Merge branch 'develop' of github.com:PaddlePaddle/Paddle into fix_cpu_pe

python/paddle/fluid/__init__.py

@@ -65,7 +65,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
               'io',
               'initializer',
               'layers',
-              'transpiler'
+              'transpiler',
               'nets',
               'optimizer',
               'learning_rate_decay',

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

+# 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.
+
+import paddle.fluid as fluid
+import paddle.v2 as paddle
+import numpy as np
+import unittest
+
+
+class TestReaderReset(unittest.TestCase):
+    def prepare_data(self):
+        def fake_data_generator():
+            for n in xrange(self.total_ins_num):
+                yield np.ones(self.ins_shape) * n, n
+
+        # Prepare data
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            reader = paddle.batch(fake_data_generator, batch_size=1)
+            feeder = fluid.DataFeeder(
+                feed_list=[
+                    fluid.layers.data(
+                        name='data', shape=[3], dtype='float32'),
+                    fluid.layers.data(
+                        name='label', shape=[1], dtype='int64'),
+                ],
+                place=fluid.CPUPlace())
+            fluid.recordio_writer.convert_reader_to_recordio_file(
+                self.data_file_name, reader, feeder)
+
+    def setUp(self):
+        self.use_cuda = fluid.core.is_compiled_with_cuda()
+        self.data_file_name = './reader_reset_test.recordio'
+        self.ins_shape = [3]
+        self.batch_size = 5
+        self.total_ins_num = self.batch_size * 20
+        self.test_pass_num = 100
+        self.prepare_data()
+
+    def main(self, with_double_buffer):
+        main_prog = fluid.Program()
+        startup_prog = fluid.Program()
+
+        with fluid.program_guard(main_prog, startup_prog):
+            data_reader_handle = fluid.layers.io.open_files(
+                filenames=[self.data_file_name],
+                shapes=[[-1] + self.ins_shape, [-1, 1]],
+                lod_levels=[0, 0],
+                dtypes=['float32', 'int64'],
+                thread_num=1,
+                pass_num=1)
+            data_reader = fluid.layers.io.batch(data_reader_handle,
+                                                self.batch_size)
+            if with_double_buffer:
+                data_reader = fluid.layers.double_buffer(data_reader)
+            image, label = fluid.layers.read_file(data_reader)
+            fetch_list = [image.name, label.name]
+
+        place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+
+        build_strategy = fluid.BuildStrategy()
+        if with_double_buffer:
+            build_strategy.enable_data_balance = True
+        exec_strategy = fluid.ExecutionStrategy()
+        parallel_exe = fluid.ParallelExecutor(
+            use_cuda=self.use_cuda,
+            main_program=main_prog,
+            build_strategy=build_strategy,
+            exec_strategy=exec_strategy)
+
+        data_appeared = [False] * self.total_ins_num
+        pass_count = 0
+        while (True):
+            try:
+                data_val, label_val = parallel_exe.run(fetch_list,
+                                                       return_numpy=True)
+                ins_num = data_val.shape[0]
+                broadcasted_label = np.ones((ins_num, ) + tuple(
+                    self.ins_shape)) * label_val.reshape((ins_num, 1))
+                self.assertEqual(data_val.all(), broadcasted_label.all())
+                for l in label_val:
+                    self.assertFalse(data_appeared[l[0]])
+                    data_appeared[l[0]] = True
+
+            except fluid.core.EOFException:
+                pass_count += 1
+                if with_double_buffer:
+                    data_appeared = data_appeared[:-parallel_exe.device_count *
+                                                  self.batch_size]
+                for i in data_appeared:
+                    self.assertTrue(i)
+                if pass_count < self.test_pass_num:
+                    data_appeared = [False] * self.total_ins_num
+                    data_reader_handle.reset()
+                else:
+                    break
+
+    def test_all(self):
+        self.main(with_double_buffer=False)
+        self.main(with_double_buffer=True)
+
+
+if __name__ == '__main__':
+    unittest.main()