update sys path

.gitignore

@@ -3,7 +3,7 @@ results/
 dataset/
 checkpoints/
 logs/
-src/.idea/
+*.idea/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

requirements.txt

@@ -7,3 +7,5 @@ torchsummary==1.5.1
 tensorboard==2.2.1
 scikit-learn==0.22.2
 wget==3.2
+tqdm==4.54.0
+matplotlib=3.3.3

sfa/data_process/demo_dataset.py

@@ -18,11 +18,15 @@
 import cv2
 import torch
 
-sys.path.append('../')
-
-from sfa.data_process.kitti_data_utils import get_filtered_lidar
-from sfa.data_process.kitti_bev_utils import makeBEVMap
-import sfa.config.kitti_config as cnf
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
+
+from data_process.kitti_data_utils import get_filtered_lidar
+from data_process.kitti_bev_utils import makeBEVMap
+import config.kitti_config as cnf
 
 
 class Demo_KittiDataset(Dataset):

sfa/data_process/kitti_bev_utils.py

@@ -4,14 +4,19 @@
 """
 
 import math
+import os
 import sys
 
 import cv2
 import numpy as np
 
-sys.path.append('../')
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
 
-import sfa.config.kitti_config as cnf
+import config.kitti_config as cnf
 
 
 def makeBEVMap(PointCloud_, boundary):

sfa/data_process/kitti_data_utils.py

@@ -9,14 +9,19 @@
 """
 
 from __future__ import print_function
+import os
 import sys
 
 import numpy as np
 import cv2
 
-sys.path.append('../')
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
 
-import sfa.config.kitti_config as cnf
+import config.kitti_config as cnf
 
 
 class Object3d(object):

sfa/data_process/kitti_dataloader.py

@@ -8,16 +8,21 @@
 # Description: This script for creating the dataloader for training/validation/test phase
 """
 
+import os
 import sys
 
 import torch
 from torch.utils.data import DataLoader
 import numpy as np
 
-sys.path.append('../')
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
 
-from sfa.data_process.kitti_dataset import KittiDataset
-from sfa.data_process.transformation import OneOf, Random_Rotation, Random_Scaling
+from data_process.kitti_dataset import KittiDataset
+from data_process.transformation import OneOf, Random_Rotation, Random_Scaling
 
 
 def create_train_dataloader(configs):

sfa/data_process/kitti_dataset.py

@@ -18,12 +18,16 @@
 import cv2
 import torch
 
-sys.path.append('../')
-
-from sfa.data_process.kitti_data_utils import gen_hm_radius, compute_radius, Calibration, get_filtered_lidar
-from sfa.data_process.kitti_bev_utils import makeBEVMap, drawRotatedBox, get_corners
-from sfa.data_process import transformation
-import sfa.config.kitti_config as cnf
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
+
+from data_process.kitti_data_utils import gen_hm_radius, compute_radius, Calibration, get_filtered_lidar
+from data_process.kitti_bev_utils import makeBEVMap, drawRotatedBox, get_corners
+from data_process import transformation
+import config.kitti_config as cnf
 
 
 class KittiDataset(Dataset):

sfa/data_process/transformation.py

@@ -4,15 +4,20 @@
 # Refer: https://github.com/ghimiredhikura/Complex-YOLOv3
 # Source : https://github.com/jeasinema/VoxelNet-tensorflow/blob/master/utils/utils.py
 """
+import os
 import sys
 import math
 
 import numpy as np
 import torch
 
-sys.path.append('../')
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
 
-from sfa.config import kitti_config as cnf
+from config import kitti_config as cnf
 
 
 def angle_in_limit(angle):

sfa/demo_2_sides.py

@@ -18,16 +18,20 @@
 import torch
 import numpy as np
 
-sys.path.append('./')
-
-from sfa.data_process.demo_dataset import Demo_KittiDataset
-from sfa.models.model_utils import create_model
-from sfa.utils.evaluation_utils import draw_predictions, convert_det_to_real_values
-import sfa.config.kitti_config as cnf
-from sfa.data_process.transformation import lidar_to_camera_box
-from sfa.utils.visualization_utils import show_rgb_image_with_boxes
-from sfa.data_process.kitti_data_utils import Calibration
-from sfa.utils.demo_utils import parse_demo_configs, do_detect, download_and_unzip, write_credit
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
+
+from data_process.demo_dataset import Demo_KittiDataset
+from models.model_utils import create_model
+from utils.evaluation_utils import draw_predictions, convert_det_to_real_values
+import config.kitti_config as cnf
+from data_process.transformation import lidar_to_camera_box
+from utils.visualization_utils import show_rgb_image_with_boxes
+from data_process.kitti_data_utils import Calibration
+from utils.demo_utils import parse_demo_configs, do_detect, download_and_unzip, write_credit
 
 if __name__ == '__main__':
     configs = parse_demo_configs()

sfa/demo_front.py

@@ -21,16 +21,20 @@
 import torch
 import numpy as np
 
-sys.path.append('./')
-
-from sfa.data_process.demo_dataset import Demo_KittiDataset
-from sfa.models.model_utils import create_model
-from sfa.utils.evaluation_utils import draw_predictions, convert_det_to_real_values
-import sfa.config.kitti_config as cnf
-from sfa.data_process.transformation import lidar_to_camera_box
-from sfa.utils.visualization_utils import merge_rgb_to_bev, show_rgb_image_with_boxes
-from sfa.data_process.kitti_data_utils import Calibration
-from sfa.utils.demo_utils import parse_demo_configs, do_detect, download_and_unzip, write_credit
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
+
+from data_process.demo_dataset import Demo_KittiDataset
+from models.model_utils import create_model
+from utils.evaluation_utils import draw_predictions, convert_det_to_real_values
+import config.kitti_config as cnf
+from data_process.transformation import lidar_to_camera_box
+from utils.visualization_utils import merge_rgb_to_bev, show_rgb_image_with_boxes
+from data_process.kitti_data_utils import Calibration
+from utils.demo_utils import parse_demo_configs, do_detect, download_and_unzip, write_credit
 
 if __name__ == '__main__':
     configs = parse_demo_configs()

sfa/losses/losses.py

@@ -6,16 +6,21 @@
 # Modified by Nguyen Mau Dung (2020.08.09)
 # ------------------------------------------------------------------------------
 
+import os
 import sys
 import math
 
 import torch.nn as nn
 import torch
 import torch.nn.functional as F
 
-sys.path.append('../')
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
 
-from sfa.utils.torch_utils import to_cpu, _sigmoid
+from utils.torch_utils import to_cpu, _sigmoid
 
 
 def _gather_feat(feat, ind, mask=None):

sfa/models/model_utils.py

@@ -8,13 +8,18 @@
 # Description: utils functions that use for model
 """
 
+import os
 import sys
 
 import torch
 
-sys.path.append('../')
+src_dir = os.path.dirname(os.path.realpath(__file__))
+while not src_dir.endswith("sfa"):
+    src_dir = os.path.dirname(src_dir)
+if src_dir not in sys.path:
+    sys.path.append(src_dir)
 
-from sfa.models import resnet, fpn_resnet
+from models import resnet, fpn_resnet
 
 
 def create_model(configs):