这是一份面向 Numpy 用户的 PyTorch 入坑指南，如果你之前对 Numpy 使用得心应手，那么有了下面这份指南，你一定可以快速了解 PyTorch 里对应的数值类型以及运算等知识。
文章目录 []
类型（Types）
NumpyPyTorch
np.ndarraytorch.Tensor
np.float32torch.float32; torch.float
np.float64torch.float64; torch.double
np.float16torch.float16; torch.half
np.int8torch.int8
np.uint8torch.uint8
np.int16torch.int16; torch.short
np.int32torch.int32; torch.int
np.int64torch.int64; torch.long
构造器（Constructor）
零和一（Ones and zeros）
NumpyPyTorch
np.empty((2, 3))torch.empty(2, 3)
np.empty_like(x)torch.empty_like(x)
np.eyetorch.eye
np.identitytorch.eye
np.onestorch.ones
np.ones_liketorch.ones_like
np.zerostorch.zeros
np.zeros_liketorch.zeros_like
从已知数据构造
NumpyPyTorch
np.array([[1, 2], [3, 4]])torch.tensor([[1, 2], [3, 4]])
np.array([3.2, 4.3], dtype=np.float16)
np.float16([3.2, 4.3])
torch.tensor([3.2, 4.3], dtype=torch.float16)
x.copy()x.clone()
np.fromfile(file)torch.tensor(torch.Storage(file))
np.frombuffer
np.fromfunction
np.fromiter
np.fromstring
np.loadtorch.load
np.loadtxt
np.concatenatetorch.cat
数值范围
NumpyPyTorch
np.arange(10)torch.arange(10)
np.arange(2, 3, 0.1)torch.arange(2, 3, 0.1)
np.linspacetorch.linspace
np.logspacetorch.logspace
构造矩阵
NumpyPyTorch
np.diagtorch.diag
np.triltorch.tril
np.triutorch.triu
参数
NumpyPyTorch
x.shapex.shape
x.stridesx.stride()
x.ndimx.dim()
x.datax.data
x.sizex.nelement()
x.dtypex.dtype
索引
NumpyPyTorch
x[0]x[0]
x[:, 0]x[:, 0]
x[indices]x[indices]
np.take(x, indices)torch.take(x, torch.LongTensor(indices))
x[x != 0]x[x != 0]
形状（Shape）变换
NumpyPyTorch
x.reshapex.reshape; x.view
x.resize()x.resize_
x.resize_as_
x.transposex.transpose or x.permute
x.flattenx.view(-1)
x.squeeze()x.squeeze()
x[:, np.newaxis]; np.expand_dims(x, 1)x.unsqueeze(1)
数据选择
NumpyPyTorch
np.put
x.putx.put_
x = np.array([1, 2, 3])
x.repeat(2) # [1, 1, 2, 2, 3, 3]
x = torch.tensor([1, 2, 3])
x.repeat(2) # [1, 2, 3, 1, 2, 3]
x.repeat(2).reshape(2, -1).transpose(1, 0).reshape(-1) # [1, 1, 2, 2, 3, 3]
np.tile(x, (3, 2))x.repeat(3, 2)
np.choose
np.sortsorted, indices = torch.sort(x, [dim])
np.argsortsorted, indices = torch.sort(x, [dim])
np.nonzerotorch.nonzero
np.wheretorch.where
x[::-1]
数值计算
NumpyPyTorch
x.minx.min
x.argminx.argmin
x.maxx.max
x.argmaxx.argmax
x.clipx.clamp
x.roundx.round
np.floor(x)torch.floor(x); x.floor()
np.ceil(x)torch.ceil(x); x.ceil()
x.tracex.trace
x.sumx.sum
x.cumsumx.cumsum
x.meanx.mean
x.stdx.std
x.prodx.prod
x.cumprodx.cumprod
x.all(x == 1).sum() == x.nelement()
x.any(x == 1).sum() > 0
数值比较
NumpyPyTorch
np.lessx.lt
np.less_equalx.le
np.greaterx.gt
np.greater_equalx.ge
np.equalx.eq
np.not_equalx.ne
希望这份指南能帮你快速了解 Numpy 和 PyTorch 之间的联系和区别。
