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Mojo function
max_pool_ceil_mode_true_shape
def max_pool_ceil_mode_true_shape[dtype: DType, int_type: DType](input: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=input.static_spec], filter: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=filter.static_spec], strides: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=strides.static_spec], dilations: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=dilations.static_spec], paddings: ManagedTensorSlice[IOSpec[_, _].Input, static_spec=paddings.static_spec]) -> IndexList[Int(4)]
Computes the output shape for the mo.max_pool_ceil_mode_true graph op.
Parameters:
- βdtype (
DType): Element type of the poolinginputtensor. - βint_type (
DType): Element type of thefilter,strides,dilations, andpaddingstensors.
Args:
- βinput (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=input.static_spec]): Rank-4 batched image input to the pooling operator. - βfilter (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=filter.static_spec]): One-dimensional tensor of filter sizes on the height and width dimensions,(filter_h, filter_w). - βstrides (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=strides.static_spec]): One-dimensional tensor of strides on the height and width dimensions,(stride_h, stride_w). - βdilations (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=dilations.static_spec]): One-dimensional tensor of dilations on the height and width dimensions,(dilation_h, dilation_w). - βpaddings (
ManagedTensorSlice[IOSpec[_, _].Input, static_spec=paddings.static_spec]): One-dimensional tensor of paddings on the height and width dimensions,(pad_h_before, pad_h_after, pad_w_before, pad_w_after).
Returns:
IndexList[Int(4)]: The output shape of the max pooling operation with ceil mode enabled.