Refactured Settings File

datasets/shapenet.py

@@ -5,16 +5,15 @@ import numpy as np
 from collections import defaultdict
 
 import os
+from torch.utils.data import Dataset
 from tqdm import tqdm
 import glob
 
 import torch
 from torch_geometric.data import InMemoryDataset
 from torch_geometric.data import Data
-from torch.utils.data import Dataset
-import re
 
-from utils.project_config import Classes, DataSplit
+from utils.project_settings import Classes, DataSplit
 
 
 def save_names(name_list, path):
@@ -198,12 +197,13 @@ class ShapeNetPartSegDataset(Dataset):
 
         # y -= 1 if self.num_classes() in y else 0   # Map label from [1, C] to [0, C-1]
 
-        sample = Data(**dict(pos=pos,          # torch.Tensor (n, 3/6)
+        data = Data(**dict(pos=pos,          # torch.Tensor (n, 3/6)
                              y=y,              # torch.Tensor (n,)
                              norm=norm         # torch.Tensor (n, 3/0)
                            )
                       )
-        return sample
+
+        return data
 
     def __len__(self):
         return len(self.dataset)

main_inference.py

@@ -16,7 +16,8 @@ from ml_lib.utils.model_io import SavedLightningModels
 
 # Datasets
 from datasets.shapenet import ShapeNetPartSegDataset
-from utils.project_config import GlobalVar, ThisConfig
+from utils.project_config import ThisConfig
+from utils.project_settings import GlobalVar
 
 
 def prepare_dataloader(config_obj):

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()
 

models/point_net_2.py

@@ -7,7 +7,7 @@ from datasets.shapenet import ShapeNetPartSegDataset
 from models._point_net_2 import _PointNetCore
 
 from utils.module_mixins import BaseValMixin, BaseTrainMixin, BaseOptimizerMixin, BaseDataloadersMixin, DatasetMixin
-from utils.project_config import GlobalVar
+from utils.project_settings import GlobalVar
 
 
 class PointNet2(BaseValMixin,

utils/module_mixins.py

@@ -22,7 +22,7 @@ from torchcontrib.optim import SWA
 from ml_lib.modules.util import LightningBaseModule
 from ml_lib.utils.tools import to_one_hot
 
-from .project_config import GlobalVar
+from .project_settings import GlobalVar
 
 
 class BaseOptimizerMixin:
@@ -86,7 +86,7 @@ class BaseValMixin:
 
     def validation_step(self, batch_pos_x_n_y_c, batch_idx, *_, **__):
         assert isinstance(self, LightningBaseModule)
-        data = self.batch_to_data(*batch_pos_x_n_y_c) if not isinstance(batch_pos_x_n_y_c, Data) else batch_pos_x_n_y_c
+        data = self.batch_to_data(batch_pos_x_n_y_c) if not isinstance(batch_pos_x_n_y_c, Data) else batch_pos_x_n_y_c
         y = self(data).main_out
         nll_loss = self.nll_loss(y, data.y)
         return dict(val_nll_loss=nll_loss,

utils/pointcloud.py

@@ -16,7 +16,7 @@ from pyod.models.hbos import HBOS
 from pyod.models.lscp import LSCP
 from pyod.models.feature_bagging import FeatureBagging
 
-from utils.project_config import Classes
+from utils.project_settings import Classes
 
 
 def polytopes_to_planes(pc):

utils/project_config.py

@@ -1,66 +1,4 @@
-from argparse import Namespace
-
 from ml_lib.utils.config import Config
-
-
-class DataClass(Namespace):
-
-    def __len__(self):
-        return len(self.__dict__())
-
-    def __dict__(self):
-        return {key: val for key, val in self.__class__.__dict__.items() if '__' not in key}
-
-    def items(self):
-        return self.__dict__().items()
-
-    def __repr__(self):
-        return f'{self.__class__.__name__}({self.__dict__().__repr__()})'
-
-    def __getitem__(self, item):
-        return self.__getattribute__(item)
-
-
-class Classes(DataClass):
-    # Object Classes for Point Segmentation
-    Sphere = 0
-    Cylinder = 1
-    Cone = 2
-    Box = 3
-    Polytope = 4
-    Torus = 5
-    Plane = 6
-
-
-class Settings(DataClass):
-    P2G = 'grid'
-    P2P = 'prim'
-    PN2 = 'pc'
-
-
-class DataSplit(DataClass):
-    # DATA SPLIT OPTIONS
-    train = 'train'
-    devel = 'devel'
-    test = 'test'
-
-
-class GlobalVar(DataClass):
-    # Variables for plotting
-    PADDING = 0.25
-    DPI = 50
-
-    data_split = DataSplit()
-
-    classes = Classes()
-
-    grid_count = 12
-
-    prim_count = -1
-
-    settings = Settings()
-
-
 from models import *
 
 

utils/project_settings.py

@@ -0,0 +1,61 @@
+from argparse import Namespace
+
+from ml_lib.utils.config import Config
+
+
+class DataClass(Namespace):
+
+    def __len__(self):
+        return len(self.__dict__())
+
+    def __dict__(self):
+        return {key: val for key, val in self.__class__.__dict__.items() if '__' not in key}
+
+    def items(self):
+        return self.__dict__().items()
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__().__repr__()})'
+
+    def __getitem__(self, item):
+        return self.__getattribute__(item)
+
+
+class Classes(DataClass):
+    # Object Classes for Point Segmentation
+    Sphere = 0
+    Cylinder = 1
+    Cone = 2
+    Box = 3
+    Polytope = 4
+    Torus = 5
+    Plane = 6
+
+
+class Settings(DataClass):
+    P2G = 'grid'
+    P2P = 'prim'
+    PN2 = 'pc'
+
+
+class DataSplit(DataClass):
+    # DATA SPLIT OPTIONS
+    train = 'train'
+    devel = 'devel'
+    test = 'test'
+
+
+class GlobalVar(DataClass):
+    # Variables for plotting
+    PADDING = 0.25
+    DPI = 50
+
+    data_split = DataSplit()
+
+    classes = Classes()
+
+    grid_count = 12
+
+    prim_count = -1
+
+    settings = Settings()