wkentaro / Pytorch For Numpy Users
Licence: mit
PyTorch for Numpy users. https://pytorch-for-numpy-users.wkentaro.com
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PyTorch for Numpy users.
PyTorch version of Torch for Numpy users.
We assume you use the latest PyTorch and Numpy.
How to contribute?
git clone https://github.com/wkentaro/pytorch-for-numpy-users.git
cd pytorch-for-numpy-users
vim conversions.yaml
git commit -m "Update conversions.yaml"
./run_tests.py
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 |
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