add ssr infer

models/recall/ssr/config.yaml

@@ -12,6 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
+evaluate:
+  reader:
+    batch_size: 1
+    class: "{workspace}/ssr_infer_reader.py"
+    test_data_path: "{workspace}/data/train"
+  is_return_numpy: True
+
 train:
   trainer:
     # for cluster training

models/recall/ssr/model.py

@@ -134,5 +134,45 @@ class Model(ModelBase):
         self.train()
 
 
+    def infer(self):
+        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
+        emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None, self._namespace)
+        hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
+
+        user_data = fluid.data(
+            name="user", shape=[None, 1], dtype="int64", lod_level=1)
+        all_item_data = fluid.data(
+            name="all_item", shape=[None, vocab_size], dtype="int64")
+        pos_label = fluid.data(name="pos_label", shape=[None, 1], dtype="int64")
+        self._infer_data_var = [user_data, all_item_data, pos_label]
+        self._infer_data_loader = fluid.io.DataLoader.from_generator(
+                feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+
+        user_emb = fluid.embedding(
+            input=user_data, size=[vocab_size, emb_dim], param_attr="emb.item")
+        all_item_emb = fluid.embedding(
+            input=all_item_data, size=[vocab_size, emb_dim], param_attr="emb.item")
+        all_item_emb_re = fluid.layers.reshape(x=all_item_emb, shape=[-1, emb_dim])
+
+        user_encoder = GrnnEncoder()
+        user_enc = user_encoder.forward(user_emb)
+        user_hid = fluid.layers.fc(input=user_enc,
+                                   size=hidden_size,
+                                   param_attr='user.w',
+                                   bias_attr="user.b")
+        user_exp = fluid.layers.expand(x=user_hid, expand_times=[1, vocab_size])
+        user_re = fluid.layers.reshape(x=user_exp, shape=[-1, hidden_size])
+
+        all_item_hid = fluid.layers.fc(input=all_item_emb_re,
+                                       size=hidden_size,
+                                       param_attr='item.w',
+                                       bias_attr="item.b")
+        cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re)
+        all_pre_ = fluid.layers.reshape(x=cos_item, shape=[-1, vocab_size])
+        acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20)
+
+        self._infer_results['recall20'] = acc
+
+
     def infer_net(self):
-        pass
+        self.infer()

models/recall/ssr/ssr_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
+from paddlerec.core.utils import envs
+import random
+import numpy as np
+
+
+class EvaluateReader(Reader):
+    def init(self):
+        self.vocab_size = envs.get_global_env("vocab_size", 10, "train.model.hyper_parameters")
+
+
+    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
+            """
+            ids = line.strip().split()
+            conv_ids = [int(i) for i in ids]
+            boundary = len(ids) - 1
+            src = conv_ids[:boundary]
+            pos_tgt = [conv_ids[boundary]]
+            feature_name = ["user", "all_item", "p_item"]
+            yield zip(feature_name, [src] + [np.arange(self.vocab_size).astype("int64").tolist()]+ [pos_tgt])
+
+        return reader