xiaoting
4 years ago
committed by
GitHub
commit
f214437538
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import sys, time
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from PyQt5 import QtWidgets
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from PyQt5.QtGui import *
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from PyQt5.QtCore import *
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from PyQt5.QtWidgets import *
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class EditInList(QListWidget):
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def __init__(self):
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super(EditInList,self).__init__()
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# click to edit
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self.clicked.connect(self.item_clicked)
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def item_clicked(self, modelindex: QModelIndex) -> None:
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self.edited_item = self.currentItem()
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self.closePersistentEditor(self.edited_item)
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item = self.item(modelindex.row())
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# time.sleep(0.2)
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self.edited_item = item
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self.openPersistentEditor(item)
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# time.sleep(0.2)
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self.editItem(item)
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def mouseDoubleClickEvent(self, event):
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# close edit
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for i in range(self.count()):
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self.closePersistentEditor(self.item(i))
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def leaveEvent(self, event):
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# close edit
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for i in range(self.count()):
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self.closePersistentEditor(self.item(i))
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Load Diff
@ -1,2 +0,0 @@
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pyqt5==5.10.1
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lxml==4.2.4
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@ -0,0 +1,152 @@
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# Copyright (c) 2021 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 yaml
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from argparse import ArgumentParser, RawDescriptionHelpFormatter
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import os.path
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import logging
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logging.basicConfig(level=logging.INFO)
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support_list = {
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'it':'italian', 'xi':'spanish', 'pu':'portuguese', 'ru':'russian', 'ar':'arabic',
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'ta':'tamil', 'ug':'uyghur', 'fa':'persian', 'ur':'urdu', 'rs':'serbian latin',
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'oc':'occitan', 'rsc':'serbian cyrillic', 'bg':'bulgarian', 'uk':'ukranian', 'be':'belarusian',
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'te':'telugu', 'ka':'kannada', 'chinese_cht':'chinese tradition','hi':'hindi','mr':'marathi',
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'ne':'nepali',
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}
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assert(
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os.path.isfile("./rec_multi_language_lite_train.yml")
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),"Loss basic configuration file rec_multi_language_lite_train.yml.\
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You can download it from \
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https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/configs/rec/multi_language/"
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global_config = yaml.load(open("./rec_multi_language_lite_train.yml", 'rb'), Loader=yaml.Loader)
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project_path = os.path.abspath(os.path.join(os.getcwd(), "../../../"))
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class ArgsParser(ArgumentParser):
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def __init__(self):
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super(ArgsParser, self).__init__(
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formatter_class=RawDescriptionHelpFormatter)
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self.add_argument(
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"-o", "--opt", nargs='+', help="set configuration options")
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self.add_argument(
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"-l", "--language", nargs='+', help="set language type, support {}".format(support_list))
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self.add_argument(
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"--train",type=str,help="you can use this command to change the train dataset default path")
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self.add_argument(
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"--val",type=str,help="you can use this command to change the eval dataset default path")
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self.add_argument(
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"--dict",type=str,help="you can use this command to change the dictionary default path")
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self.add_argument(
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"--data_dir",type=str,help="you can use this command to change the dataset default root path")
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def parse_args(self, argv=None):
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args = super(ArgsParser, self).parse_args(argv)
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args.opt = self._parse_opt(args.opt)
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args.language = self._set_language(args.language)
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return args
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def _parse_opt(self, opts):
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config = {}
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if not opts:
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return config
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for s in opts:
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s = s.strip()
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k, v = s.split('=')
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config[k] = yaml.load(v, Loader=yaml.Loader)
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return config
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def _set_language(self, type):
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assert(type),"please use -l or --language to choose language type"
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assert(
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type[0] in support_list.keys()
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),"the sub_keys(-l or --language) can only be one of support list: \n{},\nbut get: {}, " \
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"please check your running command".format(support_list, type)
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global_config['Global']['character_dict_path'] = 'ppocr/utils/dict/{}_dict.txt'.format(type[0])
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global_config['Global']['save_model_dir'] = './output/rec_{}_lite'.format(type[0])
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global_config['Train']['dataset']['label_file_list'] = ["train_data/{}_train.txt".format(type[0])]
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global_config['Eval']['dataset']['label_file_list'] = ["train_data/{}_val.txt".format(type[0])]
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global_config['Global']['character_type'] = type[0]
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assert(
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os.path.isfile(os.path.join(project_path,global_config['Global']['character_dict_path']))
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),"Loss default dictionary file {}_dict.txt.You can download it from \
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https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/ppocr/utils/dict/".format(type[0])
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return type[0]
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def merge_config(config):
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"""
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Merge config into global config.
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Args:
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config (dict): Config to be merged.
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Returns: global config
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"""
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for key, value in config.items():
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if "." not in key:
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if isinstance(value, dict) and key in global_config:
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global_config[key].update(value)
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else:
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global_config[key] = value
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else:
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sub_keys = key.split('.')
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assert (
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sub_keys[0] in global_config
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), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
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global_config.keys(), sub_keys[0])
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cur = global_config[sub_keys[0]]
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for idx, sub_key in enumerate(sub_keys[1:]):
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if idx == len(sub_keys) - 2:
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cur[sub_key] = value
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else:
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cur = cur[sub_key]
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def loss_file(path):
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assert(
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os.path.exists(path)
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),"There is no such file:{},Please do not forget to put in the specified file".format(path)
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if __name__ == '__main__':
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FLAGS = ArgsParser().parse_args()
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merge_config(FLAGS.opt)
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save_file_path = 'rec_{}_lite_train.yml'.format(FLAGS.language)
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if os.path.isfile(save_file_path):
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os.remove(save_file_path)
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if FLAGS.train:
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global_config['Train']['dataset']['label_file_list'] = [FLAGS.train]
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train_label_path = os.path.join(project_path,FLAGS.train)
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loss_file(train_label_path)
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if FLAGS.val:
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global_config['Eval']['dataset']['label_file_list'] = [FLAGS.val]
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eval_label_path = os.path.join(project_path,FLAGS.val)
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loss_file(Eval_label_path)
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if FLAGS.dict:
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global_config['Global']['character_dict_path'] = FLAGS.dict
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dict_path = os.path.join(project_path,FLAGS.dict)
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loss_file(dict_path)
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if FLAGS.data_dir:
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global_config['Eval']['dataset']['data_dir'] = FLAGS.data_dir
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global_config['Train']['dataset']['data_dir'] = FLAGS.data_dir
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data_dir = os.path.join(project_path,FLAGS.data_dir)
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loss_file(data_dir)
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with open(save_file_path, 'w') as f:
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yaml.dump(dict(global_config), f, default_flow_style=False, sort_keys=False)
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logging.info("Project path is :{}".format(project_path))
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logging.info("Train list path set to :{}".format(global_config['Train']['dataset']['label_file_list'][0]))
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logging.info("Eval list path set to :{}".format(global_config['Eval']['dataset']['label_file_list'][0]))
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logging.info("Dataset root path set to :{}".format(global_config['Eval']['dataset']['data_dir']))
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logging.info("Dict path set to :{}".format(global_config['Global']['character_dict_path']))
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logging.info("Config file set to :configs/rec/multi_language/{}".format(save_file_path))
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@ -0,0 +1,103 @@
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Global:
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use_gpu: True
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epoch_num: 500
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec_multi_language_lite
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save_epoch_step: 3
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [0, 2000]
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints:
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save_inference_dir:
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use_visualdl: False
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infer_img:
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# for data or label process
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character_dict_path:
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# Set the language of training, if set, select the default dictionary file
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character_type:
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max_text_length: 25
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infer_mode: False
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use_space_char: True
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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name: Cosine
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learning_rate: 0.001
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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model_type: rec
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algorithm: CRNN
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: small
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small_stride: [1, 2, 2, 2]
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Neck:
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name: SequenceEncoder
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encoder_type: rnn
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hidden_size: 48
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Head:
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name: CTCHead
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fc_decay: 0.00001
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/
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label_file_list: ["./train_data/train_list.txt"]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- RecAug:
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 320]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 256
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drop_last: True
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num_workers: 8
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/
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label_file_list: ["./train_data/val_list.txt"]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [3, 32, 320]
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- KeepKeys:
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keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 256
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num_workers: 8
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