def main(model_name, checkpoint_name, url):
config = Cfg.load_config_from_name(model_name)
dataset_params = {
'name': 'hw',
'data_root': '/home/longhn',
# 'train_root': '/home/fdm/Desktop/chungnph/vietocr/Annotation_2505',
# 'val_root': '/home/fdm/Desktop/chungnph/vietocr/Annotation_2505',
'train_annotation': f'{url}/train.txt',
'valid_annotation': f'{url}/valid.txt'
}
params = {
'print_every': 200,
'valid_every': 10 * 200,
'iters': 30000,
'checkpoint': f'./checkpoint/{checkpoint_name}.pth',
'export': f'./checkpoint/{checkpoint_name}.pth',
'metrics': 15000,
'batch_size': 32
}
dataloader_params = {'num_workers': 1}
# config['pretrain']['cached'] = 'checkpoint/ngaycap_0204.pth'
config['trainer'].update(params)
config['dataset'].update(dataset_params)
config['dataloader'].update(dataloader_params)
config['device'] = 'cuda'
config[
'vocab'] = '''aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ '''
# config['weights'] = 'checkpoint/ngaycap_0204.pth'
print(config)
trainer = Trainer(config, pretrained=True)
trainer.config.save(f'train_config/{checkpoint_name}.yml')
trainer.train()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', required=True, help='see example at ')
parser.add_argument('--checkpoint', required=False, help='your checkpoint')
args = parser.parse_args()
config = Cfg.load_config_from_file(args.config)
trainer = Trainer(config)
if args.checkpoint:
trainer.load_checkpoint(args.checkpoint)
trainer.train()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--img', required=True, help='foo help')
parser.add_argument('--config', required=True, help='foo help')
args = parser.parse_args()
config = Cfg.load_config_from_file(args.config)
detector = Predictor(config)
img = Image.open(args.img)
s = detector.predict(img)
print(s)
Created on Fri May 21 09:07:18 2021
@author: fdm
"""
import cv2
import numpy as np
import glob
import ntpath
import os
import re
from PIL import Image
import time
from tool.predictor import Predictor
from tool.config import Cfg
config_all = Cfg.load_config_from_file(
'./train_config/seq2seq_handwriting_0307_pretrain_32_2k.yml')
config_all[
'weights'] = './checkpoint/seq2seq_handwriting_0307_pretrain_32_2k.pth'
config_all['cnn']['pretrained'] = False
config_all['device'] = 'cuda:0'
# config_all['device'] = 'cuda:1'
config_all['predictor']['beamsearch'] = False
# config_all['vocab'] = '''aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0125456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ '''
detector_old = Predictor(config_all)
def no_accent_vietnamese(s):
s = re.sub(r'[àáạảãâầấậẩẫăằắặẳẵ]', 'a', s)
s = re.sub(r'[ÀÁẠẢÃĂẰẮẶẲẴÂẦẤẬẨẪ]', 'A', s)
s = re.sub(r'[èéẹẻẽêềếệểễ]', 'e', s)
s = re.sub(r'[ÈÉẸẺẼÊỀẾỆỂỄ]', 'E', s)
import argparse
from PIL import Image
import glob
from tool.predictor import Predictor
from model.trainer import Trainer
from tool.config import Cfg
import cv2
# config = Cfg.load_config_from_file('config/vgg_transformer.yml')
config = Cfg.load_config_from_file('config_seq2seq.yml')
dataset_params = {
'name': 'hw',
'data_root': '/home/longhn',
'train_annotation': '/home/longhn/Anotation/train.txt',
'valid_annotation': '/home/longhn/Anotation/valid.txt'
}
params = {
'print_every': 200,
'valid_every': 10 * 200,
'iters': 200000,
'checkpoint': 'checkpoint/smartdoc_seq2seq.pth',
'export': 'checkpoint/smartdoc_seq2seq.pth',
'metrics': 10000000,
'batch_size': 32
}
dataloader_params = {'num_workers': 1}
config['trainer'].update(params)
config['dataset'].update(dataset_params)
#coding: utf-8
import uvicorn
import os
from os import path
from fastapi import FastAPI, File, UploadFile
from PIL import Image
from tool.predictor import Predictor
from tool.config import Cfg
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
app = FastAPI()
config = Cfg.load_config_from_file("config/vgg_transformer.yml")
config[
'weights'] = f'{os.getenv("MODEL_PATH", "../../models")}/vietocr/transformerocr.pth'
config['cnn']['pretrained'] = False
config['device'] = 'cpu'
detector = Predictor(config)
@app.post('/recognize')
async def recognize(file: UploadFile = File(...)):
result = detector.predict_bytes(await file.read())
return {'result': result}
if __name__ == "__main__":
import sys
sys.path.append(path.join(path.dirname(__file__), '..'))
uvicorn.run(app, host="0.0.0.0", port=8002)