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74 lines
2.6 KiB
74 lines
2.6 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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from . import core
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from .layer_helper import LayerHelper
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__all__ = ['data']
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def data(name, shape, dtype='float32', type=core.VarDesc.VarType.LOD_TENSOR):
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"""
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**Data Layer**
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This function creates a variable on the global scope. The global variables
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can be accessed by all the following operators in the graph.
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Note:
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`paddle.fluid.layers.data` is deprecated. It will be removed in a future
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version. Please use this `paddle.fluid.data`.
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The `paddle.fluid.layers.data` set shape at compile time but does NOT
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check the shape of feeded data, this `paddle.fluid.data` checks the
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shape of data feeded by Executor/ParallelExecutor during run time.
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Args:
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name (str): The name/alias of the variable
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shape (list|tuple): List|Tuple of integers declaring the shape.
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dtype (np.dtype|VarType|str): The type of the data. Supported dtype:
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float16, float32, float64, int8, int16, int32, int64, uint8, bool.
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type (VarType): The output type. Supported type: VarType.LOD_TENSOR,
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VarType.SELECTED_ROWS, VarType.NCCL_ID. Default: VarType.LOD_TENSOR.
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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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# Creates a variable with fixed size [1, 2, 3]
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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=[1, 2, 3], dtype='int64')
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# Creates a variable with changable 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 [3, 224, 224]
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y = fluid.data(name='y', shape=[-1, 3, 224, 224], dtype='float32')
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"""
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helper = LayerHelper('data', **locals())
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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=type,
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stop_gradient=True,
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lod_level=0,
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is_data=True,
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need_check_feed=True)
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