add max-pool-with-mask python interface

python/paddle/trainer/config_parser.py

@@ -1250,9 +1250,9 @@ def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
 def parse_pool(pool, input_layer_name, pool_conf, ceil_mode):
     pool_conf.pool_type = pool.pool_type
     config_assert(pool.pool_type in [
-        'max-projection', 'avg-projection', 'cudnn-max-pool', 'cudnn-avg-pool'
-    ], "pool-type %s is not in "
-                  "['max-projection', 'avg-projection', "
+        'max-projection', 'avg-projection', 'max-pool-with-mask', 'cudnn-max-pool', 'cudnn-avg-pool'
+    ], "pool-type %s is not in " \
+              "['max-projection', 'avg-projection', 'max-pool-with-mask'," \
                   "'cudnn-max-pool', 'cudnn-avg-pool']" % pool.pool_type)
 
     pool_conf.channels = pool.channels

python/paddle/trainer_config_helpers/layers.py

@@ -20,7 +20,7 @@ from paddle.trainer.config_parser import *
 from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
     ReluActivation, IdentityActivation, SoftmaxActivation, BaseActivation
 from .evaluators import *
-from .poolings import MaxPooling, AvgPooling, BasePoolingType, \
+from .poolings import MaxPooling, AvgPooling, MaxWithMaskPooling, BasePoolingType, \
     CudnnAvgPooling, CudnnMaxPooling
 from .attrs import *
 from .default_decorators import *
@@ -2652,9 +2652,9 @@ def img_pool_layer(input,
     elif isinstance(pool_type, AvgPooling):
         pool_type.name = 'avg'
 
-    assert type(pool_type) in [AvgPooling, MaxPooling, CudnnAvgPooling,
+    assert type(pool_type) in [AvgPooling, MaxPooling, MaxWithMaskPooling, CudnnAvgPooling,
                                CudnnMaxPooling], \
-        "only (Cudnn)AvgPooling, (Cudnn)MaxPooling are supported"
+        "only (Cudnn)AvgPooling, (Cudnn)MaxPooling MaxWithMaskPooling are supported"
 
     type_name = pool_type.name + '-projection' \
         if (

python/paddle/trainer_config_helpers/poolings.py

@@ -15,8 +15,8 @@
 """
 
 __all__ = [
-    "BasePoolingType", "MaxPooling", "AvgPooling", "CudnnMaxPooling",
-    "CudnnAvgPooling", "SumPooling", "SquareRootNPooling"
+    "BasePoolingType", "MaxPooling", "AvgPooling", "MaxWithMaskPooling",
+    "CudnnMaxPooling", "CudnnAvgPooling", "SumPooling", "SquareRootNPooling"
 ]
 
 
@@ -55,6 +55,19 @@ class MaxPooling(BasePoolingType):
         self.output_max_index = output_max_index
 
 
+class MaxWithMaskPooling(BasePoolingType):
+    """
+    MaxWithMask pooling.
+
+    Not only return the very large values for each dimension in sequence or time steps,
+    but also the location indices of found maxinum values.
+
+    """
+
+    def __init__(self):
+        BasePoolingType.__init__(self, "max-pool-with-mask")
+
+
 class CudnnMaxPooling(BasePoolingType):
     """
     Cudnn max pooling only support GPU. Return the maxinum value in the