Merge pull request #13787 from PaddlePaddle/revert-13637-optimize-opyreader

Revert "optimize pyreader"

Author: jacquesqiao

paddle/fluid/API.spec

@@ -178,7 +178,6 @@ paddle.fluid.layers.batch ArgSpec(args=['reader', 'batch_size'], varargs=None, k
 paddle.fluid.layers.double_buffer ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.random_data_generator ArgSpec(args=['low', 'high', 'shapes', 'lod_levels', 'for_parallel'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.layers.py_reader ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True))
-paddle.fluid.layers.create_py_reader_by_data ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True))
 paddle.fluid.layers.Preprocessor.__init__ ArgSpec(args=['self', 'reader', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.Preprocessor.block ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
 paddle.fluid.layers.Preprocessor.inputs ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)

paddle/fluid/CMakeLists.txt

@@ -12,5 +12,6 @@ endif(NOT WIN32)
 if(WITH_INFERENCE)
   # NOTE: please add subdirectory inference at last.
   add_subdirectory(inference)
-  add_subdirectory(train)
 endif()
+
+add_subdirectory(train)

python/paddle/fluid/layers/io.py

@@ -30,8 +30,7 @@
 
 __all__ = [
     'data', 'open_files', 'read_file', 'shuffle', 'batch', 'double_buffer',
-    'random_data_generator', 'py_reader', 'create_py_reader_by_data',
-    'Preprocessor', 'load'
+    'random_data_generator', 'py_reader', 'Preprocessor', 'load'
 ]
 
 
@@ -471,158 +470,6 @@ 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,
-               dtypes,
-               lod_levels=None,
-               name=None,
-               use_double_buffer=True,
-               feed_list=None):
-
-    if feed_list is not None:
-        if not isinstance(feed_list, list):
-            raise TypeError("feed_list should be a list of Variable"
-                            " instead of " + str(type(feed_list)))
-        lod_levels = []
-        dtypes = []
-        shape_concat = []
-        ranks = []
-        shapes = []
-
-        for data in feed_list:
-            dtypes.append(data.dtype)
-            shape_concat.extend(data.shape)
-            ranks.append(len(data.shape))
-            shapes.append(data.shape)
-            lod_levels.append(data.lod_level)
-    else:
-        dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
-        shape_concat = []
-        ranks = []
-
-        for shape in shapes:
-            shape_concat.extend(shape)
-            ranks.append(len(shape))
-
-        if lod_levels is None:
-            lod_levels = [0] * len(shapes)
-
-    if name is None:
-        queue_name = unique_name('lod_tensor_blocking_queue')
-        reader_name = unique_name('create_py_reader')
-        double_buffer_name = unique_name('double_buffer')
-    else:
-        queue_name = "_".join([name, "queue"])
-        reader_name = "_".join([name, "reader"])
-        double_buffer_name = "_".join([name, "double_buffer"])
-
-    var = global_scope().var(queue_name)
-    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
-
-    startup_blk = default_startup_program().current_block()
-    startup_var = startup_blk.create_var(name=reader_name)
-    startup_blk.append_op(
-        type='create_py_reader',
-        inputs={'blocking_queue': [queue_name]},
-        outputs={'Out': [startup_var]},
-        attrs={
-            'shape_concat': shape_concat,
-            'lod_levels': lod_levels,
-            'ranks': ranks
-        })
-
-    startup_var.desc.set_dtypes(dtypes)
-    startup_var.persistable = True
-
-    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
-                                      startup_var)
-
-    reader = monkey_patch_reader_methods(main_prog_var)
-    if use_double_buffer:
-        double_buffer_reader = double_buffer(reader, name=double_buffer_name)
-        # we return a double buffer reader. However, the reset method comes from
-        # py_reader.
-        double_buffer_reader.reset = reader.reset
-        reader = double_buffer_reader
-
-    # monkey patch py_reader special methods
-    reader.queue = feed_queue
-    current_reset_method = reader.reset
-    reader.thread = None
-    reader.tensor_provider = None
-    reader.exited = False
-
-    def start_provide_thread(func):
-        def __provider_thread__():
-            for tensors in func():
-                array = core.LoDTensorArray()
-                for item in tensors:
-                    if not isinstance(item, core.LoDTensor):
-                        tmp = core.LoDTensor()
-                        tmp.set(item, core.CPUPlace())
-                        item = tmp
-
-                    array.append(item)
-
-                if reader.exited:
-                    break
-                feed_queue.push(array)
-                if reader.exited:
-                    break
-            feed_queue.close()
-
-        reader.thread = threading.Thread(target=__provider_thread__)
-        reader.thread.daemon = True
-        reader.thread.start()
-
-    def __set_tensor_provider__(func):
-        reader.tensor_provider = func
-
-    def __set_paddle_reader__(paddle_reader):
-        with program_guard(Program(), Program()):
-            actual_feed_list = feed_list
-            if actual_feed_list is None:
-                actual_feed_list = []
-                counter = 0
-                for dtype, shape, lod_level in zip(dtypes, shapes, lod_levels):
-                    name = str(counter)
-                    actual_feed_list.append(
-                        data(
-                            name=name,
-                            dtype=dtype,
-                            shape=shape,
-                            lod_level=lod_level))
-                    counter += 1
-
-            feeder = DataFeeder(
-                feed_list=actual_feed_list, place=core.CPUPlace())
-            paddle_reader = feeder.decorate_reader(
-                paddle_reader, multi_devices=False)
-
-        def __tensor_provider__():
-            for slots in paddle_reader():
-                yield [slots[str(idx)] for idx in six.moves.xrange(counter)]
-
-        __set_tensor_provider__(__tensor_provider__)
-
-    def __reset__():
-        current_reset_method()
-        if reader.thread is not None and reader.tensor_provider is not None:
-            reader.exited = True
-            reader.thread.join()
-            reader.exited = False
-
-    def __start__():
-        start_provide_thread(reader.tensor_provider)
-
-    reader.reset = __reset__
-    reader.decorate_tensor_provider = __set_tensor_provider__
-    reader.decorate_paddle_reader = __set_paddle_reader__
-    reader.start = __start__
-
-    return reader
-
-
 def py_reader(capacity,
               shapes,
               dtypes,
@@ -747,72 +594,128 @@ def py_reader(capacity,
         >>>     except fluid.core.EOFException:
         >>>         test_reader.reset()
     """
-    return _py_reader(
-        capacity=capacity,
-        shapes=shapes,
-        dtypes=dtypes,
-        lod_levels=lod_levels,
-        name=name,
-        use_double_buffer=use_double_buffer)
+    dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
+    shape_concat = []
+    ranks = []
 
+    for shape in shapes:
+        shape_concat.extend(shape)
+        ranks.append(len(shape))
 
-def create_py_reader_by_data(capacity,
-                             feed_list,
-                             name=None,
-                             use_double_buffer=True):
-    """
-    Create a Python reader for data feeding in Python
+    if lod_levels is None:
+        lod_levels = [0] * len(shapes)
 
-    This layer returns a Reader Variable.
+    if name is None:
+        queue_name = unique_name('lod_tensor_blocking_queue')
+        reader_name = unique_name('create_py_reader')
+        double_buffer_name = unique_name('double_buffer')
+    else:
+        queue_name = "_".join([name, "queue"])
+        reader_name = "_".join([name, "reader"])
+        double_buffer_name = "_".join([name, "double_buffer"])
 
-    Works much like py_reader except that it's input is feed_list
-    instead of shapes, dtypes and lod_levels
+    var = global_scope().var(queue_name)
+    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
 
-    Args:
-       capacity(int): The buffer capacity maintained by :code:`py_reader`.
-       feed_list(list(Variable)): The data feed list.
-       name(basestring): The prefix Python queue name and Reader name. None will
-            be generated automatically.
-       use_double_buffer(bool): Whether use double buffer or not.
+    startup_blk = default_startup_program().current_block()
+    startup_var = startup_blk.create_var(name=reader_name)
+    startup_blk.append_op(
+        type='create_py_reader',
+        inputs={'blocking_queue': [queue_name]},
+        outputs={'Out': [startup_var]},
+        attrs={
+            'shape_concat': shape_concat,
+            'lod_levels': lod_levels,
+            'ranks': ranks
+        })
 
-    Returns:
-       Variable: A Reader from which we can get feeding data.
+    startup_var.desc.set_dtypes(dtypes)
+    startup_var.persistable = True
 
-    Examples:
+    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
+                                      startup_var)
 
-        1. The basic usage of :code:`py_reader` is as follows:
+    reader = monkey_patch_reader_methods(main_prog_var)
+    if use_double_buffer:
+        double_buffer_reader = double_buffer(reader, name=double_buffer_name)
+        # we return a double buffer reader. However, the reset method comes from
+        # py_reader.
+        double_buffer_reader.reset = reader.reset
+        reader = double_buffer_reader
 
-        >>> import paddle.fluid as fluid
-        >>> import paddle.dataset.mnist as mnist
-        >>>
-        >>> image = fluid.layers.data(name='image', shape=[3,224,224], dtypes='float32')
-        >>> label = fluid.layers.data(name='label', shape=[1], dtypes='int64')
-        >>> reader = fluid.layers.create_py_reader_by_data(capacity=64, feed_list=[image, label])
-        >>> reader.decorate_paddle_reader(
-        >>>     paddle.reader.shuffle(paddle.batch(mnist.train())
-        >>>
-        >>> img, label = fluid.layers.read_file(reader)
-        >>> loss = network(img, label) # some network definition
-        >>>
-        >>> fluid.Executor(fluid.CUDAPlace(0)).run(fluid.default_startup_program())
-        >>>
-        >>> exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name)
-        >>> for epoch_id in range(10):
-        >>>     reader.start()
-        >>>     try:
-        >>>         while True:
-        >>>             exe.run(fetch_list=[loss.name])
-        >>>     except fluid.core.EOFException:
-        >>>         reader.reset()
-    """
-    return _py_reader(
-        capacity=capacity,
-        shapes=None,
-        dtypes=None,
-        lod_levels=None,
-        name=name,
-        use_double_buffer=use_double_buffer,
-        feed_list=feed_list)
+    # monkey patch py_reader special methods
+    reader.queue = feed_queue
+    current_reset_method = reader.reset
+    reader.thread = None
+    reader.tensor_provider = None
+    reader.exited = False
+
+    def start_provide_thread(func):
+        def __provider_thread__():
+            for tensors in func():
+                array = core.LoDTensorArray()
+                for item in tensors:
+                    if not isinstance(item, core.LoDTensor):
+                        tmp = core.LoDTensor()
+                        tmp.set(item, core.CPUPlace())
+                        item = tmp
+
+                    array.append(item)
+
+                if reader.exited:
+                    break
+                feed_queue.push(array)
+                if reader.exited:
+                    break
+            feed_queue.close()
+
+        reader.thread = threading.Thread(target=__provider_thread__)
+        reader.thread.daemon = True
+        reader.thread.start()
+
+    def __set_tensor_provider__(func):
+        reader.tensor_provider = func
+
+    def __set_paddle_reader__(paddle_reader):
+        with program_guard(Program(), Program()):
+            feed_list = []
+            counter = 0
+            for dtype, shape, lod_level in zip(dtypes, shapes, lod_levels):
+                name = str(counter)
+                feed_list.append(
+                    data(
+                        name=name,
+                        dtype=dtype,
+                        shape=shape,
+                        lod_level=lod_level))
+                counter += 1
+
+            feeder = DataFeeder(feed_list=feed_list, place=core.CPUPlace())
+            paddle_reader = feeder.decorate_reader(
+                paddle_reader, multi_devices=False)
+
+        def __tensor_provider__():
+            for slots in paddle_reader():
+                yield [slots[str(idx)] for idx in six.moves.xrange(counter)]
+
+        __set_tensor_provider__(__tensor_provider__)
+
+    def __reset__():
+        current_reset_method()
+        if reader.thread is not None and reader.tensor_provider is not None:
+            reader.exited = True
+            reader.thread.join()
+            reader.exited = False
+
+    def __start__():
+        start_provide_thread(reader.tensor_provider)
+
+    reader.reset = __reset__
+    reader.decorate_tensor_provider = __set_tensor_provider__
+    reader.decorate_paddle_reader = __set_paddle_reader__
+    reader.start = __start__
+
+    return reader
 
 
 def open_files(filenames,