update

README.md

@@ -15,3 +15,13 @@ A ADAS(Advanced Driver Assistance System) for Euro Truck Simulator 2 (or America
 3. Automatic control system + Decision system (Behaviour)
 
 ## Datasets 
+
+1. [BDD100K](https://doc.bdd100k.com/) - A diverse driving dataset for heterogeneous multitask learning 
+    - Multi-object Detection 
+    - Lane Detection
+    - Drivable Area Segmentation
+2. [ETS2SCDataset](https://www.kaggle.com/datasets/vjekoslavdiklic/ets2sc) - Euro Truck Simulator 2 Captured Screen and Input
+    - This dataset contains recorded screen of Euro Truck Simulator 2 and paired input from Steering wheel controller (Thrustmaster Ff430).
+    - Dataset contains 323894 frames captured at 25fps.
+    - Each frame is paired with steering wheel controller input values at that moment
+    - Using [Europilot](https://github.com/marsauto/europilot)

src/perception/base.py

@@ -0,0 +1,63 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+
+import torchvision
+import torchvision.utils as TU
+import torchvision.transforms.functional as TVTF
+
+import os
+import cv2
+import math
+import timm
+import random
+import gitinfo
+import argparse
+import coloredlogs
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from pathlib import Path
+from copy import deepcopy
+from skimage.draw import disk
+import matplotlib.pyplot as plt
+
+import albumentations as Aug
+from albumentations.pytorch import ToTensorV2
+
+
+def use_device(GPU):
+    if GPU is not None:
+        if len(GPU) == 1:
+            device = "cuda:{}".format(GPU[0])
+        else:
+            device = "cuda"
+    else:
+        device = "cpu"
+    return device
+
+def requires_grad(model, flag=True):
+    for p in model.parameters():
+        p.requires_grad = flag
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+def tensor2im(tensor=None):
+    output = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
+    return output
+
+OBJ_LABELS = {
+    'unlabeled':0, 'dynamic': 1, 'ego vehicle': 2, 'ground': 3, 
+    'static': 4, 'parking': 5, 'rail track': 6, 'road': 7, 
+    'sidewalk': 8, 'bridge': 9, 'building': 10, 'fence': 11, 
+    'garage': 12, 'guard rail': 13, 'tunnel': 14, 'wall': 15,
+    'banner': 16, 'billboard': 17, 'lane divider': 18,'parking sign': 19, 
+    'pole': 20, 'polegroup': 21, 'street light': 22, 'traffic cone': 23,
+    'traffic device': 24, 'traffic light': 25, 'traffic sign': 26, 'traffic sign frame': 27,
+    'terrain': 28, 'vegetation': 29, 'sky': 30, 'person': 31,
+    'rider': 32, 'bicycle': 33, 'bus': 34, 'car': 35, 
+    'caravan': 36, 'motorcycle': 37, 'trailer': 38, 'train': 39,
+    'truck': 40       
+}