PyTorch for Numpy Users.

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A cheatsheet for Numpy users to use PyTorch. To add/edit an entry, see it on GitHub.

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

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]])
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()
x.astype(np.float32)
x.type(torch.float32); x.float()
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

Linear algebra

Numpy PyTorch
np.dot
torch.dot   # 1D arrays only
torch.mm    # 2D arrays only
torch.mv    # matrix-vector (2D x 1D)
np.matmul
torch.matmul
np.tensordot
torch.tensordot
np.einsum
torch.einsum

Building matrices

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

Attributes

Numpy PyTorch
x.shape
x.shape; x.size()
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; x.view
x.resize()
x.resize_
x.resize_as_
x = np.arange(6).reshape(3, 2, 1)
x.transpose(2, 0, 1)  # 012 -> 201
x = torch.arange(6).reshape(3, 2, 1)
x.permute(2, 0, 1); x.transpose(1, 2).transpose(0, 1)  # 012 -> 021 -> 201
x.flatten
x.view(-1)
x.squeeze()
x.squeeze()
x[:, None]; np.expand_dims(x, 1)
x[:, None]; x.unsqueeze(1)

Item selection and manipulation

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_interleave(2)  # [1, 1, 2, 2, 3, 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]
torch.flip(x, [0])
np.unique(x)
torch.unique(x)

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); x.floor()
np.ceil(x)
torch.ceil(x); x.ceil()
x.trace
x.trace
x.sum
x.sum
x.sum(axis=0)
x.sum(0)
x.cumsum
x.cumsum
x.mean
x.mean
x.std
x.std
x.prod
x.prod
x.cumprod
x.cumprod
x.all
x.all
x.any
x.any

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

Random numbers

Numpy PyTorch
np.random.seed
torch.manual_seed
np.random.permutation(5)
torch.randperm(5)

Numerical operations

Numpy PyTorch
np.sign
torch.sign
np.sqrt
torch.sqrt