remove some code

paddle/data_converter_test.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import py_paddle.swig_paddle as api
-import numpy as np
-import paddle.trainer.PyDataProvider2 as dp2
-
-from paddle.v2.data_converter import DataConverter
-
-
-class DataConverterTest(unittest.TestCase):
-    def dense_reader(self, shape):
-        data = np.random.random(shape)
-        return data
-
-    def sparse_binary_reader(self,
-                             high,
-                             size_limit,
-                             batch_size,
-                             non_empty=False):
-        data = []
-        for i in xrange(batch_size):
-            num = np.random.randint(size_limit)  # num could be 0
-            while non_empty and num == 0:
-                num = np.random.randint(size_limit)
-            data.append(np.random.randint(high, size=num).tolist())
-
-        return data
-
-    def test_dense_vector(self):
-        def compare(input):
-            converter = DataConverter([('image', dp2.dense_vector(784))])
-            arg = converter([input], {'image': 0})
-            output = arg.getSlotValue(0).copyToNumpyMat()
-            input = np.array(input, dtype='float32')
-            self.assertAlmostEqual(input.all(), output.all())
-
-        # test numpy array
-        data = self.dense_reader(shape=[32, 784])
-        compare(data)
-
-        # test list
-        compare(data.tolist())
-
-    #def test_sparse_binary(self):
-    #    dim = 100000
-    #    data = self.sparse_binary_reader(dim, 5, 2)
-    #    converter = DataConverter([('input', dp2.sparse_binary_vector(dim))])
-    #    arg = converter([data], {'input':0})
-    #    output = arg.getSlotValue(0)
-
-    #def test_sparse(self):
-    #    dim = 100000
-    #    v = self.sparse_binary_reader(dim, 5, 2)
-    #    w = []
-    #    for dat in data:
-    #        x = self.dense_reader(shape=[1, len(dat)])
-    #        w.append(x.tolist())
-    #    data = []
-    #    for each in zip(v, w):
-    #        data.append(zip(each[0], each[1]))
-    #    
-    #    converter = DataConverter([('input', dp2.sparse_binary_vector(dim))])
-    #    arg = converter([data], {'input':0})
-    #    output = arg.getSlotValue(0)
-
-    def test_integer(self):
-        dim = 100
-        index = np.random.randint(dim, size=32)
-        print index
-        converter = DataConverter([('input', dp2.integer_value(dim))])
-        arg = converter([index], {'input': 0})
-        print arg.getSlotValue(0)
-        output = arg.getSlotValue(0).copyToNumpyArray()
-        print 'output=', output
-
-
-if __name__ == '__main__':
-    unittest.main()