o Qe12ã@svddlZddlZddlmZmZddlmZddlmZddl m Z ddl m Z gZ e d dd„ƒZ Gd d „d eƒZdS) éN)ÚcoreÚVariable)Ú LayerHelper)Ú check_type)Úconvert_np_dtype_to_dtype_)Ú static_onlyc Cs²tditƒ¤Ž}t|dtjtjfdƒt|dttfdƒt|ƒ}tj  t |ƒ¡D] }||dur4d||<q(|rG|j |||t j jjd|dddS|j ||t ¡t j jjd|dddS) aÉ **Data Layer** 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. For more details about when and how to use LoD level, see :ref:`user_guide_lod_tensor` . 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) ÚdataÚnameÚshapeNéÿÿÿÿT)r r ÚdtypeÚtypeZ stop_gradientÚ lod_levelZis_dataZneed_check_feed)r)rÚlocalsrÚsixÚ binary_typeÚ text_typeÚlistÚtupleÚmovesÚrangeÚlenZcreate_global_variablerÚVarDescÚVarTypeZ LOD_TENSORÚpaddleZget_default_dtype)r r r rÚhelperÚi©rúCD:\Projects\ConvertPro\env\Lib\site-packages\paddle/static/input.pyrs:A €ø ørc@sveZdZdZddd„Zdd„Zdd „Zedd d „ƒZedd d „ƒZ dd„Z dd„Z dd„Z dd„Z dd„Zdd„ZdS)Ú 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. Examples: .. code-block:: python 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) print(label) # InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label) Úfloat32NcCs:| |¡|_|durt|tjjƒst|ƒ}||_||_dS©N) Ú_verifyr Ú isinstancerrrrr r )Úselfr r r rrrÚ__init__œs  zInputSpec.__init__cCst|j|j|jdS)N©r r )rr r r ©r$rrrÚ_create_feed_layer¦szInputSpec._create_feed_layercCsd t|ƒj|j|j|j¡S)Nz{}(shape={}, dtype={}, name={}))Úformatr Ú__name__r r r r'rrrÚ__repr__©sÿzInputSpec.__repr__cCs@t|ttjtjjfƒr||j|j|p|jƒSt d  t |ƒj ¡ƒ‚)ay Generates a InputSpec based on the description of input tensor. Args: tensor(Tensor): the source tensor to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python import paddle from paddle.static import InputSpec paddle.disable_static() x = paddle.ones([2, 2], dtype="float32") x_spec = InputSpec.from_tensor(x, name='x') print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x) z3Input `tensor` should be a Tensor, but received {}.) r#rrZVarBaseÚeagerZTensorr r r Ú ValueErrorr)r r*)ÚclsZtensorr rrrÚ from_tensor®sÿÿzInputSpec.from_tensorcCs||j|j|ƒS)aV Generates a InputSpec based on the description of input np.ndarray. Args: tensor(Tensor): the source numpy ndarray to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python import numpy as np from paddle.static import InputSpec x = np.ones([2, 2], np.float32) x_spec = InputSpec.from_numpy(x, name='x') print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x) r&)r.Zndarrayr rrrÚ from_numpyÏszInputSpec.from_numpycCsvt|ttfƒrt|ƒdkrtd |t|ƒ¡ƒ‚|d}nt|tjƒs,td t |ƒj ¡ƒ‚|gt|j ƒ}t|ƒ|_ |S)a0 Inserts `batch_size` in front of the `shape`. Args: batch_size(int): the inserted integer value of batch size. Returns: The original InputSpec instance by inserting `batch_size` in front of `shape`. Examples: .. code-block:: python from paddle.static import InputSpec x_spec = InputSpec(shape=[64], dtype='float32', name='x') x_spec.batch(4) print(x_spec) # InputSpec(shape=(4, 64), dtype=paddle.float32, name=x) éz5Length of batch_size: {} shall be 1, but received {}.z1type(batch_size) shall be `int`, but received {}.) r#rrrr-r)rÚ integer_typesÚ TypeErrorr r*r )r$Z batch_sizeZ new_shaperrrÚbatchçs" ÿÿ  ÿÿ zInputSpec.batchcCs0t|jƒdkr tdƒ‚| |jdd…¡|_|S)aÐ 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) rz8Not support to unbatch a InputSpec when len(shape) == 0.r1N)rr r-r"r'rrrÚunbatchs ÿzInputSpec.unbatchcCsšt|ttfƒstd t|ƒj¡ƒ‚t|ƒdkrtd |¡ƒ‚t |ƒD]&\}}|durYs€ ÿz#InputSpec.__eq__..)r Úall)r$r=Úslotsrr<rÚ__eq__WsÿzInputSpec.__eq__cCs ||k Sr!r)r$r=rrrÚ__ne__]s zInputSpec.__ne__)r Nr!)r*Ú __module__Ú __qualname__Ú__doc__r%r(r+Ú classmethodr/r0r4r5r"r8rArBrrrrr}s    ( r)Nr)rrZ paddle.fluidrrZpaddle.fluid.layer_helperrZpaddle.fluid.data_feederrZpaddle.fluid.frameworkrrÚ__all__rÚobjectrrrrrÚs     a