add imdb training example and save api

python/examples/imdb/imdb_reader.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 sys
+import os
+import paddle
+import re
+import paddle.fluid.incubate.data_generator as dg
+
+class IMDBDataset(dg.MultiSlotDataGenerator):
+    def load_resource(self, dictfile):
+        self._vocab = {}
+        wid = 0
+        with open(dictfile) as f:
+            for line in f:
+                self._vocab[line.strip()] = wid
+                wid += 1
+        self._unk_id = len(self._vocab)
+        self._pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
+        self.return_value = ("words", [1, 2, 3, 4, 5, 6]), ("label", [0])
+
+    def get_words_and_label(self, line):
+        send = '|'.join(line.split('|')[:-1]).lower().replace("<br />",
+                                                              " ").strip()
+        label = [int(line.split('|')[-1])]
+        
+        words = [x for x in self._pattern.split(send) if x and x != " "]
+        feas = [
+            self._vocab[x] if x in self._vocab else self._unk_id for x in words
+        ]
+        return feas, label
+
+    def infer_reader(self, infer_filelist, batch, buf_size):
+        def local_iter():
+            for fname in infer_filelist:
+                with open(fname, "r") as fin:
+                    for line in fin:
+                        feas, label = self.get_words_and_label(line)
+                        yield feas, label
+        import paddle
+        batch_iter = paddle.batch(
+            paddle.reader.shuffle(local_iter, buf_size=buf_size),
+            batch_size=batch)
+        return batch_iter
+
+    def generate_sample(self, line):
+        def memory_iter():
+            for i in range(1000):
+                yield self.return_value
+        def data_iter():
+            feas, label = self.get_words_and_label(line)
+            yield ("words", feas), ("label", label)
+        return data_iter
+
+if __name__ == "__main__":
+    imdb = IMDBDataset()
+    imdb.load_resource("imdb.vocab")
+    imdb.run_from_stdin()
+

python/examples/imdb/local_train.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 paddle
+import logging
+import paddle.fluid as fluid
+import paddle_serving as serving
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+def load_vocab(filename):
+    vocab = {}
+    with open(filename) as f:
+        wid = 0
+        for line in f:
+            vocab[line.strip()] = wid
+            wid += 1
+    vocab["<unk>"] = len(vocab)
+    return vocab
+
+if __name__ == "__main__":
+    vocab = load_vocab('imdb.vocab')
+    dict_dim = len(vocab)
+
+    data = fluid.layers.data(name="words", shape=[1], dtype="int64", lod_level=1)
+    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+    dataset = fluid.DatasetFactory().create_dataset()
+    filelist = ["train_data/%s" % x for x in os.listdir("train_data")]
+    dataset.set_use_var([data, label])
+    pipe_command = "python imdb_reader.py"
+    dataset.set_pipe_command(pipe_command)
+    dataset.set_batch_size(4)
+    dataset.set_filelist(filelist)
+    dataset.set_thread(10)
+    from nets import cnn_net
+    avg_cost, acc, prediction = cnn_net(data, label, dict_dim)
+    optimizer = fluid.optimizer.SGD(learning_rate=0.01)
+    optimizer.minimize(avg_cost)
+
+    exe = fluid.Executor(fluid.CPUPlace())
+    exe.run(fluid.default_startup_program())
+    epochs = 30
+    save_dirname = "cnn_model"
+    for i in range(epochs):
+        exe.train_from_dataset(program=fluid.default_main_program(),
+                               dataset=dataset, debug=False)
+        logger.info("TRAIN --> pass: {}".format(i))
+        fluid.io.save_inference_model("%s/epoch%d.model" % (save_dirname, i),
+                                      [data.name, label.name], [acc], exe)
+        serving.io.save_model("%s/epoch%d.model" % (save_dirname, i),
+                              ["words", "label"], {"acc": acc}, exe)
+
+

python/examples/imdb/nets.py

+import sys
+import time
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+
+
+def bow_net(data,
+            label,
+            dict_dim,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2):
+    """
+    bow net
+    """
+    emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim], is_sparse=True)
+    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
+    bow_tanh = fluid.layers.tanh(bow)
+    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
+    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
+    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction
+
+
+def cnn_net(data,
+            label,
+            dict_dim,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2,
+            win_size=3):
+    """
+    conv net
+    """
+    emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim], is_sparse=True)
+
+    conv_3 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=win_size,
+        act="tanh",
+        pool_type="max")
+
+    fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
+
+    prediction = fluid.layers.fc(input=[fc_1], size=class_dim, act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction
+
+
+def lstm_net(data,
+             label,
+             dict_dim,
+             emb_dim=128,
+             hid_dim=128,
+             hid_dim2=96,
+             class_dim=2,
+             emb_lr=30.0):
+    """
+    lstm net
+    """
+    emb = fluid.layers.embedding(
+        input=data,
+        size=[dict_dim, emb_dim],
+        param_attr=fluid.ParamAttr(learning_rate=emb_lr),
+        is_sparse=True)
+
+    fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4)
+
+    lstm_h, c = fluid.layers.dynamic_lstm(
+        input=fc0, size=hid_dim * 4, is_reverse=False)
+
+    lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
+    lstm_max_tanh = fluid.layers.tanh(lstm_max)
+
+    fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
+
+    prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
+
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction
+
+
+def gru_net(data,
+            label,
+            dict_dim,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2,
+            emb_lr=400.0):
+    """
+    gru net
+    """
+    emb = fluid.layers.embedding(
+        input=data,
+        size=[dict_dim, emb_dim],
+        param_attr=fluid.ParamAttr(learning_rate=emb_lr))
+
+    fc0 = fluid.layers.fc(input=emb, size=hid_dim * 3)
+    gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False)
+    gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+    gru_max_tanh = fluid.layers.tanh(gru_max)
+    fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh')
+    prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
+
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction

python/paddle_serving/io/__init__.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 paddle.fluid import Executor
+from paddle.fluid.compiler import CompiledProgram
+from paddle.fluid.framework import Program
+
+def save_model(server_model_folder,
+               client_config_folder,
+               feed_var_dict,
+               fetch_var_dict,
+               main_program=None):
+    if main_program is None:
+        main_program = default_main_program()
+    elif isinstance(main_program, CompiledProgram):
+        main_program = main_program._program
+        if main_program is None:
+            raise TypeError("program should be as Program type or None")
+    if not isinstance(main_program, Program):
+        raise TypeError("program should be as Program type or None")
+
+    executor = Executor(place=paddle.fluid.CPUPlace())
+    paddle.fluid.io.save_persistables(executor, server_model_folder,
+                                      main_program)
+    
+
+    
+