numpy(ndarray)和tensor(GPU上的numpy)速查

乎语百科 125 0

类型(Types)

Numpy PyTorch
np.ndarray torch.Tensor
np.float32 torch.float32; torch.float
np.float64 torch.float64; torch.double
np.float 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
np.fromfunction
np.fromiter
np.fromstring
np.load torch.load
np.loadtxt
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_
null 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
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
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]

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

pytorch与tensorflow API速查表

方法名称 pytroch tensorflow numpy
裁剪 torch.clamp(x, min, max) tf.clip_by_value(x, min, max) np.clip(x, min, max)
取最小值 torch.min(x, dim)[0] tf.min(x, axis) np.min(x , axis)
取两个tensor的最大值 torch.max(x, y) tf.maximum(x, y) np.maximum(x, y)
取两个tensor的最小值 torch.min(x, y) torch.minimum(x, y) np.minmum(x, y)
取最大值索引 torch.max(x, dim)[1] tf.argmax(x, axis) np.argmax(x, axis)
取最小值索引 torch.min(x, dim)[1] tf.argmin(x, axis) np.argmin(x, axis)
比较(x > y) torch.gt(x, y) tf.greater(x, y) np.greater(x, y)
比较(x < y) torch.le(x, y) tf.less(x, y) np.less(x, y)
比较(x==y) torch.eq(x, y) tf.equal(x, y) np.equal(x, y)
比较(x!=y) torch.ne(x, y) tf.not_equal(x, y) np.not_queal(x , y)
取符合条件值的索引 torch.nonzero(cond) tf.where(cond) np.where(cond)
多个tensor聚合 torch.cat([x, y], dim) tf.concat([x,y], axis) np.concatenate([x,y], axis)
堆叠成一个tensor torch.stack([x1, x2], dim) tf.stack([x1, x2], axis) np.stack([x, y], axis)
tensor切成多个tensor torch.split(x1, split_size_or_sections, dim) tf.split(x1, num_or_size_splits, axis) np.split(x1, indices_or_sections, axis)
` torch.unbind(x1, dim) tf.unstack(x1,axis) NULL
随机扰乱 torch.randperm(n) 1 tf.random_shuffle(x) np.random.shuffle(x) 2 np.random.permutation(x ) 3
前k个值 torch.topk(x, n, sorted, dim) tf.nn.top_k(x, n, sorted) NULL
  1. 该方法只能对0~n-1自然数随机扰乱,所以先对索引随机扰乱,然后再根据扰乱后的索引取相应的数据得到扰乱后的数据
  2. 该方法会修改原值,没有返回值
  3. 该方法不会修改原值,返回扰乱后的值

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