Add missing file for last commit

also rename hdfs_data to make it internally.

ISSUE=4604505


git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1451 1ad973e4-5ce8-4261-8a94-b56d1f490c56

demo/image_classification/upload_hadoop.sh

-# Copyright (c) 2016 Baidu, Inc. 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.
-set -e
-hadoop fs -Dhadoop.job.ugi=paddle_demo,paddle_demo -put data/cifar-out/batches/train_batch_* /app/idl/idl-dl/paddle/demo/image_classification/train/
-hadoop fs -Dhadoop.job.ugi=paddle_demo,paddle_demo -put data/cifar-out/batches/test_batch_* /app/idl/idl-dl/paddle/demo/image_classification/test/
-hadoop fs -Dhadoop.job.ugi=paddle_demo,paddle_demo -put data/cifar-out/batches/batches.meta /app/idl/idl-dl/paddle/demo/image_classification/train_meta
-hadoop fs -Dhadoop.job.ugi=paddle_demo,paddle_demo -put data/cifar-out/batches/batches.meta /app/idl/idl-dl/paddle/demo/image_classification/test_meta

paddle/py_paddle/dataprovider_converter.py

+# Copyright (c) 2016 Baidu, Inc. 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 paddle.trainer.PyDataProvider2 as dp2
+import collections
+import swig_paddle
+
+__all__ = ['DataProviderConverter']
+
+
+class IScanner(object):
+    def __init__(self, input_type, pos):
+        self.input_type = input_type
+        assert isinstance(self.input_type, dp2.InputType)
+        self.pos = pos
+
+    def scan(self, dat):
+        pass
+
+    def finish_scan(self, argument):
+        pass
+
+
+class DenseScanner(IScanner):
+    def __init__(self, input_type, pos):
+        IScanner.__init__(self, input_type, pos)
+        self.__mat__ = []
+        self.__height__ = 0
+
+    def scan(self, dat):
+        self.__mat__.extend(dat)
+        self.__height__ += 1
+
+    def finish_scan(self, argument):
+        assert isinstance(argument, swig_paddle.Arguments)
+        assert isinstance(self.input_type, dp2.InputType)
+        m = swig_paddle.Matrix.createDense(self.__mat__,
+                                           self.__height__,
+                                           self.input_type.dim,
+                                           False)
+        argument.setSlotValue(self.pos, m)
+
+
+class SparseBinaryScanner(IScanner):
+    def __init__(self, input_type, pos):
+        IScanner.__init__(self, input_type, pos)
+        self.__rows__ = [0]
+        self.__cols__ = []
+        self.__height__ = 0
+        self.__nnz__ = 0
+        self.__value__ = []
+
+    def scan(self, dat):
+        self.extend_cols(dat)
+        self.__rows__.append(len(dat))
+
+    def extend_cols(self, dat):
+        self.__cols__.extend(dat)
+
+    def finish_scan(self, argument):
+        assert isinstance(argument, swig_paddle.Arguments)
+        assert isinstance(self.input_type, dp2.InputType)
+        m = swig_paddle.Matrix.createSparse(self.__height__,
+                                            self.input_type.dim,
+                                            len(self.__cols__),
+                                            len(self.__value__) == 0)
+        assert isinstance(m, swig_paddle.Matrix)
+        m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__)
+        argument.setSlotValue(self.pos, m)
+
+
+class SparseFloatScanner(SparseBinaryScanner):
+    def __init__(self, input_type, pos):
+        SparseBinaryScanner.__init__(self, input_type, pos)
+
+    def extend_cols(self, dat):
+        self.__cols__.extend((x[0] for x in dat))
+        self.__value__.extend((x[1] for x in dat))
+
+
+class IndexScanner(IScanner):
+    def __init__(self, input_type, pos):
+        IScanner.__init__(self, input_type, pos)
+        self.__ids__ = []
+
+    def scan(self, dat):
+        self.__ids__.append(dat)
+
+    def finish_scan(self, argument):
+        ids = swig_paddle.IVector.create(self.__ids__)
+        assert isinstance(argument, swig_paddle.Arguments)
+        argument.setSlotIds(self.pos, ids)
+
+
+class SequenceScanner(IScanner):
+    def __init__(self, input_type, pos, inner_scanner, setter):
+        IScanner.__init__(self, input_type, pos)
+        self.__seq__ = [0]
+        self.__inner_scanner__ = inner_scanner
+        self.__setter__ = setter
+
+    def scan(self, dat):
+        self.__seq__.append(self.__seq__[-1] + self.get_size(dat))
+        for each in dat:
+            self.__inner_scanner__.scan(each)
+
+    def finish_scan(self, argument):
+        seq = swig_paddle.IVector.create(self.__seq__, False)
+        self.__setter__(argument, self.pos, seq)
+        self.__inner_scanner__.finish_scan(argument)
+
+    def get_size(self, dat):
+        if isinstance(self.__inner_scanner__, SequenceScanner):
+            return sum(self.__inner_scanner__.get_size(item) for item in dat)
+        else:
+            return len(dat)
+
+
+class DataProviderConverter(object):
+    def __init__(self, input_types):
+        self.input_types = input_types
+        assert isinstance(self.input_types, collections.Sequence)
+        for each in self.input_types:
+            assert isinstance(each, dp2.InputType)
+
+    def convert(self, dat, argument=None):
+        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 each_step, scanner in zip(each_sample, scanners):
+                scanner.scan(each_step)
+
+        for scanner in scanners:
+            scanner.finish_scan(argument)
+
+        return argument
+
+    def __call__(self, dat, argument=None):
+        return self.convert(dat, argument)
+
+    @staticmethod
+    def create_scanner(i, each):
+        assert isinstance(each, dp2.InputType)
+        retv = None
+        if each.type == dp2.DataType.Dense:
+            retv = DenseScanner(each, i)
+        elif each.type == dp2.DataType.Index:
+            retv = IndexScanner(each, i)
+        elif each.type == dp2.DataType.SparseNonValue:
+            retv = SparseBinaryScanner(each, i)
+        elif each.type == dp2.DataType.SparseValue:
+            retv = SparseFloatScanner(each, i)
+        assert retv is not None
+
+        if each.seq_type == dp2.SequenceType.SUB_SEQUENCE:
+            retv = SequenceScanner(each, i, retv, lambda a, p, seq:
+            a.setSlotSubSequenceStartPositions(p, seq))
+
+        if each.seq_type in [dp2.SequenceType.SUB_SEQUENCE,
+                             dp2.SequenceType.SEQUENCE]:
+            retv = SequenceScanner(each, i, retv, lambda a, p, seq:
+            a.setSlotSequenceStartPositions(p, seq))
+        return retv