pad¶

nvtripy.pad(input: Tensor, pad: Sequence[Tuple[int | DimensionSize, int | DimensionSize]], mode: str = 'constant', value: int | float = 0) → Tensor[source]¶

Pads the input tensor.

Parameters:

input (Tensor) – [dtype=T1] The input tensor.
pad (Sequence[Tuple[int | DimensionSize, int | DimensionSize]]) – A sequence of padding sizes of each dimension. Its length must be equal to the rank of input. Each element of pad is a tuple of integers or DimensionSize s (low, high), which represents the padding sizes before the lowest index and after the highest index at the corresponding dimension.
mode (str) – The padding mode. Only “constant” is supported.
value (int | float) – The padding value for “constant” mode.

Returns:

[dtype=T1] The padded tensor.

Return type:

Tensor

DATA TYPE CONSTRAINTS:

T1: bool, float16, float32, int32, int64

Example: Constant padding.

input = tp.reshape(tp.arange(6, dtype=tp.float32), (2, 3))
output = tp.pad(input, [(1, 0), (0, 1)])

Local Variables¶

>>> input
tensor(
    [[0, 1, 2],
     [3, 4, 5]], 
    dtype=float32, loc=gpu:0, shape=(2, 3))

>>> output
tensor(
    [[0, 0, 0, 0],
     [0, 1, 2, 0],
     [3, 4, 5, 0]], 
    dtype=float32, loc=gpu:0, shape=(3, 4))