are the indices of the selected values during pooling.
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  The indices are computed as flatten 1-D tensor, and the indices do
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  not consider padding. So the values in indices are in [0, N x C x D1
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  x ... x Dn).
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  **Type Constraints**
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  * **T** in (
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  tensor(double),
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  tensor(float),
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  tensor(float16)
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  ):
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  Constrain input and output types to float tensors.
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  * **I** in (
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  tensor(int64)
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  ):
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  Constrain index tensor to int64