def create_train_dataset(args, dataset_args=None): gt_extension = args.gt_extension if args.gt_extension is not None else DataSetType.gt_extension(args.dataset) # Training dataset print("Resolving input files") input_image_files = sorted(glob_all(args.files)) if not args.text_files: if gt_extension: gt_txt_files = [split_all_ext(f)[0] + gt_extension for f in input_image_files] else: gt_txt_files = [None] * len(input_image_files) else: gt_txt_files = sorted(glob_all(args.text_files)) input_image_files, gt_txt_files = keep_files_with_same_file_name(input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of file: {} and {}".format(img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception("Some image are occurring more than once in the data set.") dataset = create_dataset( args.dataset, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, skip_invalid=not args.no_skip_invalid_gt, args=dataset_args if dataset_args else {}, ) print("Found {} files in the dataset".format(len(dataset))) return dataset
def create_test_dataset( cfg: CfgNode, dataset_args=None ) -> Union[List[Union[RawDataSet, FileDataSet, AbbyyDataSet, PageXMLDataset, Hdf5DataSet, ExtendedPredictionDataSet, GeneratedLineDataset]], None]: if cfg.DATASET.VALID.TEXT_FILES: assert len(cfg.DATASET.VALID.PATH) == len(cfg.DATASET.VALID.TEXT_FILES) if cfg.DATASET.VALID.PATH: validation_dataset_list = [] print("Resolving validation files") for i, valid_path in enumerate(cfg.DATASET.VALID.PATH): validation_image_files = glob_all(valid_path) dataregistry.register( i, os.path.basename(os.path.dirname(valid_path)), len(validation_image_files)) if not cfg.DATASET.VALID.TEXT_FILES: val_txt_files = [ split_all_ext(f)[0] + cfg.DATASET.VALID.GT_EXTENSION for f in validation_image_files ] else: val_txt_files = sorted( glob_all(cfg.DATASET.VALID.TEXT_FILES[i])) validation_image_files, val_txt_files = keep_files_with_same_file_name( validation_image_files, val_txt_files) for img, gt in zip(validation_image_files, val_txt_files): if split_all_ext( os.path.basename(img))[0] != split_all_ext( os.path.basename(gt))[0]: raise Exception( "Expected identical basenames of validation file: {} and {}" .format(img, gt)) if len(set(val_txt_files)) != len(val_txt_files): raise Exception( "Some validation images are occurring more than once in the data set." ) validation_dataset = create_dataset( cfg.DATASET.VALID.TYPE, DataSetMode.TRAIN, images=validation_image_files, texts=val_txt_files, skip_invalid=not cfg.DATALOADER.NO_SKIP_INVALID_GT, args=dataset_args, ) print("Found {} files in the validation dataset".format( len(validation_dataset))) validation_dataset_list.append(validation_dataset) else: validation_dataset_list = None return validation_dataset_list
def main(): parser = ArgumentParser() parser.add_argument("--pred", nargs="+", required=True, help="Extended prediction files (.json extension)") args = parser.parse_args() print("Resolving files") pred_files = sorted(glob_all(args.pred)) data_set = create_dataset( DataSetType.EXTENDED_PREDICTION, DataSetMode.EVAL, texts=pred_files, ) print('Average confidence: {:.2%}'.format(np.mean([s['best_prediction'].avg_char_probability for s in data_set.samples()])))
def main(): parser = ArgumentParser() parser.add_argument("--pred", nargs="+", required=True, help="Extended prediction files (.json extension)") args = parser.parse_args() print("Resolving files") pred_files = sorted(glob_all(args.pred)) data_set = create_dataset( DataSetType.EXTENDED_PREDICTION, DataSetMode.EVAL, texts=pred_files, ) data_set.load_samples(progress_bar=True) print('Average confidence: {:.2%}'.format(np.mean([s['best_prediction'].avg_char_probability for s in data_set.samples()])))
def create_train_dataset(cfg: CfgNode, dataset_args=None): gt_extension = cfg.DATASET.TRAIN.GT_EXTENSION if cfg.DATASET.TRAIN.GT_EXTENSION is not False else DataSetType.gt_extension( cfg.DATASET.TRAIN.TYPE) # Training dataset print("Resolving input files") input_image_files = sorted(glob_all(cfg.DATASET.TRAIN.PATH)) if not cfg.DATASET.TRAIN.TEXT_FILES: if gt_extension: gt_txt_files = [ split_all_ext(f)[0] + gt_extension for f in input_image_files ] else: gt_txt_files = [None] * len(input_image_files) else: gt_txt_files = sorted(glob_all(cfg.DATASET.TRAIN.TEXT_FILES)) input_image_files, gt_txt_files = keep_files_with_same_file_name( input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext( os.path.basename(gt))[0]: raise Exception( "Expected identical basenames of file: {} and {}".format( img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception( "Some image are occurring more than once in the data set.") dataset = create_dataset( cfg.DATASET.TRAIN.TYPE, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, skip_invalid=not cfg.DATALOADER.NO_SKIP_INVALID_GT, args=dataset_args if dataset_args else {}, ) print("Found {} files in the dataset".format(len(dataset))) return dataset
def main(): parser = ArgumentParser() parser.add_argument("--dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument( "--gt", nargs="+", required=True, help="Ground truth files (.gt.txt extension). " "Optionally, you can pass a single json file defining all parameters.") parser.add_argument( "--pred", nargs="+", default=None, help= "Prediction files if provided. Else files with .pred.txt are expected at the same " "location as the gt.") parser.add_argument("--pred_dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--pred_ext", type=str, default=".pred.txt", help="Extension of the predicted text files") parser.add_argument( "--n_confusions", type=int, default=10, help= "Only print n most common confusions. Defaults to 10, use -1 for all.") parser.add_argument( "--n_worst_lines", type=int, default=0, help="Print the n worst recognized text lines with its error") parser.add_argument( "--xlsx_output", type=str, help="Optionally write a xlsx file with the evaluation results") parser.add_argument("--num_threads", type=int, default=1, help="Number of threads to use for evaluation") parser.add_argument( "--non_existing_file_handling_mode", type=str, default="error", help= "How to handle non existing .pred.txt files. Possible modes: skip, empty, error. " "'Skip' will simply skip the evaluation of that file (not counting it to errors). " "'Empty' will handle this file as would it be empty (fully checking for errors)." "'Error' will throw an exception if a file is not existing. This is the default behaviour." ) parser.add_argument("--skip_empty_gt", action="store_true", default=False, help="Ignore lines of the gt that are empty.") parser.add_argument("--no_progress_bars", action="store_true", help="Do not show any progress bars") parser.add_argument( "--checkpoint", type=str, default=None, help= "Specify an optional checkpoint to parse the text preprocessor (for the gt txt files)" ) # page xml specific args parser.add_argument("--pagexml_gt_text_index", default=0) parser.add_argument("--pagexml_pred_text_index", default=1) args = parser.parse_args() # check if loading a json file if len(args.gt) == 1 and args.gt[0].endswith("json"): with open(args.gt[0], 'r') as f: json_args = json.load(f) for key, value in json_args.items(): setattr(args, key, value) print("Resolving files") gt_files = sorted(glob_all(args.gt)) if args.pred: pred_files = sorted(glob_all(args.pred)) else: pred_files = [split_all_ext(gt)[0] + args.pred_ext for gt in gt_files] args.pred_dataset = args.dataset if args.non_existing_file_handling_mode.lower() == "skip": non_existing_pred = [p for p in pred_files if not os.path.exists(p)] for f in non_existing_pred: idx = pred_files.index(f) del pred_files[idx] del gt_files[idx] text_preproc = None if args.checkpoint: with open( args.checkpoint if args.checkpoint.endswith(".json") else args.checkpoint + '.json', 'r') as f: checkpoint_params = json_format.Parse(f.read(), CheckpointParams()) text_preproc = text_processor_from_proto( checkpoint_params.model.text_preprocessor) non_existing_as_empty = args.non_existing_file_handling_mode.lower( ) != "error " gt_data_set = create_dataset( args.dataset, DataSetMode.EVAL, texts=gt_files, non_existing_as_empty=non_existing_as_empty, args={'text_index': args.pagexml_gt_text_index}, ) pred_data_set = create_dataset( args.pred_dataset, DataSetMode.EVAL, texts=pred_files, non_existing_as_empty=non_existing_as_empty, args={'text_index': args.pagexml_pred_text_index}, ) evaluator = Evaluator(text_preprocessor=text_preproc, skip_empty_gt=args.skip_empty_gt) r = evaluator.run(gt_dataset=gt_data_set, pred_dataset=pred_data_set, processes=args.num_threads, progress_bar=not args.no_progress_bars) # TODO: More output print("Evaluation result") print("=================") print("") print( "Got mean normalized label error rate of {:.2%} ({} errs, {} total chars, {} sync errs)" .format(r["avg_ler"], r["total_char_errs"], r["total_chars"], r["total_sync_errs"])) # sort descending print_confusions(r, args.n_confusions) print_worst_lines(r, gt_data_set.samples(), args.n_worst_lines) if args.xlsx_output: write_xlsx(args.xlsx_output, [{ "prefix": "evaluation", "results": r, "gt_files": gt_files, }])
def run(args): # check if loading a json file if len(args.files) == 1 and args.files[0].endswith("json"): import json with open(args.files[0], 'r') as f: json_args = json.load(f) for key, value in json_args.items(): if key == 'dataset' or key == 'validation_dataset': setattr(args, key, DataSetType.from_string(value)) else: setattr(args, key, value) # parse whitelist whitelist = args.whitelist if len(whitelist) == 1: whitelist = list(whitelist[0]) whitelist_files = glob_all(args.whitelist_files) for f in whitelist_files: with open(f) as txt: whitelist += list(txt.read()) if args.gt_extension is None: args.gt_extension = DataSetType.gt_extension(args.dataset) if args.validation_extension is None: args.validation_extension = DataSetType.gt_extension(args.validation_dataset) if args.text_generator_params is not None: with open(args.text_generator_params, 'r') as f: args.text_generator_params = json_format.Parse(f.read(), TextGeneratorParameters()) else: args.text_generator_params = TextGeneratorParameters() if args.line_generator_params is not None: with open(args.line_generator_params, 'r') as f: args.line_generator_params = json_format.Parse(f.read(), LineGeneratorParameters()) else: args.line_generator_params = LineGeneratorParameters() dataset_args = { 'line_generator_params': args.line_generator_params, 'text_generator_params': args.text_generator_params, 'pad': args.dataset_pad, 'text_index': args.pagexml_text_index, } # Training dataset dataset = create_train_dataset(args, dataset_args) # Validation dataset if args.validation: print("Resolving validation files") validation_image_files = glob_all(args.validation) if not args.validation_text_files: val_txt_files = [split_all_ext(f)[0] + args.validation_extension for f in validation_image_files] else: val_txt_files = sorted(glob_all(args.validation_text_files)) validation_image_files, val_txt_files = keep_files_with_same_file_name(validation_image_files, val_txt_files) for img, gt in zip(validation_image_files, val_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of validation file: {} and {}".format(img, gt)) if len(set(val_txt_files)) != len(val_txt_files): raise Exception("Some validation images are occurring more than once in the data set.") validation_dataset = create_dataset( args.validation_dataset, DataSetMode.TRAIN, images=validation_image_files, texts=val_txt_files, skip_invalid=not args.no_skip_invalid_gt, args=dataset_args, ) print("Found {} files in the validation dataset".format(len(validation_dataset))) else: validation_dataset = None params = CheckpointParams() params.max_iters = args.max_iters params.stats_size = args.stats_size params.batch_size = args.batch_size params.checkpoint_frequency = args.checkpoint_frequency if args.checkpoint_frequency >= 0 else args.early_stopping_frequency params.output_dir = args.output_dir params.output_model_prefix = args.output_model_prefix params.display = args.display params.skip_invalid_gt = not args.no_skip_invalid_gt params.processes = args.num_threads params.data_aug_retrain_on_original = not args.only_train_on_augmented params.early_stopping_frequency = args.early_stopping_frequency params.early_stopping_nbest = args.early_stopping_nbest params.early_stopping_best_model_prefix = args.early_stopping_best_model_prefix params.early_stopping_best_model_output_dir = \ args.early_stopping_best_model_output_dir if args.early_stopping_best_model_output_dir else args.output_dir if args.data_preprocessing is None or len(args.data_preprocessing) == 0: args.data_preprocessing = [DataPreprocessorParams.DEFAULT_NORMALIZER] params.model.data_preprocessor.type = DataPreprocessorParams.MULTI_NORMALIZER for preproc in args.data_preprocessing: pp = params.model.data_preprocessor.children.add() pp.type = DataPreprocessorParams.Type.Value(preproc) if isinstance(preproc, str) else preproc pp.line_height = args.line_height pp.pad = args.pad # Text pre processing (reading) params.model.text_preprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(params.model.text_preprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(params.model.text_preprocessor.children.add(), groups=args.text_regularization) strip_processor_params = params.model.text_preprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER # Text post processing (prediction) params.model.text_postprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(params.model.text_postprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(params.model.text_postprocessor.children.add(), groups=args.text_regularization) strip_processor_params = params.model.text_postprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER if args.seed > 0: params.model.network.backend.random_seed = args.seed if args.bidi_dir: # change bidirectional text direction if desired bidi_dir_to_enum = {"rtl": TextProcessorParams.BIDI_RTL, "ltr": TextProcessorParams.BIDI_LTR, "auto": TextProcessorParams.BIDI_AUTO} bidi_processor_params = params.model.text_preprocessor.children.add() bidi_processor_params.type = TextProcessorParams.BIDI_NORMALIZER bidi_processor_params.bidi_direction = bidi_dir_to_enum[args.bidi_dir] bidi_processor_params = params.model.text_postprocessor.children.add() bidi_processor_params.type = TextProcessorParams.BIDI_NORMALIZER bidi_processor_params.bidi_direction = TextProcessorParams.BIDI_AUTO params.model.line_height = args.line_height network_params_from_definition_string(args.network, params.model.network) params.model.network.clipping_mode = NetworkParams.ClippingMode.Value("CLIP_" + args.gradient_clipping_mode.upper()) params.model.network.clipping_constant = args.gradient_clipping_const params.model.network.backend.fuzzy_ctc_library_path = args.fuzzy_ctc_library_path params.model.network.backend.num_inter_threads = args.num_inter_threads params.model.network.backend.num_intra_threads = args.num_intra_threads params.model.network.backend.shuffle_buffer_size = args.shuffle_buffer_size # create the actual trainer trainer = Trainer(params, dataset, validation_dataset=validation_dataset, data_augmenter=SimpleDataAugmenter(), n_augmentations=args.n_augmentations, weights=args.weights, codec_whitelist=whitelist, keep_loaded_codec=args.keep_loaded_codec, preload_training=not args.train_data_on_the_fly, preload_validation=not args.validation_data_on_the_fly, ) trainer.train( auto_compute_codec=not args.no_auto_compute_codec, progress_bar=not args.no_progress_bars )
def run(args): # check if loading a json file if len(args.files) == 1 and args.files[0].endswith("json"): import json with open(args.files[0], 'r') as f: json_args = json.load(f) for key, value in json_args.items(): setattr(args, key, value) # checks if args.extended_prediction_data_format not in ["pred", "json"]: raise Exception( "Only 'pred' and 'json' are allowed extended prediction data formats" ) # add json as extension, resolve wildcard, expand user, ... and remove .json again args.checkpoint = [(cp if cp.endswith(".json") else cp + ".json") for cp in args.checkpoint] args.checkpoint = glob_all(args.checkpoint) args.checkpoint = [cp[:-5] for cp in args.checkpoint] # create voter voter_params = VoterParams() voter_params.type = VoterParams.Type.Value(args.voter.upper()) voter = voter_from_proto(voter_params) # load files input_image_files = glob_all(args.files) if args.text_files: args.text_files = glob_all(args.text_files) # skip invalid files and remove them, there wont be predictions of invalid files dataset = create_dataset( args.dataset, DataSetMode.PREDICT, input_image_files, args.text_files, skip_invalid=True, remove_invalid=True, args={'text_index': args.pagexml_text_index}, ) print("Found {} files in the dataset".format(len(dataset))) if len(dataset) == 0: raise Exception( "Empty dataset provided. Check your files argument (got {})!". format(args.files)) # predict for all models predictor = MultiPredictor(checkpoints=args.checkpoint, batch_size=args.batch_size, processes=args.processes) do_prediction = predictor.predict_dataset( dataset, progress_bar=not args.no_progress_bars) avg_sentence_confidence = 0 n_predictions = 0 # output the voted results to the appropriate files for result, sample in do_prediction: n_predictions += 1 for i, p in enumerate(result): p.prediction.id = "fold_{}".format(i) # vote the results (if only one model is given, this will just return the sentences) prediction = voter.vote_prediction_result(result) prediction.id = "voted" sentence = prediction.sentence avg_sentence_confidence += prediction.avg_char_probability if args.verbose: lr = "\u202A\u202B" print("{}: '{}{}{}'".format(sample['id'], lr[get_base_level(sentence)], sentence, "\u202C")) output_dir = args.output_dir dataset.store_text(sentence, sample, output_dir=output_dir, extension=".pred.txt") if args.extended_prediction_data: ps = Predictions() ps.line_path = sample[ 'image_path'] if 'image_path' in sample else sample['id'] ps.predictions.extend([prediction] + [r.prediction for r in result]) output_dir = output_dir if output_dir else os.path.dirname( ps.line_path) if not os.path.exists(output_dir): os.mkdir(output_dir) if args.extended_prediction_data_format == "pred": with open(os.path.join(output_dir, sample['id'] + ".pred"), 'wb') as f: f.write(ps.SerializeToString()) elif args.extended_prediction_data_format == "json": with open(os.path.join(output_dir, sample['id'] + ".json"), 'w') as f: # remove logits for prediction in ps.predictions: prediction.logits.rows = 0 prediction.logits.cols = 0 prediction.logits.data[:] = [] f.write( MessageToJson(ps, including_default_value_fields=True)) else: raise Exception("Unknown prediction format.") print("Average sentence confidence: {:.2%}".format( avg_sentence_confidence / n_predictions)) dataset.store() print("All files written")
def main(): parser = argparse.ArgumentParser() parser.add_argument('--version', action='version', version='%(prog)s v' + __version__) parser.add_argument( "--files", nargs="+", help= "List all image files that shall be processed. Ground truth fils with the same " "base name but with '.gt.txt' as extension are required at the same location", required=True) parser.add_argument( "--text_files", nargs="+", default=None, help="Optional list of GT files if they are in other directory") parser.add_argument( "--gt_extension", default=None, help="Default extension of the gt files (expected to exist in same dir)" ) parser.add_argument("--dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--line_height", type=int, default=48, help="The line height") parser.add_argument("--pad", type=int, default=16, help="Padding (left right) of the line") parser.add_argument("--processes", type=int, default=1, help="The number of threads to use for all operations") parser.add_argument("--n_cols", type=int, default=1) parser.add_argument("--n_rows", type=int, default=5) parser.add_argument("--select", type=int, nargs="+", default=[]) # text normalization/regularization parser.add_argument( "--n_augmentations", type=float, default=0, help= "Amount of data augmentation per line (done before training). If this number is < 1 " "the amount is relative.") parser.add_argument("--text_regularization", type=str, nargs="+", default=["extended"], help="Text regularization to apply.") parser.add_argument( "--text_normalization", type=str, default="NFC", help="Unicode text normalization to apply. Defaults to NFC") parser.add_argument("--data_preprocessing", nargs="+", type=DataPreprocessorParams.Type.Value, choices=DataPreprocessorParams.Type.values(), default=[DataPreprocessorParams.DEFAULT_NORMALIZER]) args = parser.parse_args() # Text/Data processing if args.data_preprocessing is None or len(args.data_preprocessing) == 0: args.data_preprocessing = [DataPreprocessorParams.DEFAULT_NORMALIZER] data_preprocessor = DataPreprocessorParams() data_preprocessor.type = DataPreprocessorParams.MULTI_NORMALIZER for preproc in args.data_preprocessing: pp = data_preprocessor.children.add() pp.type = preproc pp.line_height = args.line_height pp.pad = args.pad # Text pre processing (reading) text_preprocessor = TextProcessorParams() text_preprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(text_preprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(text_preprocessor.children.add(), groups=args.text_regularization) strip_processor_params = text_preprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER text_preprocessor = text_processor_from_proto(text_preprocessor) data_preprocessor = data_processor_from_proto(data_preprocessor) print("Resolving input files") input_image_files = sorted(glob_all(args.files)) if not args.text_files: if args.gt_extension: gt_txt_files = [ split_all_ext(f)[0] + args.gt_extension for f in input_image_files ] else: gt_txt_files = [None] * len(input_image_files) else: gt_txt_files = sorted(glob_all(args.text_files)) input_image_files, gt_txt_files = keep_files_with_same_file_name( input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext( os.path.basename(gt))[0]: raise Exception( "Expected identical basenames of file: {} and {}".format( img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception( "Some image are occurring more than once in the data set.") dataset = create_dataset( args.dataset, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, non_existing_as_empty=True, ) if len(args.select) == 0: args.select = range(len(dataset.samples())) dataset._samples = dataset.samples() else: dataset._samples = [dataset.samples()[i] for i in args.select] samples = dataset.samples() print("Found {} files in the dataset".format(len(dataset))) with StreamingInputDataset( dataset, data_preprocessor, text_preprocessor, SimpleDataAugmenter(), args.n_augmentations, ) as input_dataset: f, ax = plt.subplots(args.n_rows, args.n_cols, sharey='all') row, col = 0, 0 for i, (id, sample) in enumerate( zip(args.select, input_dataset.generator(args.processes))): line, text, params = sample if args.n_cols == 1: ax[row].imshow(line.transpose()) ax[row].set_title("ID: {}\n{}".format(id, text)) else: ax[row, col].imshow(line.transpose()) ax[row, col].set_title("ID: {}\n{}".format(id, text)) row += 1 if row == args.n_rows: row = 0 col += 1 if col == args.n_cols or i == len(samples) - 1: plt.show() f, ax = plt.subplots(args.n_rows, args.n_cols, sharey='all') row, col = 0, 0
def run(args): # check if loading a json file if len(args.files) == 1 and args.files[0].endswith("json"): import json with open(args.files[0], 'r') as f: json_args = json.load(f) for key, value in json_args.items(): setattr(args, key, value) # parse whitelist whitelist = args.whitelist if len(whitelist) == 1: whitelist = list(whitelist[0]) whitelist_files = glob_all(args.whitelist_files) for f in whitelist_files: with open(f) as txt: whitelist += list(txt.read()) if args.gt_extension is None: args.gt_extension = DataSetType.gt_extension(args.dataset) if args.validation_extension is None: args.validation_extension = DataSetType.gt_extension(args.validation_dataset) # Training dataset print("Resolving input files") input_image_files = sorted(glob_all(args.files)) if not args.text_files: gt_txt_files = [split_all_ext(f)[0] + args.gt_extension for f in input_image_files] else: gt_txt_files = sorted(glob_all(args.text_files)) input_image_files, gt_txt_files = keep_files_with_same_file_name(input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of file: {} and {}".format(img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception("Some image are occurring more than once in the data set.") dataset = create_dataset( args.dataset, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, skip_invalid=not args.no_skip_invalid_gt ) print("Found {} files in the dataset".format(len(dataset))) # Validation dataset if args.validation: print("Resolving validation files") validation_image_files = glob_all(args.validation) if not args.validation_text_files: val_txt_files = [split_all_ext(f)[0] + args.validation_extension for f in validation_image_files] else: val_txt_files = sorted(glob_all(args.validation_text_files)) validation_image_files, val_txt_files = keep_files_with_same_file_name(validation_image_files, val_txt_files) for img, gt in zip(validation_image_files, val_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of validation file: {} and {}".format(img, gt)) if len(set(val_txt_files)) != len(val_txt_files): raise Exception("Some validation images are occurring more than once in the data set.") validation_dataset = create_dataset( args.validation_dataset, DataSetMode.TRAIN, images=validation_image_files, texts=val_txt_files, skip_invalid=not args.no_skip_invalid_gt) print("Found {} files in the validation dataset".format(len(validation_dataset))) else: validation_dataset = None params = CheckpointParams() params.max_iters = args.max_iters params.stats_size = args.stats_size params.batch_size = args.batch_size params.checkpoint_frequency = args.checkpoint_frequency if args.checkpoint_frequency >= 0 else args.early_stopping_frequency params.output_dir = args.output_dir params.output_model_prefix = args.output_model_prefix params.display = args.display params.skip_invalid_gt = not args.no_skip_invalid_gt params.processes = args.num_threads params.data_aug_retrain_on_original = not args.only_train_on_augmented params.early_stopping_frequency = args.early_stopping_frequency params.early_stopping_nbest = args.early_stopping_nbest params.early_stopping_best_model_prefix = args.early_stopping_best_model_prefix params.early_stopping_best_model_output_dir = \ args.early_stopping_best_model_output_dir if args.early_stopping_best_model_output_dir else args.output_dir params.model.data_preprocessor.type = DataPreprocessorParams.DEFAULT_NORMALIZER params.model.data_preprocessor.line_height = args.line_height params.model.data_preprocessor.pad = args.pad # Text pre processing (reading) params.model.text_preprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(params.model.text_preprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(params.model.text_preprocessor.children.add(), groups=args.text_regularization) strip_processor_params = params.model.text_preprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER # Text post processing (prediction) params.model.text_postprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(params.model.text_postprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(params.model.text_postprocessor.children.add(), groups=args.text_regularization) strip_processor_params = params.model.text_postprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER if args.seed > 0: params.model.network.backend.random_seed = args.seed if args.bidi_dir: # change bidirectional text direction if desired bidi_dir_to_enum = {"rtl": TextProcessorParams.BIDI_RTL, "ltr": TextProcessorParams.BIDI_LTR, "auto": TextProcessorParams.BIDI_AUTO} bidi_processor_params = params.model.text_preprocessor.children.add() bidi_processor_params.type = TextProcessorParams.BIDI_NORMALIZER bidi_processor_params.bidi_direction = bidi_dir_to_enum[args.bidi_dir] bidi_processor_params = params.model.text_postprocessor.children.add() bidi_processor_params.type = TextProcessorParams.BIDI_NORMALIZER bidi_processor_params.bidi_direction = TextProcessorParams.BIDI_AUTO params.model.line_height = args.line_height network_params_from_definition_string(args.network, params.model.network) params.model.network.clipping_mode = NetworkParams.ClippingMode.Value("CLIP_" + args.gradient_clipping_mode.upper()) params.model.network.clipping_constant = args.gradient_clipping_const params.model.network.backend.fuzzy_ctc_library_path = args.fuzzy_ctc_library_path params.model.network.backend.num_inter_threads = args.num_inter_threads params.model.network.backend.num_intra_threads = args.num_intra_threads # create the actual trainer trainer = Trainer(params, dataset, validation_dataset=validation_dataset, data_augmenter=SimpleDataAugmenter(), n_augmentations=args.n_augmentations, weights=args.weights, codec_whitelist=whitelist, preload_training=not args.train_data_on_the_fly, preload_validation=not args.validation_data_on_the_fly, ) trainer.train( auto_compute_codec=not args.no_auto_compute_codec, progress_bar=not args.no_progress_bars )
def main(): parser = ArgumentParser() parser.add_argument("--dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--gt", nargs="+", required=True, help="Ground truth files (.gt.txt extension)") parser.add_argument("--pred", nargs="+", default=None, help="Prediction files if provided. Else files with .pred.txt are expected at the same " "location as the gt.") parser.add_argument("--pred_dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--pred_ext", type=str, default=".pred.txt", help="Extension of the predicted text files") parser.add_argument("--n_confusions", type=int, default=10, help="Only print n most common confusions. Defaults to 10, use -1 for all.") parser.add_argument("--n_worst_lines", type=int, default=0, help="Print the n worst recognized text lines with its error") parser.add_argument("--xlsx_output", type=str, help="Optionally write a xlsx file with the evaluation results") parser.add_argument("--num_threads", type=int, default=1, help="Number of threads to use for evaluation") parser.add_argument("--non_existing_file_handling_mode", type=str, default="error", help="How to handle non existing .pred.txt files. Possible modes: skip, empty, error. " "'Skip' will simply skip the evaluation of that file (not counting it to errors). " "'Empty' will handle this file as would it be empty (fully checking for errors)." "'Error' will throw an exception if a file is not existing. This is the default behaviour.") parser.add_argument("--no_progress_bars", action="store_true", help="Do not show any progress bars") parser.add_argument("--checkpoint", type=str, default=None, help="Specify an optional checkpoint to parse the text preprocessor (for the gt txt files)") # page xml specific args parser.add_argument("--pagexml_gt_text_index", default=0) parser.add_argument("--pagexml_pred_text_index", default=1) args = parser.parse_args() print("Resolving files") gt_files = sorted(glob_all(args.gt)) if args.pred: pred_files = sorted(glob_all(args.pred)) else: pred_files = [split_all_ext(gt)[0] + args.pred_ext for gt in gt_files] args.pred_dataset = args.dataset if args.non_existing_file_handling_mode.lower() == "skip": non_existing_pred = [p for p in pred_files if not os.path.exists(p)] for f in non_existing_pred: idx = pred_files.index(f) del pred_files[idx] del gt_files[idx] text_preproc = None if args.checkpoint: with open(args.checkpoint if args.checkpoint.endswith(".json") else args.checkpoint + '.json', 'r') as f: checkpoint_params = json_format.Parse(f.read(), CheckpointParams()) text_preproc = text_processor_from_proto(checkpoint_params.model.text_preprocessor) non_existing_as_empty = args.non_existing_file_handling_mode.lower() != "error " gt_data_set = create_dataset( args.dataset, DataSetMode.EVAL, texts=gt_files, non_existing_as_empty=non_existing_as_empty, args={'text_index': args.pagexml_gt_text_index}, ) pred_data_set = create_dataset( args.pred_dataset, DataSetMode.EVAL, texts=pred_files, non_existing_as_empty=non_existing_as_empty, args={'text_index': args.pagexml_pred_text_index}, ) evaluator = Evaluator(text_preprocessor=text_preproc) r = evaluator.run(gt_dataset=gt_data_set, pred_dataset=pred_data_set, processes=args.num_threads, progress_bar=not args.no_progress_bars) # TODO: More output print("Evaluation result") print("=================") print("") print("Got mean normalized label error rate of {:.2%} ({} errs, {} total chars, {} sync errs)".format( r["avg_ler"], r["total_char_errs"], r["total_chars"], r["total_sync_errs"])) # sort descending print_confusions(r, args.n_confusions) print_worst_lines(r, gt_data_set.samples(), pred_data_set.text_samples(), args.n_worst_lines) if args.xlsx_output: write_xlsx(args.xlsx_output, [{ "prefix": "evaluation", "results": r, "gt_files": gt_files, "gts": gt_data_set.text_samples(), "preds": pred_data_set.text_samples() }])
def run(args): # check if loading a json file if len(args.files) == 1 and args.files[0].endswith("json"): import json with open(args.files[0], 'r') as f: json_args = json.load(f) for key, value in json_args.items(): setattr(args, key, value) # checks if args.extended_prediction_data_format not in ["pred", "json"]: raise Exception("Only 'pred' and 'json' are allowed extended prediction data formats") # add json as extension, resolve wildcard, expand user, ... and remove .json again args.checkpoint = [(cp if cp.endswith(".json") else cp + ".json") for cp in args.checkpoint] args.checkpoint = glob_all(args.checkpoint) args.checkpoint = [cp[:-5] for cp in args.checkpoint] # create voter voter_params = VoterParams() voter_params.type = VoterParams.Type.Value(args.voter.upper()) voter = voter_from_proto(voter_params) # load files input_image_files = glob_all(args.files) if args.text_files: args.text_files = glob_all(args.text_files) # skip invalid files and remove them, there wont be predictions of invalid files dataset = create_dataset( args.dataset, DataSetMode.PREDICT, input_image_files, args.text_files, skip_invalid=True, remove_invalid=True, args={'text_index': args.pagexml_text_index}, ) print("Found {} files in the dataset".format(len(dataset))) if len(dataset) == 0: raise Exception("Empty dataset provided. Check your files argument (got {})!".format(args.files)) # predict for all models predictor = MultiPredictor(checkpoints=args.checkpoint, batch_size=args.batch_size, processes=args.processes) do_prediction = predictor.predict_dataset(dataset, progress_bar=not args.no_progress_bars) avg_sentence_confidence = 0 n_predictions = 0 # output the voted results to the appropriate files for result, sample in do_prediction: n_predictions += 1 for i, p in enumerate(result): p.prediction.id = "fold_{}".format(i) # vote the results (if only one model is given, this will just return the sentences) prediction = voter.vote_prediction_result(result) prediction.id = "voted" sentence = prediction.sentence avg_sentence_confidence += prediction.avg_char_probability if args.verbose: lr = "\u202A\u202B" print("{}: '{}{}{}'".format(sample['id'], lr[get_base_level(sentence)], sentence, "\u202C" )) output_dir = args.output_dir dataset.store_text(sentence, sample, output_dir=output_dir, extension=".pred.txt") if args.extended_prediction_data: ps = Predictions() ps.line_path = sample['image_path'] if 'image_path' in sample else sample['id'] ps.predictions.extend([prediction] + [r.prediction for r in result]) output_dir = output_dir if output_dir else os.path.dirname(ps.line_path) if not os.path.exists(output_dir): os.mkdir(output_dir) if args.extended_prediction_data_format == "pred": with open(os.path.join(output_dir, sample['id'] + ".pred"), 'wb') as f: f.write(ps.SerializeToString()) elif args.extended_prediction_data_format == "json": with open(os.path.join(output_dir, sample['id'] + ".json"), 'w') as f: # remove logits for prediction in ps.predictions: prediction.logits.rows = 0 prediction.logits.cols = 0 prediction.logits.data[:] = [] f.write(MessageToJson(ps, including_default_value_fields=True)) else: raise Exception("Unknown prediction format.") print("Average sentence confidence: {:.2%}".format(avg_sentence_confidence / n_predictions)) dataset.store() print("All files written")