首先时是align_corners为True的情况,正如大佬的链接里面说的一样,图像的像素点既可以看成点和可以看成格子,align_corners为True时,像素点看成格子,归一化坐标(-1.0,-1.0)就表示输入图像坐标的(0,0)像素点,同理(1.0,1.0)就表示输入图像(W_in - 1, H_in - 1)的位置。下图中,如果grid中包含(-1.0,-1.0)...
align_corners (bool,optional) – ifTrue, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect whenmodeis'linear','bilinear', or'trilinear'. Default:False 输入输出形状: 注意: 在align_corners = True的情况下,线性...
align_corners (bool,optional) – ifTrue, the corner pixels of the input and output tensors are aligned, and thus preserving the values at those pixels. This only has effect whenmodeis'linear','bilinear', or'trilinear'. Default:False 输入输出形状: 注意: 在align_corners = True的情况下,线性...
(1)第一种方式:像素点位于1\times 1大小方块中心,坐标转换时,HW方向的坐标都要加0.5才能对应起来,pytorch里面对应align_corners=False,假设图像大小为3\times 3上采样为5\times 5, 每个像素点都位于方形内的黑色小点,h_{src}, w_{src}为原图像的大小3\times 3, 目标图像大小h_{dst}, w_{dst}为5\time...
align_corners (bool, optional) – Geometrically, we consider the pixels of the input and output as squares rather than points. If set to True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to False...
'nearest'align_corners(bool,optional):ifTrue,thecornerpixelsoftheinputandoutputtensorsarealigned,andthuspreservingthevaluesatthosepixels.Thisonlyhaseffectwhen:attr:`mode`is`linear`,`bilinear`,or`trilinear`.Default:False..warning::With``align_corners=True``,thelinearlyinterpolatingmodes(`linear`,`...
>>> m = nn.Upsample(scale_factor=2, mode='bilinear') # align_corners=False >>> # Notice that values in top left corner are the same with the small input (except at boundary) >>> m(input_3x3) tensor([[[ 1.0000, 1.2500, 1.7500, 1.5000, 0.5000, 0.0000], [...
(1)第一种方式:像素点位于1×11×1大小方块中心,坐标转换时,HWHW方向的坐标都要加0.50.5才能对应起来,pytorch里面对应align_corners=False,假设图像大小为3×33×3上采样为5×55×5, 每个像素点都位于方形内的黑色小点,hsrc,wsrchsrc,wsrc为原图像的大小3×33×3, 目标图像大小hdst,wdsthdst,wdst为5×55×...
align_corners (Optional[bool]): if set to ``True``, the extrema [-1, 1] are considered as referring to the center points of the input's corner pixels; if set to ``False``, they are instead considered as referring to the corner points of the input's corner pixels, making the ...
- align_corners (bool, optional): 如果 align_corners=True，则对齐 input 和 output 的⾓点像素(corner pixels)，保持在⾓点像素的值. 只会对 mode=linear, bilinear 和 trilinear 有作⽤. 默认是 False. """from numbers import Integral from .modules.utils import _ntuple def _check_size_...