Add SliceProjection and slice_projection.

proto/ModelConfig.proto

@@ -225,7 +225,8 @@ message ProjectionConfig {
   optional PoolConfig pool_conf = 12;
 
   // For slice
-  repeated SliceConfig slice = 13;
+  // Each slice output is the input[start, end)
+  repeated SliceConfig slices = 13;
 }
 
 message OperatorConfig {

python/paddle/trainer/config_parser.py

@@ -565,6 +565,35 @@ class IdentityOffsetProjection(Projection):
         return []
 
 
+@config_class
+class SliceProjection(Projection):
+    type = 'slice'
+
+    def __init__(self, input_layer_name, slices, **xargs):
+        super(SliceProjection, self).__init__(input_layer_name, **xargs)
+        input = g_layer_map[input_layer_name]
+        if input.type in ["exconv", "cudnn_conv"]:
+            # the slice operator is for the channel dimension
+            assert input.num_filters is not None
+            channels = input.num_filters
+            image_size = input.size / channels
+            assert slices[len(slices) - 1][1] <= channels
+            for i in xrange(len(slices)):
+                slice = self.proj_conf.slices.add()
+                slice.start = slices[i][0] * image_size
+                slice.end = slices[i][1] * image_size
+                self.size += slice.end - slice.start
+        else:
+            config_assert(False,
+                          'Currently the input should be convolution layer')
+
+    def calc_parameter_size(self, input_size, output_size):
+        return 0
+
+    def calc_parameter_dims(self, input_size, output_size):
+        return []
+
+
 # DotMulProjection performs element-wise multiplication with weight
 @config_class
 class DotMulProjection(Projection):

python/paddle/trainer_config_helpers/layers.py

@@ -128,6 +128,7 @@ __all__ = [
     'prelu_layer',
     'gated_unit_layer',
     'crop_layer',
+    'slice_projection',
 ]
 
 
@@ -536,6 +537,45 @@ def identity_projection(input, offset=None, size=None):
     return proj
 
 
+def slice_projection(input, slices):
+    """
+    slice_projection can get multiple outputs, and each output is a slice
+    of the input.
+
+    .. math::
+       output[i] = input.slice(slices[i])
+
+    The example usage is:
+
+    .. code-block:: python
+
+       proj = slice_projection(input=layer, slices=[(0, 10), (20, 30)])
+
+    Note that slice_projection should not have any parameter.
+
+    :param input: Input Layer.
+    :type input: LayerOutput
+    :param slices: An array of slice parameters.
+                   Each slice contains the start and end offsets based
+                   on the input.
+    :type offset: pair of int
+    :return: A SliceProjection object
+    :rtype: SliceProjection
+    """
+    assert len(slices) >= 1
+    start = 0
+    for i in xrange(len(slices)):
+        assert len(slices[i]) == 2
+        # The start position of the next slice needs to be greater than
+        # or equal to the end position of the previous slice.
+        assert slices[i][0] >= start
+        assert slices[i][1] >= slices[i][0]
+        start = slices[i][1]
+    proj = SliceProjection(input_layer_name=input.name, slices=slices)
+    proj.origin = input
+    return proj
+
+
 @wrap_param_attr_default()
 def scaling_projection(input, param_attr=None):
     """