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Mojo function

max_pool_cpu

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

Computes fp32 pooling.

Parameters:

dtype (DType): Data type of the input and output tensors.
int_type (DType): Data type of the filter, strides, dilations, and paddings tensors.

Args:

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