\;j<ddlZddlZddlZddlmZmZddlmZddl m Z m Z m Z ddl mZddlmZddlmZgZd efd Ze dd ZGd d ZdZy)N)Variablecore) check_type)convert_np_dtype_to_dtype_ in_pir_mode static_only) LayerHelper)DataType_setitem_staticreturnc8t|jdvS)N)falseoff0none)strlower)vals \G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/static/input.py evaluate_flagr#s s8>> #@ @@c d}tdit}t|dttfdt|dt t fdt |}tt|D] }|| d||<|tj}tr|}t|ts)tjjj!|}|tj"j%|||tj&}tjj)|S|j+|||tj,j.j0d|dd}t2j4j7d d} t9| r^tdit}t|tj,j.s t!|}|j;did |i||d |d  |S)a_ This function creates a variable on the global block. The global variable can be accessed by all the following operators in the graph. The variable is a placeholder that could be fed with input, such as Executor can feed input into the variable. When `dtype` is None, the dtype will get from the global dtype by `paddle.get_default_dtype()`. Args: name (str): The name/alias of the variable, see :ref:`api_guide_Name` for more details. shape (list|tuple): List|Tuple of integers declaring the shape. You can set None or -1 at a dimension to indicate the dimension can be of any size. For example, it is useful to set changeable batch size as None or -1. dtype (np.dtype|str, optional): The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: None. When `dtype` is not set, the dtype will get from the global dtype by `paddle.get_default_dtype()`. lod_level (int, optional): The LoD level of the LoDTensor. Usually users don't have to set this value. Default: 0. Returns: Variable: The global variable that gives access to the data. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> paddle.enable_static() # Creates a variable with fixed size [3, 2, 1] # User can only feed data of the same shape to x # the dtype is not set, so it will set "float32" by # paddle.get_default_dtype(). You can use paddle.get_default_dtype() to # change the global dtype >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) # Creates a variable with changeable batch size -1. # Users can feed data of any batch size into y, # but size of each data sample has to be [2, 1] >>> y = paddle.static.data(name='y', shape=[-1, 2, 1], dtype='float32') >>> z = x + y # In this example, we will feed x and y with np-ndarray "1" # and fetch z, like implementing "1 + 1 = 2" in PaddlePaddle >>> feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32) >>> exe = paddle.static.Executor(paddle.framework.CPUPlace()) >>> out = exe.run(paddle.static.default_main_program(), ... feed={ ... 'x': feed_data, ... 'y': feed_data ... }, ... fetch_list=[z.name]) # np-ndarray of shape=[3, 2, 1], dtype=float32, whose elements are 2 >>> print(out) [array([[[2.], [2.]], [[2.], [2.]], [[2.], [2.]]], dtype=float32)] ctjjj}|j j }t |dk(ry|D]6}|jdk7stjj|yy)Nrz pd_op.data) paddlepirrdefault_main_program global_blockopslennameset_insertion_point)rr ops r_reset_data_op_insertion_pointz,data.._reset_data_op_insertion_pointmsh%zzCCE"//155 s8q= BwwyL( ..r2rdatar"shapeNT)r"r'dtypetype stop_gradient lod_levelis_dataneed_check_feedFLAGS_enable_pir_in_executoroutr)r'r)placer")r*inputsoutputsattrs)r&)r localsrbytesrlisttupleranger!rget_default_dtyper isinstancer rrr_pir_opsr&Placereset_insertion_point_to_endcreate_global_variableVarDescVarType LOD_TENSORosenvirongetr append_op) r"r'r)r,r%helperiir_dtyper0 is_pir_modes rr&r&'sL ,68 ,FtVeS\62uge}f5 KE 3u:  8 E!H }((*}(H-zzAA%HH&(oo""4$**,G //1  ' '  \\ ! ! , , ( C**..!?FK[!0vx0%!5!56.u5ECL    JrcleZdZdZddZdZdZeddZeddZ dZ d Z d Z d Z d Zd Zy) InputSpeca@ InputSpec describes the signature information of the model input, such as ``shape`` , ``dtype`` , ``name`` . This interface is often used to specify input tensor information of models in high-level API. It's also used to specify the tensor information for each input parameter of the forward function decorated by `@paddle.jit.to_static`. Args: shape (tuple(integers)|list[integers]): List|Tuple of integers declaring the shape. You can set "None" or -1 at a dimension to indicate the dimension can be of any size. For example, it is useful to set changeable batch size as "None" or -1. dtype (np.dtype|str, optional): The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: float32. name (str): The name/alias of the variable, see :ref:`api_guide_Name` for more details. stop_gradient (bool, optional): A boolean that mentions whether gradient should flow. Default is False, means don't stop calculate gradients. Examples: .. code-block:: python >>> import paddle >>> from paddle.static import InputSpec >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> print(input) InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x, stop_gradient=False) >>> print(label) InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label, stop_gradient=False) Nc|j||_|+t|tjt fr t |}||_||_||_yN) _verifyr'r;npr)rrr"r+)selfr'r)r"r+s r__init__zInputSpec.__init__sJ\\%(  %"((C1259  *rcZt|j|j|jS)Nr'r))r&r"r'r)rQs r_create_feed_layerzInputSpec._create_feed_layersDIITZZtzzBBrcdjt|j|j|j|j |j S)Nz1{}(shape={}, dtype={}, name={}, stop_gradient={}))formatr*__name__r'r)r"r+rUs r__repr__zInputSpec.__repr__s@BII J   JJ JJ II      rct|ttjjfr,||j |j |xs |jStdjt|j)a Generates a InputSpec based on the description of input tensor. Args: tensor(Tensor): the source tensor to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python >>> import paddle >>> from paddle.static import InputSpec >>> paddle.disable_static() >>> x = paddle.ones([2, 2], dtype="float32") >>> x_spec = InputSpec.from_tensor(x, name='x') >>> print(x_spec) InputSpec(shape=(2, 2), dtype=paddle.float32, name=x, stop_gradient=False) z3Input `tensor` should be a Tensor, but received {}.) r;rreagerTensorr'r)r" ValueErrorrXr*rY)clstensorr"s r from_tensorzInputSpec.from_tensorse2 fx):):; <v||V\\43F6;;G GELLL)) rc>||j|j|S)a Generates a InputSpec based on the description of input np.ndarray. Args: tensor(Tensor): the source numpy ndarray to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python >>> import numpy as np >>> from paddle.static import InputSpec >>> x = np.ones([2, 2], np.float32) >>> x_spec = InputSpec.from_numpy(x, name='x') >>> print(x_spec) InputSpec(shape=(2, 2), dtype=paddle.float32, name=x, stop_gradient=False) rT)r_ndarrayr"s r from_numpyzInputSpec.from_numpys.7=='--66rcnt|ttfr8t|dk7r$t dj |t||d}n=t|t s-tdj t|j|gt|jz}t||_ |S)ac Inserts `batch_size` in front of the `shape`. Args: batch_size(int): the inserted integer value of batch size. Returns: The original InputSpec instance by inserting `batch_size` in front of `shape`. Examples: .. code-block:: python >>> from paddle.static import InputSpec >>> x_spec = InputSpec(shape=[64], dtype='float32', name='x') >>> x_spec.batch(4) >>> print(x_spec) InputSpec(shape=(4, 64), dtype=paddle.float32, name=x, stop_gradient=False) z5Length of batch_size: {} shall be 1, but received {}.z1type(batch_size) shall be `int`, but received {}.) r;r7r8r!r^rXint TypeErrorr*rYr')rQ batch_size new_shapes rbatchzInputSpec.batch!s* j4- 0:!# KRR"C O $AJJ,CJJ$--   L4 #33 9%  rct|jdk(r td|j|jdd|_|S)a: Removes the first element of `shape`. Returns: The original InputSpec instance by removing the first element of `shape` . Examples: .. code-block:: python >>> from paddle.static import InputSpec >>> x_spec = InputSpec(shape=[4, 64], dtype='float32', name='x') >>> x_spec.unbatch() >>> print(x_spec) # InputSpec(shape=(64,), dtype=paddle.float32, name=x) InputSpec(shape=(64,), dtype=paddle.float32, name=x, stop_gradient=False) rz8Not support to unbatch a InputSpec when len(shape) == 0.rfN)r!r'r^rOrUs runbatchzInputSpec.unbatchJsE$ tzz?a J \\$**QR.1  rc `t|ttfs-tdj t |j t|D]S\}}|?t|ts/tdj |t |j |||dksOd||<Ut|S)zI Verifies the input shape and modifies `None` into `-1`. zMType of `shape` in InputSpec should be one of (tuple, list), but received {}.z3shape[{}] should be an `int`, but received `{}`:{}.r() r;r7r8rhrXr*rY enumeratergr^)rQr'rHeles rrOzInputSpec._verifyds%$/_ffK((   &FAs!#s+$MTTtCy113 {cBha'U|rcltt|j|j|jfSrN)hashr8r'r)r+rUs r__hash__zInputSpec.__hash__|s)U4::& D4F4FGHHrchgd}ttuxrtfd|DS)N)r'r)r"r+c3PK|]}t|t|k(ywrN)getattr).0attrotherrQs r z#InputSpec.__eq__..s(1 DIDGD$ 75$#7 7Es#&)r*all)rQryslotss`` r__eq__zInputSpec.__eq__s6;DzT%[( S1 DI1 .  rc||k( SrN)rQrys r__ne__zInputSpec.__ne__s5=  r)float32NFrN)rY __module__ __qualname____doc__rRrVrZ classmethodrardrkrmrOrsr}rrrrrLrLsa!F +C B770'R40I" !rrLct|||S)a= x(Tensor): input Tensor. index(Scalar|Tuple|List|Tensor): Where should be set value. value(Scalar|Tensor): The value which is going to be set. [How to write index?] 1. ':' -> slice(), (1) a[:]=v -> setitem(a, slice(None,None,None), v) (2) a[1::2] -> setitem(a, slice(1,None,2), v) 2. if there are multiple indexes for axes, use TUPLE (Not LIST) to pack them. (1) a[1, 2]=v -> setitem(a, (1, 2), v) (2) a[[1,2],[2,3]]=v -> setitem(a, ([1,2],[2,3]), v) (3) a[1,:, 3] = v -> setitem(a, (1, slice(None,None,None),3), v) (4) a[1, ..., 2]=v -> setitem(a, (1, ..., 2), v) 3. You can always use TUPLE as index input, even there is only one index. (1) a[Tensor([10,10])]=v -> setitem(a, (Tensor([10,10]),), v) (2) a[1] = v -> setitem(a, (1,), v) r )xindexvalues rsetitemrs* 1eU ++r)Nr)rCnumpyrPr paddle.baserrpaddle.base.data_feederrpaddle.base.frameworkrrrpaddle.base.layer_helperr paddle.base.libpaddler base.variable_indexr __all__boolrr&rLrrrrrsg  &. 1*1 A$A  Dj!j!Z,r