Skip to content

H
hapi

PaddlePaddle / hapi

通知 11
Star 2
Fork 0
H
hapi
未验证 提交 33ece5d7 编写于 作者: P pkpk 提交者： GitHub
浏览文件
操作

Merge pull request #45 from 0YuanZhang0/sequence_tagging 

Sequence tagging
上级 c54980b9 6462645b
隐藏空白更改
内联 并排
Showing with 613 addition and 155 deletion
sequence_tagging/README.md → examples/sequence_tagging/README.md
浏览文件 @ 33ece5d7
......@@ -54,7 +54,7 @@ python downloads.py dataset
我们开源了在自建数据集上训练的词法分析模型，可供用户直接使用，可通过下述链接进行下载:
```bash
# download baseline model
python downloads.py lac
python downloads.py model
```
### 模型训练
......@@ -66,65 +66,36 @@ GPU上单卡训练
export CUDA_VISIBLE_DEVICES=0
python -u train.py \
--train_file ./data/train.tsv \
--test_file ./data/test.tsv \
--word_dict_path ./conf/word.dic \
--label_dict_path ./conf/tag.dic \
--word_rep_dict_path ./conf/q2b.dic \
--device gpu \
--grnn_hidden_dim 128 \
--word_emb_dim 128 \
--bigru_num 2 \
--base_learning_rate 1e-3 \
--batch_size 300 \
--epoch 10 \
--save_dir ./model \
--num_devices 1 \
-d
# -d： 是否使用动态图模式进行训练，如果使用静态图训练，命令行请删除-d参数
--dynamic False
# --device: 使用gpu设备还是cpu设备
# --dynamic： 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
```
GPU上多卡训练
```
# setting visible devices for training
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --selected_gpus=0,1,2,3 train.py \
--train_file ./data/train.tsv \
--test_file ./data/test.tsv \
--word_dict_path ./conf/word.dic \
--label_dict_path ./conf/tag.dic \
--word_rep_dict_path ./conf/q2b.dic \
--device gpu \
--grnn_hidden_dim 128 \
--word_emb_dim 128 \
--bigru_num 2 \
--base_learning_rate 1e-3 \
--batch_size 300 \
--epoch 10 \
--save_dir ./model \
-d
--dynamic False
# -d： 是否使用动态图模式进行训练，如果使用静态图训练，命令行请删除-d参数
# --device: 使用gpu设备还是cpu设备
# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
```
CPU上训练
```
python -u train.py \
--train_file ./data/train.tsv \
--test_file ./data/test.tsv \
--word_dict_path ./conf/word.dic \
--label_dict_path ./conf/tag.dic \
--word_rep_dict_path ./conf/q2b.dic \
--device cpu \
--grnn_hidden_dim 128 \
--word_emb_dim 128 \
--bigru_num 2 \
--base_learning_rate 1e-3 \
--batch_size 300 \
--epoch 10 \
--save_dir ./model \
-d
--dynamic False
# --device: 使用gpu设备还是cpu设备
# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
```
### 模型预测
......@@ -132,18 +103,17 @@ python -u train.py \
加载已有的模型，对未知的数据进行预测
```bash
python predict.py \
--predict_file ./data/infer.tsv \
--word_dict_path ./conf/word.dic \
--label_dict_path ./conf/tag.dic \
--word_rep_dict_path ./conf/q2b.dic \
--init_from_checkpoint model_baseline/params \
--output_file predict.result \
--mode predict \
--device cpu \
-d
# -d： 是否使用动态图模式进行训练，如果使用静态图训练，命令行请删除-d参数
--dynamic False
# --init_from_checkpoint: 初始化模型
# --output_file: 预测结果文件
# --device: 使用gpu还是cpu设备
# --mode: 开启模式, 设置为train时，进行训练，设置为predict时进行预测
# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
```
### 模型评估
......@@ -152,15 +122,15 @@ python predict.py \
```bash
# baseline model
python eval.py \
--test_file ./data/test.tsv \
--word_dict_path ./conf/word.dic \
--label_dict_path ./conf/tag.dic \
--word_rep_dict_path ./conf/q2b.dic \
--init_from_checkpoint ./model_baseline/params \
--mode predict \
--device cpu \
-d
--dynamic False
# -d： 是否使用动态图模式进行训练，如果使用静态图训练，命令行请删除-d参数
# --init_from_checkpoint: 初始化模型
# --device: 使用gpu还是cpu设备
# --mode: 开启模式, 设置为train时，进行训练，设置为predict时进行预测
# --dynamic: 是否使用动态图模式进行训练，如果使用静态图训练，设置为True, 动态图设置为False
```
......
sequence_tagging/downloads.py → examples/sequence_tagging/downloads.py
浏览文件 @ 33ece5d7
......@@ -33,19 +33,19 @@ FILE_INFO = {
'name': 'lexical_analysis-dataset-2.0.0.tar.gz',
'md5': '71e4a9a36d0f0177929a1bccedca7dba'
},
'LAC_MODEL': {
'name': 'lexical_analysis-2.0.0.tar.gz',
'md5': "fc1daef00de9564083c7dc7b600504ca"
'MODEL': {
'name': 'sequence_tagging_dy.tar.gz',
'md5': "1125d374c03c8218b6e47325dcf607e3"
},
}
def usage():
desc = ("\nDownload datasets and pretrained models for LAC.\n"
desc = ("\nDownload datasets and pretrained models for sequence tagging.\n"
"Usage:\n"
" 1. python download.py all\n"
" 2. python download.py dataset\n"
" 3. python download.py lac\n")
" 3. python download.py model\n")
print(desc)
......@@ -136,13 +136,13 @@ if __name__ == '__main__':
if sys.argv[1] == 'all':
download('DATA', pwd)
download('LAC_MODEL', pwd)
download('MODEL', pwd)
if sys.argv[1] == "dataset":
download('DATA', pwd)
elif sys.argv[1] == "lac":
download('LAC_MODEL', pwd)
elif sys.argv[1] == "model":
download('MODEL', pwd)
else:
usage()
sequence_tagging/downloads.sh → examples/sequence_tagging/downloads.sh
浏览文件 @ 33ece5d7
......@@ -5,9 +5,9 @@ if [ -d ./model_baseline/ ]
then
echo "./model_baseline/ directory already existed, ignore download"
else
wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/lexical_analysis-2.0.0.tar.gz
tar xvf lexical_analysis-2.0.0.tar.gz
/bin/rm lexical_analysis-2.0.0.tar.gz
wget --no-check-certificate https://baidu-nlp.bj.bcebos.com/sequence_tagging_dy.tar.gz
tar xvf sequence_tagging_dy.tar.gz
/bin/rm sequence_tagging_dy.tar.gz
fi
# download dataset file to ./data/
......
sequence_tagging/eval.py → examples/sequence_tagging/eval.py
浏览文件 @ 33ece5d7
......@@ -26,12 +26,13 @@ import argparse
import numpy as np
from train import SeqTagging
from utils.configure import PDConfig
from utils.check import check_gpu, check_version
from utils.metrics import chunk_count
from reader import LacDataset, create_lexnet_data_generator, create_dataloader
work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(work_dir)
sys.path.append(os.path.join(work_dir, "../"))
from hapi.model import set_device, Input
import paddle.fluid as fluid
......@@ -87,95 +88,12 @@ def main(args):
print("[test] P: %.5f, R: %.5f, F1: %.5f" % (precision, recall, f1))
if __name__ == '__main__':
parser = argparse.ArgumentParser("sequence tagging training")
parser.add_argument(
"-wd",
"--word_dict_path",
default=None,
type=str,
help='word dict path')
parser.add_argument(
"-ld",
"--label_dict_path",
default=None,
type=str,
help='label dict path')
parser.add_argument(
"-wrd",
"--word_rep_dict_path",
default=None,
type=str,
help='The path of the word replacement Dictionary.')
parser.add_argument(
"-dev",
"--device",
type=str,
default='gpu',
help="device to use, gpu or cpu")
parser.add_argument(
"-d", "--dynamic", action='store_true', help="enable dygraph mode")
parser.add_argument(
"-e", "--epoch", default=10, type=int, help="number of epoch")
parser.add_argument(
'-lr',
'--base_learning_rate',
default=1e-3,
type=float,
metavar='LR',
help='initial learning rate')
parser.add_argument(
"--word_emb_dim",
default=128,
type=int,
help='word embedding dimension')
parser.add_argument(
"--grnn_hidden_dim", default=128, type=int, help="hidden dimension")
parser.add_argument(
"--bigru_num", default=2, type=int, help='the number of bi-rnn')
parser.add_argument("-elr", "--emb_learning_rate", default=1.0, type=float)
parser.add_argument("-clr", "--crf_learning_rate", default=1.0, type=float)
parser.add_argument(
"-b", "--batch_size", default=300, type=int, help="batch size")
parser.add_argument(
"--max_seq_len", default=126, type=int, help="max sequence length")
parser.add_argument(
"-n", "--num_devices", default=1, type=int, help="number of devices")
parser.add_argument(
"-o",
"--save_dir",
default="./model",
type=str,
help="save model path")
parser.add_argument(
"--init_from_checkpoint",
default=None,
type=str,
help="load init model parameters")
parser.add_argument(
"--init_from_pretrain_model",
default=None,
type=str,
help="load pretrain model parameters")
parser.add_argument(
"-sf", "--save_freq", default=1, type=int, help="save frequency")
parser.add_argument(
"-ef", "--eval_freq", default=1, type=int, help="eval frequency")
parser.add_argument(
"--output_file", default="predict.result", type=str, help="predict output file")
parser.add_argument(
"--predict_file", default="./data/infer.tsv", type=str, help="predict output file")
parser.add_argument(
"--test_file", default="./data/test.tsv", type=str, help="predict and eval output file")
parser.add_argument(
"--train_file", default="./data/train.tsv", type=str, help="train file")
parser.add_argument(
"--mode", default="predict", type=str, help="train|test|predict")
args = parser.parse_args()
print(args)
use_gpu = True if args.device == "gpu" else False
if __name__ == '__main__':
args = PDConfig(yaml_file="sequence_tagging.yaml")
args.build()
args.Print()
use_gpu = True if args.device == "gpu" else False
check_gpu(use_gpu)
check_version()
main(args)
sequence_tagging/predict.py → examples/sequence_tagging/predict.py
浏览文件 @ 33ece5d7
......@@ -27,10 +27,11 @@ import numpy as np
from train import SeqTagging
from utils.check import check_gpu, check_version
from utils.configure import PDConfig
from reader import LacDataset, create_lexnet_data_generator, create_dataloader
work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(work_dir)
sys.path.append(os.path.join(work_dir, "../"))
from hapi.model import set_device, Input
import paddle.fluid as fluid
......@@ -66,7 +67,7 @@ def main(args):
model.mode = "test"
model.prepare(inputs=inputs)
model.load(args.init_from_checkpoint)
model.load(args.init_from_checkpoint, skip_mismatch=True)
f = open(args.output_file, "wb")
for data in predict_dataset():
......@@ -82,91 +83,12 @@ def main(args):
f.write("\002".join(tags) + "\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser("sequence tagging training")
parser.add_argument(
"-wd",
"--word_dict_path",
default=None,
type=str,
help='word dict path')
parser.add_argument(
"-ld",
"--label_dict_path",
default=None,
type=str,
help='label dict path')
parser.add_argument(
"-wrd",
"--word_rep_dict_path",
default=None,
type=str,
help='The path of the word replacement Dictionary.')
parser.add_argument(
"-dev",
"--device",
type=str,
default='gpu',
help="device to use, gpu or cpu")
parser.add_argument(
"-d", "--dynamic", action='store_true', help="enable dygraph mode")
parser.add_argument(
"-e", "--epoch", default=10, type=int, help="number of epoch")
parser.add_argument(
'-lr',
'--base_learning_rate',
default=1e-3,
type=float,
metavar='LR',
help='initial learning rate')
parser.add_argument(
"--word_emb_dim",
default=128,
type=int,
help='word embedding dimension')
parser.add_argument(
"--grnn_hidden_dim", default=128, type=int, help="hidden dimension")
parser.add_argument(
"--bigru_num", default=2, type=int, help='the number of bi-rnn')
parser.add_argument("-elr", "--emb_learning_rate", default=1.0, type=float)
parser.add_argument("-clr", "--crf_learning_rate", default=1.0, type=float)
parser.add_argument(
"-b", "--batch_size", default=300, type=int, help="batch size")
parser.add_argument(
"--max_seq_len", default=126, type=int, help="max sequence length")
parser.add_argument(
"-n", "--num_devices", default=1, type=int, help="number of devices")
parser.add_argument(
"-o",
"--save_dir",
default="./model",
type=str,
help="save model path")
parser.add_argument(
"--init_from_checkpoint",
default=None,
type=str,
help="load init model parameters")
parser.add_argument(
"--init_from_pretrain_model",
default=None,
type=str,
help="load pretrain model parameters")
parser.add_argument(
"-sf", "--save_freq", default=1, type=int, help="save frequency")
parser.add_argument(
"-ef", "--eval_freq", default=1, type=int, help="eval frequency")
parser.add_argument(
"--output_file", default="predict.result", type=str, help="predict output file")
parser.add_argument(
"--predict_file", default="./data/infer.tsv", type=str, help="predict output file")
parser.add_argument(
"--mode", default="train", type=str, help="train|test|predict")
args = parser.parse_args()
print(args)
if __name__ == '__main__':
args = PDConfig(yaml_file="sequence_tagging.yaml")
args.build()
args.Print()
use_gpu = True if args.device == "gpu" else False
check_gpu(use_gpu)
check_version()
main(args)
examples/sequence_tagging/sequence_tagging.yaml 0 → 100644
浏览文件 @ 33ece5d7
word_dict_path: "./conf/word.dic"
label_dict_path: "./conf/tag.dic"
word_rep_dict_path: "./conf/q2b.dic"
device: "cpu"
dynamic: True
epoch: 10
base_learning_rate: 0.001
word_emb_dim: 128
grnn_hidden_dim: 128
bigru_num: 2
emb_learning_rate: 1.0
crf_learning_rate: 1.0
batch_size: 300
max_seq_len: 126
num_devices: 1
save_dir: "model"
init_from_checkpoint: "model_baseline/params"
init_from_pretrain_model: ""
save_freq: 1
eval_freq: 1
output_file: "predict.result"
test_file: "./data/test.tsv"
train_file: "./data/train.tsv"
predict_file: "./data/infer.tsv"
mode: "train"
sequence_tagging/train.py → examples/sequence_tagging/train.py
浏览文件 @ 33ece5d7
......@@ -26,13 +26,15 @@ import argparse
import numpy as np
work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(os.path.join(work_dir))
sys.path.append(os.path.join(work_dir, "../"))
from hapi.metrics import Metric
from hapi.model import Model, Input, Loss, set_device
from hapi.text.text import SequenceTagging
from utils.check import check_gpu, check_version
from utils.configure import PDConfig
from reader import LacDataset, create_lexnet_data_generator, create_dataloader
import paddle.fluid as fluid
......@@ -258,92 +260,10 @@ def main(args):
if __name__ == '__main__':
parser = argparse.ArgumentParser("sequence tagging training")
parser.add_argument(
"-wd",
"--word_dict_path",
default=None,
type=str,
help='word dict path')
parser.add_argument(
"-ld",
"--label_dict_path",
default=None,
type=str,
help='label dict path')
parser.add_argument(
"-wrd",
"--word_rep_dict_path",
default=None,
type=str,
help='The path of the word replacement Dictionary.')
parser.add_argument(
"-dev",
"--device",
type=str,
default='gpu',
help="device to use, gpu or cpu")
parser.add_argument(
"-d", "--dynamic", action='store_true', help="enable dygraph mode")
parser.add_argument(
"-e", "--epoch", default=10, type=int, help="number of epoch")
parser.add_argument(
'-lr',
'--base_learning_rate',
default=1e-3,
type=float,
metavar='LR',
help='initial learning rate')
parser.add_argument(
"--word_emb_dim",
default=128,
type=int,
help='word embedding dimension')
parser.add_argument(
"--grnn_hidden_dim", default=128, type=int, help="hidden dimension")
parser.add_argument(
"--bigru_num", default=2, type=int, help='the number of bi-rnn')
parser.add_argument("-elr", "--emb_learning_rate", default=1.0, type=float)
parser.add_argument("-clr", "--crf_learning_rate", default=1.0, type=float)
parser.add_argument(
"-b", "--batch_size", default=300, type=int, help="batch size")
parser.add_argument(
"--max_seq_len", default=126, type=int, help="max sequence length")
parser.add_argument(
"-n", "--num_devices", default=1, type=int, help="number of devices")
parser.add_argument(
"-o",
"--save_dir",
default="./model",
type=str,
help="save model path")
parser.add_argument(
"--init_from_checkpoint",
default=None,
type=str,
help="load init model parameters")
parser.add_argument(
"--init_from_pretrain_model",
default=None,
type=str,
help="load pretrain model parameters")
parser.add_argument(
"-sf", "--save_freq", default=1, type=int, help="save frequency")
parser.add_argument(
"-ef", "--eval_freq", default=1, type=int, help="eval frequency")
parser.add_argument(
"--output_file", default="predict.result", type=str, help="predict output file")
parser.add_argument(
"--predict_file", default="./data/infer.tsv", type=str, help="predict output file")
parser.add_argument(
"--test_file", default="./data/test.tsv", type=str, help="predict and eval output file")
parser.add_argument(
"--train_file", default="./data/train.tsv", type=str, help="train file")
parser.add_argument(
"--mode", default="train", type=str, help="train|test|predict")
args = parser.parse_args()
print(args)
args = PDConfig(yaml_file="sequence_tagging.yaml")
args.build()
args.Print()
use_gpu = True if args.device == "gpu" else False
check_gpu(use_gpu)
check_version()
......
examples/sequence_tagging/utils/configure.py 0 → 100644
浏览文件 @ 33ece5d7
# Copyright (c) 2019 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 absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import argparse
import json
import yaml
import six
import logging
logging_only_message = "%(message)s"
logging_details = "%(asctime)s.%(msecs)03d %(levelname)s %(module)s - %(funcName)s: %(message)s"
class JsonConfig(object):
"""
A high-level api for handling json configure file.
"""
def __init__(self, config_path):
self._config_dict = self._parse(config_path)
def _parse(self, config_path):
try:
with open(config_path) as json_file:
config_dict = json.load(json_file)
except:
raise IOError("Error in parsing bert model config file '%s'" %
config_path)
else:
return config_dict
def __getitem__(self, key):
return self._config_dict[key]
def print_config(self):
for arg, value in sorted(six.iteritems(self._config_dict)):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
class ArgumentGroup(object):
def __init__(self, parser, title, des):
self._group = parser.add_argument_group(title=title, description=des)
def add_arg(self, name, type, default, help, **kwargs):
type = str2bool if type == bool else type
self._group.add_argument(
"--" + name,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
class ArgConfig(object):
"""
A high-level api for handling argument configs.
"""
def __init__(self):
parser = argparse.ArgumentParser()
train_g = ArgumentGroup(parser, "training", "training options.")
train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
train_g.add_arg("learning_rate", float, 5e-5,
"Learning rate used to train with warmup.")
train_g.add_arg(
"lr_scheduler",
str,
"linear_warmup_decay",
"scheduler of learning rate.",
choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay", float, 0.01,
"Weight decay rate for L2 regularizer.")
train_g.add_arg(
"warmup_proportion", float, 0.1,
"Proportion of training steps to perform linear learning rate warmup for."
)
train_g.add_arg("save_steps", int, 1000,
"The steps interval to save checkpoints.")
train_g.add_arg("use_fp16", bool, False,
"Whether to use fp16 mixed precision training.")
train_g.add_arg(
"loss_scaling", float, 1.0,
"Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled."
)
train_g.add_arg("pred_dir", str, None,
"Path to save the prediction results")
log_g = ArgumentGroup(parser, "logging", "logging related.")
log_g.add_arg("skip_steps", int, 10,
"The steps interval to print loss.")
log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")
run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda", bool, True,
"If set, use GPU for training.")
run_type_g.add_arg(
"use_fast_executor", bool, False,
"If set, use fast parallel executor (in experiment).")
run_type_g.add_arg(
"num_iteration_per_drop_scope", int, 1,
"Ihe iteration intervals to clean up temporary variables.")
run_type_g.add_arg("do_train", bool, True,
"Whether to perform training.")
run_type_g.add_arg("do_predict", bool, True,
"Whether to perform prediction.")
custom_g = ArgumentGroup(parser, "customize", "customized options.")
self.custom_g = custom_g
self.parser = parser
def add_arg(self, name, dtype, default, descrip):
self.custom_g.add_arg(name, dtype, default, descrip)
def build_conf(self):
return self.parser.parse_args()
def str2bool(v):
# because argparse does not support to parse "true, False" as python
# boolean directly
return v.lower() in ("true", "t", "1")
def print_arguments(args, log=None):
if not log:
print('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
else:
log.info('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
log.info('%s: %s' % (arg, value))
log.info('------------------------------------------------')
class PDConfig(object):
"""
A high-level API for managing configuration files in PaddlePaddle.
Can jointly work with command-line-arugment, json files and yaml files.
"""
def __init__(self, json_file="", yaml_file="", fuse_args=True):
"""
Init funciton for PDConfig.
json_file: the path to the json configure file.
yaml_file: the path to the yaml configure file.
fuse_args: if fuse the json/yaml configs with argparse.
"""
assert isinstance(json_file, str)
assert isinstance(yaml_file, str)
if json_file != "" and yaml_file != "":
raise Warning(
"json_file and yaml_file can not co-exist for now. please only use one configure file type."
)
return
self.args = None
self.arg_config = {}
self.json_config = {}
self.yaml_config = {}
parser = argparse.ArgumentParser()
self.default_g = ArgumentGroup(parser, "default", "default options.")
self.yaml_g = ArgumentGroup(parser, "yaml", "options from yaml.")
self.json_g = ArgumentGroup(parser, "json", "options from json.")
self.com_g = ArgumentGroup(parser, "custom", "customized options.")
self.default_g.add_arg("do_train", bool, False,
"Whether to perform training.")
self.default_g.add_arg("do_predict", bool, False,
"Whether to perform predicting.")
self.default_g.add_arg("do_eval", bool, False,
"Whether to perform evaluating.")
self.default_g.add_arg("do_save_inference_model", bool, False,
"Whether to perform model saving for inference.")
# NOTE: args for profiler
self.default_g.add_arg("is_profiler", int, 0, "the switch of profiler tools. (used for benchmark)")
self.default_g.add_arg("profiler_path", str, './', "the profiler output file path. (used for benchmark)")
self.default_g.add_arg("max_iter", int, 0, "the max train batch num.(used for benchmark)")
self.parser = parser
if json_file != "":
self.load_json(json_file, fuse_args=fuse_args)
if yaml_file:
self.load_yaml(yaml_file, fuse_args=fuse_args)
def load_json(self, file_path, fuse_args=True):
if not os.path.exists(file_path):
raise Warning("the json file %s does not exist." % file_path)
return
with open(file_path, "r") as fin:
self.json_config = json.loads(fin.read())
fin.close()
if fuse_args:
for name in self.json_config:
if isinstance(self.json_config[name], list):
self.json_g.add_arg(
name,
type(self.json_config[name][0]),
self.json_config[name],
"This is from %s" % file_path,
nargs=len(self.json_config[name]))
continue
if not isinstance(self.json_config[name], int) \
and not isinstance(self.json_config[name], float) \
and not isinstance(self.json_config[name], str) \
and not isinstance(self.json_config[name], bool):
continue
self.json_g.add_arg(name,
type(self.json_config[name]),
self.json_config[name],
"This is from %s" % file_path)
def load_yaml(self, file_path, fuse_args=True):
if not os.path.exists(file_path):
raise Warning("the yaml file %s does not exist." % file_path)
return
with open(file_path, "r") as fin:
self.yaml_config = yaml.load(fin, Loader=yaml.SafeLoader)
fin.close()
if fuse_args:
for name in self.yaml_config:
if isinstance(self.yaml_config[name], list):
self.yaml_g.add_arg(
name,
type(self.yaml_config[name][0]),
self.yaml_config[name],
"This is from %s" % file_path,
nargs=len(self.yaml_config[name]))
continue
if not isinstance(self.yaml_config[name], int) \
and not isinstance(self.yaml_config[name], float) \
and not isinstance(self.yaml_config[name], str) \
and not isinstance(self.yaml_config[name], bool):
continue
self.yaml_g.add_arg(name,
type(self.yaml_config[name]),
self.yaml_config[name],
"This is from %s" % file_path)
def build(self):
self.args = self.parser.parse_args()
self.arg_config = vars(self.args)
def __add__(self, new_arg):
assert isinstance(new_arg, list) or isinstance(new_arg, tuple)
assert len(new_arg) >= 3
assert self.args is None
name = new_arg[0]
dtype = new_arg[1]
dvalue = new_arg[2]
desc = new_arg[3] if len(
new_arg) == 4 else "Description is not provided."
self.com_g.add_arg(name, dtype, dvalue, desc)
return self
def __getattr__(self, name):
if name in self.arg_config:
return self.arg_config[name]
if name in self.json_config:
return self.json_config[name]
if name in self.yaml_config:
return self.yaml_config[name]
raise Warning("The argument %s is not defined." % name)
def Print(self):
print("-" * 70)
for name in self.arg_config:
print("%s:\t\t\t\t%s" % (str(name), str(self.arg_config[name])))
for name in self.json_config:
if name not in self.arg_config:
print("%s:\t\t\t\t%s" %
(str(name), str(self.json_config[name])))
for name in self.yaml_config:
if name not in self.arg_config:
print("%s:\t\t\t\t%s" %
(str(name), str(self.yaml_config[name])))
print("-" * 70)
if __name__ == "__main__":
"""
pd_config = PDConfig(json_file = "./test/bert_config.json")
pd_config.build()
print(pd_config.do_train)
print(pd_config.hidden_size)
pd_config = PDConfig(yaml_file = "./test/bert_config.yaml")
pd_config.build()
print(pd_config.do_train)
print(pd_config.hidden_size)
"""
pd_config = PDConfig(yaml_file="./test/bert_config.yaml")
pd_config += ("my_age", int, 18, "I am forever 18.")
pd_config.build()
print(pd_config.do_train)
print(pd_config.hidden_size)
print(pd_config.my_age)
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或