o #j+@sddlZddlZddlmZddlmZmZddlmZm Z ddl m Z ddl m Z mZdd lmZmZgZd d Zd d ZddZddZddZdddZdddZdS)N)_C_ops) check_typecheck_variable_and_dtype)in_dynamic_or_pir_mode in_pir_mode)Variable) LayerHelpercore)_complex_to_real_dtypeassigncCs4t|dttjjfdt|j}tt |d}|S)a% Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor. Args: input (Tensor): The input Tensor with shape of :math:`[N_1, N_2, ..., N_k]`, the data type is arbitrary. Returns: Tensor, the output data type is int32.: The 0-D tensor with the dimensions of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> input = paddle.rand((3, 100, 100)) >>> rank = paddle.rank(input) >>> print(rank.numpy()) 3 inputint32) rrpaddlepirValuelenshaper nparray)rZndimsoutrX/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/tensor/attribute.pyranks rcCsltr t|}d|_|St|dgddtd it}|jdd}|jdd|id|idd d|_|S) a Get the shape of the input. .. code-block:: text Case1: Given N-D Tensor: input = [ [1, 2, 3, 4], [5, 6, 7, 8] ] Then: input.shape = [2, 4] Case2: Given SelectedRows: input.rows = [0, 4, 19] input.height = 20 input.value = [ [1, 2], [3, 4], [5, 6] ] # inner tensor Then: input.shape = [3, 2] Args: input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, bfloat16, float16, float32, float64, int32, int64. If input variable is type of SelectedRows, returns the shape of it's inner tensor. Returns: Variable (Tensor): The shape of the input variable. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> paddle.enable_static() >>> inputs = paddle.static.data(name="x", shape=[3, 100, 100], dtype="float32") >>> output = paddle.shape(inputs) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) >>> img = np.ones((3, 100, 100)).astype(np.float32) >>> res = exe.run(paddle.static.default_main_program(), feed={'x':img}, fetch_list=[output]) >>> print(res) [array([ 3, 100, 100], dtype=int32)] Tr) booluint16Zfloat16Zfloat32Zfloat64rZint64 complex64 complex128rrrdtypeZInputOut)typeinputsoutputs stop_gradientN)r) rrrr%rr locals"create_variable_for_type_inference append_op)rrhelperrrrr;s(/   rcCsjt|tjtjjtjjfstdt||j }|t j j j kp2|t j j jkp2|t jj kp2|t jjk}|S)aReturn whether x is a tensor of complex data type(complex64 or complex128). Args: x (Tensor): The input tensor. Returns: bool: True if the data type of the input is complex data type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1 + 2j, 3 + 4j]) >>> print(paddle.is_complex(x)) True >>> x = paddle.to_tensor([1.1, 1.2]) >>> print(paddle.is_complex(x)) False >>> x = paddle.to_tensor([1, 2, 3]) >>> print(paddle.is_complex(x)) False )Expected Tensor, but received type of x: ) isinstancerTensorstaticrrr TypeErrorr"r r VarDescVarTypeZ COMPLEX64Z COMPLEX128DataType)xr Zis_complex_dtyperrr is_complexs   r3cCsht|tjtjjfstdt||j}|tj j j kp1|tj j j kp1|tj j j kp1|tj j jk}|S)aK Returns whether the dtype of `x` is one of paddle.float64, paddle.float32, paddle.float16, and paddle.bfloat16. Args: x (Tensor): The input tensor. Returns: bool: True if the dtype of `x` is floating type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.arange(1., 5., dtype='float32') >>> y = paddle.arange(1, 5, dtype='int32') >>> print(paddle.is_floating_point(x)) True >>> print(paddle.is_floating_point(y)) False r*)r+rr,r-rr.r"r r r/r0ZFP32ZFP64ZFP16ZBF16)r2r Z is_fp_dtyperrris_floating_points   r4cCst|tjtjjtjjfstdt||j }d}t sC|t j j jkp@|t j j jkp@|t j j jkp@|t j j jkp@|t j j jk}|S|t jjkp`|t jjkp`|t jjkp`|t jjkp`|t jjk}|S)aqReturn whether x is a tensor of integeral data type. Args: x (Tensor): The input tensor. Returns: bool: True if the data type of the input is integer data type, otherwise false. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor([1 + 2j, 3 + 4j]) >>> print(paddle.is_integer(x)) False >>> x = paddle.to_tensor([1.1, 1.2]) >>> print(paddle.is_integer(x)) False >>> x = paddle.to_tensor([1, 2, 3]) >>> print(paddle.is_integer(x)) True r*F)r+rr,r-rrZOpResultr.r"r rr r/r0ZUINT8ZINT8ZINT16ZINT32ZINT64r1)r2r Z is_int_dtyperrr is_integers6        r5cCbtrt|St|dddgdtd it}|jt|d}|j dd|id|id|S) a Returns a new Tensor containing real values of the input Tensor. Args: x (Tensor): the input Tensor, its data type could be complex64 or complex128. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: a Tensor containing real values of the input Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]]) >>> print(x) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+6j), (2+5j), (3+4j)], [(4+3j), (5+2j), (6+1j)]]) >>> real_res = paddle.real(x) >>> print(real_res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) >>> real_t = x.real() >>> print(real_t) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) r2rrrealrXr!r"r#r$N)r7) rrr7rr r&r'r input_dtyper(r2namer)rrrrr7 $  r7cCr6) a Returns a new tensor containing imaginary values of input tensor. Args: x (Tensor): the input tensor, its data type could be complex64 or complex128. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: a tensor containing imaginary values of the input tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.to_tensor( ... [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]]) >>> print(x) Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, [[(1+6j), (2+5j), (3+4j)], [(4+3j), (5+2j), (6+1j)]]) >>> imag_res = paddle.imag(x) >>> print(imag_res) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[6., 5., 4.], [3., 2., 1.]]) >>> imag_t = x.imag() >>> print(imag_t) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[6., 5., 4.], [3., 2., 1.]]) r2rrimagrr8r!r9N)r>) rrr>rr r&r'r r:r(r;rrrr>;r=r>)N)numpyrrrZbase.data_feederrrZbase.frameworkrrZcommon_ops_importrZ frameworkr r Zcreationr r __all__rrr3r4r5r7r>rrrrs   Q(" 50