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125 lines
4.8 KiB
125 lines
4.8 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import six
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from paddle.fluid import core
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from paddle.fluid.layer_helper import LayerHelper
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from paddle.fluid.data_feeder import check_dtype, check_type
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from ..utils import deprecated
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from paddle.fluid.framework import static_only
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__all__ = ['data']
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@static_only
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@deprecated(since="2.0.0", update_to="paddle.static.data")
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def data(name, shape, dtype='float32', lod_level=0):
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"""
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**Data Layer**
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This function creates a variable on the global block. The global variable
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can be accessed by all the following operators in the graph. The variable
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is a placeholder that could be fed with input, such as Executor can feed
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input into the variable.
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Note:
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`paddle.fluid.layers.data` is deprecated. It will be removed in a
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future version. Please use this `paddle.fluid.data`.
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The `paddle.fluid.layers.data` set shape and dtype at compile time but
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does NOT check the shape or the dtype of fed data, this
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`paddle.fluid.data` checks the shape and the dtype of data fed by
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Executor or ParallelExecutor during run time.
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To feed variable size inputs, users can set None or -1 on the variable
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dimension when using :code:`paddle.fluid.data`, or feed variable size
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inputs directly to :code:`paddle.fluid.layers.data` and PaddlePaddle
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will fit the size accordingly.
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The default :code:`stop_gradient` attribute of the Variable created by
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this API is true, which means the gradient won't be passed backward
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through the data Variable. Set :code:`var.stop_gradient = False` If
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user would like to pass backward gradient.
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Args:
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name (str): The name/alias of the variable, see :ref:`api_guide_Name`
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for more details.
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shape (list|tuple): List|Tuple of integers declaring the shape. You can
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set "None" or -1 at a dimension to indicate the dimension can be of any
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size. For example, it is useful to set changeable batch size as "None" or -1.
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dtype (np.dtype|VarType|str, optional): The type of the data. Supported
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dtype: bool, float16, float32, float64, int8, int16, int32, int64,
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uint8. Default: float32.
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lod_level (int, optional): The LoD level of the LoDTensor. Usually users
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don't have to set this value. For more details about when and how to
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use LoD level, see :ref:`user_guide_lod_tensor` . Default: 0.
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Returns:
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Variable: The global variable that gives access to the data.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import numpy as np
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# Creates a variable with fixed size [3, 2, 1]
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# User can only feed data of the same shape to x
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x = fluid.data(name='x', shape=[3, 2, 1], dtype='float32')
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# Creates a variable with changeable batch size -1.
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# Users can feed data of any batch size into y,
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# but size of each data sample has to be [2, 1]
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y = fluid.data(name='y', shape=[-1, 2, 1], dtype='float32')
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z = x + y
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# In this example, we will feed x and y with np-ndarray "1"
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# and fetch z, like implementing "1 + 1 = 2" in PaddlePaddle
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feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32)
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exe = fluid.Executor(fluid.CPUPlace())
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out = exe.run(fluid.default_main_program(),
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feed={
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'x': feed_data,
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'y': feed_data
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},
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fetch_list=[z.name])
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# np-ndarray of shape=[3, 2, 1], dtype=float32, whose elements are 2
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print(out)
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"""
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helper = LayerHelper('data', **locals())
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check_type(name, 'name', (six.binary_type, six.text_type), 'data')
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check_type(shape, 'shape', (list, tuple), 'data')
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shape = list(shape)
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for i in six.moves.range(len(shape)):
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if shape[i] is None:
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shape[i] = -1
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return helper.create_global_variable(
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name=name,
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shape=shape,
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dtype=dtype,
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type=core.VarDesc.VarType.LOD_TENSOR,
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stop_gradient=True,
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lod_level=lod_level,
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is_data=True,
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need_check_feed=True)
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