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这是一份面向 Numpy 用户的 PyTorch 入坑指南，如果你之前对 Numpy 使用得心应手，那么有了下面这份指南，你一定可以快速了解 PyTorch 里对应的数值类型以及运算等知识。
类型（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 |
构造器（Constructor）
零和一（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 |
从已知数据构造
| 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() |
| np.fromfile(file) |
torch.tensor(torch.Storage(file)) |
| np.frombuffer |
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| np.fromfunction |
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| np.fromiter |
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| np.fromstring |
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| np.load |
torch.load |
| np.loadtxt |
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| np.concatenate |
torch.cat |
数值范围
| 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 |
构造矩阵
| Numpy |
PyTorch |
| np.diag |
torch.diag |
| np.tril |
torch.tril |
| np.triu |
torch.triu |
参数
| Numpy |
PyTorch |
| x.shape |
x.shape |
| x.strides |
x.stride() |
| x.ndim |
x.dim() |
| x.data |
x.data |
| x.size |
x.nelement() |
| x.dtype |
x.dtype |
索引
| 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] |
形状（Shape）变换
| Numpy |
PyTorch |
| x.reshape |
x.reshape; x.view |
| x.resize() |
x.resize_ |
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x.resize_as_ |
| x.transpose |
x.transpose or x.permute |
| x.flatten |
x.view(-1) |
| x.squeeze() |
x.squeeze() |
| x[:, np.newaxis]; np.expand_dims(x, 1) |
x.unsqueeze(1) |
数据选择
| Numpy |
PyTorch |
| np.put |
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| 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(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 |
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| 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] |
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数值计算
| 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.cumsum |
x.cumsum |
| x.mean |
x.mean |
| x.std |
x.std |
| x.prod |
x.prod |
| x.cumprod |
x.cumprod |
| x.all |
(x == 1).sum() == x.nelement() |
| x.any |
(x == 1).sum() > 0 |
数值比较
| 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 |
希望这份指南能帮你快速了解 Numpy 和 PyTorch 之间的联系和区别。
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