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Torch flipud、Torch flip、Torch empty在PTT/mobile01評價與討論,在ptt社群跟網路上大家這樣說

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Torch flipud在torch.flipud — PyTorch 1.10.0 documentation的討論與評價

torch.flipud makes a copy of input 's data. This is different from NumPy's np.flipud , which returns a view in constant time. Since copying a tensor's data ...

Torch flipud在torch.flipud - 在上/下方向翻转张量,返回一个新 ... - Runebook.dev的討論與評價

按上/下方向翻转每列中的条目。行被保留,但显示顺序与之前不同。 Note 要求张量至少是一维的。 Note torch.flipud 制作input 数据的副本。这与NumPy 的np.flipud 不同 ...

Torch flipud在torch.flipud - AI研习社的討論與評價

torch. flipud (input) → Tensor¶. Flip array in the up/down direction, returning a new tensor. Flip the entries in each column in the up/down direction.

Torch flipud在ptt上的文章推薦目錄

    Torch flipud在Pytorch torch.flipud() | Newbedev的討論與評價

    Pytorch torch.flipud(). It may be missing from the source documentation or this could be a bug. Go back ...

    Torch flipud在torch.flipud— PyTorch 1.8.0 documentation - 金莎国际的討論與評價

    torch.flipud 复制 输入 的数据。这和NumPy的不一样 np.flipud ,它以常量时间返回视图。由于复制一个张量的数据比查看数据要复杂, torch.flipud 预计会比 np.flipud ...

    Torch flipud在flipud的討論與評價

    flipud ¶. dragon.vm.torch. flipud (input)[source]¶. Reverse elements along the first dimension. Examples: x = torch.tensor([[1, 2, 3], [4, 5, ...

    Torch flipud在Flipud - R-Project.org的討論與評價

    torch_flipud {torch}, R Documentation. Flipud. Description. Flipud ... flipud(input) -> Tensor. Flip array in the up/down direction, returning a new tensor.

    Torch flipud在bns090020/01-tensor-operations - Jovian的討論與評價

    function: torch.arrange(); function: torch.unique(); function: torch.count_nonzero(); function: torch.flipud(); function: torch.lcm().

    Torch flipud在基于Pytorch使用FFT,矩阵乘法,Conv2d计算卷积 - Python成神 ...的討論與評價

    导入所需模块 import torch import torch.nn as nn from timeit import ... [1]) # 对卷积核h_n进行左右翻转 h_trans = torch.flipud(h_trans) # 对卷 ...

    Torch flipud在DnCNN/data_generator.py at master - GitHub的討論與評價

    from torch.utils.data import Dataset. import torch ... noise = torch.randn(batch_x.size()).mul_(self.sigma/255.0) ... return np.flipud(np.rot90(img, k=2)).

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