refine data feeder and add unit test

python/CMakeLists.txt

@@ -25,6 +25,7 @@ add_custom_target(paddle_python ALL DEPENDS
 
 add_subdirectory(paddle/trainer_config_helpers/tests)
 add_subdirectory(paddle/reader/tests)
+add_subdirectory(paddle/v2/tests)
 
 install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/
     DESTINATION opt/paddle/share/wheels

python/paddle/v2/data_feeder.py

@@ -12,49 +12,89 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from py_paddle import swig_paddle
 from py_paddle import DataProviderConverter
+import data_type
 
 __all__ = ['DataFeeder']
-"""
-DataFeeder converts the data returned by paddle.reader into a data structure
-of Arguments which is defined in the API. The paddle.reader usually returns
-a list of mini-batch data. Each item in the list is a tuple or list, which is
-one sample with multiple features. DataFeeder converts this mini-batch data
-into Arguments in order to feed it to C++ interface.
-
-The example usage:
-
-    data_types = [paddle.data_type.dense_vector(784),
-                  paddle.data_type.integer_value(10)]
-    feeder = DataFeeder(input_types=data_types)
+
+
+class DataFeeder(DataProviderConverter):
+    """
+    DataFeeder converts the data returned by paddle.reader into a data structure
+    of Arguments which is defined in the API. The paddle.reader usually returns
+    a list of mini-batch data. Each item in the list is a list or a tuple,
+    which is one sample with one or multiple features. DataFeeder converts this
+    mini-batch data into Arguments in order to feed it to C++ interface.
+    
+    The example usage:
+    
+        data_types = [('image', paddle.data_type.dense_vector(784)),
+                      ('label', paddle.data_type.integer_value(10))]
+        reader_dict = {'image':0, 'label':1}
+        feeder = DataFeeder(data_types=data_types, reader_dict=reader_dict)
         minibatch_data = [
                            ( [1.0,2.0,3.0,4.0], 5, [6,7,8] ),  # first sample
                            ( [1.0,2.0,3.0,4.0], 5, [6,7,8] )   # second sample
                          ]
-
-    #  or 
-    #  minibatch_data = [
-    #                     [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ],  # first sample
-    #                     [ [1.0,2.0,3.0,4.0], 5, [6,7,8] ]   # second sample
-    #                   ]
         arg = feeder(minibatch_data)
+    """
+
+    def __init__(self, data_types, reader_dict):
+        """
+        :param data_types: A list to specify data name and type. Each item is
+                           a tuple of (data_name, data_type). For example:
+                           [('image', paddle.data_type.dense_vector(784)),
+                            ('label', paddle.data_type.integer_value(10))]
 
+        :type data_types: A list of tuple
+        :param reader_dict: A dictionary to specify the position of each data
+                            in the input data.
+        :type reader_dict: dict()
+        """
+        self.input_names = []
+        self.input_types = []
+        self.reader_dict = reader_dict
+        for each in data_types:
+            self.input_names.append(each[0])
+            self.input_types.append(each[1])
+            assert isinstance(each[1], data_type.InputType)
+        DataProviderConverter.__init__(self, self.input_types)
 
-Args:
-    input_types: A list of input data types. It's length is equal to the length
-                 of data returned by paddle.reader. Each item specifies the type
-                 of each feature.
-    mintbatch_data: A list of mini-batch data. Each item is a list or tuple,
+    def convert(self, dat, argument=None):
+        """
+        :param dat: A list of mini-batch data. Each item is a list or tuple,
                     for example:
                     [ 
                       (feature_0, feature_1, feature_2, ...), # first sample
                       (feature_0, feature_1, feature_2, ...), # second sample
                       ...
                     ]
+        :type dat: List
+        :param argument: An Arguments object contains this mini-batch data with
+                         one or multiple features. The Arguments definition is
+                         in the API.
+        :type argument: swig_paddle.Arguments
+        """
+
+        if argument is None:
+            argument = swig_paddle.Arguments.createArguments(0)
+        assert isinstance(argument, swig_paddle.Arguments)
+        argument.resize(len(self.input_types))
+
+        scanners = [
+            DataProviderConverter.create_scanner(i, each_type)
+            for i, each_type in enumerate(self.input_types)
+        ]
+
+        for each_sample in dat:
+            for name, scanner in zip(self.input_names, scanners):
+                scanner.scan(each_sample[self.reader_dict[name]])
+
+        for scanner in scanners:
+            scanner.finish_scan(argument)
 
-Returns:
-    An Arguments object contains this mini-batch data with multiple features.
-    The Arguments definition is in the API.
-"""
+        return argument
 
-DataFeeder = DataProviderConverter
+    def __call__(self, dat, argument=None):
+        return self.convert(dat, argument)

python/paddle/v2/tests/CMakeLists.txt

+add_test(NAME test_v2_api
+    COMMAND bash ${PROJ_ROOT}/python/paddle/v2/tests/run_tests.sh ${PYTHON_EXECUTABLE})

python/paddle/v2/tests/run_tests.sh

+#!/bin/bash
+# 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.
+
+
+pushd `dirname $0` > /dev/null
+SCRIPTPATH=$PWD
+popd > /dev/null
+
+cd $SCRIPTPATH
+
+$1 -m pip install ../../../../paddle/dist/*.whl
+
+test_list="test_data_feeder.py"
+
+export PYTHONPATH=$PWD/../../../../python/
+
+for fn in $test_list
+do
+  echo "test $fn"
+  $1 $fn
+  if [ $? -ne 0 ]; then
+    exit 1
+  fi
+done

python/paddle/v2/tests/test_data_feeder.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
+
+from paddle.v2 import data_type
+from paddle.v2.data_feeder import DataFeeder
+
+
+class DataFeederTest(unittest.TestCase):
+    def dense_reader(self, size):
+        data = np.random.random(size)
+        return data
+
+    def sparse_binary_reader(self, high, size_limit, non_empty=False):
+        num = np.random.randint(size_limit)  # num could be 0
+        while non_empty and num == 0:
+            num = np.random.randint(size_limit)
+        return np.random.randint(high, size=num).tolist()
+
+    def test_dense_vector(self):
+        def compare(input):
+            feeder = DataFeeder([('image', data_type.dense_vector(784))],
+                                {'image': 0})
+            arg = feeder([input])
+            output = arg.getSlotValue(0).copyToNumpyMat()
+            input = np.array(input, dtype='float32')
+            self.assertAlmostEqual(input.all(), output.all())
+
+        # test numpy array
+        batch_size = 32
+        dim = 784
+        data = []
+        for i in xrange(batch_size):
+            data.append(self.dense_reader(784))
+        compare(data)
+
+        # test list
+        data = []
+        for i in xrange(batch_size):
+            data.append(self.dense_reader(784).tolist())
+        compare(data)
+
+    def test_sparse_binary(self):
+        dim = 10000
+        batch_size = 32
+        data = []
+        for i in xrange(batch_size):
+            data.append([self.sparse_binary_reader(dim, 50)])
+        feeder = DataFeeder([('input', data_type.sparse_binary_vector(dim))],
+                            {'input': 0})
+        arg = feeder(data)
+        output = arg.getSlotValue(0)
+        assert isinstance(output, api.Matrix)
+        for i in xrange(batch_size):
+            self.assertEqual(output.getSparseRowCols(i), data[i][0])
+
+    def test_sparse(self):
+        dim = 10000
+        batch_size = 32
+        v = []
+        w = []
+        data = []
+        for dat in xrange(batch_size):
+            a = self.sparse_binary_reader(dim, 40, non_empty=True)
+            b = self.dense_reader(len(a)).tolist()
+            v.append(a)
+            w.append(b[0])
+            data.append([zip(a, b)])
+
+        feeder = DataFeeder([('input', data_type.sparse_vector(dim))],
+                            {'input': 0})
+        arg = feeder(data)
+        output = arg.getSlotValue(0)
+        assert isinstance(output, api.Matrix)
+        for i in xrange(batch_size):
+            self.assertEqual(output.getSparseRowCols(i), v[i])
+
+    def test_integer(self):
+        dim = 100
+        batch_size = 32
+        index = []
+        for i in xrange(batch_size):
+            index.append([np.random.randint(dim)])
+        feeder = DataFeeder([('input', data_type.integer_value(dim))],
+                            {'input': 0})
+        arg = feeder(index)
+        output = arg.getSlotIds(0).copyToNumpyArray()
+        index = np.array(index, dtype='int')
+        self.assertEqual(output.all(), index.flatten().all())
+
+    def test_multiple_slots(self):
+        batch_size = 2
+        data = []
+        for i in xrange(batch_size):
+            each_sample = []
+            each_sample.append(np.random.randint(10))  # size of feature 2: 10
+            each_sample.append(
+                self.sparse_binary_reader(
+                    20000, 40, non_empty=True))  # size of feature 1: 20000
+            each_sample.append(self.dense_reader(100))  # size of feature 0: 100
+            data.append(each_sample)
+
+        # test multiple features
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea1', data_type.sparse_binary_vector(20000)),
+                      ('fea2', data_type.integer_value(10))]
+        feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
+        arg = feeder(data)
+        output_dense = arg.getSlotValue(0).copyToNumpyMat()
+        output_sparse = arg.getSlotValue(1)
+        output_index = arg.getSlotIds(2).copyToNumpyArray()
+        for i in xrange(batch_size):
+            self.assertEqual(output_dense[i].all(), data[i][2].all())
+            self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1])
+            self.assertEqual(output_index[i], data[i][0])
+
+        # reader returns 3 featreus, but only use 2 features
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea2', data_type.integer_value(10))]
+        feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0})
+        arg = feeder(data)
+        output_dense = arg.getSlotValue(0).copyToNumpyMat()
+        output_index = arg.getSlotIds(1).copyToNumpyArray()
+        for i in xrange(batch_size):
+            self.assertEqual(output_dense[i].all(), data[i][2].all())
+            self.assertEqual(output_index[i], data[i][0])
+
+
+if __name__ == '__main__':
+    api.initPaddle("--use_gpu=0")
+    unittest.main()
+
+if __name__ == '__main__':
+    api.initPaddle("--use_gpu=0")
+    unittest.main()