面向 Numpy 用户的 PyTorch 速查表

PyTorch入门实战教程

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。

类型(Types)

Numpy PyTorch
np.ndarray torch.Tensor
np.float32 torch.float32; torch.float
np.float64 torch.float64; torch.double
np.float16 torch.float16; torch.half
np.int8 torch.int8
np.uint8 torch.uint8
np.int16 torch.int16; torch.short
np.int32 torch.int32; torch.int
np.int64 torch.int64; torch.long

构造器(Constructor)

零和一(Ones and zeros)

Numpy PyTorch
np.empty((2, 3)) torch.empty(2, 3)
np.empty_like(x) torch.empty_like(x)
np.eye torch.eye
np.identity torch.eye
np.ones torch.ones
np.ones_like torch.ones_like
np.zeros torch.zeros
np.zeros_like torch.zeros_like

从已知数据构造

Numpy PyTorch
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.load torch.load
np.loadtxt
np.concatenate torch.cat

数值范围

Numpy PyTorch
np.arange(10) torch.arange(10)
np.arange(2, 3, 0.1) torch.arange(2, 3, 0.1)
np.linspace torch.linspace
np.logspace torch.logspace

构造矩阵

Numpy PyTorch
np.diag torch.diag
np.tril torch.tril
np.triu torch.triu

参数

Numpy PyTorch
x.shape x.shape
x.strides x.stride()
x.ndim x.dim()
x.data x.data
x.size x.nelement()
x.dtype x.dtype

索引

Numpy PyTorch
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

Numpy PyTorch
x.reshape x.reshape; x.view
x.resize() x.resize_
x.resize_as_
x.transpose x.transpose or x.permute
x.flatten x.view(-1)
x.squeeze() x.squeeze()
x[:, np.newaxis]; np.expand_dims(x, 1) x.unsqueeze(1)

数据选择

Numpy PyTorch
np.put
x.put x.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.sort sorted, indices = torch.sort(x, [dim])
np.argsort sorted, indices = torch.sort(x, [dim])
np.nonzero torch.nonzero
np.where torch.where
x[::-1]

数值计算

Numpy PyTorch
x.min x.min
x.argmin x.argmin
x.max x.max
x.argmax x.argmax
x.clip x.clamp
x.round x.round
np.floor(x) torch.floor(x); x.floor()
np.ceil(x) torch.ceil(x); x.ceil()
x.trace x.trace
x.sum x.sum
x.cumsum x.cumsum
x.mean x.mean
x.std x.std
x.prod x.prod
x.cumprod x.cumprod
x.all (x == 1).sum() == x.nelement()
x.any (x == 1).sum() > 0

数值比较

Numpy PyTorch
np.less x.lt
np.less_equal x.le
np.greater x.gt
np.greater_equal x.ge
np.equal x.eq
np.not_equal x.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。

PyTorch入门实战教程

Leave a Reply

Your email address will not be published. Required fields are marked *

返回顶部