Merge branch 'complete_py_reader_python' of https://github.com/sneaxiy/Paddle into complete_py_reader_python

python/paddle/fluid/layers/io.py

@@ -446,7 +446,7 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
     return monkey_patch_reader_methods(main_prog_var)
 
 
-def py_reader(capacity, shapes, lod_levels, dtypes):
+def py_reader(capacity, shapes, dtypes, lod_levels=None):
     """
     Create a reader and blocking queue for data feeding in Python
     
@@ -461,8 +461,8 @@ def py_reader(capacity, shapes, lod_levels, dtypes):
     Args:
        capacity(int): The maximum capacity of the BlockingQueue.
        shapes(list): List of tuples which declaring data shapes.
+       dtypes(list): List of strs which declaring data type. 
        lod_levels(list): List of ints which declaring data lod_level.
-       dtypes(list): List of strs which declaring data type.
 
     Returns:
        tuple(Variable, BlockingQueue):
@@ -477,7 +477,6 @@ def py_reader(capacity, shapes, lod_levels, dtypes):
             reader, queue = fluid.layers.py_reader(
                                              capacity=10,
                                              shapes=[[-1,3,224,224], [-1,1]],
-                                             lod_levels=[0, 0],
                                              dtypes=['float32', 'int64'])
             # Via the reader, we can use 'read_file' layer to get data:
             image, label = fluid.layers.read_file(reader)
@@ -501,6 +500,9 @@ def py_reader(capacity, shapes, lod_levels, dtypes):
         shape_concat.extend(shape)
         ranks.append(len(shape))
 
+    if lod_levels is None:
+        lod_levels = [0] * len(shapes)
+
     queue_name = unique_name('lod_tensor_blocking_queue')
     var = global_scope().var(queue_name)
     feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)