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81 lines
2.8 KiB
81 lines
2.8 KiB
# Copyright (c) 2018 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 unittest
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import paddle.fluid.layers as layers
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from paddle.fluid.executor import Executor
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import paddle.fluid.core as core
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from paddle.fluid.backward import append_backward
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import numpy
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class TestWhileOp(unittest.TestCase):
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def test_simple_forward(self):
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d0 = layers.data(
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"d0", shape=[10], append_batch_size=False, dtype='float32')
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d1 = layers.data(
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"d1", shape=[10], append_batch_size=False, dtype='float32')
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d2 = layers.data(
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"d2", shape=[10], append_batch_size=False, dtype='float32')
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i = layers.zeros(shape=[1], dtype='int64')
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i.stop_gradient = True
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init = layers.zeros(shape=[10], dtype='float32')
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mem_array = layers.array_write(x=init, i=i)
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data_array = layers.array_write(x=d0, i=i)
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i = layers.increment(i)
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layers.array_write(d1, i, array=data_array)
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i = layers.increment(i)
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layers.array_write(d2, i, array=data_array)
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i = layers.zeros(shape=[1], dtype='int64')
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i.stop_gradient = True
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array_len = layers.fill_constant(shape=[1], dtype='int64', value=3)
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array_len.stop_gradient = True
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cond = layers.less_than(x=i, y=array_len)
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while_op = layers.While(cond=cond)
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with while_op.block():
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d = layers.array_read(array=data_array, i=i)
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prev = layers.array_read(array=mem_array, i=i)
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result = layers.sums(input=[d, prev])
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i = layers.increment(x=i, in_place=True)
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layers.array_write(result, i=i, array=mem_array)
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layers.less_than(x=i, y=array_len, cond=cond)
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sum_result = layers.array_read(array=mem_array, i=i)
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loss = layers.mean(sum_result)
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append_backward(loss)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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d = []
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for i in xrange(3):
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d.append(numpy.random.random(size=[10]).astype('float32'))
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outs = exe.run(feed={'d0': d[0],
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'd1': d[1],
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'd2': d[2]},
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fetch_list=[sum_result])
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self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
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if __name__ == '__main__':
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unittest.main()
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