Mojo function

avg_pool_gpu

avg_pool_gpu[dtype: DType, int_type: DType, count_boundary: Bool = False](ctx: DeviceContext, 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 the average pool on GPU.

Params: count_boundary: Whether to count the boundary in the average computation.

Args:

ctx (DeviceContext): The DeviceContext to use for GPU execution.
input (TileTensor[dtype, address_space=input.address_space, linear_idx_type=input.linear_idx_type, element_size=input.element_size]): (On device) 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]): (On host) 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]): (On host) Strides on height and width dimensions with assumed tuple (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]): (On host) Dilations on height and width dimensions with assumed tuple (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]): (On host) Paddings on height and width dimensions with assumed tuple (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]): (On device) Pre-allocated output tensor space.
ceil_mode (Bool): Ceiling mode defines the output shape and implicit padding.