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wkentaro/pytorch-for-numpy-users

TopGit repo profile for wkentaro/pytorch-for-numpy-users, with GitHub repository stats and README context.

Stars

★ 706

Forks

⑂ 86

Language

HTML

Topic

AI Tools

License

MIT

Updated

3.5y ago

Contributors

—

Homepage

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QUICK VERDICT

TopGit tracks wkentaro/pytorch-for-numpy-users on GitHub as part of the AI Tools family. The project has 706 stars. PyTorch for Numpy users. https://pytorch-for-numpy-users.wkentaro.com

Snapshot summary built from the project's own GitHub metadata — there's no written TopGit review yet. The page will update automatically when a full review is published.

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Snapshot

Stars★ 706

Forks⑂ 86

LanguageHTML

TopicAI Tools

LicenseMIT

Homepage—

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Top contributors

Show top contributors

Top 21 contributors to wkentaro/pytorch-for-numpy-users

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

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

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

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

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

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

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

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]

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)

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)
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)

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

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

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

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

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wkentaro/pytorch-for-numpy-users (wkentaro/pytorch-for-numpy-users) is a HTML project on GitHub. From the project's own README: PyTorch for Numpy users. https://pytorch-for-numpy-users.wkentaro.com

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GitHub

wkentaro/pytorch-for-numpy-users

Snapshot

Top contributors

PyTorch for Numpy users.

How to contribute?

Types

Ones and zeros

From existing data

Numerical ranges

Linear algebra

Building matrices

Attributes

Indexing

Shape manipulation

Item selection and manipulation

Calculation

Arithmetic and comparison operations

Random numbers

Numerical operations

Related repositories

Quick answers

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