scripts/application_packager/convert_to_onnx.py

@@ -2,8 +2,8 @@ import os
 
 import numpy as np
 import torch
+from scripts.training.mobilenetv3 import RFClassifier, mobilenetv3
 
-from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
 from helpers.app_settings import get_app_settings
 
 

scripts/application_packager/convert_to_ort.py

@@ -1,4 +1,5 @@
 import subprocess
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings()

scripts/application_packager/profile_onnx.py

@@ -1,9 +1,11 @@
-import onnxruntime as ort
+import json
-import numpy as np
-from helpers.app_settings import get_app_settings
 import os
 import time
-import json
+
+import numpy as np
+import onnxruntime as ort
+
+from helpers.app_settings import get_app_settings
 
 
 def profile_onnx_model(

scripts/dataset_manager/data_gen.py

@@ -1,7 +1,9 @@
-from utils.data import Recording
-import numpy as np
-from utils.signal import block_generator
 import argparse
+
+import numpy as np
+from utils.data import Recording
+from utils.signal import block_generator
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings().dataset

scripts/dataset_manager/produce_dataset.py

@@ -1,7 +1,11 @@
-import os, h5py, numpy as np
+import os
 from typing import List
-from utils.io import from_npy
+
+import h5py
+import numpy as np
 from split_dataset import split, split_recording
+from utils.io import from_npy
+
 from helpers.app_settings import DataSetConfig, get_app_settings
 
 meta_dtype = np.dtype(

scripts/dataset_manager/split_dataset.py

@@ -1,6 +1,7 @@
 import random
 from collections import defaultdict
-from typing import List, Tuple, Dict
+from typing import Dict, List, Tuple
+
 import numpy as np
 
 

scripts/model_builder/cm_plotter.py

@@ -1,5 +1,6 @@
-import numpy as np
 from typing import Optional
+
+import numpy as np
 from matplotlib import pyplot as plt
 from sklearn.metrics import confusion_matrix
 

scripts/model_builder/mobilenetv3.py

@@ -1,8 +1,8 @@
-import numpy as np
-import torch
-import timm
-from torch import nn
 import lightning as L
+import numpy as np
+import timm
+import torch
+from torch import nn
 
 sizes = [
     "mobilenetv3_large_075",

scripts/model_builder/modulation_dataset.py

@@ -1,10 +1,12 @@
-import sys, os
+import os
+import sys
 
 sys.path.insert(0, os.path.abspath("../.."))  # or ".." if needed
+import h5py
 import numpy as np
 import torch
 from torch.utils.data import Dataset
-import h5py
+
 from helpers.app_settings import get_app_settings
 
 settings = get_app_settings()

scripts/model_builder/plot_data.py

@@ -1,16 +1,17 @@
 import os
-import torch
+
 import numpy as np
+import torch
 from sklearn.metrics import classification_report
 
 os.environ["NNPACK"] = "0"
-from matplotlib import pyplot as plt
-
-from scripts.training.mobilenetv3 import mobilenetv3, RFClassifier
-from helpers.app_settings import get_app_settings
 from cm_plotter import plot_confusion_matrix
+from matplotlib import pyplot as plt
+from scripts.training.mobilenetv3 import RFClassifier, mobilenetv3
 from scripts.training.modulation_dataset import ModulationH5Dataset
 
+from helpers.app_settings import get_app_settings
+
 
 def load_validation_data():
     val_dataset = ModulationH5Dataset(

scripts/model_builder/train.py

@@ -1,14 +1,16 @@
-import sys, os
+import os
+import sys
 
 os.environ["NNPACK"] = "0"
 import lightning as L
-from lightning.pytorch.callbacks import ModelCheckpoint
+import mobilenetv3
 import torch
 import torch.nn.functional as F
 import torchmetrics
-from helpers.app_settings import get_app_settings
+from lightning.pytorch.callbacks import ModelCheckpoint
 from modulation_dataset import ModulationH5Dataset
-import mobilenetv3
+
+from helpers.app_settings import get_app_settings
 
 script_dir = os.path.dirname(os.path.abspath(__file__))
 data_dir = os.path.abspath(os.path.join(script_dir, ".."))