update kitti eval, test seg

Rhett98 · Jan 30, 2024 · 9a7cf4a · 9a7cf4a
1 parent ee406df
commit 9a7cf4a
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Showing 21 changed files with 23,611 additions and 419 deletions.
diff --git a/configs.py b/configs.py
@@ -12,7 +12,7 @@ def dynamic_seg_args():
     parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 3]')
     parser.add_argument('--multi_gpu', type=str, default=None, help='The gpu [default : null]')
     parser.add_argument('--limit_or_filter', type=bool, default=True, help='if False, filter will reserve 40m~50m points')
-    parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 16]')
+    parser.add_argument('--batch_size', type=int, default=2, help='Batch Size during training [default: 16]')
     parser.add_argument('--eval_batch_size', type=int, default=2, help='Batch Size during evaling [default: 64]')
     parser.add_argument('--eval_before', type=int, default=0, help='if 1, eval before train')
 
@@ -56,7 +56,7 @@ def dynamic_seg_school_args():
     parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 3]')
     parser.add_argument('--multi_gpu', type=str, default=None, help='The gpu [default : null]')
     parser.add_argument('--limit_or_filter', type=bool, default=True, help='if False, filter will reserve 40m~50m points')
-    parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 16]')
+    parser.add_argument('--batch_size', type=int, default=2, help='Batch Size during training [default: 16]')
     parser.add_argument('--eval_batch_size', type=int, default=2, help='Batch Size during evaling [default: 64]')
     parser.add_argument('--eval_before', type=int, default=0, help='if 1, eval before train')
 
@@ -178,7 +178,7 @@ def odometry_school_args():
     parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 3]')
     parser.add_argument('--multi_gpu', type=str, default=None, help='The gpu [default : null]')
     parser.add_argument('--limit_or_filter', type=bool, default=True, help='if False, filter will reserve 40m~50m points')
-    parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 16]')
+    parser.add_argument('--batch_size', type=int, default=2, help='Batch Size during training [default: 16]')
     parser.add_argument('--eval_batch_size', type=int, default=1, help='Batch Size during evaling [default: 64]')
     parser.add_argument('--eval_before', type=int, default=1, help='if 1, eval before train')
 

diff --git a/evaluation.py b/evaluation.py
diff --git a/ground_truth_pose/00.txt b/ground_truth_pose/00.txt
diff --git a/ground_truth_pose/01.txt b/ground_truth_pose/01.txt
diff --git a/ground_truth_pose/02.txt b/ground_truth_pose/02.txt
diff --git a/ground_truth_pose/03.txt b/ground_truth_pose/03.txt
diff --git a/ground_truth_pose/04.txt b/ground_truth_pose/04.txt
diff --git a/ground_truth_pose/05.txt b/ground_truth_pose/05.txt
diff --git a/ground_truth_pose/06.txt b/ground_truth_pose/06.txt
diff --git a/ground_truth_pose/07.txt b/ground_truth_pose/07.txt
diff --git a/ground_truth_pose/08.txt b/ground_truth_pose/08.txt
diff --git a/ground_truth_pose/09.txt b/ground_truth_pose/09.txt
diff --git a/ground_truth_pose/10.txt b/ground_truth_pose/10.txt
diff --git a/infer_pwc.py b/infer_pwc.py
@@ -42,7 +42,8 @@ def main():
 
     global args
 
-    eval_list = [0,1, 2,3,4, 5, 6, 7, 8,9,10]#[0, 1, 2, 3, 4, 5, 6]
+    # eval_list = [0,1, 2,3,4, 5, 6, 7, 8,9,10]
+    eval_list = [3]
 
     logger = creat_logger(log_dir, args.model_name)
     logger.info('----------------------------------------TRAINING----------------------------------')
@@ -173,7 +174,7 @@ def main():
             os.makedirs(data_dir)
         np.save(fname_file, T)
         np.savetxt(fname_txt, T)
-        os.system('cp %s %s' % (fname_file, data_dir))  ###SAVE THE txt FILE
+        os.system('cp %s %s' % (fname_txt, data_dir))  ###SAVE THE txt FILE
         # print('python evaluation.py --result_dir ' + data_dir + ' --eva_seqs ' + str(item).zfill(2) + '_pred')
         os.system('python evaluation.py --result_dir ' + data_dir + ' --eva_seqs ' + str(item).zfill(2) + '_pred')
 

diff --git a/infer_pwc_school_dataset.py b/infer_pwc_school_dataset.py
@@ -42,7 +42,7 @@ def main():
 
     global args
 
-    eval_list = [0]
+    eval_list = [3]
 
     logger = creat_logger(log_dir, args.model_name)
     logger.info('----------------------------------------TRAINING----------------------------------')

diff --git a/kitti_pytorch.py b/kitti_pytorch.py
@@ -551,7 +551,7 @@ def __init__(self, is_training: int = 1, num_point: int = 24000, data_dir_list:
 
         self.data_list = data_dir_list
         self.lidar_root = config.lidar_root
-        self.data_len_sequence = [5836,4814]
+        self.data_len_sequence = [5836,4814,3736,5448]
         # self.mean = [0.485, 0.456, 0.406]
         # self.std = [0.229, 0.224, 0.225]
 
@@ -671,6 +671,10 @@ def get_index(self, value, mylist):
                 cb+=1
             if i == 2:
                 cc+=1
+        # if cb == 0:
+        #     print("no static:",sample_id)
+        # if cc == 0:
+        #     print("no dynamic:",sample_id)
         total_static_p+=cb
         total_moving_p+=cc
         # print("num of point: ", ca, cb, cc)

diff --git a/tools/dataset_config.yaml b/tools/dataset_config.yaml
@@ -5,32 +5,149 @@ labels:
   1 : "outlier"
   9 : "static"     # for lidar-mos static 
   10: "car"
+  11: "bicycle"
+  13: "bus"
+  15: "motorcycle"
+  16: "on-rails"
+  18: "truck"
+  20: "other-vehicle"
+  30: "person"
+  31: "bicyclist"
+  32: "motorcyclist"
+  40: "road"
+  44: "parking"
+  48: "sidewalk"
+  49: "other-ground"
+  50: "building"
+  51: "fence"
+  52: "other-structure"
+  60: "lane-marking"
+  70: "vegetation"
+  71: "trunk"
+  72: "terrain"
+  80: "pole"
+  81: "traffic-sign"
   99: "other-object"
   251: "moving"      # lidar-mos moving
   252: "moving-car"
+  253: "moving-bicyclist"
+  254: "moving-person"
+  255: "moving-motorcyclist"
+  256: "moving-on-rails"
+  257: "moving-bus"
+  258: "moving-truck"
+  259: "moving-other-vehicle"
 color_map: # bgr
   0 : [0, 0, 0]
   1 : [0, 0, 0]  # [0, 0, 255] 
   9 : [0, 0, 0]     # for lidar-mos static
   10: [245, 150, 100]
+  11: [245, 230, 100]
+  13: [250, 80, 100]
+  15: [150, 60, 30]
+  16: [255, 0, 0]
+  18: [180, 30, 80]
+  20: [255, 0, 0]
+  30: [30, 30, 255]
+  31: [200, 40, 255]
+  32: [90, 30, 150]
+  40: [255, 0, 255]
+  44: [255, 150, 255]
+  48: [75, 0, 75]
+  49: [75, 0, 175]
+  50: [0, 200, 255]
+  51: [50, 120, 255]
+  52: [0, 150, 255]
+  60: [170, 255, 150]
+  70: [0, 175, 0]
+  71: [0, 60, 135]
+  72: [80, 240, 150]
+  80: [150, 240, 255]
+  81: [0, 0, 255]
   99: [255, 255, 50]
   251: [0, 0, 255]  # lidar-mos moving
   252: [245, 150, 100]
+  256: [255, 0, 0]
+  253: [200, 40, 255]
+  254: [30, 30, 255]
+  255: [90, 30, 150]
+  257: [250, 80, 100]
+  258: [180, 30, 80]
+  259: [255, 0, 0]
 content: # as a ratio with the total number of points
-  0: 0
-  10: 0
-  99: 0.987640703
-  252: 0.01235929297
+  0: 0.018889854628292943
+  1: 0.0002937197336781505
+  10: 0.040818519255974316
+  11: 0.00016609538710764618
+  13: 2.7879693665067774e-05
+  15: 0.00039838616015114444
+  16: 0.0
+  18: 0.0020633612104619787
+  20: 0.0016218197275284021
+  30: 0.00017698551338515307
+  31: 1.1065903904919655e-08
+  32: 5.532951952459828e-09
+  40: 0.1987493871255525
+  44: 0.014717169549888214
+  48: 0.14392298360372
+  49: 0.0039048553037472045
+  50: 0.1326861944777486
+  51: 0.0723592229456223
+  52: 0.002395131480328884
+  60: 4.7084144280367186e-05
+  70: 0.26681502148037506
+  71: 0.006035012012626033
+  72: 0.07814222006271769
+  80: 0.002855498193863172
+  81: 0.0006155958086189918
+  99: 0.009923127583046915
+  252: 0.001789309418528068
+  253: 0.00012709999297008662
+  254: 0.00016059776092534436
+  255: 3.745553104802113e-05
+  256: 0.0
+  257: 0.00011351574470342043
+  258: 0.00010157861367183268
+  259: 4.3840131989471124e-05
 # classes that are indistinguishable from single scan or inconsistent in
 # ground truth are mapped to their closest equivalent
 learning_map:
   0 : 0     # "unlabeled"       mapped to "unlabeled" ------------------------mapped
   1 : 0     # "outlier"         mapped to "unlabeled" ------------------------mapped
   9 : 1     # "static"          mapped to "static" ---------------------------mapped
-  10: 1     # "car"             mapped to "static" ---------------------------mapped
+  10: 2     # "car"             mapped to "static" ---------------------------mapped
+  11: 1     # "bicycle"         mapped to "static" ---------------------------mapped
+  13: 1     # "bus"             mapped to "static" ---------------------------mapped
+  15: 1     # "motorcycle"      mapped to "static" ---------------------------mapped
+  16: 1     # "on-rails"        mapped to "static" ---------------------------mapped
+  18: 1     # "truck"           mapped to "static" ---------------------------mapped
+  20: 1     # "other-vehicle"   mapped to "static" ---------------------------mapped
+  30: 1     # "person"          mapped to "static" ---------------------------mapped
+  31: 1     # "bicyclist"       mapped to "static" ---------------------------mapped
+  32: 1     # "motorcyclist"    mapped to "static" ---------------------------mapped
+  40: 1     # "road"            mapped to "static" ---------------------------mapped
+  44: 1    # "parking"          mapped to "static" ---------------------------mapped
+  48: 1    # "sidewalk"         mapped to "static" ---------------------------mapped
+  49: 1    # "other-ground"     mapped to "static" ---------------------------mapped
+  50: 1    # "building"         mapped to "static" ---------------------------mapped
+  51: 1    # "fence"            mapped to "static" ---------------------------mapped
+  52: 1     # "other-structure" mapped to "static" ---------------------------mapped
+  60: 1     # "lane-marking"    mapped to "static" ---------------------------mapped
+  70: 1    # "vegetation"       mapped to "static" ---------------------------mapped
+  71: 1    # "trunk"            mapped to "static" ---------------------------mapped
+  72: 1    # "terrain"          mapped to "static" ---------------------------mapped
+  80: 1    # "pole"             mapped to "static" ---------------------------mapped
+  81: 1    # "traffic-sign"     mapped to "static" ---------------------------mapped
   99: 1     # "other-object"    mapped to "static" ---------------------------mapped
   251: 2    # "moving"              mapped to "moving" ---------------------------mapped
   252: 2    # "moving-car"          mapped to "moving" ---------------------------mapped
+  253: 2    # "moving-bicyclist"    mapped to "moving" ---------------------------mapped
+  254: 2    # "moving-person"       mapped to "moving" ---------------------------mapped
+  255: 2    # "moving-motorcyclist" mapped to "moving" ---------------------------mapped
+  256: 2    # "moving-on-rails"     mapped to "moving" ---------------------------mapped
+  257: 2    # "moving-bus"          mapped to "moving" ---------------------------mapped
+  258: 2    # "moving-truck"        mapped to "moving" ---------------------------mapped
+  259: 2    # "moving-other"        mapped to "moving" ---------------------------mapped
 learning_map_inv: # inverse of previous map
   0: 0      # "unlabeled", and others ignored
   1: 9      # "static"

diff --git a/tools/dataset_config_school.yaml b/tools/dataset_config_school.yaml
@@ -5,149 +5,32 @@ labels:
   1 : "outlier"
   9 : "static"     # for lidar-mos static 
   10: "car"
-  11: "bicycle"
-  13: "bus"
-  15: "motorcycle"
-  16: "on-rails"
-  18: "truck"
-  20: "other-vehicle"
-  30: "person"
-  31: "bicyclist"
-  32: "motorcyclist"
-  40: "road"
-  44: "parking"
-  48: "sidewalk"
-  49: "other-ground"
-  50: "building"
-  51: "fence"
-  52: "other-structure"
-  60: "lane-marking"
-  70: "vegetation"
-  71: "trunk"
-  72: "terrain"
-  80: "pole"
-  81: "traffic-sign"
   99: "other-object"
   251: "moving"      # lidar-mos moving
   252: "moving-car"
-  253: "moving-bicyclist"
-  254: "moving-person"
-  255: "moving-motorcyclist"
-  256: "moving-on-rails"
-  257: "moving-bus"
-  258: "moving-truck"
-  259: "moving-other-vehicle"
 color_map: # bgr
   0 : [0, 0, 0]
   1 : [0, 0, 0]  # [0, 0, 255] 
   9 : [0, 0, 0]     # for lidar-mos static
   10: [245, 150, 100]
-  11: [245, 230, 100]
-  13: [250, 80, 100]
-  15: [150, 60, 30]
-  16: [255, 0, 0]
-  18: [180, 30, 80]
-  20: [255, 0, 0]
-  30: [30, 30, 255]
-  31: [200, 40, 255]
-  32: [90, 30, 150]
-  40: [255, 0, 255]
-  44: [255, 150, 255]
-  48: [75, 0, 75]
-  49: [75, 0, 175]
-  50: [0, 200, 255]
-  51: [50, 120, 255]
-  52: [0, 150, 255]
-  60: [170, 255, 150]
-  70: [0, 175, 0]
-  71: [0, 60, 135]
-  72: [80, 240, 150]
-  80: [150, 240, 255]
-  81: [0, 0, 255]
   99: [255, 255, 50]
   251: [0, 0, 255]  # lidar-mos moving
   252: [245, 150, 100]
-  256: [255, 0, 0]
-  253: [200, 40, 255]
-  254: [30, 30, 255]
-  255: [90, 30, 150]
-  257: [250, 80, 100]
-  258: [180, 30, 80]
-  259: [255, 0, 0]
 content: # as a ratio with the total number of points
-  0: 0.018889854628292943
-  1: 0.0002937197336781505
-  10: 0.040818519255974316
-  11: 0.00016609538710764618
-  13: 2.7879693665067774e-05
-  15: 0.00039838616015114444
-  16: 0.0
-  18: 0.0020633612104619787
-  20: 0.0016218197275284021
-  30: 0.00017698551338515307
-  31: 1.1065903904919655e-08
-  32: 5.532951952459828e-09
-  40: 0.1987493871255525
-  44: 0.014717169549888214
-  48: 0.14392298360372
-  49: 0.0039048553037472045
-  50: 0.1326861944777486
-  51: 0.0723592229456223
-  52: 0.002395131480328884
-  60: 4.7084144280367186e-05
-  70: 0.26681502148037506
-  71: 0.006035012012626033
-  72: 0.07814222006271769
-  80: 0.002855498193863172
-  81: 0.0006155958086189918
-  99: 0.009923127583046915
-  252: 0.001789309418528068
-  253: 0.00012709999297008662
-  254: 0.00016059776092534436
-  255: 3.745553104802113e-05
-  256: 0.0
-  257: 0.00011351574470342043
-  258: 0.00010157861367183268
-  259: 4.3840131989471124e-05
+  0: 0
+  10: 0
+  99: 0.992517993
+  252: 0.0074820062
 # classes that are indistinguishable from single scan or inconsistent in
 # ground truth are mapped to their closest equivalent
 learning_map:
   0 : 0     # "unlabeled"       mapped to "unlabeled" ------------------------mapped
   1 : 0     # "outlier"         mapped to "unlabeled" ------------------------mapped
   9 : 1     # "static"          mapped to "static" ---------------------------mapped
   10: 2     # "car"             mapped to "static" ---------------------------mapped
-  11: 1     # "bicycle"         mapped to "static" ---------------------------mapped
-  13: 1     # "bus"             mapped to "static" ---------------------------mapped
-  15: 1     # "motorcycle"      mapped to "static" ---------------------------mapped
-  16: 1     # "on-rails"        mapped to "static" ---------------------------mapped
-  18: 1     # "truck"           mapped to "static" ---------------------------mapped
-  20: 1     # "other-vehicle"   mapped to "static" ---------------------------mapped
-  30: 1     # "person"          mapped to "static" ---------------------------mapped
-  31: 1     # "bicyclist"       mapped to "static" ---------------------------mapped
-  32: 1     # "motorcyclist"    mapped to "static" ---------------------------mapped
-  40: 1     # "road"            mapped to "static" ---------------------------mapped
-  44: 1    # "parking"          mapped to "static" ---------------------------mapped
-  48: 1    # "sidewalk"         mapped to "static" ---------------------------mapped
-  49: 1    # "other-ground"     mapped to "static" ---------------------------mapped
-  50: 1    # "building"         mapped to "static" ---------------------------mapped
-  51: 1    # "fence"            mapped to "static" ---------------------------mapped
-  52: 1     # "other-structure" mapped to "static" ---------------------------mapped
-  60: 1     # "lane-marking"    mapped to "static" ---------------------------mapped
-  70: 1    # "vegetation"       mapped to "static" ---------------------------mapped
-  71: 1    # "trunk"            mapped to "static" ---------------------------mapped
-  72: 1    # "terrain"          mapped to "static" ---------------------------mapped
-  80: 1    # "pole"             mapped to "static" ---------------------------mapped
-  81: 1    # "traffic-sign"     mapped to "static" ---------------------------mapped
   99: 1     # "other-object"    mapped to "static" ---------------------------mapped
   251: 2    # "moving"              mapped to "moving" ---------------------------mapped
   252: 2    # "moving-car"          mapped to "moving" ---------------------------mapped
-  253: 2    # "moving-bicyclist"    mapped to "moving" ---------------------------mapped
-  254: 2    # "moving-person"       mapped to "moving" ---------------------------mapped
-  255: 2    # "moving-motorcyclist" mapped to "moving" ---------------------------mapped
-  256: 2    # "moving-on-rails"     mapped to "moving" ---------------------------mapped
-  257: 2    # "moving-bus"          mapped to "moving" ---------------------------mapped
-  258: 2    # "moving-truck"        mapped to "moving" ---------------------------mapped
-  259: 2    # "moving-other"        mapped to "moving" ---------------------------mapped
 learning_map_inv: # inverse of previous map
   0: 0      # "unlabeled", and others ignored
   1: 9      # "static"

diff --git a/train_pillar_raft.py b/train_pillar_raft.py
@@ -20,7 +20,7 @@
 from ioueval import iouEval
 from pointpillar_encoder import PillarLayer
 
-f = open('dataset_config.yaml')
+f = open('tools/ataset_config.yaml')
 dataset_config = yaml.load(f, Loader=yaml.FullLoader)
 
 args = dynamic_seg_args()