pytorch-for-numpy-users/conversions.yaml

types:
  - numpy: np.ndarray
    pytorch: torch.Tensor
  - numpy: np.float32
    pytorch: torch.FloatTensor
  - numpy: np.float64
    pytorch: torch.DoubleTensor
  - numpy: np.int8
    pytorch: torch.CharTensor
  - numpy: np.uint8
    pytorch: torch.ByteTensor
  - numpy: np.int16
    pytorch: torch.ShortTensor
  - numpy: np.int32
    pytorch: torch.IntTensor
  - numpy: np.int64
    pytorch: torch.LongTensor

constructors:
  ones and zeros:
    - numpy: np.empty((2, 3))
      pytorch: torch.Tensor(2, 3)
    - numpy: np.empty_like(x)
      pytorch: x.new(x.size()).type(x.type())
    - numpy: np.eye
      pytorch: torch.eye
    - numpy: np.identity
      pytorch: torch.eye
    - numpy: np.ones
      pytorch: torch.ones
    - numpy: np.ones_like
      pytorch: torch.ones(x.size()).type(x.type())
    - numpy: np.zeros
      pytorch: torch.zeros
    - numpy: np.zeros_like
      pytorch: torch.zeros(x.size()).type(x.type())
  from existing data:
    - numpy: np.array([[1, 2], [3, 4]])
      pytorch: torch.Tensor([[1, 2], [3, 4])
    - numpy: x.copy()
      pytorch: x.clone()
    - numpy: np.fromfile(file)
      pytorch: torch.Tensor(torch.Storage(file))
    - numpy: np.frombuffer
      pytorch:
    - numpy: np.fromfunction
      pytorch:
    - numpy: np.fromiter
      pytorch:
    - numpy: np.fromstring
      pytorch:
    - numpy: np.loadtxt
      pytorch:
    - numpy: np.concatenate
      pytorch: torch.cat
  numerical ranges:
    - numpy: np.arange(10)
      pytorch: torch.range(0, 9)
    - numpy: np.arange(2, 3, 0.1)
      pytorch: torch.range(2, 2.9, 10)
    - numpy: np.linspace
      pytorch: torch.linspace
    - numpy: np.logspace
      pytorch: torch.logspace
  building matrices:
    - numpy: np.diag
      pytorch: torch.diag
    - numpy: np.tril
      pytorch: torch.tril
    - numpy: np.triu
      pytorch: torch.triu
  attributes:
    - numpy: x.shape
      pytorch: x.size()
    - numpy: x.strides
      pytorch: x.stride()
    - numpy: x.ndim
      pytorch: x.dim()
    - numpy: x.data
      pytorch: x.data()
    - numpy: x.size
      pytorch: x.nelement()
    - numpy: x.dtype
      pytorch: x.type()
  indexing:
    - numpy: x[0]
      pytorch: x[0]
    - numpy: x[:, 0]
      pytorch: x[:, 0]
    - numpy: x[indices]
      pytorch: x[torch.LongTensor(indices)]
    - numpy: np.take(x, indices)
      pytorch: x[torch.LongTensor(indices)]
    - numpy: x[x != 0]
      pytorch: x[x != 0]
  shape manipulation:
    - numpy: x.reshape
      pytorch: x.view
    - numpy: x.resize
      pytorch: x.resize_
    - numpy:
      pytorch: x.resize_as_
    - numpy: x.transpose
      pytorch: x.permute
    - numpy: x.flatten()
      pytorch: x.view(-1)
    - numpy: x.squeeze
      pytorch: x.squeeze
    - numpy: x[:, np.newaxis]
      pytorch: x.unsqueeze(1)
  item selection and manipulation:
    - numpy: np.put
      pytorch:
    - numpy: x.repeat
      pytorch:
    - numpy: x.tile
      pytorch: x.repeat
    - numpy: np.choose
      pytorch:
    - numpy: np.sort
      pytorch: sorted, indices = torch.sort(x, [dim])
    - numpy: np.argsort
      pytorch: sorted, indices = torch.sort(x, [dim])
    - numpy: np.nonzero
      pytorch: torch.nonzero
    - numpy: np.where
      pytorch: torch.nonzero
  calculation:
    - numpy: x.min
      pytorch: mins, indices = torch.min(x, [dim])
    - numpy: x.argmin
      pytorch: mins, indices = torch.min(x, [dim])
    - numpy: x.max
      pytorch: maxs, indices = torch.max(x, [dim])
    - numpy: x.argmax
      pytorch: maxs, indices = torch.max(x, [dim])
    - numpy: x.clip
      pytorch: x.clamp
    - numpy: x.round
      pytorch: x.round
    - numpy: np.floor(x)
      pytorch: x.floor()
    - numpy: np.ceil(x)
      pytorch: x.ceil()
    - numpy: x.trace
      pytorch: x.trace
    - numpy: x.sum
      pytorch: x.sum
    - numpy: x.cumsum
      pytorch: x.cumsum
    - numpy: x.mean
      pytorch: x.mean
    - numpy: x.std
      pytorch: x.std
    - numpy: x.prod
      pytorch: x.prod
    - numpy: x.cumprod
      pytorch: x.cumprod
    - numpy: x.all
      pytorch: (x == 1).sum() == x.nelement()
    - numpy: x.any
      pytorch: (x == 1).sum() > 0
  arithmetic and comparison operations:
    - numpy: x.lt
      pytorch: x.lt
    - numpy: x.le
      pytorch: x.le
    - numpy: x.gt
      pytorch: x.gt
    - numpy: x.ge
      pytorch: x.ge
    - numpy: x.eq
      pytorch: x.eq
    - numpy: x.ne
      pytorch: x.ne