def main():
parser = argparse.ArgumentParser()
parser.add_argument("--base_dir", type=str, required=True,
help="The base directory where to store all working files")
parser.add_argument("--eval_files", type=str, nargs="+", required=True,
help="All files that shall be used for evaluation")
parser.add_argument("--train_files", type=str, nargs="+", required=True,
help="All files that shall be used for (cross-fold) training")
parser.add_argument("--n_lines", type=int, default=[-1], nargs="+",
help="Optional argument to specify the number of lines (images) used for training. "
"On default, all available lines will be used.")
parser.add_argument("--run", type=str, default=None,
help="An optional command that will receive the train calls. Useful e.g. when using a resource "
"manager such as slurm.")
parser.add_argument("--n_folds", type=int, default=5,
help="The number of fold, that is the number of models to train")
parser.add_argument("--max_parallel_models", type=int, default=-1,
help="Number of models to train in parallel per fold. Defaults to all.")
parser.add_argument("--weights", type=str, nargs="+", default=[],
help="Load network weights from the given file. If more than one file is provided the number "
"models must match the number of folds. Each fold is then initialized with the weights "
"of each model, respectively")
parser.add_argument("--single_fold", type=int, nargs="+", default=[],
help="Only train a single (list of single) specific fold(s).")
parser.add_argument("--skip_train", action="store_true",
help="Skip the cross fold training")
parser.add_argument("--skip_eval", action="store_true",
help="Skip the cross fold evaluation")
parser.add_argument("--verbose", action="store_true",
help="Verbose output")
parser.add_argument("--n_confusions", type=int, default=0,
help="Only print n most common confusions. Defaults to 0, use -1 for all.")
parser.add_argument("--xlsx_output", type=str,
help="Optionally write a xlsx file with the evaluation results")
setup_train_args(parser, omit=["files", "validation", "weights",
"early_stopping_best_model_output_dir", "early_stopping_best_model_prefix",
"output_dir"])
args = parser.parse_args()
args.base_dir = os.path.abspath(os.path.expanduser(args.base_dir))
np.random.seed(args.seed)
random.seed(args.seed)
# argument checks
args.weights = glob_all(args.weights)
if len(args.weights) > 1 and len(args.weights) != args.n_folds:
raise Exception("Either no, one or n_folds (={}) models are required for pretraining but got {}.".format(
args.n_folds, len(args.weights)
))
if len(args.single_fold) > 0:
if len(set(args.single_fold)) != len(args.single_fold):
raise Exception("Repeated fold id's found.")
for fold_id in args.single_fold:
if fold_id < 0 or fold_id >= args.n_folds:
raise Exception("Invalid fold id found: 0 <= id <= {}, but id == {}".format(args.n_folds, fold_id))
actual_folds = args.single_fold
else:
actual_folds = list(range(args.n_folds))
# run for all lines
single_args = [copy.copy(args) for _ in args.n_lines]
for s_args, n_lines in zip(single_args, args.n_lines):
s_args.n_lines = n_lines
predictions = parallel_map(run_for_single_line, single_args, progress_bar=False, processes=len(single_args), use_thread_pool=True)
# output predictions as csv:
header = "lines," + ",".join([str(fold) for fold in range(args.n_folds)])\
+ ",avg,std,seq. vot., def. conf. vot., fuz. conf. vot."
print(header)
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
data = "{}".format(n_lines)
folds_lers = []
for fold in range(len(actual_folds)):
eval = prediction[str(fold)]["eval"]
data += ",{}".format(eval['avg_ler'])
folds_lers.append(eval['avg_ler'])
data += ",{},{}".format(np.mean(folds_lers), np.std(folds_lers))
for voter in ['sequence_voter', 'confidence_voter_default_ctc', 'confidence_voter_fuzzy_ctc']:
eval = prediction[voter]["eval"]
data += ",{}".format(eval['avg_ler'])
print(data)
if args.n_confusions != 0:
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
print("")
print("CONFUSIONS (lines = {})".format(n_lines))
print("==========")
print()
for fold in range(len(actual_folds)):
print("FOLD {}".format(fold))
print_confusions(prediction[str(fold)]['eval'], args.n_confusions)
for voter in ['sequence_voter', 'confidence_voter_default_ctc', 'confidence_voter_fuzzy_ctc']:
print("VOTER {}".format(voter))
print_confusions(prediction[voter]['eval'], args.n_confusions)
if args.xlsx_output:
data_list = []
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
for fold in actual_folds:
pred = prediction[str(fold)]
data_list.append({
"prefix": "L{} - Fold{}".format(n_lines, fold),
"results": pred['eval'],
"gt_files": prediction_map['gt_txts'],
"gts": prediction_map['gt'],
"preds": pred['data']
})
for voter in ['sequence_voter', 'confidence_voter_default_ctc']:
pred = prediction[voter]
data_list.append({
"prefix": "L{} - {}".format(n_lines, voter[:3]),
"results": pred['eval'],
"gt_files": prediction_map['gt_txts'],
"gts": prediction_map['gt'],
"preds": pred['data']
})
write_xlsx(args.xlsx_output, data_list)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--base_dir", type=str, required=True,
help="The base directory where to store all working files")
parser.add_argument("--eval_files", type=str, nargs="+", required=True,
help="All files that shall be used for evaluation")
parser.add_argument("--train_files", type=str, nargs="+", required=True,
help="All files that shall be used for (cross-fold) training")
parser.add_argument("--n_lines", type=int, default=[-1], nargs="+",
help="Optional argument to specify the number of lines (images) used for training. "
"On default, all available lines will be used.")
parser.add_argument("--run", type=str, default=None,
help="An optional command that will receive the train calls. Useful e.g. when using a resource "
"manager such as slurm.")
parser.add_argument("--n_folds", type=int, default=5,
help="The number of fold, that is the number of models to train")
parser.add_argument("--max_parallel_models", type=int, default=-1,
help="Number of models to train in parallel per fold. Defaults to all.")
parser.add_argument("--weights", type=str, nargs="+", default=[],
help="Load network weights from the given file. If more than one file is provided the number "
"models must match the number of folds. Each fold is then initialized with the weights "
"of each model, respectively")
parser.add_argument("--single_fold", type=int, nargs="+", default=[],
help="Only train a single (list of single) specific fold(s).")
parser.add_argument("--skip_train", action="store_true",
help="Skip the cross fold training")
parser.add_argument("--skip_eval", action="store_true",
help="Skip the cross fold evaluation")
parser.add_argument("--verbose", action="store_true",
help="Verbose output")
parser.add_argument("--n_confusions", type=int, default=0,
help="Only print n most common confusions. Defaults to 0, use -1 for all.")
parser.add_argument("--xlsx_output", type=str,
help="Optionally write a xlsx file with the evaluation results")
setup_train_args(parser, omit=["files", "validation", "weights",
"early_stopping_best_model_output_dir", "early_stopping_best_model_prefix",
"output_dir"])
args = parser.parse_args()
args.base_dir = os.path.abspath(os.path.expanduser(args.base_dir))
np.random.seed(args.seed)
random.seed(args.seed)
# argument checks
args.weights = glob_all(args.weights)
if len(args.weights) > 1 and len(args.weights) != args.n_folds:
raise Exception("Either no, one or n_folds (={}) models are required for pretraining but got {}.".format(
args.n_folds, len(args.weights)
))
if len(args.single_fold) > 0:
if len(set(args.single_fold)) != len(args.single_fold):
raise Exception("Repeated fold id's found.")
for fold_id in args.single_fold:
if fold_id < 0 or fold_id >= args.n_folds:
raise Exception("Invalid fold id found: 0 <= id <= {}, but id == {}".format(args.n_folds, fold_id))
actual_folds = args.single_fold
else:
actual_folds = list(range(args.n_folds))
# run for all lines
single_args = [copy.copy(args) for _ in args.n_lines]
for s_args, n_lines in zip(single_args, args.n_lines):
s_args.n_lines = n_lines
predictions = parallel_map(run_for_single_line, single_args, progress_bar=False, processes=len(single_args), use_thread_pool=True)
# output predictions as csv:
header = "lines," + ",".join([str(fold) for fold in range(args.n_folds)])\
+ ",avg,std,seq. vot., def. conf. vot., fuz. conf. vot."
print(header)
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
data = "{}".format(n_lines)
folds_lers = []
for fold in range(len(actual_folds)):
eval = prediction[str(fold)]["eval"]
data += ",{}".format(eval['avg_ler'])
folds_lers.append(eval['avg_ler'])
data += ",{},{}".format(np.mean(folds_lers), np.std(folds_lers))
for voter in ['sequence_voter', 'confidence_voter_default_ctc']:
eval = prediction[voter]["eval"]
data += ",{}".format(eval['avg_ler'])
print(data)
if args.n_confusions != 0:
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
print("")
print("CONFUSIONS (lines = {})".format(n_lines))
print("==========")
print()
for fold in range(len(actual_folds)):
print("FOLD {}".format(fold))
print_confusions(prediction[str(fold)]['eval'], args.n_confusions)
for voter in ['sequence_voter', 'confidence_voter_default_ctc']:
print("VOTER {}".format(voter))
print_confusions(prediction[voter]['eval'], args.n_confusions)
if args.xlsx_output:
data_list = []
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
for fold in actual_folds:
pred = prediction[str(fold)]
data_list.append({
"prefix": "L{} - Fold{}".format(n_lines, fold),
"results": pred['eval'],
"gt_files": prediction_map['gt_txts'],
"gts": prediction_map['gt'],
"preds": pred['data']
})
for voter in ['sequence_voter', 'confidence_voter_default_ctc']:
pred = prediction[voter]
data_list.append({
"prefix": "L{} - {}".format(n_lines, voter[:3]),
"results": pred['eval'],
"gt_files": prediction_map['gt_txts'],
"gts": prediction_map['gt'],
"preds": pred['data']
})
write_xlsx(args.xlsx_output, data_list)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--base_dir", type=str, required=True,
help="The base directory where to store all working files")
parser.add_argument("--eval_files", type=str, nargs="+", required=True,
help="All files that shall be used for evaluation")
parser.add_argument("--n_lines", type=int, default=[-1], nargs="+",
help="Optional argument to specify the number of lines (images) used for training. "
"On default, all available lines will be used.")
parser.add_argument("--run", type=str, default=None,
help="An optional command that will receive the train calls. Useful e.g. when using a resource "
"manager such as slurm.")
parser.add_argument("--skip_train", action="store_true",
help="Skip the cross fold training")
parser.add_argument("--skip_eval", action="store_true",
help="Skip the cross fold evaluation")
parser.add_argument("--verbose", action="store_true",
help="Verbose output")
parser.add_argument("--n_confusions", type=int, default=0,
help="Only print n most common confusions. Defaults to 0, use -1 for all.")
parser.add_argument("--xlsx_output", type=str,
help="Optionally write a xlsx file with the evaluation results")
setup_train_args(parser, omit=["early_stopping_best_model_output_dir", "output_dir"])
args = parser.parse_args()
args.base_dir = os.path.abspath(os.path.expanduser(args.base_dir))
np.random.seed(args.seed)
random.seed(args.seed)
# run for all lines
single_args = [copy.copy(args) for _ in args.n_lines]
for s_args, n_lines in zip(single_args, args.n_lines):
s_args.n_lines = n_lines
predictions = parallel_map(run_for_single_line, single_args, progress_bar=False, processes=len(single_args), use_thread_pool=True)
predictions = list(predictions)
# output predictions as csv:
header = "lines," + ",".join([str(fold) for fold in range(len(predictions[0]["full"]) - 1)])\
+ ",avg,std,voted"
print(header)
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
data = "{}".format(n_lines)
folds_lers = []
for fold, pred in prediction.items():
if fold == 'voted':
continue
eval = pred["eval"]
data += ",{}".format(eval['avg_ler'])
folds_lers.append(eval['avg_ler'])
data += ",{},{}".format(np.mean(folds_lers), np.std(folds_lers))
eval = prediction['voted']["eval"]
data += ",{}".format(eval['avg_ler'])
print(data)
if args.n_confusions != 0:
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
print("")
print("CONFUSIONS (lines = {})".format(n_lines))
print("==========")
print()
for fold, pred in prediction.items():
print("FOLD {}".format(fold))
print_confusions(pred['eval'], args.n_confusions)
if args.xlsx_output:
data_list = []
for prediction_map, n_lines in zip(predictions, args.n_lines):
prediction = prediction_map["full"]
for fold, pred in prediction.items():
data_list.append({
"prefix": "L{} - Fold{}".format(n_lines, fold),
"results": pred['eval'],
"gt_files": prediction_map['gt_txts'],
"gts": prediction_map['gt'],
"preds": pred['data']
})
for voter in ['sequence_voter', 'confidence_voter_default_ctc']:
pred = prediction[voter]
data_list.append({
"prefix": "L{} - {}".format(n_lines, voter[:3]),
"results": pred['eval'],
"gt_files": prediction_map['gt_txts'],
"gts": prediction_map['gt'],
"preds": pred['data']
})
write_xlsx(args.xlsx_output, data_list)