add dataset_generator.py

dataset_generator.py is a framework for generating data with python
the generated data with a fixed format will be feeded into c++ reader
test=develop

paddle/fluid/framework/data_feed.h

@@ -60,6 +60,7 @@ class DataFeed {
   // Otherwise, Init() function will init finish_set_filelist_ flag.
   virtual bool SetFileList(const std::vector<std::string>& files);
   virtual bool Start() = 0;
+
   // The trainer calls the Next() function, and the DataFeed will load a new
   // batch to the feed_vec. The return value of this function is the batch
   // size of the current batch.

paddle/fluid/framework/data_feed.proto

@@ -28,4 +28,5 @@ message DataFeedDesc {
   optional int32 batch_size = 2 [ default = 32 ];
   optional MultiSlotDesc multi_slot_desc = 3;
   optional string pipe_command = 4;
+  optional int32 thread_num = 5;
 }

paddle/fluid/framework/executor_thread_worker.cc

@@ -284,6 +284,7 @@ void ExecutorThreadWorker::TrainFilesWithTimer() {
         for (int i = 0; i < fetch_var_num; ++i) {
           print_fetch_var(thread_scope_, fetch_var_names_[i]);
         }
+        fprintf(stderr, "IO percent: %f\n", read_time / total_time);
       }
     }
     timeline.Start();

python/paddle/fluid/data_feed_desc.py

@@ -139,6 +139,10 @@ class DataFeedDesc(object):
             self.proto_desc.multi_slot_desc.slots[self.__name_to_index[
                 name]].is_used = True
 
+    def global_shuffle(self):
+        self.data.global_shuffle()
+        pass
+
     def desc(self):
         """
         Returns a protobuf message for this DataFeedDesc

python/paddle/fluid/dataset.py

@@ -0,0 +1,109 @@
+#   Copyright (c) 2018 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.
+
+from paddle.fluid.proto import data_feed_pb2
+from google.protobuf import text_format
+from . import core
+__all__ = ['DatasetFactory']
+
+
+class DatasetFactory(object):
+    def __init__(self):
+        pass
+
+    def create_dataset(self, datafeed_class):
+        datafeed_class = datafeed_class.capitalize()
+        try:
+            dataset = globals()[datafeed_class]()
+        except:
+            raise ValueError("datafeed class %s does not exist" %
+                             datafeed_class)
+
+
+class DatasetBase(object):
+    def __init__(self):
+        # define class name here
+        # to decide whether we need create in memory instance
+        self.proto_desc = data_feed_pb2.DataFeedDesc()
+        self.proto_desc.pipe_command = "cat"
+
+    def set_pipe_command(self, pipe_command):
+        """
+        Set pipe command of current dataset
+        A pipe command is a UNIX pipeline command that can be used only
+
+        """
+        self.proto_desc.pipe_command = pipe_command
+
+    def set_batch_size(self, batch_size):
+        """
+        Set batch size. Will be effective during training
+
+        Example:
+            >>> data_feed = fluid.DataFeedDesc('data.proto')
+            >>> data_feed.set_batch_size(128)
+
+        Args:
+            batch_size: batch size
+
+        """
+        self.proto_desc.batch_size = batch_size
+
+    def set_use_var(self, var_list):
+        multi_slot = self.proto_desc.multi_slot_desc()
+        for var in var_list:
+            slot_var = multi_slot.add()
+            slot_var.is_used = True
+            slot_var.name = var.name
+            if var.lod_level == 0:
+                slot_var.is_dense = True
+            if var.dtype == core.VarType.FP32:
+                slot_var.type = "float32"
+            elif var.dtype == core.VarType.INT64:
+                slot_var.type = "uint64"
+            else:
+                raise ValueError(
+                    "Currently, fluid.dataset only supports dtype=float32 and dtype=int64"
+                )
+
+    def desc(self):
+        """
+        Returns a protobuf message for this DataFeedDesc
+
+        Example:
+            >>> data_feed = fluid.DataFeedDesc('data.proto')
+            >>> print(data_feed.desc())
+
+        Returns:
+            A string message
+        """
+        return text_format.MessageToString(self.proto_desc)
+
+
+class InMemoryDataset(DatasetBase):
+    def __init__(self):
+        super(InMemoryDataset.__init__())
+        self.proto_desc.name = "InMemoryDataFeed"
+
+    def local_shuffle(self):
+        pass
+
+    def global_shuffle(self):
+        pass
+
+
+class QueueDataset(DatasetBase):
+    def __init__(self):
+        super(QueueDataset.__init__())
+        self.proto_desc.name = "MultiSlotDataFeed"