Feat/release centerpoint 2 6 1

autoware_ml/configs/detection3d/dataset/t4dataset/j6gen2_base.py

@@ -33,8 +33,8 @@
 ]
 
 dataset_test_groups = {
-    "largebus": ("t4dataset_largebus_infos_test.pkl", False),
-    "j6gen2": ("t4dataset_j6gen2_infos_test.pkl", False),
+    # "largebus": ("t4dataset_largebus_infos_test.pkl", False),
+    # "j6gen2": ("t4dataset_j6gen2_infos_test.pkl", False),
     "j6gen2_base": ("t4dataset_j6gen2_base_infos_test.pkl", True),
 }
 

autoware_ml/configs/detection3d/dataset/t4dataset/jpntaxi_base.py

@@ -18,8 +18,8 @@
 
 # dataset scene setting
 dataset_test_groups = {
-    "jpntaxi_gen2": ("t4dataset_jpntaxi_gen2_infos_test.pkl", False),
-    "jpntaxi_base": ("t4dataset_jpntaxi_base_infos_test.pkl", True),
+    "jpntaxi_gen2": ("t4dataset_jpntaxi_gen2_infos_test.pkl", True),
+    # "jpntaxi_base": ("t4dataset_jpntaxi_base_infos_test.pkl", True),
 }
 
 dataset_version_list = [

projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_8xb16_121m_base_amp.py

@@ -42,15 +42,15 @@
 
 # user setting
 data_root = "data/t4dataset/"
-info_directory_path = "info/user_name/"
+info_directory_path = "info/kokseang_2_6_1/"
 train_gpu_size = 8
 train_batch_size = 16
 test_batch_size = 2
 num_workers = 32
 val_interval = 5
 max_epochs = 50
 
-experiment_group_name = "centerpoint/base/" + _base_.dataset_type
+experiment_group_name = "centerpoint_2.6.1/base/" + _base_.dataset_type
 experiment_name = "second_secfpn_8xb16_121m_base_amp"
 work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name
 
@@ -353,7 +353,8 @@
 
 # learning rate
 # Since mmengine doesn't support OneCycleMomentum yet, we use CosineAnnealing from the default configs
-lr = 0.0003
+# 0.0003 * sqrt(2) = 0.0004242
+lr = 0.00042
 t_max = 15
 param_scheduler = [
     # learning rate scheduler
@@ -439,13 +440,13 @@
     dict(type="LocalVisBackend"),
     dict(type="TensorboardVisBackend"),
     # Update info accordingly
-    dict(
-        type="SafeMLflowVisBackend",
-        exp_name="(UserName) CenterPoint",
-        run_name="CenterPoint base",
-        tracking_uri="http://localhost:5000",
-        artifact_suffix=(),
-    ),
+    # dict(
+    #     type="SafeMLflowVisBackend",
+    #     exp_name="(UserName) CenterPoint",
+    #     run_name="CenterPoint base",
+    #     tracking_uri="http://localhost:5000",
+    #     artifact_suffix=(),
+    # ),
 ]
 visualizer = dict(type="Det3DLocalVisualizer", vis_backends=vis_backends, name="visualizer")
 

projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_8xb16_121m_j6gen2_base_amp.py

@@ -42,15 +42,15 @@
 
 # user setting
 data_root = "data/t4dataset/"
-info_directory_path = "info/user_name/"
+info_directory_path = "info/kokseang_2_6_1/"
 train_gpu_size = 8
 train_batch_size = 16
 test_batch_size = 2
 num_workers = 16
 val_interval = 1
 max_epochs = 30
 
-experiment_group_name = "centerpoint/j6gen2_base/" + _base_.dataset_type
+experiment_group_name = "centerpoint_2.6.2/j6gen2_base/" + _base_.dataset_type
 experiment_name = "second_secfpn_8xb16_121m_j6gen2_base_amp"
 work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name
 
@@ -352,7 +352,8 @@
 
 randomness = dict(seed=0, diff_rank_seed=False, deterministic=True)
 
-lr = 3e-4
+# 0.0003 * sqrt(2) = 0.0004242
+lr = 0.00042
 param_scheduler = [
     # learning rate scheduler
     # During the first (max_epochs * 0.3) epochs, learning rate increases from 0 to lr * 10
@@ -437,13 +438,13 @@
     dict(type="LocalVisBackend"),
     dict(type="TensorboardVisBackend"),
     # Update info accordingly
-    dict(
-        type="SafeMLflowVisBackend",
-        exp_name="(UserName) CenterPoint",
-        run_name="CenterPoint base",
-        tracking_uri="http://localhost:5000",
-        artifact_suffix=(),
-    ),
+    # dict(
+    #     type="SafeMLflowVisBackend",
+    #     exp_name="(UserName) CenterPoint",
+    #     run_name="CenterPoint base",
+    #     tracking_uri="http://localhost:5000",
+    #     artifact_suffix=(),
+    # ),
 ]
 visualizer = dict(type="Det3DLocalVisualizer", vis_backends=vis_backends, name="visualizer")
 
@@ -459,6 +460,6 @@
 ]
 
 # Update the load_from path accordingly
-load_from = "<best_checkpoint>"
+load_from = "work_dirs/centerpoint_2.6.1/base/T4Dataset/second_secfpn_8xb16_121m_base_amp_rfs/epoch_49.pth"
 
 activation_checkpointing = ["pts_backbone"]

projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_8xb16_121m_jpntaxi_base_amp.py

@@ -42,15 +42,15 @@
 
 # user setting
 data_root = "data/t4dataset/"
-info_directory_path = "info/user_name/"
+info_directory_path = "info/kokseang_2_6_1/"
 train_gpu_size = 8
 train_batch_size = 16
 test_batch_size = 2
 num_workers = 16
 val_interval = 1
 max_epochs = 30
 
-experiment_group_name = "centerpoint/jpntaxi_base/" + _base_.dataset_type
+experiment_group_name = "centerpoint_2.6.2/jpntaxi_base/" + _base_.dataset_type
 experiment_name = "second_secfpn_8xb16_121m_jpntaxi_base_amp"
 work_dir = "work_dirs/" + experiment_group_name + "/" + experiment_name
 
@@ -81,9 +81,9 @@
     ),
     dict(
         type="GlobalRotScaleTrans",
-        rot_range=[-1.571, 1.571],
-        scale_ratio_range=[0.80, 1.20],
-        translation_std=[1.0, 1.0, 0.2],
+        scale_ratio_range=[0.95, 1.05],
+        rot_range=[-0.78539816, 0.78539816],
+        translation_std=[0.5, 0.5, 0.2],
     ),
     dict(type="PointsRangeFilter", point_cloud_range=point_cloud_range),
     dict(type="ObjectRangeFilter", point_cloud_range=point_cloud_range),
@@ -356,7 +356,8 @@
 
 randomness = dict(seed=0, diff_rank_seed=False, deterministic=True)
 
-lr = 3e-4
+# 0.0003 * sqrt(2) = 0.0004242
+lr = 0.00042
 param_scheduler = [
     # learning rate scheduler
     # During the first (max_epochs * 0.3) epochs, learning rate increases from 0 to lr * 10
@@ -441,13 +442,13 @@
     dict(type="LocalVisBackend"),
     dict(type="TensorboardVisBackend"),
     # Update info accordingly
-    dict(
-        type="SafeMLflowVisBackend",
-        exp_name="(UserName) CenterPoint",
-        run_name="CenterPoint base",
-        tracking_uri="http://localhost:5000",
-        artifact_suffix=(),
-    ),
+    # dict(
+    #     type="SafeMLflowVisBackend",
+    #     exp_name="(UserName) CenterPoint",
+    #     run_name="CenterPoint base",
+    #     tracking_uri="http://localhost:5000",
+    #     artifact_suffix=(),
+    # ),
 ]
 visualizer = dict(type="Det3DLocalVisualizer", vis_backends=vis_backends, name="visualizer")
 
@@ -463,6 +464,7 @@
 ]
 
 # Update the load_from path accordingly
-load_from = "<best_checkpoint>"
+# load_from = "<best_checkpoint>"
+load_from = "work_dirs/centerpoint_2.6.1/base/T4Dataset/second_secfpn_8xb16_121m_base_amp_rfs/epoch_49.pth"
 
 activation_checkpointing = ["pts_backbone"]

projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_8xb16_121m_jpntaxi_base_amp_rfs.py

@@ -3,7 +3,7 @@
 ]
 
 # user setting
-experiment_name = "second_secfpn_8xb16_121m_jpntaxi_base_amp_rfs"
+experiment_name = "second_secfpn_8xb16_121m_jpntaxi_base_amp_rfs_less_rotation_1e-3_lr"
 work_dir = "work_dirs/" + _base_.experiment_group_name + "/" + experiment_name
 
 train_frame_object_sampler = dict(

tools/detection3d/t4dataset_converters/t4converter.py

@@ -627,6 +627,10 @@ def get_lidarseg_annotations(
     if not hasattr(t4, "lidarseg") or not t4.lidarseg:
         return dict()
 
+    if sd_record.info_filename is None:
+        print(f"sample {lidar_token} doesn't have lidar info_filename")
+        return dict()
+
     assert i < len(t4.lidarseg), "Index exceeds number of lidarseg records!"
     assert t4.lidarseg[i].sample_data_token == lidar_token, "Sample data token mismatch!"
     return dict(