fix listwise

models/rerank/listwise/config.yaml

@@ -12,44 +12,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/random_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+workspace: "paddlerec.models.rerank.listwise"
 
-  epochs: 3
-  workspace: "paddlerec.models.rerank.listwise"
-  device: cpu
+dataset:
+- name: dataset_train
+  type: DataLoader
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/random_reader.py"
+- name: dataset_infer
+  type: DataLoader
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/random_reader.py"
 
-  reader:
-    batch_size: 2
-    class: "{workspace}/random_reader.py"
-    train_data_path: "{workspace}/data/train"
-    dataset_class: "DataLoader"
+hyper_parameters:
+  hidden_size: 128
+  user_vocab: 200
+  item_vocab: 1000
+  item_len: 5
+  embed_size: 16
+  batch_size: 1
+  optimizer: 
+    class: sgd
+    learning_rate: 0.01
+    strategy: async
 
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      hidden_size: 128
-      user_vocab: 200
-      item_vocab: 1000
-      item_len: 5
-      embed_size: 16
-      learning_rate: 0.01
-      optimizer: sgd
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
+  epochs: 3
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
+  device: cpu
+  epochs: 3
 
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/rerank/listwise/model.py

@@ -25,18 +25,13 @@ class Model(ModelBase):
         ModelBase.__init__(self, config)
 
     def _init_hyper_parameters(self):
-        self.item_len = envs.get_global_env("hyper_parameters.self.item_len",
-                                            None, self._namespace)
-        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size",
-                                               None, self._namespace)
-        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
-                                              None, self._namespace)
-        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
-                                              None, self._namespace)
-        self.embed_size = envs.get_global_env("hyper_parameters.embed_size",
-                                              None, self._namespace)
-
-    def input_data(self, is_infer=False):
+        self.item_len = envs.get_global_env("hyper_parameters.self.item_len")
+        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
+        self.embed_size = envs.get_global_env("hyper_parameters.embed_size")
+
+    def input_data(self, is_infer=False, **kwargs):
         user_slot_names = fluid.data(
             name='user_slot_names',
             shape=[None, 1],

models/rerank/listwise/random_reader.py

@@ -23,14 +23,10 @@ from collections import defaultdict
 
 class TrainReader(Reader):
     def init(self):
-        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
-                                              None, "train.model")
-        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
-                                              None, "train.model")
-        self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
-                                            "train.model")
-        self.batch_size = envs.get_global_env("batch_size", None,
-                                              "train.reader")
+        self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab")
+        self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab")
+        self.item_len = envs.get_global_env("hyper_parameters.item_len")
+        self.batch_size = envs.get_global_env("hyper_parameters.batch_size")
 
     def reader_creator(self):
         def reader():