Make buffers of DoubleBufferReader and open_files bigger

paddle/fluid/operators/reader/create_double_buffer_reader_op.cc

@@ -23,13 +23,13 @@ namespace reader {
 
 // 'Double buffer' means we shall maintain two batches of input data at the same
 // time. So the kCacheSize shoul be at least 2.
-static constexpr size_t kCacheSize = 3;
+static constexpr size_t kCacheSize = 5;
 // There will be two bacthes out of the channel during training:
 // 1. the one waiting to be sent to the channel
 // 2. the one just be received from the channel, which is also being used by
 // subsequent operators.
 // So the channel size should be kChacheSize - 2
-static constexpr size_t kChannelSize = 1;  // kCacheSize - 2
+static constexpr size_t kChannelSize = 3;  // kCacheSize - 2
 
 class DoubleBufferReader : public framework::DecoratedReader {
  public:

python/paddle/fluid/layers/io.py

@@ -469,10 +469,13 @@ def open_files(filenames,
        lod_levels(list): List of ints which declaring data lod_level.
        dtypes(list): List of strs which declaring data type.
        thread_num(int): The maximal concurrent prefetch thread number.
-       buffer_size(int): The size of prefetch buffer.
+       buffer_size(int|None): The size of prefetch buffer. If it is setted None, 
+            buffer size will be thread_num * 3.
+            Default: None
        pass_num(int): Number of passes to run.
        for_parallel(Bool): Set it as True if you are going to run 
             subsequent operators in parallel.
+            Default: True
 
     Returns:
        Variable: A Reader Variable via which we can get file data.
@@ -492,7 +495,7 @@ def open_files(filenames,
          image, label = fluid.layers.io.read_file(reader)
     """
     if buffer_size is None:
-        buffer_size = thread_num
+        buffer_size = thread_num * 3
     if isinstance(filenames, basestring):
         filenames = [filenames]
     dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]