add sample_generator

test=develop

paddle/fluid/API.spec

@@ -61,8 +61,9 @@ paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program',
 paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
 paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.io.PyReader.__init__ ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable'], varargs=None, keywords=None, defaults=(True, True))
+paddle.fluid.io.PyReader.__init__ ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable'], varargs=None, keywords=None, defaults=(True, False))
 paddle.fluid.io.PyReader.decorate_paddle_reader ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.io.PyReader.decorate_sample_generator ArgSpec(args=['self', 'sample_generator', 'batch_size', 'drop_last', 'places'], varargs=None, keywords=None, defaults=(True, None))
 paddle.fluid.io.PyReader.decorate_tensor_provider ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.io.PyReader.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.io.PyReader.start ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)

paddle/fluid/operators/reader/blocking_queue.h

@@ -16,6 +16,7 @@
 
 #include <condition_variable>  // NOLINT
 #include <deque>
+#include <utility>
 
 #include "paddle/fluid/platform/enforce.h"
 

paddle/fluid/operators/reader/buffered_reader.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/reader/buffered_reader.h"
+#include <memory>
 #include <vector>
 #include "paddle/fluid/framework/data_type.h"
 

paddle/fluid/operators/reader/buffered_reader.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include <list>
+#include <memory>
 #include <queue>
 #include <vector>
 #include "ThreadPool.h"

paddle/fluid/operators/reader/py_reader.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/reader/py_reader.h"
+#include <memory>
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/reader/py_reader.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include <atomic>
+#include <memory>
 #include <vector>
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"

paddle/fluid/pybind/reader_py.cc

@@ -13,7 +13,10 @@
 // limitations under the License.
 
 #include "paddle/fluid/pybind/reader_py.h"
+#include <memory>
 #include <string>
+#include <unordered_map>
+#include <utility>
 #include <vector>
 #include "paddle/fluid/framework/reader.h"
 #include "paddle/fluid/operators/reader/buffered_reader.h"

python/paddle/fluid/data_feeder.py

@@ -26,6 +26,24 @@ from .framework import Variable, default_main_program
 __all__ = ['DataFeeder']
 
 
+def convert_dtype(dtype):
+    if dtype == core.VarDesc.VarType.FP32:
+        return 'float32'
+    elif dtype == core.VarDesc.VarType.INT64:
+        return 'int64'
+    elif dtype == core.VarDesc.VarType.FP64:
+        return 'float64'
+    elif dtype == core.VarDesc.VarType.FP16:
+        return 'float16'
+    elif dtype == core.VarDesc.VarType.INT32:
+        return 'int32'
+    elif dtype == core.VarDesc.VarType.UINT8:
+        return 'uint8'
+    else:
+        raise ValueError("dtype must be any of [int32, float32, int64, "
+                         "float64, uint8]")
+
+
 class DataToLoDTensorConverter(object):
     def __init__(self, place, lod_level, shape, dtype):
         self.place = place
@@ -38,27 +56,12 @@ class DataToLoDTensorConverter(object):
             if negtive_count > 1:
                 self.shape = None
                 break
-        if dtype == core.VarDesc.VarType.FP32:
-            self.dtype = 'float32'
-        elif dtype == core.VarDesc.VarType.INT64:
-            self.dtype = 'int64'
-        elif dtype == core.VarDesc.VarType.FP64:
-            self.dtype = 'float64'
-        elif dtype == core.VarDesc.VarType.FP16:
-            self.dtype = 'float16'
-        elif dtype == core.VarDesc.VarType.INT32:
-            self.dtype = 'int32'
-        elif dtype == core.VarDesc.VarType.UINT8:
-            self.dtype = 'uint8'
-        else:
-            raise ValueError("dtype must be any of [int32, float32, int64, "
-                             "float64, uint8]")
+        self.dtype = convert_dtype(dtype)
+        self._reset()
 
+    def _reset(self):
         self.data = []
-        self.lod = []
-
-        for i in six.moves.range(lod_level):
-            self.lod.append([])
+        self.lod = [[] for _ in six.moves.range(self.lod_level)]
 
     def feed(self, data):
         self._feed_impl_(data, self.lod, self.lod_level)
@@ -88,15 +91,52 @@ class DataToLoDTensorConverter(object):
                     raise ValueError(
                         "Reshape error. What is defined in data layer is {}, but receive {}"
                         .format(self.shape, arr.shape))
-            #else:
-            #    self._check_shape(arr.shape)
         t = core.LoDTensor()
         t.set(arr, self.place)
         if self.lod_level > 0:
             t.set_recursive_sequence_lengths(self.lod)
+        self._reset()
         return t
 
 
+class BatchedTensorProvider(object):
+    def __init__(self, feed_list, place, batch_size, generator, drop_last):
+        self.place = place
+        self.batch_size = batch_size
+        self.generator = generator
+        self.converters = []
+        self.drop_last = drop_last
+
+        for var in feed_list:
+            assert var.lod_level == 0, "lod_level must be 0"
+            self.converters.append(
+                DataToLoDTensorConverter(
+                    place=self.place,
+                    lod_level=0,
+                    shape=var.shape,
+                    dtype=var.dtype))
+
+    def _done(self):
+        return [c.done() for c in self.converters]
+
+    def __call__(self):
+        idx = 0
+        for each_sample in self.generator():
+            for each_slot, each_converter in six.moves.zip(each_sample,
+                                                           self.converters):
+                each_converter.data.append(each_slot)
+
+            idx += 1
+            if idx == self.batch_size:
+                idx = 0
+                yield self._done()
+
+        if not self.drop_last and idx > 0:
+            yield self._done()
+        else:
+            [c._reset() for c in self.converters]
+
+
 class DataFeeder(object):
     """
     DataFeeder converts the data that returned by a reader into a data

python/paddle/fluid/reader.py

@@ -17,7 +17,7 @@ import six
 import threading
 from .framework import Program, Variable, program_guard, default_main_program, default_startup_program
 from .executor import global_scope
-from .data_feeder import DataFeeder
+from .data_feeder import DataFeeder, BatchedTensorProvider
 from .layers.io import monkey_patch_reader_methods, _copy_reader_var_, double_buffer
 from .unique_name import UniqueNameGenerator
 
@@ -46,7 +46,7 @@ class PyReader(object):
                  feed_list,
                  capacity,
                  use_double_buffer=True,
-                 iterable=True):
+                 iterable=False):
         """
         Create a reader object for data feeding in Python. 
         Data would be prefetched using Python thread and be pushed
@@ -269,6 +269,54 @@ class PyReader(object):
         self._thread.daemon = True
         self._thread.start()
 
+    def decorate_sample_generator(self,
+                                  sample_generator,
+                                  batch_size,
+                                  drop_last=True,
+                                  places=None):
+        '''
+        Set the data source of the PyReader object.
+        
+        The provided :code:`sample_generator` should be a Python generator,
+        which yields numpy.ndarray typed data of each sample.
+
+        :code:`places` must be set when the PyReader object is iterable.
+
+        If all inputs have no lods, this method is faster than 
+        :code:`decorate_paddle_reader(paddle.batch(sample_generator, ...))` .
+
+        Args:
+            sample_generator (generator): Python generator that yields
+                numpy.ndarray-typed sample data.
+            batch_size (int): batch size. Must be larger than 0.
+            drop_last (bool): Whether to drop the last batch when sample number
+                is less than batch_size. 
+            places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
+                be provided when PyReader is iterable.
+        '''
+        assert batch_size > 0, "batch_size must be larger than 0"
+        has_lod = False
+        for f in self._feed_list:
+            if f.lod_level != 0:
+                has_lod = True
+                break
+
+        if has_lod:
+            self.decorate_paddle_reader(
+                paddle.batch(
+                    sample_generator,
+                    batch_size=batch_size,
+                    drop_last=drop_last),
+                places=places)
+        else:
+            reader = BatchedTensorProvider(
+                feed_list=self._feed_list,
+                place=core.CPUPlace(),
+                batch_size=batch_size,
+                generator=sample_generator,
+                drop_last=drop_last)
+            self.decorate_tensor_provider(reader, places=places)
+
     def decorate_paddle_reader(self, reader, places=None):
         '''
         Set the data source of the PyReader object. 
@@ -279,8 +327,10 @@ class PyReader(object):
         :code:`places` must be set when the PyReader object is iterable.
 
         Args:
-            reader (generator): Python generator that yields numpy-typed
-                batched data. 
+            reader (generator): Python generator that yields 
+                list(numpy.ndarray)-typed batched data. 
+            places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
+                be provided when PyReader is iterable.
         '''
         assert self._tensor_reader is None, \
             "Cannot reset the data source of PyReader"
@@ -307,6 +357,8 @@ class PyReader(object):
         Args:
             reader (generator): Python generator that yields LoDTensor-typed
                 batched data.
+			places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
+                be provided when PyReader is iterable.
         '''
         assert self._tensor_reader is None, \
             "Cannot reset the data source of PyReader"

python/paddle/fluid/tests/unittests/test_decoupled_py_reader.py

@@ -127,7 +127,6 @@ class TestBase(unittest.TestCase):
                     step_list.append(step)
             end_t = time.time()
             ret = {"time": end_t - start_t, "step": step_list}
-            scope._remove_from_pool()
             return ret
 
     def prepare_places(self, with_data_parallel, with_cpu=True, with_gpu=True):

python/paddle/fluid/tests/unittests/test_py_reader_sample_generator.py

@@ -0,0 +1,137 @@
+# Copyright (c) 2019 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.
+
+import paddle
+import paddle.fluid as fluid
+import math
+import unittest
+import numpy as np
+import os
+
+os.environ['CPU_NUM'] = '1'
+
+
+def random_reader(sample_num):
+    def __impl__():
+        for _ in range(sample_num):
+            yield np.random.random(
+                size=[784]).astype('float32'), np.random.random_integers(
+                    low=0, high=9, size=[1]).astype('int64')
+
+    return paddle.reader.cache(__impl__)
+
+
+class TestCaseBase(unittest.TestCase):
+    def setUp(self):
+        self.batch_size = 32
+        self.epoch_num = 2
+        self.sample_num = 165
+
+    def generate_all_data(self, reader):
+        ret = []
+        for d in reader():
+            slots = [[], []]
+            for item in d:
+                slots[0].append(item[0])
+                slots[1].append(item[1])
+            slots = [np.array(slot) for slot in slots]
+            ret.append(slots)
+        return ret
+
+    def run_main(self, reader, use_sample_generator, iterable, drop_last):
+        image = fluid.layers.data(name='image', dtype='float32', shape=[784])
+        label = fluid.layers.data(name='label', dtype='int64', shape=[1])
+        py_reader = fluid.io.PyReader(
+            feed_list=[image, label],
+            capacity=16,
+            iterable=iterable,
+            use_double_buffer=False)
+
+        batch_reader = paddle.batch(reader, self.batch_size, drop_last)
+        all_datas = self.generate_all_data(batch_reader)
+
+        if not use_sample_generator:
+            py_reader.decorate_paddle_reader(
+                batch_reader, places=fluid.cpu_places())
+        else:
+            py_reader.decorate_sample_generator(
+                reader, self.batch_size, drop_last, places=fluid.cpu_places())
+
+        if drop_last:
+            batch_num = int(self.sample_num / self.batch_size)
+        else:
+            batch_num = math.ceil(float(self.sample_num) / self.batch_size)
+
+        exe = fluid.Executor(fluid.CPUPlace())
+        exe.run(fluid.default_startup_program())
+        for _ in range(self.epoch_num):
+            if py_reader.iterable:
+                step = 0
+                for data in py_reader():
+                    img, lbl = exe.run(feed=data, fetch_list=[image, label])
+                    self.assertArrayEqual(img, all_datas[step][0])
+                    self.assertArrayEqual(lbl, all_datas[step][1])
+                    step += 1
+                self.assertEqual(step, len(all_datas))
+            else:
+                step = 0
+                try:
+                    py_reader.start()
+                    while True:
+                        img, lbl = exe.run(fetch_list=[image, label])
+                        self.assertArrayEqual(img, all_datas[step][0])
+                        self.assertArrayEqual(lbl, all_datas[step][1])
+                        step += 1
+                except fluid.core.EOFException:
+                    py_reader.reset()
+                    self.assertEqual(step, len(all_datas))
+                    break
+
+    def assertArrayEqual(self, arr1, arr2):
+        self.assertEqual(arr1.shape, arr2.shape)
+        self.assertTrue((arr1 == arr2).all())
+
+    def test_main(self):
+        reader = random_reader(self.sample_num)
+        for use_sample_generator in [False, True]:
+            for iterable in [False, True]:
+                for drop_last in [False, True]:
+                    with fluid.program_guard(fluid.Program(), fluid.Program()):
+                        self.run_main(reader, use_sample_generator, iterable,
+                                      drop_last)
+
+
+class TestCase1(TestCaseBase):
+    def setUp(self):
+        self.batch_size = 32
+        self.epoch_num = 10
+        self.sample_num = 160
+
+
+class TestCase2(TestCaseBase):
+    def setUp(self):
+        self.batch_size = 32
+        self.epoch_num = 2
+        self.sample_num = 200
+
+
+class TestCase3(TestCaseBase):
+    def setUp(self):
+        self.batch_size = 32
+        self.epoch_num = 2
+        self.sample_num = 159
+
+
+if __name__ == '__main__':
+    unittest.main()