Merge pull request #2218 from luotao1/rename

rename AggregateLevel and ExpandLevel

doc/howto/deep_model/rnn/hierarchical_layer_cn.rst

@@ -28,17 +28,17 @@ pooling 的使用示例如下，详细见 :ref:`api_v2.layer_pooling` 配置API
 
         seq_pool = pooling(input=layer,
                            pooling_type=pooling.Max(),
-                           agg_level=AggregateLevel.EACH_SEQUENCE)
+                           agg_level=AggregateLevel.TO_SEQUENCE)
         
 - `pooling_type` 目前支持两种，分别是：pooling.Max()和pooling.Avg()。
 
-- `agg_level=AggregateLevel.EACH_TIMESTEP` 时（默认值）：
+- `agg_level=AggregateLevel.TO_NO_SEQUENCE` 时（默认值）：
 
   - 作用：双层序列经过运算变成一个0层序列，或单层序列经过运算变成一个0层序列
   - 输入：一个双层序列，或一个单层序列
   - 输出：一个0层序列，即整个输入序列（单层或双层）的平均值（或最大值）
 
-- `agg_level=AggregateLevel.EACH_SEQUENCE` 时：
+- `agg_level=AggregateLevel.TO_SEQUENCE` 时：
 
   - 作用：一个双层序列经过运算变成一个单层序列
   - 输入：必须是一个双层序列
@@ -52,15 +52,15 @@ last_seq 的使用示例如下（ :ref:`api_v2.layer_first_seq` 类似），详
 ..	code-block:: bash
 
         last = last_seq(input=layer,
-                        agg_level=AggregateLevel.EACH_SEQUENCE)
+                        agg_level=AggregateLevel.TO_SEQUENCE)
         
-- `agg_level=AggregateLevel.EACH_TIMESTEP` 时（默认值）：
+- `agg_level=AggregateLevel.TO_NO_SEQUENCE` 时（默认值）：
 
   - 作用：一个双层序列经过运算变成一个0层序列，或一个单层序列经过运算变成一个0层序列
   - 输入：一个双层序列或一个单层序列
   - 输出：一个0层序列，即整个输入序列（双层或者单层）最后一个，或第一个元素。
 
-- `agg_level=AggregateLevel.EACH_SEQUENCE` 时：
+- `agg_level=AggregateLevel.TO_SEQUENCE` 时：
   - 作用：一个双层序列经过运算变成一个单层序列
   - 输入：必须是一个双层序列
   - 输出：一个单层序列，其中每个元素是双层序列中每个subseq最后一个（或第一个）元素。
@@ -74,9 +74,9 @@ expand 的使用示例如下，详细见 :ref:`api_v2.layer_expand` 配置API。
 
         ex = expand(input=layer1,
                     expand_as=layer2,
-                    expand_level=ExpandLevel.FROM_TIMESTEP)
+                    expand_level=ExpandLevel.FROM_NO_SEQUENCE)
         
-- `expand_level=ExpandLevel.FROM_TIMESTEP` 时（默认值）：
+- `expand_level=ExpandLevel.FROM_NO_SEQUENCE` 时（默认值）：
 
   - 作用：一个0层序列经过运算扩展成一个单层序列，或者一个双层序列
   - 输入：layer1必须是一个0层序列，是待扩展的数据；layer2 可以是一个单层序列，或者是一个双层序列，提供扩展的长度信息

doc/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.rst

@@ -81,7 +81,7 @@
   
   * 在本例中，我们将原始数据的每一组，通过\ :code:`recurrent_group`\ 进行拆解，拆解成的每一句话再通过一个LSTM网络。这和单层RNN的配置是等价的。
 
-* 与单层RNN的配置类似，我们只需要使用LSTM encode成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但和单层RNN不同，我们是对每一个子序列取最后一个元素，因此\ :code:`agg_level=AggregateLevel.EACH_SEQUENCE`\ 。
+* 与单层RNN的配置类似，我们只需要使用LSTM encode成的最后一个向量。所以对\ :code:`recurrent_group`\ 进行了\ :code:`last_seq`\ 操作。但和单层RNN不同，我们是对每一个子序列取最后一个元素，因此\ :code:`agg_level=AggregateLevel.TO_SEQUENCE`\ 。
 
 * 至此，\ :code:`lstm_last`\ 便和单层RNN配置中的\ :code:`lstm_last`\ 具有相同的结果了。
 

paddle/gserver/tests/sequence_nest_layer_group.conf

@@ -59,7 +59,7 @@ lstm_nest_group = recurrent_group(
     input=SubsequenceInput(emb_group), step=lstm_group, name="lstm_nest_group")
 # hasSubseq ->(seqlastins) seq
 lstm_last = last_seq(
-    input=lstm_nest_group, agg_level=AggregateLevel.EACH_SEQUENCE)
+    input=lstm_nest_group, agg_level=AggregateLevel.TO_SEQUENCE)
 
 # seq ->(expand) hasSubseq
 lstm_expand = expand_layer(
@@ -71,7 +71,7 @@ lstm_expand = expand_layer(
 lstm_average = pooling_layer(
     input=lstm_expand,
     pooling_type=AvgPooling(),
-    agg_level=AggregateLevel.EACH_SEQUENCE)
+    agg_level=AggregateLevel.TO_SEQUENCE)
 
 with mixed_layer(
         size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:

python/paddle/trainer_config_helpers/layers.py

@@ -237,16 +237,19 @@ class AggregateLevel(object):
 
     Accordingly, AggregateLevel supports two modes:
 
-    - :code:`AggregateLevel.EACH_TIMESTEP` means the aggregation acts on each
+    - :code:`AggregateLevel.TO_NO_SEQUENCE` means the aggregation acts on each
       timestep of a sequence, both :code:`SUB_SEQUENCE` and :code:`SEQUENCE` will
       be aggregated to :code:`NO_SEQUENCE`.
 
-    - :code:`AggregateLevel.EACH_SEQUENCE` means the aggregation acts on each
+    - :code:`AggregateLevel.TO_SEQUENCE` means the aggregation acts on each
       sequence of a nested sequence, :code:`SUB_SEQUENCE` will be aggregated to
       :code:`SEQUENCE`.
     """
-    EACH_TIMESTEP = 'non-seq'
-    EACH_SEQUENCE = 'seq'
+    TO_NO_SEQUENCE = 'non-seq'
+    TO_SEQUENCE = 'seq'
+    # compatible with previous configuration
+    EACH_TIMESTEP = TO_NO_SEQUENCE
+    EACH_SEQUENCE = TO_SEQUENCE
 
 
 class LayerOutput(object):
@@ -1083,7 +1086,7 @@ def pooling_layer(input,
                   pooling_type=None,
                   name=None,
                   bias_attr=None,
-                  agg_level=AggregateLevel.EACH_TIMESTEP,
+                  agg_level=AggregateLevel.TO_NO_SEQUENCE,
                   layer_attr=None):
     """
     Pooling layer for sequence inputs, not used for Image.
@@ -1094,10 +1097,10 @@ def pooling_layer(input,
 
        seq_pool = pooling_layer(input=layer,
                                 pooling_type=AvgPooling(),
-                                agg_level=AggregateLevel.EACH_SEQUENCE)
+                                agg_level=AggregateLevel.TO_NO_SEQUENCE)
 
-    :param agg_level: AggregateLevel.EACH_TIMESTEP or
-                      AggregateLevel.EACH_SEQUENCE
+    :param agg_level: AggregateLevel.TO_NO_SEQUENCE or
+                      AggregateLevel.TO_SEQUENCE
     :type agg_level: AggregateLevel
     :param name: layer name.
     :type name: basestring
@@ -1367,7 +1370,7 @@ def grumemory(input,
 @layer_support()
 def last_seq(input,
              name=None,
-             agg_level=AggregateLevel.EACH_TIMESTEP,
+             agg_level=AggregateLevel.TO_NO_SEQUENCE,
              stride=-1,
              layer_attr=None):
     """
@@ -1402,7 +1405,7 @@ def last_seq(input,
                        " series information at all. Maybe you want to use"
                        " first_seq instead.")
 
-    if agg_level == AggregateLevel.EACH_SEQUENCE:
+    if agg_level == AggregateLevel.TO_SEQUENCE:
         assert stride == -1
 
     Layer(
@@ -1423,7 +1426,7 @@ def last_seq(input,
 @layer_support()
 def first_seq(input,
               name=None,
-              agg_level=AggregateLevel.EACH_TIMESTEP,
+              agg_level=AggregateLevel.TO_NO_SEQUENCE,
               stride=-1,
               layer_attr=None):
     """
@@ -1459,7 +1462,7 @@ def first_seq(input,
                        ' time series information at all. Maybe you want to use'
                        ' last_seq instead.')
 
-    if agg_level == AggregateLevel.EACH_SEQUENCE:
+    if agg_level == AggregateLevel.TO_SEQUENCE:
         assert stride == -1
 
     Layer(
@@ -1482,16 +1485,18 @@ class ExpandLevel(object):
 
     ExpandLevel supports two modes:
 
-    - :code:`ExpandLevel.FROM_TIMESTEP` means the expandation acts on each
-      timestep of a sequence, :code:`NO_SEQUENCE` will be expanded to
+    - :code:`ExpandLevel.FROM_NO_SEQUENCE` means the expansion acts on
+      :code:`NO_SEQUENCE`, which will be expanded to
       :code:`SEQUENCE` or :code:`SUB_SEQUENCE`.
 
-    - :code:`ExpandLevel.FROM_SEQUENCE` means the expandation acts on each
-      sequence of a nested sequence, :code:`SEQUENCE` will be expanded to
+    - :code:`ExpandLevel.FROM_SEQUENCE` means the expansion acts on
+      :code:`SEQUENCE`, which will be expanded to
       :code:`SUB_SEQUENCE`.
     """
-    FROM_TIMESTEP = AggregateLevel.EACH_TIMESTEP
-    FROM_SEQUENCE = AggregateLevel.EACH_SEQUENCE
+    FROM_NO_SEQUENCE = AggregateLevel.TO_NO_SEQUENCE
+    FROM_SEQUENCE = AggregateLevel.TO_SEQUENCE
+    # compatible with previous configuration
+    FROM_TIMESTEP = FROM_NO_SEQUENCE
 
 
 @wrap_name_default()
@@ -1500,7 +1505,7 @@ def expand_layer(input,
                  expand_as,
                  name=None,
                  bias_attr=False,
-                 expand_level=ExpandLevel.FROM_TIMESTEP,
+                 expand_level=ExpandLevel.FROM_NO_SEQUENCE,
                  layer_attr=None):
     """
     A layer for "Expand Dense data or (sequence data where the length of each
@@ -1512,7 +1517,7 @@ def expand_layer(input,
 
        expand = expand_layer(input=layer1,
                              expand_as=layer2,
-                             expand_level=ExpandLevel.FROM_TIMESTEP)
+                             expand_level=ExpandLevel.FROM_NO_SEQUENCE)
 
     :param input: Input layer
     :type input: LayerOutput

python/paddle/trainer_config_helpers/tests/configs/last_first_seq.py

@@ -6,7 +6,7 @@ din = data_layer(name='data', size=30)
 
 seq_op = [first_seq, last_seq]
 
-agg_level = [AggregateLevel.EACH_SEQUENCE, AggregateLevel.EACH_TIMESTEP]
+agg_level = [AggregateLevel.TO_SEQUENCE, AggregateLevel.TO_NO_SEQUENCE]
 
 opts = []
 
@@ -15,6 +15,7 @@ for op in seq_op:
         opts.append(op(input=din, agg_level=al))
 
 for op in seq_op:
-    opts.append(op(input=din, agg_level=AggregateLevel.EACH_TIMESTEP, stride=5))
+    opts.append(
+        op(input=din, agg_level=AggregateLevel.TO_NO_SEQUENCE, stride=5))
 
 outputs(opts)

python/paddle/trainer_config_helpers/tests/configs/test_expand_layer.py

@@ -9,4 +9,6 @@ outputs(
     expand_layer(
         input=din, expand_as=data_seq, expand_level=ExpandLevel.FROM_SEQUENCE),
     expand_layer(
-        input=din, expand_as=data_seq, expand_level=ExpandLevel.FROM_TIMESTEP))
+        input=din,
+        expand_as=data_seq,
+        expand_level=ExpandLevel.FROM_NO_SEQUENCE))

python/paddle/trainer_config_helpers/tests/configs/test_sequence_pooling.py

@@ -6,7 +6,7 @@ din = data_layer(name='dat_in', size=100)
 
 POOL_TYPE = [MaxPooling, AvgPooling, SumPooling]
 
-AGG_LEVEL = [AggregateLevel.EACH_SEQUENCE, AggregateLevel.EACH_TIMESTEP]
+AGG_LEVEL = [AggregateLevel.TO_SEQUENCE, AggregateLevel.TO_NO_SEQUENCE]
 
 opts = []
 

python/paddle/v2/tests/test_layer.py

@@ -73,7 +73,7 @@ class AggregateLayerTest(unittest.TestCase):
         pool = layer.pooling(
             input=pixel,
             pooling_type=pooling.Avg(),
-            agg_level=layer.AggregateLevel.EACH_SEQUENCE)
+            agg_level=layer.AggregateLevel.TO_SEQUENCE)
         last_seq = layer.last_seq(input=pixel)
         first_seq = layer.first_seq(input=pixel)
         concat = layer.concat(input=[last_seq, first_seq])
@@ -109,7 +109,7 @@ class ReshapeLayerTest(unittest.TestCase):
         expand = layer.expand(
             input=weight,
             expand_as=pixel,
-            expand_level=layer.ExpandLevel.FROM_TIMESTEP)
+            expand_level=layer.ExpandLevel.FROM_NO_SEQUENCE)
         repeat = layer.repeat(input=pixel, num_repeats=4)
         reshape = layer.seq_reshape(input=pixel, reshape_size=4)
         rotate = layer.rotate(input=pixel, height=16, width=49)