api_predict.py

# Copyright (c) 2016 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, sys
import numpy as np
from optparse import OptionParser
from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import sparse_binary_vector
from paddle.trainer.config_parser import parse_config
"""
Usage: run following command to show help message.
  python api_predict.py -h
"""


class QuickStartPrediction():
    def __init__(self, train_conf, dict_file, model_dir=None, label_file=None):
        """
        train_conf: trainer configure.
        dict_file: word dictionary file name.
        model_dir: directory of model.
        """
        self.train_conf = train_conf
        self.dict_file = dict_file
        self.word_dict = {}
        self.dict_dim = self.load_dict()
        self.model_dir = model_dir
        if model_dir is None:
            self.model_dir = os.path.dirname(train_conf)

        self.label = None
        if label_file is not None:
            self.load_label(label_file)

        conf = parse_config(train_conf, "is_predict=1")
        self.network = swig_paddle.GradientMachine.createFromConfigProto(
            conf.model_config)
        self.network.loadParameters(self.model_dir)
        input_types = [sparse_binary_vector(self.dict_dim)]
        self.converter = DataProviderConverter(input_types)

    def load_dict(self):
        """
        Load dictionary from self.dict_file.
        """
        for line_count, line in enumerate(open(self.dict_file, 'r')):
            self.word_dict[line.strip().split('\t')[0]] = line_count
        return len(self.word_dict)

    def load_label(self, label_file):
        """
        Load label.
        """
        self.label = {}
        for v in open(label_file, 'r'):
            self.label[int(v.split('\t')[1])] = v.split('\t')[0]

    def get_index(self, data):
        """
        transform word into integer index according to the dictionary.
        """
        words = data.strip().split()
        word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
        return word_slot

    def batch_predict(self, data_batch):
        input = self.converter(data_batch)
        output = self.network.forwardTest(input)
        prob = output[0]["id"].tolist()
        print("predicting labels is:")
        print prob


def option_parser():
    usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
    parser = OptionParser(usage="usage: %s [options]" % usage)
    parser.add_option(
        "-n",
        "--tconf",
        action="store",
        dest="train_conf",
        help="network config")
    parser.add_option(
        "-d",
        "--dict",
        action="store",
        dest="dict_file",
        help="dictionary file")
    parser.add_option(
        "-b",
        "--label",
        action="store",
        dest="label",
        default=None,
        help="dictionary file")
    parser.add_option(
        "-c",
        "--batch_size",
        type="int",
        action="store",
        dest="batch_size",
        default=1,
        help="the batch size for prediction")
    parser.add_option(
        "-w",
        "--model",
        action="store",
        dest="model_path",
        default=None,
        help="model path")
    return parser.parse_args()


def main():
    options, args = option_parser()
    train_conf = options.train_conf
    batch_size = options.batch_size
    dict_file = options.dict_file
    model_path = options.model_path
    label = options.label
    swig_paddle.initPaddle("--use_gpu=0")
    predict = QuickStartPrediction(train_conf, dict_file, model_path, label)

    batch = []
    labels = []
    for line in sys.stdin:
        [label, text] = line.split("\t")
        labels.append(int(label))
        batch.append([predict.get_index(text)])
    print("lables is:")
    print labels
    predict.batch_predict(batch)


if __name__ == '__main__':
    main()