add infer for multiview-simnet

fluid/PaddleRec/multiview_simnet/README.cn.md

@@ -15,8 +15,13 @@
 ```bash
 python train.py
 ```
+## 
+如下
+如下命令行可以获得预测工具的具体选项，`python infer -h`内容可以参考说明
+```bash
+python infer.py
+```
 ## 未来的工作
 - 多种pairwise的损失函数会被加入到这个项目中。对于不同视角的特征，用户-项目之间的匹配关系可以使用不同的损失函数进行联合优化。整个模型会在真实数据中进行验证。
-- 推理工具会被加入
 - Parallel Executor选项会被加入
 - 分布式训练能力会被加入

fluid/PaddleRec/multiview_simnet/README.md

@@ -15,8 +15,13 @@ The command line options for training can be listed by `python train.py -h`
 python train.py
 ```
 
+## Infer
+The command line options for inference can be listed by `python infer.py -h`
+```bash
+python infer.py
+```
+
 ## Future work
 - Multiple types of pairwise loss will be added in this project. For different views of features between a user and an item, multiple losses will be supported. The model will be verified in real world dataset.
-- infer will be added
 - Parallel Executor will be added in this project
 - Distributed Training will be added

fluid/PaddleRec/multiview_simnet/infer.py

+# Copyright (c) 2018 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 os
+import sys
+import time
+import six
+import numpy as np
+import math
+import argparse
+import logging
+import paddle.fluid as fluid
+import paddle
+import time
+import reader as reader
+from nets import MultiviewSimnet, SimpleEncoderFactory
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("multi-view simnet")
+    parser.add_argument(
+        "--train_file", type=str, help="Training file")
+    parser.add_argument(
+        "--valid_file", type=str, help="Validation file")
+    parser.add_argument(
+        "--epochs", type=int, default=10, help="Number of epochs for training")
+    parser.add_argument(
+        "--model_dir", type=str, default='model_output', help="Model output folder")
+    parser.add_argument(
+        "--query_slots", type=int, default=1, help="Number of query slots")
+    parser.add_argument(
+        "--title_slots", type=int, default=1, help="Number of title slots")
+    parser.add_argument(
+        "--query_encoder", type=str, default="bow", help="Encoder module for slot encoding")
+    parser.add_argument(
+        "--title_encoder", type=str, default="bow", help="Encoder module for slot encoding")
+    parser.add_argument(
+        "--query_encode_dim", type=int, default=128, help="Dimension of query encoder output")
+    parser.add_argument(
+        "--title_encode_dim", type=int, default=128, help="Dimension of title encoder output")
+    parser.add_argument(
+        "--batch_size", type=int, default=128, help="Batch size for training")
+    parser.add_argument(
+        "--embedding_dim", type=int, default=128, help="Default Dimension of Embedding")
+    parser.add_argument(
+        "--sparse_feature_dim", type=int, default=1000001, help="Sparse feature hashing space for index processing")
+    parser.add_argument(
+        "--hidden_size", type=int, default=128, help="Hidden dim")
+    return parser.parse_args()
+
+
+def start_infer(args, model_path):
+    dataset = reader.SyntheticDataset(args.sparse_feature_dim, args.query_slots,
+                                      args.title_slots)
+    test_reader = paddle.batch(
+        paddle.reader.shuffle(
+            dataset.valid(), buf_size=args.batch_size * 100),
+        batch_size=args.batch_size)
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    
+    with fluid.scope_guard(fluid.core.Scope()):
+        infer_program, feed_target_names, fetch_vars = fluid.io.load_inference_model(
+                args.model_dir, exe)
+        t0 = time.time()
+        step_id = 0
+        feeder = fluid.DataFeeder(program=infer_program, feed_list=feed_target_names, place=place)
+        for batch_id, data in enumerate(test_reader()):
+            step_id += 1
+            loss_val, correct_val = exe.run(infer_program,
+                                            feed=feeder.feed(data),
+                                            fetch_list=fetch_vars)
+            logger.info("TRAIN --> pass: {} batch_id: {} avg_cost: {}, acc: {}"
+                        .format(step_id, batch_id, loss_val,
+                                float(correct_val) / args.batch_size))
+
+def main():
+    args = parse_args()
+    start_infer(args, args.model_dir)
+
+if __name__ == "__main__":
+    main()