Merge pull request #2369 from emailweixu/print_layer

Correctly handle print_layer in V2 API

python/paddle/trainer_config_helpers/layers.py

@@ -111,6 +111,7 @@ __all__ = [
     'block_expand_layer',
     'maxout_layer',
     'out_prod_layer',
+    'printer_layer',
     'print_layer',
     'priorbox_layer',
     'cross_channel_norm_layer',
@@ -969,7 +970,7 @@ def fc_layer(input,
 
 
 @wrap_name_default("print")
-def print_layer(input, name=None):
+def printer_layer(input, name=None):
     """
     Print the output value of input layers. This layer is useful for debugging.
 
@@ -991,6 +992,13 @@ def print_layer(input, name=None):
         inputs=[l.name for l in input], )
     # this layer don't return anything, can not be input of other layer.
 
+# Keep print_layer for compatibility with V1 API.
+# 'print_layer' does not work for V2 API because it will be changed to
+# 'print' for V2 API. But 'print' is a reserved key word in python.
+
+
+print_layer = printer_layer
+
 
 @wrap_name_default("priorbox")
 def priorbox_layer(input,

python/paddle/v2/layer.py

@@ -149,6 +149,20 @@ def __get_used_layers__(output_layers, extra_layers=None):
     for layer in output_layers:
         dfs_travel(layer.full_name)
 
+    # print layer needs to be specially handled because no other
+    # layer depends on it. It is used to print the result of some
+    # layers when running the model for debug purpose. So we explicitly
+    # add a print layer to the topolty if its input is in the toplogy.
+    for layer in cp.g_config.model_config.layers:
+        if layer.type == 'print':
+            used = True
+            for inp in layer.inputs:
+                if inp.input_layer_name not in layer_names:
+                    used = False
+                    break
+            if used:
+                layer_names.add(layer.name)
+
     return layer_names
 
 

python/paddle/v2/tests/test_layer.py

@@ -164,6 +164,7 @@ class OtherLayerTest(unittest.TestCase):
         maxid = layer.max_id(input=inference)
         sampling_id = layer.sampling_id(input=inference)
         eos = layer.eos(input=maxid, eos_id=5)
+        layer.printer(maxid)
         print layer.parse_network([maxid, sampling_id, eos])
 
     def test_slicing_joining_layer(self):