fix gru4rec

models/recall/gru4rec/config.yaml

@@ -12,47 +12,59 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/rsc15_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-  is_return_numpy: False
+workspace: "paddlerec.models.recall.gru4rec"
 
+dataset:
+- name: dataset_train
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/rsc15_reader.py"
+- name: dataset_infer
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/rsc15_reader.py"
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+hyper_parameters:
+  vocab_size: 1000
+  hid_size: 100
+  emb_lr_x: 10.0
+  gru_lr_x: 1.0
+  fc_lr_x: 1.0
+  init_low_bound: -0.04
+  init_high_bound: 0.04
+  optimizer: 
+    class: adagrad
+    learning_rate: 0.01
+    strategy: async
 
+#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
-  workspace: "paddlerec.models.recall.gru4rec"
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
   device: cpu
+  epochs: 3
 
-  reader:
-    batch_size: 5
-    class: "{workspace}/rsc15_reader.py"
-    train_data_path: "{workspace}/data/train"
-
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      vocab_size: 1000
-      hid_size: 100
-      emb_lr_x: 10.0
-      gru_lr_x: 1.0
-      fc_lr_x: 1.0
-      init_low_bound: -0.04
-      init_high_bound: 0.04
-      learning_rate: 0.01
-      optimizer: adagrad
-
-  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/recall/gru4rec/model.py

@@ -22,84 +22,72 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def all_vocab_network(self, is_infer=False):
-        """ network definition """
-        recall_k = envs.get_global_env("hyper_parameters.recall_k", None,
-                                       self._namespace)
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        hid_size = envs.get_global_env("hyper_parameters.hid_size", None,
-                                       self._namespace)
-        init_low_bound = envs.get_global_env("hyper_parameters.init_low_bound",
-                                             None, self._namespace)
-        init_high_bound = envs.get_global_env(
-            "hyper_parameters.init_high_bound", None, self._namespace)
-        emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x", None,
-                                       self._namespace)
-        gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x", None,
-                                       self._namespace)
-        fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x", None,
-                                      self._namespace)
+    def _init_hyper_parameters(self):
+        self.recall_k = envs.get_global_env("hyper_parameters.recall_k")
+        self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+        self.hid_size = envs.get_global_env("hyper_parameters.hid_size")
+        self.init_low_bound = envs.get_global_env(
+            "hyper_parameters.init_low_bound")
+        self.init_high_bound = envs.get_global_env(
+            "hyper_parameters.init_high_bound")
+        self.emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x")
+        self.gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x")
+        self.fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x")
+
+    def input_data(self, is_infer=False, **kwargs):
+
         # Input data
         src_wordseq = fluid.data(
             name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
         dst_wordseq = fluid.data(
             name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
 
-        if is_infer:
-            self._infer_data_var = [src_wordseq, dst_wordseq]
-            self._infer_data_loader = fluid.io.DataLoader.from_generator(
-                feed_list=self._infer_data_var,
-                capacity=64,
-                use_double_buffer=False,
-                iterable=False)
+        return [src_wordseq, dst_wordseq]
+
+    def net(self, inputs, is_infer=False):
+        src_wordseq = inputs[0]
+        dst_wordseq = inputs[1]
 
         emb = fluid.embedding(
             input=src_wordseq,
-            size=[vocab_size, hid_size],
+            size=[self.vocab_size, self.hid_size],
             param_attr=fluid.ParamAttr(
                 name="emb",
                 initializer=fluid.initializer.Uniform(
-                    low=init_low_bound, high=init_high_bound),
-                learning_rate=emb_lr_x),
+                    low=self.init_low_bound, high=self.init_high_bound),
+                learning_rate=self.emb_lr_x),
             is_sparse=True)
         fc0 = fluid.layers.fc(input=emb,
-                              size=hid_size * 3,
+                              size=self.hid_size * 3,
                               param_attr=fluid.ParamAttr(
                                   initializer=fluid.initializer.Uniform(
-                                      low=init_low_bound,
-                                      high=init_high_bound),
-                                  learning_rate=gru_lr_x))
+                                      low=self.init_low_bound,
+                                      high=self.init_high_bound),
+                                  learning_rate=self.gru_lr_x))
         gru_h0 = fluid.layers.dynamic_gru(
             input=fc0,
-            size=hid_size,
+            size=self.hid_size,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Uniform(
-                    low=init_low_bound, high=init_high_bound),
-                learning_rate=gru_lr_x))
+                    low=self.init_low_bound, high=self.init_high_bound),
+                learning_rate=self.gru_lr_x))
 
         fc = fluid.layers.fc(input=gru_h0,
-                             size=vocab_size,
+                             size=self.vocab_size,
                              act='softmax',
                              param_attr=fluid.ParamAttr(
                                  initializer=fluid.initializer.Uniform(
-                                     low=init_low_bound, high=init_high_bound),
-                                 learning_rate=fc_lr_x))
+                                     low=self.init_low_bound,
+                                     high=self.init_high_bound),
+                                 learning_rate=self.fc_lr_x))
         cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
-        acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=recall_k)
+        acc = fluid.layers.accuracy(
+            input=fc, label=dst_wordseq, k=self.recall_k)
         if is_infer:
             self._infer_results['recall20'] = acc
             return
         avg_cost = fluid.layers.mean(x=cost)
 
-        self._data_var.append(src_wordseq)
-        self._data_var.append(dst_wordseq)
         self._cost = avg_cost
         self._metrics["cost"] = avg_cost
         self._metrics["acc"] = acc
-
-    def train_net(self):
-        self.all_vocab_network()
-
-    def infer_net(self):
-        self.all_vocab_network(is_infer=True)

models/recall/gru4rec/rsc15_infer_reader.py

-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-
-from paddlerec.core.reader import Reader
-
-
-class EvaluateReader(Reader):
-    def init(self):
-        pass
-
-    def generate_sample(self, line):
-        """
-        Read the data line by line and process it as a dictionary
-        """
-
-        def reader():
-            """
-            This function needs to be implemented by the user, based on data format
-            """
-            l = line.strip().split()
-            l = [w for w in l]
-            src_seq = l[:len(l) - 1]
-            src_seq = [int(e) for e in src_seq]
-            trg_seq = l[1:]
-            trg_seq = [int(e) for e in trg_seq]
-            feature_name = ["src_wordseq", "dst_wordseq"]
-            yield zip(feature_name, [src_seq] + [trg_seq])
-
-        return reader