fix

core/reader.py

@@ -59,10 +59,12 @@ class SlotReader(dg.MultiSlotDataGenerator):
     def init(self, sparse_slots, dense_slots, padding=0):
         from operator import mul
         self.sparse_slots = []
-        if sparse_slots.strip() != "#":
+        if sparse_slots.strip() != "#" and sparse_slots.strip(
+        ) != "?" and sparse_slots.strip() != "":
             self.sparse_slots = sparse_slots.strip().split(" ")
         self.dense_slots = []
-        if dense_slots.strip() != "#":
+        if dense_slots.strip() != "#" and dense_slots.strip(
+        ) != "?" and dense_slots.strip() != "":
             self.dense_slots = dense_slots.strip().split(" ")
         self.dense_slots_shape = [
             reduce(mul,

core/trainers/single_infer.py

@@ -78,14 +78,14 @@ class SingleInfer(TranspileTrainer):
             pipe_cmd = "python {} {} {} {}".format(reader, reader_class,
                                                    "TRAIN", self._config_yaml)
         else:
-            if sparse_slots is None:
-                sparse_slots = "#"
-            if dense_slots is None:
-                dense_slots = "#"
+            if sparse_slots == "":
+                sparse_slots = "?"
+            if dense_slots == "":
+                dense_slots = "?"
             padding = envs.get_global_env(name + "padding", 0)
             pipe_cmd = "python {} {} {} {} {} {} {} {}".format(
                 reader, "slot", "slot", self._config_yaml, "fake", \
-                sparse_slots.replace(" ", "#"), dense_slots.replace(" ", "#"), str(padding))
+                sparse_slots.replace(" ", "?"), dense_slots.replace(" ", "?"), str(padding))
 
         dataset = fluid.DatasetFactory().create_dataset()
         dataset.set_batch_size(envs.get_global_env(name + "batch_size"))
@@ -290,7 +290,7 @@ class SingleInfer(TranspileTrainer):
 
     def load(self, is_fleet=False):
         name = "runner." + self._runner_name + "."
-        dirname = envs.get_global_env("epoch.init_model_path", None)
+        dirname = envs.get_global_env(name + "init_model_path", None)
         if dirname is None or dirname == "":
             return
         print("single_infer going to load ", dirname)

core/trainers/single_trainer.py

@@ -73,13 +73,13 @@ class SingleTrainer(TranspileTrainer):
                                                    "TRAIN", self._config_yaml)
         else:
             if sparse_slots == "":
-                sparse_slots = "#"
+                sparse_slots = "?"
             if dense_slots == "":
-                dense_slots = "#"
+                dense_slots = "?"
             padding = envs.get_global_env(name + "padding", 0)
             pipe_cmd = "python {} {} {} {} {} {} {} {}".format(
                 reader, "slot", "slot", self._config_yaml, "fake", \
-                sparse_slots.replace(" ", "#"), dense_slots.replace(" ", "#"), str(padding))
+                sparse_slots.replace(" ", "?"), dense_slots.replace(" ", "?"), str(padding))
 
         dataset = fluid.DatasetFactory().create_dataset()
         dataset.set_batch_size(envs.get_global_env(name + "batch_size"))

core/utils/dataset_instance.py

@@ -32,8 +32,8 @@ elif sys.argv[2].upper() == "EVALUATE":
 else:
     reader_name = "SlotReader"
     namespace = sys.argv[4]
-    sparse_slots = sys.argv[5].replace("#", " ")
-    dense_slots = sys.argv[6].replace("#", " ")
+    sparse_slots = sys.argv[5].replace("?", " ")
+    dense_slots = sys.argv[6].replace("?", " ")
     padding = int(sys.argv[7])
 
 yaml_abs_path = sys.argv[3]

models/rank/dnn/model.py

@@ -36,7 +36,7 @@ class Model(ModelBase):
 
     def net(self, input, is_infer=False):
         self.sparse_inputs = self._sparse_data_var[1:]
-        self.dense_input = self._dense_data_var[0]
+        self.dense_input = []  #self._dense_data_var[0]
         self.label_input = self._sparse_data_var[0]
 
         def embedding_layer(input):
@@ -52,8 +52,8 @@ class Model(ModelBase):
             return emb_sum
 
         sparse_embed_seq = list(map(embedding_layer, self.sparse_inputs))
-        concated = fluid.layers.concat(
-            sparse_embed_seq + [self.dense_input], axis=1)
+        concated = fluid.layers.concat(sparse_embed_seq, axis=1)
+        #sparse_embed_seq + [self.dense_input], axis=1)
 
         fcs = [concated]
         hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")