Refactoring ML_Lib

datasets/paired_dataset.py

@@ -6,8 +6,8 @@ import torch
 from torch.utils.data import Dataset, ConcatDataset
 
 from datasets.utils import DatasetMapping
-from lib.preprocessing.generator import Generator
-from lib.objects.map import Map
+from ml_lib.preprocessing.generator import Generator
+from ml_lib.objects.map import Map
 
 
 class TrajPairDataset(Dataset):

datasets/preprocessing/generator.py

@@ -7,7 +7,7 @@ from pathlib import Path
 
 from tqdm import tqdm
 
-from lib.objects.map import Map
+from ml_lib.objects.map import Map
 
 
 class Generator:

datasets/trajectory_dataset.py

@@ -14,11 +14,11 @@ from torch.utils.data import ConcatDataset, Dataset
 import numpy as np
 from tqdm import tqdm
 
-from lib.objects.map import Map
-import lib.variables as V
+from ml_lib.objects.map import Map
+import ml_lib.variables as V
 from PIL import Image
 
-from lib.utils.tools import write_to_shelve
+from ml_lib.utils.tools import write_to_shelve
 
 
 class TrajDataShelve(VisionDataset):

generator_eval.py

@@ -7,7 +7,7 @@ from sklearn.cluster import Birch, DBSCAN, KMeans
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE
 
-import lib.variables as V
+import ml_lib.variables as V
 import numpy as np
 
 

main.py

@@ -11,10 +11,10 @@ import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 
-from lib.modules.utils import LightningBaseModule
-from lib.utils.config import Config
-from lib.utils.logging import Logger
-from lib.utils.model_io import SavedLightningModels
+from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.utils.config import Config
+from ml_lib.utils.logging import Logger
+from ml_lib.utils.model_io import SavedLightningModels
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)

ml_lib/modules/blocks.py

@@ -2,7 +2,7 @@ from typing import Union
 
 import torch
 from torch import nn
-from lib.modules.utils import AutoPad, Interpolate
+from ml_lib.modules.utils import AutoPad, Interpolate
 
 
 #

ml_lib/modules/losses.py

@@ -3,9 +3,9 @@ from typing import List
 import torch
 from torch import nn
 
-from lib.modules.utils import FlipTensor
-from lib.objects.map import MapStorage, Map
-from lib.objects.trajectory import Trajectory
+from ml_lib.modules.utils import FlipTensor
+from ml_lib.objects.map import MapStorage, Map
+from ml_lib.objects.trajectory import Trajectory
 
 
 class BinaryHomotopicLoss(nn.Module):

ml_lib/utils/config.py

@@ -5,12 +5,12 @@ from collections import defaultdict
 from configparser import ConfigParser
 from pathlib import Path
 
-from lib.models.generators.cnn import CNNRouteGeneratorModel
-from lib.models.generators.cnn_discriminated import CNNRouteGeneratorDiscriminated
+from ml_lib.models.generators.cnn import CNNRouteGeneratorModel
+from ml_lib.models.generators.cnn_discriminated import CNNRouteGeneratorDiscriminated
 
-from lib.models.homotopy_classification.cnn_based import ConvHomDetector
-from lib.utils.model_io import ModelParameters
-from lib.utils.transforms import AsArray
+from ml_lib.models.homotopy_classification.cnn_based import ConvHomDetector
+from ml_lib.utils.model_io import ModelParameters
+from ml_lib.utils.transforms import AsArray
 
 
 def is_jsonable(x):

ml_lib/utils/logging.py

@@ -4,7 +4,7 @@ from pytorch_lightning.loggers.base import LightningLoggerBase
 from pytorch_lightning.loggers.neptune import NeptuneLogger
 from pytorch_lightning.loggers.test_tube import TestTubeLogger
 
-from lib.utils.config import Config
+from ml_lib.utils.config import Config
 import numpy as np
 
 

ml_lib/visualization/tools.py

@@ -1,26 +1,26 @@
-from pathlib import Path
-import matplotlib.pyplot as plt
-
-
-class Plotter(object):
-    def __init__(self, root_path=''):
-        self.root_path = Path(root_path)
-
-    def save_current_figure(self, path, extention='.png'):
-        fig, _ = plt.gcf(), plt.gca()
-        # Prepare save location and check img file extention
-        path = self.root_path / Path(path if str(path).endswith(extention) else f'{str(path)}{extention}')
-        path.parent.mkdir(exist_ok=True, parents=True)
-        fig.savefig(path)
-        fig.clf()
-
-    def show_current_figure(self):
-        fig, _ = plt.gcf(), plt.gca()
-        fig.show()
-        fig.clf()
-
-
-if __name__ == '__main__':
-    output_root = Path('..') / 'output'
-    p = Plotter(output_root)
-    p.save_current_figure('test.png')
+from pathlib import Path
+import matplotlib.pyplot as plt
+
+
+class Plotter(object):
+    def __init__(self, root_path=''):
+        self.root_path = Path(root_path)
+
+    def save_current_figure(self, path, extention='.png'):
+        fig, _ = plt.gcf(), plt.gca()
+        # Prepare save location and check img file extention
+        path = self.root_path / Path(path if str(path).endswith(extention) else f'{str(path)}{extention}')
+        path.parent.mkdir(exist_ok=True, parents=True)
+        fig.savefig(path)
+        fig.clf()
+
+    def show_current_figure(self):
+        fig, _ = plt.gcf(), plt.gca()
+        fig.show()
+        fig.clf()
+
+
+if __name__ == '__main__':
+    output_root = Path('..') / 'output'
+    p = Plotter(output_root)
+    p.save_current_figure('test.png')

models/generators/cnn.py

@@ -10,12 +10,12 @@ from torch.optim import Adam
 
 from datasets.mnist import MyMNIST
 from datasets.trajectory_dataset import TrajData
-from lib.modules.blocks import ConvModule, DeConvModule
-from lib.modules.utils import LightningBaseModule, Flatten
+from ml_lib.modules.blocks import ConvModule, DeConvModule
+from ml_lib.modules.utils import LightningBaseModule, Flatten
 
 import matplotlib.pyplot as plt
-import lib.variables as V
-from lib.visualization.generator_eval import GeneratorVisualizer
+import variables as V
+from generator_eval import GeneratorVisualizer
 
 
 class CNNRouteGeneratorModel(LightningBaseModule):

models/generators/cnn_discriminated.py

@@ -1,18 +1,12 @@
-from random import choices, seed
-import numpy as np
-
 import torch
 from functools import reduce
 from operator import mul
 
 from torch import nn
-from torch.optim import Adam
 
 from datasets.trajectory_dataset import TrajData
-from lib.evaluation.classification import ROCEvaluation
-from lib.models.generators.cnn import CNNRouteGeneratorModel
-from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
-from lib.modules.utils import LightningBaseModule, Flatten
+from ml_lib.evaluation.classification import ROCEvaluation
+from models.generators.cnn import CNNRouteGeneratorModel
 
 import matplotlib.pyplot as plt
 
@@ -79,7 +73,7 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
 
         maps, trajectories, labels, val_restul_dict = self.generate_random()
 
-        from lib.visualization.generator_eval import GeneratorVisualizer
+        from generator_eval import GeneratorVisualizer
         g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
         fig = g.draw()
         self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)

models/generators/full.py

@@ -1,7 +1,7 @@
-from lib.modules.losses import BinaryHomotopicLoss
-from lib.modules.utils import LightningBaseModule
-from lib.objects.map import Map
-from lib.objects.trajectory import Trajectory
+from ml_lib.modules.losses import BinaryHomotopicLoss
+from ml_lib.modules.utils import LightningBaseModule
+from objects.map import Map
+from objects.trajectory import Trajectory
 
 import torch.nn as nn
 

models/generators/recurrent.py

@@ -8,9 +8,9 @@ from torch import nn
 from torch.optim import Adam
 
 from datasets.trajectory_dataset import TrajData
-from lib.evaluation.classification import ROCEvaluation
-from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
-from lib.modules.utils import LightningBaseModule, Flatten
+from ml_lib.evaluation.classification import ROCEvaluation
+from ml_lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
+from ml_lib.modules.utils import LightningBaseModule, Flatten
 
 import matplotlib.pyplot as plt
 
@@ -55,7 +55,7 @@ class CNNRouteGeneratorModel(LightningBaseModule):
     def _test_val_epoch_end(self, outputs, test=False):
         maps, trajectories, labels, val_restul_dict = self.generate_random()
 
-        from lib.visualization.generator_eval import GeneratorVisualizer
+        from ml_lib.visualization.generator_eval import GeneratorVisualizer
         g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
         fig = g.draw()
         self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
@@ -312,7 +312,7 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
 
         maps, trajectories, labels, val_restul_dict = self.generate_random()
 
-        from lib.visualization.generator_eval import GeneratorVisualizer
+        from ml_lib.visualization.generator_eval import GeneratorVisualizer
         g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
         fig = g.draw()
         self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)

models/homotopy_classification/cnn_based.py

@@ -8,9 +8,9 @@ from torch.optim import Adam
 from torch.utils.data import DataLoader
 
 from datasets.trajectory_dataset import TrajData
-from lib.evaluation.classification import ROCEvaluation
-from lib.modules.utils import LightningBaseModule, Flatten
-from lib.modules.blocks import ConvModule, ResidualModule
+from ml_lib.evaluation.classification import ROCEvaluation
+from ml_lib.modules.utils import LightningBaseModule, Flatten
+from ml_lib.modules.blocks import ConvModule, ResidualModule
 import matplotlib.pyplot as plt
 
 

multi_run.py

@@ -1,6 +1,6 @@
 import warnings
 
-from lib.utils.config import Config
+from ml_lib.utils.config import Config
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)

objects/map.py

@@ -11,8 +11,8 @@ from PIL import Image, ImageDraw
 import networkx as nx
 from matplotlib import pyplot as plt
 
-from lib.objects.trajectory import Trajectory
-import lib.variables as V
+from ml_lib.objects.trajectory import Trajectory
+import ml_lib.variables as V
 
 
 class Map(object):

objects/trajectory.py

@@ -2,7 +2,7 @@ from math import atan2
 from typing import List, Tuple, Union
 
 from matplotlib import pyplot as plt
-from lib import variables as V
+from ml_lib import variables as V
 
 import numpy as np
 

variables.py

@@ -1,6 +1,3 @@
-from pathlib import Path
-_ROOT = Path('..')
-
 # Labels for classes
 HOMOTOPIC = 1
 ALTERNATIVE = 0