AvgPoolBackprop
AvgPoolBackprop#
Versioned name: AvgPoolBackprop-1
Category: Pooling
Short description: Reference
Detailed description: Reference
Attributes:
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: non-negative s64 values.
Type: s64[]
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: non-negative s64 values.
Type: s64[]
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: non-negative s64 values.
Type: s64[]
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: positive s64 values.
Type: s64[]
Required: yes
exclude_pad
Description: exclude_pad is a type of pooling strategy for values in the padding area. For example, if exclude_pad is true, zero-values in the padding are not used.
Range of values: True or False
Type: bool
Required: yes
auto_pad
Description: auto_pad how the padding is calculated. Possible values:
none (not specified): use explicit padding values.
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: none
Required: no
Note: pads_begin and pads_end attributes are ignored when auto_pad is specified.
data_format
Description: data_format denotes the data format of the output_delta and input_delta.
Range of values: NXC or NCX (X means HW for 2D, DHW for 3D)
Type: string
Default value: NXC
Required: no
input_shape
Description: input_shape denotes the shape of the forward input tensor.
Type: s64[]
Required: no
Inputs:
1:
output_delta
- the gradient tensor with respect to output of AvgPool. Required.Type: T
2:
input_shape
- the dimensions of forward input. Optional. If specified, input_shape attribute will be ignored. If not specified, users should define input_shape through attribute.Type: s64
Outputs
1:
input_delta
- the gradient tensor with respect to the input of AvgPool.Type: T
Types:
T: f32, f16, bf16.
Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.