user define trainer

fleetrec/examples/user_define_trainer.py

-from fleetrec.trainer.trainer import Trainer
+# 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.
 
+import paddle.fluid as fluid
 
-class UserDefineTrainer(Trainer):
+from fleetrec.trainer.transpiler_trainer import TranspileTrainer
+from fleetrec.utils import envs
+
+
+class UserDefineTrainer(TranspileTrainer):
     def __init__(self, config=None):
-        Trainer.__init__(self, config)
+        TranspileTrainer.__init__(self, config)
+
+    def processor_register(self):
+        self.regist_context_processor('uninit', self.instance)
+        self.regist_context_processor('init_pass', self.init)
+        self.regist_context_processor('train_pass', self.train)
+
+    def init(self, context):
+        self.model.input()
+        self.model.net()
+        self.model.metrics()
+        self.model.avg_loss()
+        optimizer = self.model.optimizer()
+        optimizer.minimize(self.model._cost)
+
+        self.fetch_vars = []
+        self.fetch_alias = []
+        self.fetch_period = self.model.get_fetch_period()
+
+        metrics = self.model.get_metrics()
+        if metrics:
+            self.fetch_vars = metrics.values()
+            self.fetch_alias = metrics.keys()
+        context['status'] = 'train_pass'
+
+    def train(self, context):
+        # run startup program at once
+        self._exe.run(fluid.default_startup_program())
+
+        dataset = self._get_dataset()
+
+        epochs = envs.get_global_env("train.epochs")
+
+        for i in range(epochs):
+            self._exe.train_from_dataset(program=fluid.default_main_program(),
+                                         dataset=dataset,
+                                         fetch_list=self.fetch_vars,
+                                         fetch_info=self.fetch_alias,
+                                         print_period=self.fetch_period)
+
+        context['is_exit'] = True

fleetrec/trainer/factory.py

@@ -42,7 +42,7 @@ class TrainerFactory(object):
         elif train_mode == "CtrTrainer":
             trainer = CtrPaddleTrainer(config)
         elif train_mode == "UserDefineTrainer":
-            train_location = envs.get_global_env("train.trainer.location")
+            train_location = envs.get_global_env("train.location")
             train_dirname = os.path.dirname(train_location)
             base_name = os.path.splitext(os.path.basename(train_location))[0]
             sys.path.append(train_dirname)