def test_parse_arguments4(): """Testing specfied arguments""" args = ["--absentees", "maria", "--round-robin", "--group-size", "3"] parsed_args = parse_arguments.parse_arguments(args) assert parsed_args.group_size == 3 assert parsed_args.grouping_method == constants.ALGORITHM_ROUND_ROBIN assert parsed_args.absentees == ["maria"]
def test_parse_arguments2(): """Testing specfied arguments""" args = ["--debug", "--students-file", "students.csv", "--random"] parsed_args = parse_arguments.parse_arguments(args) assert parsed_args.logging_level == logging.DEBUG assert parsed_args.students_file == "students.csv" assert parsed_args.grouping_method == "random"
def test_parse_arguments1(): """General testing of arguments - if arguments exists""" args = [] parsed_args = parse_arguments.parse_arguments(args) assert parsed_args.logging_level == logging.ERROR assert parsed_args.group_size == defaults.DEFAULT_GRPSIZE assert parsed_args.students_file == defaults.DEFAULT_CSVFILE assert (parsed_args.grouping_method == group_random) is False
def main(): args, _ = parse_arguments() input_path = Path(args.input_path) output_path = Path(args.output_path) pretrained_ocr = Path(args.finetuned_ocr) bsheet_pages = args.balancesheet_page_nums profitloss_pages = args.profitloss_page_nums page_numbers = [] if bsheet_pages is not None: bsheet_pages = bsheet_pages.lstrip('[').rstrip(']').split(',') bsheet_pages = [int(p_no) for p_no in bsheet_pages] page_numbers.extend(bsheet_pages) if profitloss_pages is not None: profitloss_pages = profitloss_pages.lstrip('[').rstrip(']').split(',') profitloss_pages = [int(p_no) for p_no in profitloss_pages] page_numbers.extend(profitloss_pages) if bsheet_pages is None and profitloss_pages is None: print("Enter the page numbers.") return if args.double_page == 'True': double_page = True else: double_page = False if args.next_page == 'True': next_page = True else: next_page = False table_extractor = PDFToCSV(input_path, output_path, pretrained_ocr, page_numbers, double_page, next_page) table_extractor.pdf_to_jpg() table_extractor.image_to_txt() table_extractor.txt_to_csv() table_extractor.combine_csv_files()
sys.stderr.write('\n') acc, top5, test_loss = test_model(dataset, checkpoint.model, args) checkpoint.save_results({'epoch': checkpoint.epoch, 'acc': acc, 'top5': top5, 'loss': test_loss, 'ingoing': ingoing, 'outgoing': outgoing, 'a': last_a, 'norm': l2_norm(checkpoint.model), 'pruned_param_count': checkpoint.model.compute_params_count(args.pruning_type), 'pruned_flops_count': checkpoint.model.compute_flops_count(), 'epoch_duration': duration}) checkpoint.epoch += 1 checkpoint.scheduler.step() checkpoint.save() if __name__ == '__main__': arguments = parse_arguments() torch.manual_seed(arguments.seed) np.random.seed(arguments.seed) if arguments.fix_a is None and arguments.reg_type == "swd" and arguments.pruning_iterations != 1: print('Progressive a is not compatible with iterative pruning') raise ValueError if arguments.no_ft and arguments.pruning_iterations != 1: print("You can't specify a pruning_iteration value if there is no fine-tuning at all") raise ValueError get_mask = get_mask_function(arguments.pruning_type) _dataset = get_dataset(arguments) _targets = [int((n + 1) * (arguments.target / arguments.pruning_iterations)) for n in range(arguments.pruning_iterations)] # Train model print('Train model !')
def just_do_it(): options = parse_arguments.parse_arguments() track.track(options.video_path, options.show_result) print("End of game, have a nice day!")
def main(): #parsing the arguments args, _ = parse_arguments() #setup logging #output_dir = Path('/content/drive/My Drive/image-captioning/output') output_dir = Path(args.output_directory) output_dir.mkdir(parents=True, exist_ok=True) logfile_path = Path(output_dir / "output.log") setup_logging(logfile=logfile_path) #setup and read config.ini #config_file = Path('/content/drive/My Drive/image-captioning/config.ini') config_file = Path('../config.ini') reading_config(config_file) #tensorboard tensorboard_logfile = Path(output_dir / 'tensorboard') tensorboard_writer = SummaryWriter(tensorboard_logfile) #load dataset #dataset_dir = Path('/content/drive/My Drive/Flickr8k_Dataset') dataset_dir = Path(args.dataset) images_path = Path(dataset_dir / Config.get("images_dir")) captions_path = Path(dataset_dir / Config.get("captions_dir")) training_loader, validation_loader, testing_loader = data_loaders( images_path, captions_path) #load the model (encoder, decoder, optimizer) embed_size = Config.get("encoder_embed_size") hidden_size = Config.get("decoder_hidden_size") batch_size = Config.get("training_batch_size") epochs = Config.get("epochs") feature_extraction = Config.get("feature_extraction") raw_captions = read_captions(captions_path) id_to_word, word_to_id = dictionary(raw_captions, threshold=5) vocab_size = len(id_to_word) encoder = Encoder(embed_size, feature_extraction) decoder = Decoder(embed_size, hidden_size, vocab_size, batch_size) #load pretrained embeddings #pretrained_emb_dir = Path('/content/drive/My Drive/word2vec') pretrained_emb_dir = Path(args.pretrained_embeddings) pretrained_emb_file = Path(pretrained_emb_dir / Config.get("pretrained_emb_path")) pretrained_embeddings = load_pretrained_embeddings(pretrained_emb_file, id_to_word) #load the optimizer learning_rate = Config.get("learning_rate") optimizer = adam_optimizer(encoder, decoder, learning_rate) #loss funciton criterion = cross_entropy #load checkpoint checkpoint_file = Path(output_dir / Config.get("checkpoint_file")) checkpoint_captioning = load_checkpoint(checkpoint_file) #using available device(gpu/cpu) encoder = encoder.to(Config.get("device")) decoder = decoder.to(Config.get("device")) pretrained_embeddings = pretrained_embeddings.to(Config.get("device")) start_epoch = 1 if checkpoint_captioning is not None: start_epoch = checkpoint_captioning['epoch'] + 1 encoder.load_state_dict(checkpoint_captioning['encoder']) decoder.load_state_dict(checkpoint_captioning['decoder']) optimizer.load_state_dict(checkpoint_captioning['optimizer']) logger.info( 'Initialized encoder, decoder and optimizer from loaded checkpoint' ) del checkpoint_captioning #image captioning model model = ImageCaptioning(encoder, decoder, optimizer, criterion, training_loader, validation_loader, testing_loader, pretrained_embeddings, output_dir, tensorboard_writer) #training and testing the model if args.training: validate_every = Config.get("validate_every") model.train(epochs, validate_every, start_epoch) elif args.testing: images_path = Path(images_path / Config.get("images_dir")) model.testing(id_to_word, images_path)
def test_parse_arguments5(): """Testing specfied arguments""" args = ["--num-group", "3"] parsed_args = parse_arguments.parse_arguments(args) assert parsed_args.num_group == 3
def test_parse_gatorgrouper_arguments3(): """Testing specfied arguments""" args = ["--verbose", "--round-robin"] parsed_args = parse_arguments.parse_arguments(args) assert parsed_args.logging_level == logging.INFO assert parsed_args.grouping_method == constants.ALGORITHM_ROUND_ROBIN