标签：users torch arange PyTorch np Numpy like
转自：https://github.com/wkentaro/pytorch-for-numpy-users
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) |
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) |
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 |
标签：users,torch,arange,PyTorch,np,Numpy,like 来源： https://www.cnblogs.com/ziwenblog/p/15898267.html
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