MaxPool#

Versioned name: MaxPool-1

Category: Pooling

Short description: Performs max pooling operation on input.

Detailed description: Input shape can be either 3D, 4D or 5D. Max Pooling operation is performed with the respect to input shape from the third dimension to the last dimension. If paddings are used then during the pooling calculation their value are -inf. The Max Pooling operation involves sliding a filter over each channel of feature map and downsampling by choosing the biggest value within the region covered by the filter. Article about max pooling in Convolutional Networks.

Attributes: Pooling attributes are specified in the data node, which is a child of the layer node.

  • strides

    • Description: strides is a distance (in pixels) to slide the window on the feature map over the (z, y, x) axes for 3D poolings and (y, x) axes for 2D poolings. For example, strides equal “4,2,1” means sliding the window 4 pixel at a time over depth dimension, 2 over height dimension and 1 over width dimension.

    • Range of values: integer values starting from 0

    • Type: int[]

    • Required: yes

  • pads_begin

    • Description: pads_begin is a number of pixels to add to the beginning along each axis. For example, pads_begin equal “1,2” means adding 1 pixel to the top of the input and 2 to the left of the input.

    • Range of values: integer values starting from 0

    • Type: int[]

    • Required: yes

    • Note: the attribute is ignored when auto_pad attribute is specified.

  • pads_end

    • Description: pads_end is a number of pixels to add to the ending along each axis. For example, pads_end equal “1,2” means adding 1 pixel to the bottom of the input and 2 to the right of the input.

    • Range of values: integer values starting from 0

    • Type: int[]

    • Required: yes

    • Note: the attribute is ignored when auto_pad attribute is specified.

  • kernel

    • Description: kernel is a size of each filter. For example, kernel equal (2, 3) means that each filter has height equal to 2 and width equal to 3.

    • Range of values: integer values starting from 1

    • Type: int[]

    • Required: yes

  • rounding_type

    • Description: rounding_type is a type of rounding to be used to compute output shape.

    • Range of values:

      • ceil

      • floor

    • Type: string

    • Default value: floor

    • Required: no

  • auto_pad

    • Description: auto_pad how the padding is calculated. Possible values:

      • explicit: use explicit padding values from pads_begin and pads_end.

      • same_upper (same_lower) the input is padded to match the output size. In case of odd padding value an extra padding is added at the end (at the beginning).

      • valid - do not use padding.

    • Type: string

    • Default value: explicit

    • Required: no

    • Note: pads_begin and pads_end attributes are ignored when auto_pad is not equal to explicit.

Inputs:

  • 1: 3D, 4D or 5D input tensor of type T. Required.

Outputs:

  • 1: Input shape can be either [N, C, H], [N, C, H, W] or [N, C, H, W, D]. Then the corresponding output shape will be [N, C, H_out], [N, C, H_out, W_out] or [N, C, H_out, W_out, D_out]. Output tensor has the same data type as input tensor.

Types

  • T: floating-point or integer type.

Mathematical Formulation

Output shape calculation based on auto_pad and rounding_type:

  • auto_pad = explicit and rounding_type = floor

    H_out = floor(H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1 W_out = floor(W + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1 D_out = floor(D + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1

  • auto_pad = valid and rounding_type = floor

    H_out = floor(H - kernel[0] / strides[0]) + 1 W_out = floor(W - kernel[1] / strides[1]) + 1 D_out = floor(D - kernel[2] / strides[2]) + 1

  • auto_pad = same_upper/same_lower and rounding_type = floor

    H_out = H W_out = W D_out = D

  • auto_pad = explicit and rounding_type = ceil

    H_out = ceil(H + pads_begin[0] + pads_end[0] - kernel[0] / strides[0]) + 1 W_out = ceil(W + pads_begin[1] + pads_end[1] - kernel[1] / strides[1]) + 1 D_out = ceil(D + pads_begin[2] + pads_end[2] - kernel[2] / strides[2]) + 1

  • auto_pad = valid and rounding_type = ceil

    H_out = ceil(H - kernel[0] / strides[0]) + 1 W_out = ceil(W - kernel[1] / strides[1]) + 1 D_out = ceil(D - kernel[2] / strides[2]) + 1

  • auto_pad = same_upper/same_lower and rounding_type = ceil

    H_out = H W_out = W D_out = D

If H + pads_begin[i] + pads_end[i] - kernel[i] is not divided by strides[i] evenly then the result is rounded with the respect to rounding_type attribute.

  1. Example 1 shows how MaxPool operates with 4D input using 2D kernel and auto_pad = explicit

    input = [[[[-1, 2, 3],
           [4, 5, -6],
           [-7, 8, 9]]]]
strides = [1, 1]
pads_begin = [1, 1]
pads_end = [1, 1]
kernel = [2, 2]
rounding_type = "floor"
auto_pad = "explicit"
output = [[[[-1, 2, 3, 3],
            [4, 5, 5, -6],
            [4, 8, 9, 9],
            [-7, 8, 9, 9]]]]

  2. Example 2 shows how MaxPool operates with 3D input using 1D kernel and auto_pad = valid

    input = [[[-1, 2, 3, 5, -7, 9, 1]]]
strides = [1]
kernel = [3]
rounding_type = "floor"
auto_pad = "valid"
output = [[[3, 5, 5, 9, 9]]]

  3. Example 3 shows how MaxPool operates with 4D input using 2D kernel and auto_pad = same_lower

    input = [[[[-1, 2, 3],
         [4, 5, -6],
         [-7, 8, 9]]]]
strides = [1, 1]
kernel = [2, 2]
rounding_type = "floor"
auto_pad = "same_lower"
output = [[[[-1, 2, 3],
            [4, 5, 5]
            [4, 8, 9]]]]

  4. Example 4 shows how MaxPool operates with 4D input using 2D kernel and auto_pad = same_upper

    input = [[[[-1, 2, 3],
           [4, 5, -6],
           [-7, 8, 9]],
          [[2, -1, 5],
           [6, -7, 1],
           [8, 2, -3]]]]
strides = [1, 1]
kernel = [2, 2]
rounding_type = "floor"
auto_pad = "same_upper"
output = [[[[5, 5, 3],
            [8, 9, 9]
            [8, 9, 9]],
           [[6, 5, 5],
            [8, 2, 1],
            [8, 2, -3]]]]

  5. Example 5 shows how MaxPool operates with 4D input using 2D kernel, auto_pad = valid and rounding_type = ceil

    input = [[[[-1, 2, 3],
           [4, 5, -6],
           [-7, 8, 9]]]]
strides = [2, 2]
kernel = [2, 2]
rounding_type = "ceil"
auto_pad = "valid"
output = [[[[5, 3],
            [8, 9]]]]

Examples

<layer ... type="MaxPool" ... >
    <data auto_pad="same_upper" kernel="2,2" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>3</dim>
            <dim>32</dim>
            <dim>32</dim>
        </port>
    </input>
    <output>
        <port id="1">
            <dim>1</dim>
            <dim>3</dim>
            <dim>32</dim>
            <dim>32</dim>
        </port>
    </output>
</layer>

<layer ... type="MaxPool" ... >
    <data auto_pad="explicit" kernel="2,2" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>3</dim>
            <dim>32</dim>
            <dim>32</dim>
        </port>
    </input>
    <output>
        <port id="1">
            <dim>1</dim>
            <dim>3</dim>
            <dim>17</dim>
            <dim>17</dim>
        </port>
    </output>
</layer>

<layer ... type="MaxPool" ... >
    <data auto_pad="valid" kernel="2,2" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>3</dim>
            <dim>32</dim>
            <dim>32</dim>
        </port>
    </input>
    <output>
        <port id="1">
            <dim>1</dim>
            <dim>3</dim>
            <dim>16</dim>
            <dim>16</dim>
        </port>
    </output>
</layer>