GitHub - wkentaro/pytorch-for-numpy-users: PyTorch for Numpy users.
source link: https://github.com/wkentaro/pytorch-for-numpy-users
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README.md
PyTorch for Numpy users.
PyTorch version of Torch for Numpy users.
Types
Numpy PyTorchnp.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 PyTorchnp.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 PyTorchnp.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 PyTorchnp.diag
torch.diag
np.tril
torch.tril
np.triu
torch.triu
Attributes
Numpy PyTorchx.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 PyTorchx[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 PyTorchx.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 PyTorchx.lt
x.lt
x.le
x.le
x.gt
x.gt
x.ge
x.ge
x.eq
x.eq
x.ne
x.ne
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