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GitHub - wkentaro/pytorch-for-numpy-users: PyTorch for Numpy users.

 5 years ago
source link: https://github.com/wkentaro/pytorch-for-numpy-users
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README.md

PyTorch for Numpy users.

Build Status

PyTorch version of Torch for Numpy users.

Types

Numpy PyTorch np.ndarray torch.Tensor np.float32 torch.FloatTensor np.float64 torch.DoubleTensor np.int8 torch.CharTensor np.uint8 torch.ByteTensor np.int16 torch.ShortTensor np.int32 torch.IntTensor np.int64 torch.LongTensor

Constructors

Ones and zeros

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

From existing data

Numpy PyTorch np.array([[1, 2], [3, 4]]) torch.Tensor([[1, 2], [3, 4]) x.copy() x.clone() np.fromfile(file) torch.Tensor(torch.Storage(file)) np.frombuffer

np.fromfunction

np.fromiter

np.fromstring

np.loadtxt

np.concatenate torch.cat

Numerical ranges

Numpy PyTorch np.arange(10) torch.range(0, 9) np.arange(2, 3, 0.1) torch.range(2, 2.9, 10) np.linspace torch.linspace np.logspace torch.logspace

Building matrices

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

Attributes

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

Indexing

Numpy PyTorch x[0] x[0] x[:, 0] x[:, 0] x[indices] x[torch.LongTensor(indices)] np.take(x, indices) x[torch.LongTensor(indices)] x[x != 0] x[x != 0]

Shape manipulation

Numpy PyTorch x.reshape x.view x.resize x.resize_

x.resize_as_ x.transpose x.permute x.flatten() x.view(-1) x.squeeze x.squeeze x[:, np.newaxis] x.unsqueeze(1)

Item selection and manipulation

Numpy PyTorch np.put

x.repeat

x.tile x.repeat 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.nonzero x[::-1] a workaround

Calculation

Numpy PyTorch x.min mins, indices = torch.min(x, [dim]) x.argmin mins, indices = torch.min(x, [dim]) x.max maxs, indices = torch.max(x, [dim]) x.argmax maxs, indices = torch.max(x, [dim]) x.clip x.clamp x.round x.round np.floor(x) x.floor() np.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

Arithmetic and comparison operations

Numpy PyTorch x.lt x.lt x.le x.le x.gt x.gt x.ge x.ge x.eq x.eq x.ne x.ne

report

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