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| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Sat Oct 29 14:56:09 2022 | |
| @author: Santiago Moreno | |
| """ | |
| import os | |
| import argparse | |
| from functions import json_to_txt, training_model, characterize_data, upsampling_data, str2bool, usage_cuda,copy_data | |
| default_path = os.path.dirname(os.path.abspath(__file__)) | |
| os.chdir(default_path) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(add_help=True, usage='Train a new model with given data (GPU optional)') | |
| parser.add_argument('-f','--fast', type=str2bool, nargs='?',const=True, default=False, help='Training fast option (Only for functioning test)', choices=(True, False), required=False) | |
| parser.add_argument('-m','--model', type=str, nargs='?', help='New model name', required=True) | |
| parser.add_argument('-s','--standard', type=str2bool, nargs='?',const=True, default=False, help='Standard CONLL input or not', choices=(True, False), required=False) | |
| parser.add_argument('-id','--input_dir', type=str, nargs='?', help='Absolute path input directory', required=True) | |
| parser.add_argument('-u','--up_sample_flag', type=str2bool, nargs='?',const=True, default=False , help='Boolean value to upsampling the data = True or not upsampling = False', required=False, choices=(True, False)) | |
| parser.add_argument('-cu','--cuda', type=str2bool, nargs='?', const=True, default=False, help='Boolean value for using cuda to Train the model (True). By defaul False.', choices=(True, False), required=False) | |
| args = parser.parse_args() | |
| if args.fast: epochs = 1 | |
| else: epochs = 20 | |
| if args.standard: | |
| copy_data(args.input_dir) | |
| not_error=True | |
| else: | |
| Error = json_to_txt(args.input_dir) | |
| if type(Error)==int: | |
| print('Error processing the input documents, code error {}'.format(Error)) | |
| not_error=False | |
| else: | |
| not_error=True | |
| if not_error: | |
| if args.up_sample_flag: | |
| entities_dict=characterize_data() | |
| entities = list(entities_dict.keys()) | |
| entities_to_upsample = [entities[i] for i,value in enumerate(entities_dict.values()) if value < 200] | |
| upsampling_data(entities_to_upsample, 0.8, entities) | |
| if args.cuda: cuda_info = usage_cuda(True) | |
| else: cuda_info = usage_cuda(False) | |
| print(cuda_info) | |
| Error = training_model(args.model,epochs) | |
| if type(Error)==int: | |
| print('Error training the model, code error {}'.format(Error)) | |
| else: | |
| print('Training complete') | |