o #j;@sddlZddlZddlZddlmZmZddlmZddl m Z m Z m Z ddl mZddlmZddlmZgZd efd d Ze dd d ZGdddZddZdS)N)Variablecore) check_type)convert_np_dtype_to_dtype_ in_pir_mode static_only) LayerHelper)DataType_setitem_staticreturncCst|dvS)N)falseoff0none)strlower)valrT/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/static/input.py evaluate_flag#src CsFdd}tdit}t|dttfdt|dttfdt|}tt|D] }||dur4d||<q(|dur=t }t rc|}t |t sNt jj|}|t j|||t}t j|S|j|||tjjjd|ddd }tjd d} t| rtdit}t |tjjst|}|jdid |i||d |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)] cSsRtjj}|j}t|dkrdS|D]}|dkr&tj|dSqdS)Nrz pd_op.data) paddlepirrdefault_main_programZ global_blockopslennameZset_insertion_point)rroprrr_reset_data_op_insertion_pointms     z,data.._reset_data_op_insertion_pointdatarshapeNT)rr!dtypetype stop_gradient lod_levelZis_dataZneed_check_feedZFLAGS_enable_pir_in_executoroutr)r!r#Zplacer)r$ZinputsZoutputsattrs)r )rlocalsrbytesrlisttuplerangerrZget_default_dtyper isinstancer rrrZ_pir_opsr ZPlaceZreset_insertion_point_to_endZcreate_global_variableZVarDescZVarTypeZ LOD_TENSORosenvirongetrZ append_op) rr!r#r&rhelperiZir_dtyper'Z is_pir_moderrrr 'sZF      r c@sveZdZdZdddZddZd d Zedd d Zedd dZ ddZ ddZ ddZ ddZ ddZddZdS) InputSpeca@ InputSpec describes the signature information of the model input, such as ``shape`` , ``dtype`` , ``name`` . This interface is often used to specify input tensor information of models in high-level API. It's also used to specify the tensor information for each input parameter of the forward function decorated by `@paddle.jit.to_static`. Args: shape (tuple(integers)|list[integers]): List|Tuple of integers declaring the shape. You can set "None" or -1 at a dimension to indicate the dimension can be of any size. For example, it is useful to set changeable batch size as "None" or -1. dtype (np.dtype|str, optional): The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: float32. name (str): The name/alias of the variable, see :ref:`api_guide_Name` for more details. stop_gradient (bool, optional): A boolean that mentions whether gradient should flow. Default is False, means don't stop calculate gradients. Examples: .. code-block:: python >>> import paddle >>> from paddle.static import InputSpec >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> print(input) InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x, stop_gradient=False) >>> print(label) InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label, stop_gradient=False) float32NFcCsB|||_|durt|tjtfrt|}||_||_||_dSN) _verifyr!r.npr#rrrr%)selfr!r#rr%rrr__init__s  zInputSpec.__init__cCst|j|j|jdS)Nr!r#)r rr!r#r9rrr_create_feed_layerszInputSpec._create_feed_layercCs dt|j|j|j|j|jS)Nz1{}(shape={}, dtype={}, name={}, stop_gradient={}))formatr$__name__r!r#rr%r<rrr__repr__szInputSpec.__repr__cCs<t|ttjjfr||j|j|p|jStd t |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.rreagerZTensorr!r#r ValueErrorr>r$r?)clsZtensorrrrr from_tensorszInputSpec.from_tensorcCs||j|j|S)a Generates a InputSpec based on the description of input np.ndarray. Args: tensor(Tensor): the source numpy ndarray to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python >>> import numpy as np >>> from paddle.static import InputSpec >>> x = np.ones([2, 2], np.float32) >>> x_spec = InputSpec.from_numpy(x, name='x') >>> print(x_spec) InputSpec(shape=(2, 2), dtype=paddle.float32, name=x, stop_gradient=False) r;)rCZndarrayrrrr from_numpyszInputSpec.from_numpycCstt|ttfrt|dkrtd|t||d}nt|ts+tdt|j |gt|j }t||_ |S)ac Inserts `batch_size` in front of the `shape`. Args: batch_size(int): the inserted integer value of batch size. Returns: The original InputSpec instance by inserting `batch_size` in front of `shape`. Examples: .. code-block:: python >>> from paddle.static import InputSpec >>> x_spec = InputSpec(shape=[64], dtype='float32', name='x') >>> x_spec.batch(4) >>> print(x_spec) InputSpec(shape=(4, 64), dtype=paddle.float32, name=x, stop_gradient=False) z5Length of batch_size: {} shall be 1, but received {}.z1type(batch_size) shall be `int`, but received {}.) r.r+r,rrBr>int TypeErrorr$r?r!)r9Z batch_sizeZ new_shaperrrbatch!s"    zInputSpec.batchcCs0t|jdkr td||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)rr!rBr7r<rrrunbatchJs zInputSpec.unbatchcCs~t|ttfstdt|jt|D]%\}}|dur.t|ts.t d|t|j||dus6|dkr:d||<qt|S)zI Verifies the input shape and modifies `None` into `-1`. zMType of `shape` in InputSpec should be one of (tuple, list), but received {}.Nz3shape[{}] should be an `int`, but received `{}`:{}.r") r.r+r,rHr>r$r? enumeraterGrB)r9r!r3Zelerrrr7ds$  zInputSpec._verifycCstt|j|j|jfSr6)hashr,r!r#r%r<rrr__hash__|szInputSpec.__hash__cs0gd}ttuotfdd|DS)N)r!r#rr%c3s$|] }t|t|kVqdSr6)getattr).0attrotherr9rr s z#InputSpec.__eq__..)r$all)r9rRslotsrrQr__eq__szInputSpec.__eq__cCs ||k Sr6r)r9rRrrr__ne__s zInputSpec.__ne__)r5NFr6)r? __module__ __qualname____doc__r:r=r@ classmethodrDrErIrJr7rMrVrWrrrrr4s #  ! ) r4cCs t|||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 )xindexvaluerrrsetitems r_)Nr)r/numpyr8rZ paddle.baserrZpaddle.base.data_feederrZpaddle.base.frameworkrrrZpaddle.base.layer_helperrZpaddle.base.libpaddler Zbase.variable_indexr __all__boolrr r4r_rrrrs"      n