"add sequence conv layer" (#5117)

* "add sequence conv layer"

* "add sequence layer"

* add networks

* "fix based comment"

* Update layers.py

python/paddle/v2/framework/layers.py

@@ -5,7 +5,7 @@ import re
 
 __all__ = [
     'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat',
-    'StaticRNN', 'cast', 'batch_norm'
+    'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool'
 ]
 
 
@@ -165,6 +165,18 @@ _create_op_func_('dropout')
 _create_op_func_('reshape')
 
 
+def cast(x, data_type, program=None):
+    helper = LayerHelper('cast', **locals())
+    out = helper.create_tmp_variable(dtype=data_type)
+    helper.append_op(
+        type='cast',
+        inputs={'X': [x]},
+        outputs={'Out': [out]},
+        attrs={'in_data_type': x.data_type,
+               'out_data_type': out.data_type})
+    return out
+
+
 def cast(x, data_type, program=None):
     helper = LayerHelper('cast', **locals())
     out = helper.create_tmp_variable(dtype=data_type)
@@ -220,6 +232,46 @@ def square_error_cost(input, label, **kwargs):
     return square_out
 
 
+def sequence_conv(input,
+                  num_filters,
+                  name=None,
+                  filter_size=3,
+                  act=None,
+                  stride=1,
+                  padding=None,
+                  bias_attr=None,
+                  param_attr=None,
+                  program=None,
+                  init_program=None):
+    # FIXME(dzh) : want to unify the argument of python layer
+    # function. So we ignore some unecessary attributes.
+    # such as, padding_trainable, context_start.
+
+    helper = LayerHelper('sequence_conv', **locals())
+    dtype = helper.input_dtype()
+
+    filter_shape = [num_filters, filter_size]
+    filter = helper.create_parameter(
+        attr=helper.param_attr, shape=filter_shape, dtype=dtype)
+    pre_bias = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='sequence_conv',
+        inputs={
+            'X': [input],
+            'Filter': filter,
+        },
+        outputs={"Out": pre_bias},
+        attrs={
+            'context_stride': stride,
+            'context_start': 0,
+            'context_length': filter_size
+        })
+
+    pre_act = helper.append_bias_op(pre_bias)
+    return helper.append_activation(pre_act)
+
+
 def conv2d(input,
            num_filters,
            name=None,
@@ -272,6 +324,35 @@ def conv2d(input,
     return helper.append_activation(pre_act)
 
 
+def sequence_pool(input,
+                  pool_size,
+                  pool_type,
+                  pool_stride=1,
+                  pool_padding=0,
+                  global_pooling=False,
+                  program=None,
+                  init_program=None):
+    # FIXME(dzh) : want to unify the argument of python layer
+    # function. So we ignore some unecessary attributes
+
+    ENUM_POOL_TYPE = set(["max", "avg", "sqrt", "last", "first"])
+    if pool_type not in ENUM_POOL_TYPE:
+        raise ValueError("Unknown pool_type: '%s'. It can only be %s.",
+                         str(pool_type), " ".join(ENUM_POOL_TYPE))
+
+    helper = LayerHelper('sequence_pool', **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type="sequence_pool",
+        inputs={"X": [input]},
+        outputs={"Out": pool_out},
+        attrs={"strategy": pool_type})
+
+    return pool_out
+
+
 def pool2d(input,
            pool_size,
            pool_type,
@@ -291,7 +372,7 @@ def pool2d(input,
     if isinstance(pool_padding, int):
         pool_padding = [pool_padding, pool_padding]
 
-    helper = LayerHelper('conv2d', **locals())
+    helper = LayerHelper('pool2d', **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_tmp_variable(dtype)
 

python/paddle/v2/framework/nets.py

 import paddle.v2.framework.layers as layers
 
+__all__ = ["simple_img_conv_pool", "sequence_conv_pool"]
+
 
 def simple_img_conv_pool(input,
-                         filter_size,
                          num_filters,
+                         filter_size,
                          pool_size,
                          pool_stride,
                          act,
@@ -94,3 +96,29 @@ def img_conv_group(input,
         program=program,
         init_program=init_program)
     return pool_out
+
+
+def sequence_conv_pool(input,
+                       num_filters,
+                       filter_size,
+                       pool_size,
+                       pool_stride,
+                       act,
+                       program=None,
+                       init_program=None):
+    conv_out = layers.sequence_conv(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        act=act,
+        program=program,
+        init_program=init_program)
+
+    pool_out = layers.sequence_pool(
+        input=conv_out,
+        pool_size=pool_size,
+        pool_type='max',
+        pool_stride=pool_stride,
+        program=program,
+        init_program=init_program)
+    return pool_out