PyTorch for Numpy users.

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

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

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

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

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

np.diag

torch.diag

np.tril

torch.tril

np.triu

torch.triu

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

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]

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)

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)

x = np.array([[0, 1], [2, 3], [4, 5]])
idxs = np.array([0, 2])
np.choose(idxs, x) # [0, 5]

x = torch.tensor([[0, 1], [2, 3], [4, 5]])
idxs = torch.tensor([0, 2])
x[idxs, torch.arange(x.shape[1])] # [0, 5]
torch.gather(x, 0, idxs[None, :])[0] # [0, 5]

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)

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

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

np.random.seed

torch.manual_seed

np.random.permutation(5)

torch.randperm(5)

np.sign

torch.sign

np.sqrt

torch.sqrt