Mojo function
avg_pool
avg_pool(input: Symbol, filter_shape: Tuple[Int, Int], stride: Tuple[Int, Int] = VariadicPack(<store_to_mem({1}), store_to_mem({1})>, 1), dilation: Tuple[Int, Int] = VariadicPack(<store_to_mem({1}), store_to_mem({1})>, 1), padding: Tuple[Int, Int, Int, Int] = VariadicPack(<store_to_mem({0}), store_to_mem({0}), store_to_mem({0}), store_to_mem({0})>, 1), count_boundary: Bool = 1) -> Symbol
Computes average pooling with the given filter shape, strides, and dilations.
The op supports 2D avg pooling (so input and filter must be 4D), with the following layout assumption:
- input has layout NHWC, i.e., (batch_size, height, width, in_channels)
Individual elements in the hyperparameters applies to corresponding dimensions of the input (after ignoring the batch and channel dimensions), with padding representing a before/after pair for each axis. The padding values are expected to take the form (pad_dim1_before, pad_dim1_after, pad_dim2_before, pad_dim2_after...). In 2D Convolution, dim1 here repesents H and dim2 represents W.
This op currently only supports strides and dilations on the filter.
Args:
- input (
Symbol
): The rank-4 input tensor to perform the pooling upon, in NHWC layout. - filter_shape (
Tuple[Int, Int]
): The shape of the pooling filter. - stride (
Tuple[Int, Int]
): The stride of the pooling operation. - dilation (
Tuple[Int, Int]
): The spacing between the kernel points. - padding (
Tuple[Int, Int, Int, Int]
): The amount of padding applied to the input. - count_boundary (
Bool
): Whether to include the zero-padding in the averaging calculation.
Returns:
A symbolic tensor value with the pooling applied.
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