文章目录
torch equivalents of numpy functions
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Types
| Numpy |
Torch |
| np.ndarray |
torch.Tensor |
| np.float32 |
torch.FloatTensor |
| np.float64 |
torch.DoubleTensor |
| np.int8 |
torch.CharTensor |
| np.uint8 |
torch.ByteTensor |
| np.int16 |
torch.ShortTensor |
| np.int32 |
torch.IntTensor |
| np.int64 |
torch.LongTensor |
Constructors
Ones and zeros
| Numpy |
Torch |
| np.empty([2,2]) |
torch.Tensor(2,2) |
| np.empty_like(x) |
x.new(x:size()) |
| np.eye |
torch.eye |
| np.identity |
torch.eye |
| np.ones |
torch.ones |
| np.ones_like |
torch.ones(x:size()) |
| np.zeros |
torch.zeros |
| np.zeros_like |
torch.zeros(x:size()) |
From existing data
| Numpy |
Torch |
| np.array([ [1,2],[3,4] ]) |
torch.Tensor({{1,2},{3,4}}) |
| np.ascontiguousarray(x) |
x:contiguous() |
| np.copy(x) |
x:clone() |
| np.fromfile(file) |
torch.Tensor(torch.Storage(file)) |
| np.frombuffer |
??? |
| np.fromfunction |
??? |
| np.fromiter |
??? |
| np.fromstring |
??? |
| np.loadtxt |
??? |
| np.concatenate |
torch.cat |
| np.multiply |
torch.cmul |
Numerical Ranges
| Numpy |
Torch |
| np.arange(10) |
torch.range(0,9) |
| np.arange(2, 3, 0.1) |
torch.linspace(2, 2.9, 10) |
| np.linspace(1, 4, 6) |
torch.linspace(1, 4, 6) |
| np.logspace |
torch.logspace |
Building Matrices
| Numpy |
Torch |
| np.diag |
torch.diag |
| np.tril |
torch.tril |
| np.triu |
torch.triu |
Attributes
| Numpy |
Torch |
| x.shape |
x:size() |
| x.strides |
x:stride() |
| x.ndim |
x:dim() |
| x.data |
x:data() |
| x.size |
x:nElement() |
| x.size == y.size |
x:isSameSizeAs(y) |
| x.dtype |
x:type() |
Indexing
Shape Manipulation
| Numpy |
Torch |
| x.reshape |
x:reshape |
| x.resize |
x:resize |
| ? |
x:resizeAs |
| x.transpose |
x:transpose() |
| x.flatten |
x:view(x:nElement()) |
| x.squeeze |
x:squeeze |
Item selection and manipulation
| Numpy |
Torch |
| np.take(a, indices) |
a[indices] |
| x[:,0] |
x[{{},1}] |
| np.put |
??? |
| x.repeat |
x:repeatTensor |
| x.fill |
x:fill |
| np.choose |
??? |
| np.sort |
sorted, indices = torch.sort(x, [dim]) |
| np.argsort |
sorted, indices = torch.sort(x, [dim]) |
| np.nonzero |
torch.find(x:gt(0), 1) (torchx) |
Calculation
| Numpy |
Torch |
| ndarray.min |
mins, indices = torch.min(x, [dim]) |
| ndarray.argmin |
mins, indices = torch.min(x, [dim]) |
| ndarray.max |
maxs, indices = torch.max(x, [dim]) |
| ndarray.argmax |
maxs, indices = torch.max(x, [dim]) |
| ndarray.clip |
torch.clamp |
| ndarray.round |
|
| ndarray.trace |
torch.trace |
| ndarray.sum |
torch.sum |
| ndarray.cumsum |
torch.cumsum |
| ndarray.mean |
torch.mean |
| ndarray.std |
torch.std |
| ndarray.prod |
torch.prod |
| ndarray.dot |
torch.mm |
| ndarray.cumprod |
torch.cumprod |
| ndarray.all |
??? |
| ndarray.any |
??? |
Arithmetic and comparison operations
| Numpy |
Torch |
| ndarray.lt
|
torch.lt |
| ndarray.le
|
torch.le |
| ndarray.gt
|
torch.gt |
| ndarray.ge
|
torch.ge |
| ndarray.eq
|
torch.eq |
| ndarray.ne
|
torch.ne |