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read_results.py
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read_results.py
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import os
import evaluation
import pandas as pd
from operator import itemgetter
from config import load_from_dict
def get_params(cfg):
params_lines = []
for k, v in sorted(cfg.get_as_dict().items(), key=lambda x: x[0]):
params_lines.append(f'{k}:{v}')
return ','.join(params_lines)
def get_run_config(run_path, fold):
params = {}
if fold is not None:
params_file = os.path.join(run_path, f'FOLD_{fold}', 'run_params.txt')
else:
params_file = os.path.join(run_path, 'run_params.txt')
with open(params_file, 'r') as f:
for l in f.readlines():
k, v = l.split(':')
params[k] = v.strip()
return load_from_dict(params)
def read_results(results_path, dataset, folds=None, dagm_join=False, sortkey=itemgetter(0)):
results = []
results_columns = ['RUN_NAME',
'TN', "N_SEG",
'W_SEG_LOSS', 'W_P', 'W_MAX',
'FRQ_SMP', 'DYN_B_L', 'DELTA',
'EPS', 'LR',
'AUC', 'AP',
'FP', 'FN', 'FALSES', 'THRESH',
"F_MSR", "CLS_ACC", "TPR", "TNR",
'50_FP', '50_FN', '50_FALSES', '50_FMS', '50_CA',
'FP@FN=0', 'THRESH@FN=0',
'PATH', 'CONFIGURATION'
]
if dataset == "DAGM" and not dagm_join:
for f in folds:
process_dataset(results_path, dataset, [f], results, dagm_join)
else:
process_dataset(results_path, dataset, folds, results, dagm_join)
results = sorted(results, key=sortkey)
df = pd.DataFrame(results, columns=results_columns)
return df
def process_dataset(results_path, dataset, folds, results, dagm_join):
for run_name in os.listdir(os.path.join(results_path, dataset)):
run_path = os.path.join(results_path, dataset, run_name)
try:
print(f"Processing run_path: {run_path}")
cfg = get_run_config(run_path, None if folds is None else folds[0])
ap, auc, fps, fns, t50_fps, t50_fns, fn0s, f_measure, cls_acc, f_measure_50, cls_acc_50, tpr, tnr = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
best_t, fn0_t = -1, -1
for f in folds:
t_dec, t_folds, t_gt, t_img_names, t_preds = evaluation.read_predictions(f, '', run_path)
fold_eval_res = evaluation.evaluate_fold(run_path, t_folds, t_gt, t_img_names, t_preds)
if len(folds) == 1:
best_t = fold_eval_res["best_t"]
fn0_t = fold_eval_res["fn0_t"]
ap += fold_eval_res["ap"]
auc += fold_eval_res["auc"]
fps += fold_eval_res["fps"]
fns += fold_eval_res["fns"]
t50_fps += fold_eval_res["t50_fps"]
t50_fns += fold_eval_res["t50_fns"]
fn0s += fold_eval_res["fn0s"]
f_measure += fold_eval_res["f_measure"]
cls_acc += fold_eval_res["cls_acc"]
f_measure_50 += fold_eval_res["f_measure_50"]
cls_acc_50 += fold_eval_res["cls_acc_50"]
tpr += fold_eval_res["tpr"]
tnr += fold_eval_res["tnr"]
ap /= len(folds)
auc /= len(folds)
f_measure /= len(folds)
cls_acc /= len(folds)
f_measure_50 /= len(folds)
cls_acc_50 /= len(folds)
tpr /= len(folds)
tnr /= len(folds)
if dataset == "DAGM" and not dagm_join:
run_name = f"{run_name}_FOLD_{folds[0]}"
results.append(
[run_name,
cfg.TRAIN_NUM, cfg.NUM_SEGMENTED,
cfg.WEIGHTED_SEG_LOSS, cfg.WEIGHTED_SEG_LOSS_P, cfg.WEIGHTED_SEG_LOSS_MAX,
cfg.FREQUENCY_SAMPLING, cfg.DYN_BALANCED_LOSS, cfg.DELTA_CLS_LOSS,
cfg.EPOCHS, cfg.LEARNING_RATE,
f"{auc:.5f}", f"{ap:.5f}",
fps, fns, fps + fns, f"{best_t:.5f}",
f"{f_measure:.5f}", f"{cls_acc:.5f}", f"{tpr:.5f}", f"{tnr:.5f}",
t50_fps, t50_fns, t50_fps + t50_fns, f"{f_measure_50:.5f}", f"{cls_acc_50:.5f}",
fn0s, f"{fn0_t:.5f}",
run_path, get_params(cfg)]
)
except Exception as f:
print(f'Error reading RUN {run_path} with Exception {f} ')
def main():
# dataset,results_folder = "STEEL", '/home/jakob/outputs/WEAKLY_LABELED/STEEL/GRADIENT'
# dataset, results_folder = "KSDD2", '/home/jakob/outputs/WEAKLY_LABELED/KSDD2/GRADIENT'
# dataset, results_folder = "DAGM", '/home/jakob/outputs/WEAKLY_LABELED/DAGM/GS'
dataset, results_folder = "KSDD", '/home/jakob/outputs/WEAKLY_LABELED/RELEASE/'
dagm_join = False # If True will join(average) results for all classes
folds_dict = {
'KSDD': [0, 1, 2],
'KSDD2': [None],
'STEEL': [None],
'DAGM': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
}
results = read_results(results_folder, dataset, folds_dict[dataset], dagm_join, sortkey=itemgetter(0))
results.to_csv(os.path.join(results_folder, f'{dataset}_summary{f"_joined" if dataset == "DAGM" and dagm_join else ""}.csv'), index=False)
if __name__ == '__main__':
main()