Mojo function

max_pool_cpu

max_pool_cpu[dtype: DType, int_type: DType](input: TileTensor[dtype, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size], filter: TileTensor[int_type, address_space=filter.address_space, linear_idx_type=filter.linear_idx_type, element_size=filter.element_size], strides: TileTensor[int_type, address_space=strides.address_space, linear_idx_type=strides.linear_idx_type, element_size=strides.element_size], dilations: TileTensor[int_type, address_space=dilations.address_space, linear_idx_type=dilations.linear_idx_type, element_size=dilations.element_size], paddings: TileTensor[int_type, address_space=paddings.address_space, linear_idx_type=paddings.linear_idx_type, element_size=paddings.element_size], output: TileTensor[dtype, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size], ceil_mode: Bool = False)

Computes fp32 pooling.

Args:

input (TileTensor[dtype, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size]): Batched image input to the pool2d operator.
filter (TileTensor[int_type, address_space=filter.address_space, linear_idx_type=filter.linear_idx_type, element_size=filter.element_size]): Filter size on height and width dimensions with assumed tuple (filter_h, filter_w).
strides (TileTensor[int_type, address_space=strides.address_space, linear_idx_type=strides.linear_idx_type, element_size=strides.element_size]): Strides on height and width dimensions with assumed (stride_h, stride_w).
dilations (TileTensor[int_type, address_space=dilations.address_space, linear_idx_type=dilations.linear_idx_type, element_size=dilations.element_size]): Dilations on height and width dimensions with assumed (dilation_h, dilation_w).
paddings (TileTensor[int_type, address_space=paddings.address_space, linear_idx_type=paddings.linear_idx_type, element_size=paddings.element_size]): Paddings on height and width dimensions with assumed (pad_h_before, pad_h_after, pad_w_before, pad_w_after).
output (TileTensor[dtype, address_space=output.address_space, linear_idx_type=output.linear_idx_type, element_size=output.element_size]): Pre-allocated output tensor space.
ceil_mode (Bool): Ceiling mode defines the output shape and implicit padding.