Merge pull request #1537 from helinwang/batch

move paddle.reader.batch to paddle.batch

Author: reyoung

demo/image_classification/api_v2_train.py

@@ -66,7 +66,7 @@ def event_handler(event):
                 sys.stdout.flush()
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(
-                reader=paddle.reader.batched(
+                reader=paddle.batch(
                     paddle.dataset.cifar.test10(), batch_size=128),
                 reader_dict={'image': 0,
                              'label': 1})
@@ -77,7 +77,7 @@ def event_handler(event):
                                  parameters=parameters,
                                  update_equation=momentum_optimizer)
     trainer.train(
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.shuffle(
                 paddle.dataset.cifar.train10(), buf_size=50000),
             batch_size=128),

demo/mnist/api_train_v2.py

@@ -98,7 +98,7 @@ def event_handler(event):
                           result.metrics['classification_error_evaluator']))
 
     trainer.train(
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.shuffle(
                 paddle.dataset.mnist.train(), buf_size=8192),
             batch_size=128),
@@ -115,7 +115,7 @@ def event_handler(event):
     probs = paddle.infer(
         output=predict,
         parameters=parameters,
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.firstn(
                 paddle.reader.map_readers(lambda item: (item[0], ),
                                           paddle.dataset.mnist.test()),

python/paddle/v2/__init__.py

@@ -28,6 +28,7 @@
 import inference
 import networks
 import py_paddle.swig_paddle as api
+import minibatch
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
@@ -45,3 +46,4 @@ def init(**kwargs):
 
 
 infer = inference.infer
+batch = minibatch.batch

python/paddle/v2/minibatch.py

@@ -0,0 +1,35 @@
+# Copyright (c) 2016 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.
+
+
+def batch(reader, batch_size):
+    """
+    Create a batch reader.
+    :param reader: the data reader to read from.
+    :param batch_size: batch_size
+    :return: the batch reader.
+    """
+
+    def batch_reader():
+        r = reader()
+        batch = []
+        for instance in r:
+            batch.append(instance)
+            if len(batch) == batch_size:
+                yield batch
+                batch = []
+        if batch:
+            yield batch
+
+    return batch_reader

python/paddle/v2/reader/decorator.py

@@ -14,7 +14,7 @@
 
 __all__ = [
     'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
-    'ComposeNotAligned', 'batched', 'firstn'
+    'ComposeNotAligned', 'firstn'
 ]
 
 import itertools
@@ -193,28 +193,6 @@ def data_reader():
     return data_reader
 
 
-def batched(reader, batch_size):
-    """
-    Create a batched reader.
-    :param reader: the data reader to read from.
-    :param batch_size: batch_size
-    :return: the batched reader.
-    """
-
-    def batched_reader():
-        r = reader()
-        batch = []
-        for instance in r:
-            batch.append(instance)
-            if len(batch) == batch_size:
-                yield batch
-                batch = []
-        if batch:
-            yield batch
-
-    return batched_reader
-
-
 def firstn(reader, n):
     """
     Limit the max number of samples that reader could return.