116 lines
2.8 KiB
Plaintext
116 lines
2.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-07-23T04:19:23.488642Z",
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"start_time": "2021-07-23T04:19:21.854534Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import torch.backends.cudnn as cudnn\n",
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"import yaml\n",
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"from train import train\n",
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"from utils import AttrDict\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-07-23T04:19:23.885144Z",
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"start_time": "2021-07-23T04:19:23.880564Z"
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},
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"code_folding": []
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},
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"outputs": [],
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"source": [
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"cudnn.benchmark = True\n",
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"cudnn.deterministic = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-07-23T04:19:24.119144Z",
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"start_time": "2021-07-23T04:19:24.112032Z"
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},
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"code_folding": [
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0
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]
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},
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"outputs": [],
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"source": [
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"def get_config(file_path):\n",
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" with open(file_path, 'r', encoding=\"utf8\") as stream:\n",
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" opt = yaml.safe_load(stream)\n",
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" opt = AttrDict(opt)\n",
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" if opt.lang_char == 'None':\n",
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" characters = ''\n",
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" for data in opt['select_data'].split('-'):\n",
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" csv_path = os.path.join(opt['train_data'], data, 'labels.csv')\n",
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" df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False)\n",
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" all_char = ''.join(df['words'])\n",
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" characters += ''.join(set(all_char))\n",
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" characters = sorted(set(characters))\n",
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" opt.character= ''.join(characters)\n",
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" else:\n",
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" opt.character = opt.number + opt.symbol + opt.lang_char\n",
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" os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)\n",
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" return opt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-07-23T04:49:07.045060Z",
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"start_time": "2021-07-23T04:20:15.050992Z"
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}
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},
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"outputs": [],
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"source": [
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"opt = get_config(\"config_files/en_filtered_config.yaml\")\n",
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"train(opt, amp=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "easyocr",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.20"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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