greed2411 / pytorch-for-numpy-users

PyTorch for Numpy users.

PyTorch for Numpy users.

PyTorch version of Torch for Numpy users.

Types

Numpy	PyTorch
np.ndarray	torch.Tensor or torch.tensor
np.float32	torch.FloatTensor or torch.tensor(x, dtype = torch.float)
np.float64	torch.DoubleTensor or torch.tensor(x, dtype = torch.double
np.float16	torch.HalfTensor or torch.tensor(x, dtype = torch.half)
np.int8	torch.CharTensor or torch.tensor(x, dtype = torch.int8)
np.uint8	torch.ByteTensor or torch.tensor(x, dtype = torch.uint8)
np.int16	torch.ShortTensor or torch.tensor(x, dtype = torch.short)
np.int32	torch.IntTensor or torch.tensor(x, dtype = torch.int)
np.int64	torch.LongTensor or torch.tensor(x, dtype = torch.long)

Constructors

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

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.load	torch.load
np.loadtxt
np.concatenate	torch.cat

Numerical ranges

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

Building matrices

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

Attributes

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

Indexing

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]

Shape manipulation

Numpy	PyTorch
x.reshape	x.reshape or 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] or np.expand_dims(x, 1)	x.unsqueeze(1)

Item selection and manipulation

Numpy	PyTorch
np.put
x.put	x.put_
x.repeat	x.repeat
np.tile
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]	a workaround

Calculation

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) or x.floor()
np.ceil(x)	torch.ceil() or 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
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