深入浅出PaddlePaddle函数——paddle.transpose

分类目录：《深入浅出PaddlePaddle函数》总目录
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· 深入浅出PaddlePaddle函数——paddle.transpose

根据 perm对输入的多维Tensor进行数据重排。返回多维Tensor的第$i$维对应输入Tensor的$\text{perm}[i]$维。

语法

paddle.transpose(x, perm, name=None)

参数

• x：[Tensor] 输入的多维Tensor，可选的数据类型为 bool、float16、float32、float64、int32、int64。
• perm：[list/tuple] perm的长度必须和x的维度相同，并依照perm中数据进行重排。
• name：[可选, str] 具体用法请参见Name，一般无需设置，默认值为None。

返回值

多维Tensor。

实例

输入：

x = paddle.to_tensor([[2, 3, 4]])
paddle.transpose(x, perm=[1, 0])

输出：

Tensor(shape=[3, 1], dtype=int64, place=Place(cpu), stop_gradient=True,[[2],[3],[4]])

函数实现

def transpose(x, perm, name=None):"""Permute the data dimensions of `input` according to `perm`.The `i`-th dimension  of the returned tensor will correspond to theperm[i]-th dimension of `input`.Args:x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32.perm (list|tuple): Permute the input according to the data of perm.name (str): The name of this layer. It is optional.Returns:Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64.For Example:.. code-block:: textx = [[[ 1  2  3  4] [ 5  6  7  8] [ 9 10 11 12]][[13 14 15 16] [17 18 19 20] [21 22 23 24]]]shape(x) =  [2,3,4]# Example 1perm0 = [1,0,2]y_perm0 = [[[ 1  2  3  4] [13 14 15 16]][[ 5  6  7  8]  [17 18 19 20]][[ 9 10 11 12]  [21 22 23 24]]]shape(y_perm0) = [3,2,4]# Example 2perm1 = [2,1,0]y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]][[ 2 14] [ 6 18] [10 22]][[ 3 15]  [ 7 19]  [11 23]][[ 4 16]  [ 8 20]  [12 24]]]shape(y_perm1) = [4,3,2]Examples:.. code-block:: pythonimport paddlex = paddle.randn([2, 3, 4])x_transposed = paddle.transpose(x, perm=[1, 0, 2])print(x_transposed.shape)# [3L, 2L, 4L]"""if in_dygraph_mode():return _C_ops.transpose(x, perm)else:if _in_legacy_dygraph():out, _ = _legacy_C_ops.transpose2(x, 'axis', perm)return outcheck_variable_and_dtype(x,'x',['bool','float16','float32','float64','int32','int64','complex64','complex128',],'transpose',)check_type(perm, 'perm', (list, tuple), 'transpose')if isinstance(perm, tuple):perm = list(perm)if len(perm) != len(x.shape):raise ValueError("Input(perm) is the permutation of dimensions of Input(x), ""its length should be equal to dimensions of Input(x), ""but received dimension of Input(x) is %s, ""the length of Input(perm) is %s." % (len(x.shape), len(perm)))for idx, dim in enumerate(perm):if dim >= len(x.shape):raise ValueError("Each element in Input(perm) should be less than Input(x)'s dimension, ""but %d-th element in Input(perm) is %d which exceeds Input(x)'s ""dimension %d." % (idx, perm[idx], len(x.shape)))helper = LayerHelper('transpose', **locals())out = helper.create_variable_for_type_inference(x.dtype)x_shape = helper.create_variable_for_type_inference(x.dtype)helper.append_op(type='transpose2',inputs={'X': [x]},outputs={'Out': [out], 'XShape': [x_shape]},attrs={'axis': perm},)return out