CrossLee1
9 years ago
committed by
GitHub
commit
4a0cb3db6c
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./doc/howto/dev/contribute_to_paddle_en.md
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# External dependency to Google protobuf.
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http_archive(
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name="protobuf",
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url="http://github.com/google/protobuf/archive/v3.1.0.tar.gz",
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sha256="0a0ae63cbffc274efb573bdde9a253e3f32e458c41261df51c5dbc5ad541e8f7",
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strip_prefix="protobuf-3.1.0")
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# External dependency to gtest 1.7.0. This method comes from
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# https://www.bazel.io/versions/master/docs/tutorial/cpp.html.
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new_http_archive(
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name="gtest",
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url="https://github.com/google/googletest/archive/release-1.7.0.zip",
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sha256="b58cb7547a28b2c718d1e38aee18a3659c9e3ff52440297e965f5edffe34b6d0",
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build_file="third_party/gtest.BUILD",
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strip_prefix="googletest-release-1.7.0")
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# External dependency to gflags. This method comes from
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# https://github.com/gflags/example/blob/master/WORKSPACE.
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new_git_repository(
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name="gflags",
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tag="v2.2.0",
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remote="https://github.com/gflags/gflags.git",
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build_file="third_party/gflags.BUILD")
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# External dependency to glog. This method comes from
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# https://github.com/reyoung/bazel_playground/blob/master/WORKSPACE
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new_git_repository(
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name="glog",
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remote="https://github.com/google/glog.git",
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commit="b6a5e0524c28178985f0d228e9eaa43808dbec3c",
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build_file="third_party/glog.BUILD")
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Load Diff
File diff suppressed because it is too large
Load Diff
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dataprovider.pyc
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empty.list
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train.log
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output
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train.list
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, sys
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import numpy as np
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from optparse import OptionParser
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from py_paddle import swig_paddle, DataProviderConverter
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from paddle.trainer.PyDataProvider2 import sparse_binary_vector
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from paddle.trainer.config_parser import parse_config
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"""
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Usage: run following command to show help message.
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python api_predict.py -h
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"""
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class QuickStartPrediction():
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def __init__(self, train_conf, dict_file, model_dir=None, label_file=None):
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"""
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train_conf: trainer configure.
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dict_file: word dictionary file name.
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model_dir: directory of model.
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"""
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self.train_conf = train_conf
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self.dict_file = dict_file
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self.word_dict = {}
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self.dict_dim = self.load_dict()
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self.model_dir = model_dir
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if model_dir is None:
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self.model_dir = os.path.dirname(train_conf)
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self.label = None
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if label_file is not None:
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self.load_label(label_file)
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conf = parse_config(train_conf, "is_predict=1")
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self.network = swig_paddle.GradientMachine.createFromConfigProto(
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conf.model_config)
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self.network.loadParameters(self.model_dir)
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input_types = [sparse_binary_vector(self.dict_dim)]
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self.converter = DataProviderConverter(input_types)
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def load_dict(self):
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"""
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Load dictionary from self.dict_file.
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"""
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for line_count, line in enumerate(open(self.dict_file, 'r')):
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self.word_dict[line.strip().split('\t')[0]] = line_count
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return len(self.word_dict)
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def load_label(self, label_file):
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"""
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Load label.
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"""
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self.label = {}
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for v in open(label_file, 'r'):
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self.label[int(v.split('\t')[1])] = v.split('\t')[0]
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def get_index(self, data):
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"""
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transform word into integer index according to the dictionary.
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"""
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words = data.strip().split()
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word_slot = [self.word_dict[w] for w in words if w in self.word_dict]
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return word_slot
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def batch_predict(self, data_batch):
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input = self.converter(data_batch)
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output = self.network.forwardTest(input)
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prob = output[0]["id"].tolist()
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print("predicting labels is:")
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print prob
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def option_parser():
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usage = "python predict.py -n config -w model_dir -d dictionary -i input_file "
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parser = OptionParser(usage="usage: %s [options]" % usage)
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parser.add_option(
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"-n",
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"--tconf",
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action="store",
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dest="train_conf",
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help="network config")
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parser.add_option(
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"-d",
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"--dict",
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action="store",
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dest="dict_file",
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help="dictionary file")
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parser.add_option(
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"-b",
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"--label",
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action="store",
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dest="label",
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default=None,
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help="dictionary file")
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parser.add_option(
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"-c",
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"--batch_size",
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type="int",
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action="store",
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dest="batch_size",
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default=1,
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help="the batch size for prediction")
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parser.add_option(
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"-w",
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"--model",
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action="store",
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dest="model_path",
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default=None,
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help="model path")
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return parser.parse_args()
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def main():
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options, args = option_parser()
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train_conf = options.train_conf
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batch_size = options.batch_size
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dict_file = options.dict_file
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model_path = options.model_path
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label = options.label
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swig_paddle.initPaddle("--use_gpu=0")
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predict = QuickStartPrediction(train_conf, dict_file, model_path, label)
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batch = []
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labels = []
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for line in sys.stdin:
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[label, text] = line.split("\t")
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labels.append(int(label))
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batch.append([predict.get_index(text)])
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print("labels is:")
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print labels
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predict.batch_predict(batch)
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if __name__ == '__main__':
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main()
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@ -0,0 +1,30 @@
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#!/bin/bash
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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set -e
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#Note the default model is pass-00002, you shold make sure the model path
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#exists or change the mode path.
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#only test on trainer_config.lr.py
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model=output/pass-00001/
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config=trainer_config.lr.py
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label=data/labels.list
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dict=data/dict.txt
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batch_size=20
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head -n$batch_size data/test.txt | python api_predict.py \
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--tconf=$config\
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--model=$model \
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--label=$label \
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--dict=$dict \
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--batch_size=$batch_size
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