megengine.module.padding 源代码

from typing import Tuple

from ..functional import nn
from .module import Module


[文档]class Pad(Module): r"""Pads the input tensor. Args: pad_width(Tuple[Tuple[int, int], ...]): A tuple. Each element in the tuple is the tuple of 2-elements, the 2 elements represent the padding size on both sides of the current dimension, ``(front_offset, back_offset)`` mode(str): One of the following string values. Default: ``'constant'`` * ``'constant'``: Pads with a constant value. * ``'reflect'``: Pads with the edge values of tensor. * ``'replicate'``: Pads with the reflection of the tensor mirrored on the first and last values of the tensor along each axis. constant_val(float): Fill value for ``'constant'`` padding. Default: 0 Returns: Return type: module. The instance of the ``Pad`` module. Examples: >>> import numpy as np >>> inp = Tensor([[1., 2., 3.],[4., 5., 6.]]) >>> inp Tensor([[1. 2. 3.] [4. 5. 6.]], device=xpux:0) >>> m = M.Pad(pad_width=((1, 1),), mode="constant") >>> m(inp) Tensor([[0. 0. 0.] [1. 2. 3.] [4. 5. 6.] [0. 0. 0.]], device=xpux:0) >>> m = M.Pad(pad_width=((1, 1),), mode="constant", constant_val=9) >>> m(inp) Tensor([[9. 9. 9.] [1. 2. 3.] [4. 5. 6.] [9. 9. 9.]], device=xpux:0) >>> m = M.Pad(pad_width=((1, 1), (1, 2)), mode="reflect") >>> m(inp) Tensor([[5. 4. 5. 6. 5. 4.] [2. 1. 2. 3. 2. 1.] [5. 4. 5. 6. 5. 4.] [2. 1. 2. 3. 2. 1.]], device=xpux:0) >>> m = M.Pad(pad_width=((1, 1), (1, 2)), mode="replicate") >>> m(inp) Tensor([[1. 1. 2. 3. 3. 3.] [1. 1. 2. 3. 3. 3.] [4. 4. 5. 6. 6. 6.] [4. 4. 5. 6. 6. 6.]], device=xpux:0) """ def __init__( self, pad_width: Tuple[Tuple[int, int], ...], mode: str = "constant", constant_val: float = 0.0, ): super().__init__() self.pad_width = pad_width self.mode = mode self.pad_val = constant_val def forward(self, src): return nn.pad( src, pad_width=self.pad_width, mode=self.mode, constant_value=self.pad_val )