Refactured Settings File

main_pipeline.py

@@ -1,8 +1,6 @@
 from pathlib import Path
 
 import torch
-from torch_geometric.data import Data
-from tqdm import tqdm
 
 import polyscope as ps
 import numpy as np
@@ -20,8 +18,8 @@ from ml_lib.utils.model_io import SavedLightningModels
 # Datasets
 from datasets.shapenet import ShapeNetPartSegDataset
 from utils.pointcloud import read_pointcloud, normalize_pointcloud, cluster_cubes, append_onehotencoded_type, \
-    label2color, polytopes_to_planes
-from utils.project_config import GlobalVar, ThisConfig
+    label2color
+from utils.project_settings import GlobalVar
 
 
 def prepare_dataloader(config_obj):
@@ -56,14 +54,15 @@ def predict_prim_type(input_pc, model):
 
     return np.concatenate((input_pc, y_primary.reshape(-1,1)), axis=1)
 
+
 if __name__ == '__main__':
 
-    input_pc_path = 'data/pc/pc.txt'
+    input_pc_path = Path('data') / 'pc' / 'pc.txt'
 
-    model_path = Path('trained_models/version_1')
-    config_filename = 'config.ini'
-    config = ThisConfig()
-    config.read_file((Path(model_path) / config_filename).open('r'))
+    model_path = Path('output') / 'PN2' / 'PN_26512907a2de0664bfad2349a6bffee3' / 'version_0'
+    # config_filename = 'config.ini'
+    # config = ThisConfig()
+    # config.read_file((Path(model_path) / config_filename).open('r'))
     loaded_model = restore_logger_and_model(model_path)
     loaded_model.eval()