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93 lines
3.0 KiB
93 lines
3.0 KiB
# Copyright (c) 2016 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 py_paddle.swig_paddle as api
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import numpy as np
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import paddle.trainer.PyDataProvider2 as dp2
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from paddle.v2.data_converter import DataConverter
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class DataConverterTest(unittest.TestCase):
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def dense_reader(self, shape):
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data = np.random.random(shape)
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return data
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def sparse_binary_reader(self,
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high,
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size_limit,
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batch_size,
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non_empty=False):
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data = []
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for i in xrange(batch_size):
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num = np.random.randint(size_limit) # num could be 0
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while non_empty and num == 0:
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num = np.random.randint(size_limit)
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data.append(np.random.randint(high, size=num).tolist())
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return data
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def test_dense_vector(self):
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def compare(input):
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converter = DataConverter([('image', dp2.dense_vector(784))])
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arg = converter([input], {'image': 0})
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output = arg.getSlotValue(0).copyToNumpyMat()
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input = np.array(input, dtype='float32')
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self.assertAlmostEqual(input.all(), output.all())
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# test numpy array
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data = self.dense_reader(shape=[32, 784])
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compare(data)
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# test list
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compare(data.tolist())
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#def test_sparse_binary(self):
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# dim = 100000
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# data = self.sparse_binary_reader(dim, 5, 2)
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# converter = DataConverter([('input', dp2.sparse_binary_vector(dim))])
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# arg = converter([data], {'input':0})
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# output = arg.getSlotValue(0)
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#def test_sparse(self):
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# dim = 100000
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# v = self.sparse_binary_reader(dim, 5, 2)
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# w = []
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# for dat in data:
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# x = self.dense_reader(shape=[1, len(dat)])
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# w.append(x.tolist())
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# data = []
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# for each in zip(v, w):
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# data.append(zip(each[0], each[1]))
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#
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# converter = DataConverter([('input', dp2.sparse_binary_vector(dim))])
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# arg = converter([data], {'input':0})
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# output = arg.getSlotValue(0)
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def test_integer(self):
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dim = 100
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index = np.random.randint(dim, size=32)
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print index
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converter = DataConverter([('input', dp2.integer_value(dim))])
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arg = converter([index], {'input': 0})
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print arg.getSlotValue(0)
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output = arg.getSlotValue(0).copyToNumpyArray()
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print 'output=', output
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if __name__ == '__main__':
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unittest.main()
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