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- .gitattributes +553 -0
- config.json +260 -0
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config.json
ADDED
|
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|
| 1 |
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{
|
| 2 |
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|
| 3 |
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"num_available_cameras": 3,
|
| 4 |
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"used_cameras": [
|
| 5 |
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true,
|
| 6 |
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true,
|
| 7 |
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true
|
| 8 |
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|
| 9 |
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|
| 10 |
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| 12 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 23 |
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| 25 |
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| 26 |
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|
| 27 |
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|
| 28 |
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| 29 |
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"id": "Experiment 1",
|
| 30 |
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|
| 31 |
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| 32 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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"1152",
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| 38 |
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"384",
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| 39 |
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| 40 |
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"64",
|
| 41 |
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| 42 |
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"40",
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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"True",
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| 47 |
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| 48 |
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"10"
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| 49 |
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| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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| 54 |
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| 55 |
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| 61 |
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| 63 |
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|
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| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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| 85 |
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| 86 |
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|
| 87 |
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|
| 88 |
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| 89 |
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| 94 |
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| 95 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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| 103 |
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| 104 |
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| 106 |
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|
| 114 |
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config: {'target_dataset': <TargetDataset.CARLA_LEADERBOARD2_3CAMERAS: 1>, 'num_available_cameras': 3, 'used_cameras': [True, True, True], 'num_used_cameras': 3, 'pixels_per_meter': 4.0, 'min_x_meter': -32, 'max_x_meter': 64, 'min_y_meter': -40, 'max_y_meter': 40, 'lidar_width_pixel': 384, 'lidar_height_pixel': 320, 'lidar_width_meter': 96, 'lidar_height_meter': 80, 'model_type': 'adapt', 'visualize_dataset': False, 'visualize_failed_scenarios': False, 'load_bev_3rd_person_images': False, 'seed': 0, 'wandb_id': None, 'wandb_resume': 'never', 'wandb_project_name': 'lead_posttrain', 'description': 'An example experiment description.', 'visualize_training': True, 'id': 'Experiment 1', 'load_file': '/home/divyanshu/carla/lead-adapt/outputs/local_training/posttrain/model_0019.pth', 'continue_failed_training': False, 'epoch_checkpoints_keep': [], 'use_training_session_cache': True, 'use_persistent_cache': True, 'force_rebuild_data_cache': False, 'carla_cache_path': ('1152', '384', '-32', 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'num_way_points_prediction': 8, 'waypoints_spacing': 5, 'horizontal_fov_reduction': 0, 'crop_height': 0, 'carla_crop_height_type': <CarlaImageCroppingType.NONE: 3>, 'image_width_before_camera_subselection': 1152, 'final_image_width': 1152, 'final_image_height': 384, 'img_vert_anchors': 12, 'img_horz_anchors': 36, 'freeze_backbone': False, 'image_architecture': 'resnet34', 'lidar_architecture': 'resnet34', 'LTF': False, 'block_exp': 4, 'n_layer': 2, 'n_head': 4, 'embd_pdrop': 0.1, 'resid_pdrop': 0.1, 'attn_pdrop': 0.1, 'gpt_linear_layer_init_mean': 0.0, 'gpt_linear_layer_init_std': 0.02, 'gpt_layer_norm_init_weight': 1.0, 'use_semantic': True, 'num_semantic_classes': 10, 'perspective_downsample_factor': 1, 'deconv_channel_num_0': 128, 'deconv_channel_num_1': 64, 'deconv_channel_num_2': 32, 'deconv_scale_factor_0': 4, 'deconv_scale_factor_1': 8, 'use_bev_semantic': True, 'num_bev_semantic_classes': 13, 'scale_pedestrian_bev_semantic_size': 2.5, 'pedestrian_bev_min_extent': 0.4, 'bev_features_chanels': 64, 'bev_down_sample_factor': 4, 'bev_upsample_factor': 2, 'use_carla_data': True, 'carla_num_samples': -1, 'use_navsim_data': False, 'navsim_data_root': 'data/navsim_training_cache/navtrain', 'navsim_num_samples': -1, 'schedule_carla_num_samples': False, 'use_waymo_e2e_data': False, 'waymo_e2e_num_training_samples': -1, 'waymo_e2e_training_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_training', 'waymo_e2e_val_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_val_rfm', 'waymo_e2e_test_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_test_submission', 'waymo_e2e_subsample_factor': 5, 'navsim_num_bev_semantic_classes': 7, 'navsim_num_bb_classes': 1, 'mixed_data_training': False, 'carla_leaderboard_mode': True, 'log_scalars_frequency': 1, 'log_images_frequency': 100, 'log_wandb': True, 'rank': 0, 'world_size': 2, 'local_rank': 0, 'device': device(type='cuda', index=0), 'assigned_cpu_cores': 8, 'workers_per_cpu_cores': 1, 'debug_mode': True, 'use_radars': False, '_loaded_config': {'target_dataset': 1, 'num_available_cameras': 3, 'used_cameras': [True, True, True], 'num_used_cameras': 3, 'pixels_per_meter': 4.0, 'min_x_meter': -32, 'max_x_meter': 64, 'min_y_meter': -40, 'max_y_meter': 40, 'lidar_width_pixel': 384, 'lidar_height_pixel': 320, 'lidar_width_meter': 96, 'lidar_height_meter': 80, 'model_type': 'adapt', 'visualize_dataset': False, 'visualize_failed_scenarios': False, 'load_bev_3rd_person_images': False, 'seed': 0, 'wandb_id': None, 'wandb_resume': 'never', 'wandb_project_name': 'lead_posttrain', 'description': 'An example experiment description.', 'visualize_training': True, 'id': 'Experiment 1', 'load_file': '/home/divyanshu/carla/lead-adapt/outputs/local_training/posttrain/model_0019.pth', 'continue_failed_training': False, 'epoch_checkpoints_keep': [], 'use_training_session_cache': True, 'use_persistent_cache': True, 'force_rebuild_data_cache': False, 'carla_cache_path': ['1152', '384', '-32', '64', '-40', '40', 'True', 'True', 'True', 'True', 'False', '10'], 'training_session_cache_path': '/tmp/divyanshu', 'carla_root': 'data/carla_leaderboard2', 'carla_data': 'data/carla_leaderboard2/data', 'logdir': 'outputs/local_training/adapt_finetune', 'training_png_compression_level': 6, 'vehicle_min_num_lidar_points': 1, 'vehicle_min_num_visible_pixels': 1, 'pedestrian_min_num_lidar_points': 5, 'pedestrian_min_num_visible_pixels': 15, 'parking_vehicle_min_num_lidar_points': 3, 'parking_vehicle_min_num_visible_pixels': 5, 'grad_scaler_init_scale': 1024, 'grad_scaler_growth_factor': 2, 'grad_scaler_backoff_factor': 0.5, 'grad_scaler_growth_interval': 256, 'grad_scaler_max_grad_scale': 65536, 'sync_batchnorm': False, 'epochs': 5, 'batch_size': 128, 'use_mixed_precision_training': True, 'need_grad_scaler': False, 'use_zero_redundancy': False, 'save_model_checkpoint': True, 'is_pretraining': False, 'prefetch_factor': 16, 'compile': True, 'channel_last': True, 'lr': 1e-05, 'skip_first': 8, 'skip_last': 8, 'radar_detection': False, 'use_radar_detection': False, 'num_radar_points_per_sensor': 75, 'num_radar_queries': 20, 'radar_hidden_dim_tokenizer': 1024, 'radar_token_dim': 256, 'radar_tf_dim_ff': 1024, 'radar_dropout': 0.1, 'radar_num_heads': 8, 'radar_num_layers': 4, 'radar_hidden_dim_decoder': 1024, 'radar_classification_loss_weight': 1.0, 'radar_regression_loss_weight': 5.0, 'num_radar_sensors': 4, 'force_rebuild_bucket': False, 'randomize_route_order': False, 'hold_out_town13_routes': False, 'bucket_collection_path': 'data/carla_leaderboard2/buckets', 'use_sensor_perburtation': True, 'use_sensor_perburtation_prob': 0.5, 'use_color_aug': True, 'use_color_aug_prob': 0.2, 'weight_decay': 0.01, 'use_cosine_annealing_with_restarts': True, 'use_rgb': True, 'use_depth': True, 'use_lidar': True, 'training_used_lidar_steps': 10, 'min_z': -4, 'max_z': 4, 'hist_max_per_pixel': 5, 'lidar_vert_anchors': 10, 'lidar_horz_anchors': 12, 'detect_boxes': True, 'data_bb_static_types_white_list': ['static.prop.constructioncone', 'static.prop.trafficwarning'], 'bb_confidence_threshold': 0.3, 'max_num_bbs': 90, 'num_dir_bins': 12, 'top_k_center_keypoints': 100, 'center_net_max_pooling_kernel': 3, 'bb_input_channel': 64, 'car_open_door_extra_width': 1.2, 'num_bb_classes': 8, 'use_discrete_command': True, 'discrete_command_dim': 6, 'use_noisy_tp': False, 'use_kalman_filter_for_gps': True, 'use_velocity': True, 'max_speed': 25.0, 'use_acceleration': False, 'max_acceleration': 10.0, 'use_previous_tp': True, 'use_next_tp': True, 'use_tp': True, 'target_points_normalization_constants': [[200.0, 50.0]], 'tp_pop_distance': 3.25, 'use_past_positions': False, 'use_past_speeds': False, 'num_past_samples_used': 0, 'transfuser_num_bev_cross_attention_layers': 6, 'transfuser_num_bev_cross_attention_heads': 8, 'transfuser_token_dim': 256, 'predict_target_speed': True, 'predict_spatial_path': True, 'predict_temporal_spatial_waypoints': True, 'use_planning_decoder': False, 'use_adapt_decoder': True, 'kdisks_vocab_path': 'lead/adapt/codebooks/kdisks_carla.pkl', 'use_kdisks': True, 'kdisks_heading_weight': 1.0, 'normalize_kinematics': False, 'use_soft_ce': False, 'soft_ce_sigma': 0.1, 'soft_ce_min_prob': 1e-06, 'kinematic_vocab_size': 4096, 'kinematic_embed_dim': 256, 'decoder_ffn_dim': 1024, 'decoder_num_heads': 8, 'decoder_dropout': 0.1, 'decoder_num_layers': 6, 'max_sequence_length': 64, 'frechet_gamma': 0.1, 'num_history_poses': 5, 'use_history_poses': True, 'target_speed_classes': [0.0, 4.0, 8.0, 10.0, 13.88888888, 16.0, 17.77777777, 20.0], 'target_speeds': [0.0, 4.0, 8.0, 10.0, 13.88888888, 16.0, 17.77777777, 20.0], 'smooth_route': True, 'num_route_points_smoothing': 20, 'num_route_points_prediction': 10, 'max_distance_future_waypoint': 10.0, 'num_way_points_prediction': 8, 'waypoints_spacing': 5, 'horizontal_fov_reduction': 0, 'crop_height': 0, 'carla_crop_height_type': 3, 'image_width_before_camera_subselection': 1152, 'final_image_width': 1152, 'final_image_height': 384, 'img_vert_anchors': 12, 'img_horz_anchors': 36, 'freeze_backbone': False, 'image_architecture': 'resnet34', 'lidar_architecture': 'resnet34', 'LTF': False, 'block_exp': 4, 'n_layer': 2, 'n_head': 4, 'embd_pdrop': 0.1, 'resid_pdrop': 0.1, 'attn_pdrop': 0.1, 'gpt_linear_layer_init_mean': 0.0, 'gpt_linear_layer_init_std': 0.02, 'gpt_layer_norm_init_weight': 1.0, 'use_semantic': True, 'num_semantic_classes': 10, 'perspective_downsample_factor': 1, 'deconv_channel_num_0': 128, 'deconv_channel_num_1': 64, 'deconv_channel_num_2': 32, 'deconv_scale_factor_0': 4, 'deconv_scale_factor_1': 8, 'use_bev_semantic': True, 'num_bev_semantic_classes': 13, 'scale_pedestrian_bev_semantic_size': 2.5, 'pedestrian_bev_min_extent': 0.4, 'bev_features_chanels': 64, 'bev_down_sample_factor': 4, 'bev_upsample_factor': 2, 'use_carla_data': True, 'carla_num_samples': -1, 'use_navsim_data': False, 'navsim_data_root': 'data/navsim_training_cache/navtrain', 'navsim_num_samples': -1, 'schedule_carla_num_samples': False, 'use_waymo_e2e_data': False, 'waymo_e2e_num_training_samples': -1, 'waymo_e2e_training_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_training', 'waymo_e2e_val_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_val_rfm', 'waymo_e2e_test_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_test_submission', 'waymo_e2e_subsample_factor': 5, 'navsim_num_bev_semantic_classes': 7, 'navsim_num_bb_classes': 1, 'mixed_data_training': False, 'carla_leaderboard_mode': True, 'log_scalars_frequency': 1, 'log_images_frequency': 100, 'log_wandb': True, 'rank': 0, 'world_size': 2, 'local_rank': 0, 'assigned_cpu_cores': 8, 'workers_per_cpu_cores': 1, 'debug_mode': True, 'use_radars': False}, '_wandb': {}}
|
| 9 |
+
2026-05-19 08:18:41,912 INFO MainThread:879065 [wandb_init.py:init():896] starting backend
|
| 10 |
+
2026-05-19 08:18:42,234 INFO MainThread:879065 [wandb_init.py:init():911] sending inform_init request
|
| 11 |
+
2026-05-19 08:18:42,251 INFO MainThread:879065 [wandb_init.py:init():919] backend started and connected
|
| 12 |
+
2026-05-19 08:18:42,277 INFO MainThread:879065 [wandb_init.py:init():989] updated telemetry
|
| 13 |
+
2026-05-19 08:18:42,398 INFO MainThread:879065 [wandb_init.py:init():1013] communicating run to backend with 90.0 second timeout
|
| 14 |
+
2026-05-19 08:18:55,246 INFO MainThread:879065 [wandb_init.py:init():1058] starting run threads in backend
|
| 15 |
+
2026-05-19 08:18:55,782 INFO MainThread:879065 [wandb_run.py:_console_start():2542] atexit reg
|
| 16 |
+
2026-05-19 08:18:55,783 INFO MainThread:879065 [wandb_run.py:_redirect():2391] redirect: wrap_raw
|
| 17 |
+
2026-05-19 08:18:55,783 INFO MainThread:879065 [wandb_run.py:_redirect():2460] Wrapping output streams.
|
| 18 |
+
2026-05-19 08:18:55,783 INFO MainThread:879065 [wandb_run.py:_redirect():2483] Redirects installed.
|
| 19 |
+
2026-05-19 08:18:55,788 INFO MainThread:879065 [wandb_init.py:init():1098] run started, returning control to user process
|
wandb/run-20260519_041525-rmnehalb/files/output.log
ADDED
|
@@ -0,0 +1,7 @@
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| 1 |
+
[34m[1mwandb[0m: Detected [agents] in use.
|
| 2 |
+
[34m[1mwandb[0m: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
|
| 3 |
+
[34m[1mwandb[0m: For more information, check out the docs at: https://weave-docs.wandb.ai
|
| 4 |
+
2026-05-19 04:15:31 [[94mINFO[0m] [lead/training/logger.py:74] WandB logger will log scalar every 1 steps
|
| 5 |
+
2026-05-19 04:15:31 [[94mINFO[0m] [lead/training/logger.py:77] WandB logger will log images every 100 steps
|
| 6 |
+
2026-05-19 04:15:35 [[94mINFO[0m] [lead/data_loader/carla_dataset.py:1174] Loaded 968710 images from 1 buckets.
|
| 7 |
+
0%| | 0/7568 [00:00<?, ?it/s]
|
wandb/run-20260519_041525-rmnehalb/files/requirements.txt
ADDED
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|
| 1 |
+
lead==0.0.1
|
| 2 |
+
packaging==26.0
|
| 3 |
+
rfc3986-validator==0.1.1
|
| 4 |
+
wheel==0.46.3
|
| 5 |
+
pip==26.0.1
|
| 6 |
+
easydict==1.13
|
| 7 |
+
manifold3d==3.4.1
|
| 8 |
+
mdurl==0.1.2
|
| 9 |
+
itsdangerous==2.2.0
|
| 10 |
+
tomli==2.4.1
|
| 11 |
+
tifffile==2025.5.10
|
| 12 |
+
stack-data==0.6.3
|
| 13 |
+
MarkupSafe==3.0.3
|
| 14 |
+
exceptiongroup==1.3.1
|
| 15 |
+
iniconfig==2.3.0
|
| 16 |
+
Jinja2==3.1.6
|
| 17 |
+
referencing==0.37.0
|
| 18 |
+
jupyterlab_pygments==0.3.0
|
| 19 |
+
ruff==0.12.4
|
| 20 |
+
pexpect==4.9.0
|
| 21 |
+
contourpy==1.3.2
|
| 22 |
+
PyYAML==6.0.3
|
| 23 |
+
pure_eval==0.2.3
|
| 24 |
+
httpcore==1.0.9
|
| 25 |
+
python-dateutil==2.9.0.post0
|
| 26 |
+
cffi==2.0.0
|
| 27 |
+
pluggy==1.6.0
|
| 28 |
+
pycollada==0.9.3
|
| 29 |
+
antlr4-python3-runtime==4.9.3
|
| 30 |
+
nest-asyncio==1.6.0
|
| 31 |
+
cachetools==7.0.5
|
| 32 |
+
pyparsing==3.3.2
|
| 33 |
+
h5py==3.7.0
|
| 34 |
+
ephem==4.1.5
|
| 35 |
+
kiwisolver==1.5.0
|
| 36 |
+
jsonpickle==3.0.3
|
| 37 |
+
zipp==3.23.1
|
| 38 |
+
lazy-loader==0.5
|
| 39 |
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frozenlist==1.8.0
|
| 40 |
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hydra-core==1.3.2
|
| 41 |
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cfgv==3.5.0
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| 42 |
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async-timeout==5.0.1
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| 43 |
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| 44 |
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| 45 |
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markdown-it-py==4.0.0
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addict==2.4.0
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| 48 |
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jupyter_server_terminals==0.5.4
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| 50 |
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identify==2.6.18
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aiohappyeyeballs==2.6.1
|
| 54 |
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tqdm==4.66.1
|
| 55 |
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importlib_metadata==8.7.1
|
| 56 |
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aiosignal==1.4.0
|
| 57 |
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ConfigArgParse==1.7.5
|
| 58 |
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laspy==2.5.4
|
| 59 |
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lead==0.0.1
|
| 60 |
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|
| 61 |
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google-api-core==2.30.3
|
| 62 |
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wrapt==2.1.2
|
| 63 |
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attrs==26.1.0
|
| 64 |
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omegaconf==2.3.0
|
| 65 |
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decorator==5.2.1
|
| 66 |
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| 67 |
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tinycss2==1.4.0
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|
| 69 |
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xmlformatter==0.2.8
|
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tzdata==2026.1
|
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transforms3d==0.4.1
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| 72 |
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Flask==3.1.1
|
| 74 |
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colorlog==6.10.1
|
| 75 |
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tabulate==0.9.0
|
| 76 |
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matplotlib==3.10.3
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| 77 |
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svg.path==7.0
|
| 78 |
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proto-plus==1.27.2
|
| 79 |
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argon2-cffi-bindings==25.1.0
|
| 80 |
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jupyter==1.1.1
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| 82 |
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jsonpointer==3.1.1
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| 84 |
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blinker==1.9.0
|
| 85 |
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python-discovery==1.2.2
|
| 86 |
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imgaug==0.4.0
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| 87 |
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basedpyright==1.31.3
|
| 88 |
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timm==1.0.19
|
| 89 |
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smmap==5.0.3
|
| 90 |
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joblib==1.5.3
|
| 91 |
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nvidia-cufft-cu12==11.2.1.3
|
| 92 |
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googleapis-common-protos==1.74.0
|
| 93 |
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opencv-python==4.6.0.66
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google-auth==2.49.2
|
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| 96 |
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| 98 |
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ujson==5.9.0
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| 99 |
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|
| 100 |
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wadler_lindig==0.1.7
|
| 101 |
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webencodings==0.5.1
|
| 102 |
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threadpoolctl==3.6.0
|
| 103 |
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matplotlib-inline==0.2.1
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| 104 |
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|
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|
| 106 |
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|
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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idna==3.11
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| 115 |
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| 116 |
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|
| 117 |
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| 118 |
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|
| 119 |
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|
| 120 |
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| 121 |
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|
| 122 |
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|
| 123 |
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| 124 |
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py-trees==0.8.3
|
| 125 |
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|
| 126 |
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mapbox_earcut==2.0.0
|
| 127 |
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|
| 128 |
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pycparser==3.0
|
| 129 |
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retrying==1.4.2
|
| 130 |
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|
| 131 |
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pyasn1==0.6.3
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| 132 |
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|
| 133 |
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|
| 134 |
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yarl==1.23.0
|
| 135 |
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click==8.3.2
|
| 136 |
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python-json-logger==4.1.0
|
| 137 |
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filelock==3.25.2
|
| 138 |
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vhacdx==0.0.10
|
| 139 |
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| 141 |
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ImageIO==2.37.3
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| 142 |
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| 143 |
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h11==0.16.0
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| 144 |
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| 145 |
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| 159 |
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|
| 160 |
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|
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safetensors==0.7.0
|
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|
| 163 |
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| 164 |
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pyproj==3.7.1
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| 188 |
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simple_watchdog_timer==0.1.1
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| 192 |
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gitdb==4.0.12
|
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bleach==6.3.0
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| 200 |
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| 201 |
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rtree==1.4.1
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| 202 |
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| 203 |
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protobuf==4.25.9
|
| 204 |
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numba==0.61.2
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| 205 |
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zstandard==0.25.0
|
| 206 |
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virtualenv==21.2.3
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| 207 |
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grpcio==1.80.0
|
| 208 |
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pyglet==1.5.0
|
| 209 |
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async-lru==2.3.0
|
| 210 |
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nvidia-cuda-runtime-cu12==12.4.127
|
| 211 |
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tokenizers==0.20.3
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| 212 |
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| 213 |
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pydot==4.0.1
|
| 214 |
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rich==14.3.4
|
| 215 |
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absl-py==2.4.0
|
| 216 |
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aiohttp==3.13.5
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| 217 |
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soupsieve==2.8.3
|
| 218 |
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sentry-sdk==2.58.0
|
| 219 |
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msgspec==0.21.1
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| 220 |
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torchvision==0.20.0+cu124
|
| 221 |
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torchaudio==2.5.0+cu124
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| 222 |
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nvidia-nvtx-cu12==12.4.127
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| 223 |
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trimesh==4.11.5
|
| 224 |
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prometheus_client==0.25.0
|
| 225 |
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six==1.16.0
|
| 226 |
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nbconvert==7.16.6
|
| 227 |
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prompt_toolkit==3.0.52
|
| 228 |
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ptyprocess==0.7.0
|
| 229 |
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aiohttp-cors==0.8.1
|
| 230 |
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nvidia-cuda-nvrtc-cu12==12.4.127
|
| 231 |
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| 232 |
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|
| 233 |
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gymnasium==1.2.3
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| 234 |
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| 235 |
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httpx==0.28.1
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| 240 |
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opentelemetry-semantic-conventions==0.62b0
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| 244 |
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| 245 |
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| 246 |
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| 247 |
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| 248 |
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opentelemetry-exporter-prometheus==0.62b0
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pydantic==2.13.0
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| 250 |
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babel==2.18.0
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| 251 |
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GitPython==3.1.46
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| 252 |
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terminado==0.18.1
|
| 253 |
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DracoPy==2.0.0
|
| 254 |
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py-spy==0.4.1
|
| 255 |
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scipy==1.14.1
|
| 256 |
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opencensus==0.11.4
|
| 257 |
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requests==2.32.5
|
| 258 |
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widgetsnbextension==4.0.15
|
| 259 |
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nvidia-nccl-cu12==2.21.5
|
| 260 |
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nvidia-cudnn-cu12==9.1.0.70
|
| 261 |
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notebook==7.5.5
|
| 262 |
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Markdown==3.10.2
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| 263 |
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jupyterlab_widgets==3.0.16
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| 264 |
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websocket-client==1.9.0
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| 265 |
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huggingface_hub==0.36.2
|
| 266 |
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xmlschema==1.0.18
|
| 267 |
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nvidia-cublas-cu12==12.4.5.8
|
| 268 |
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py123d==0.2.1
|
| 269 |
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narwhals==2.19.0
|
| 270 |
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future==1.0.0
|
| 271 |
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tensorboard-data-server==0.7.2
|
| 272 |
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scikit-image==0.25.2
|
| 273 |
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viser==1.0.26
|
| 274 |
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smart_open==7.6.0
|
| 275 |
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opentelemetry-sdk==1.41.0
|
| 276 |
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pyogrio==0.12.1
|
| 277 |
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hf-xet==1.4.3
|
| 278 |
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debugpy==1.8.20
|
| 279 |
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ipython==8.39.0
|
| 280 |
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wandb==0.26.0
|
| 281 |
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scikit-learn==1.7.2
|
| 282 |
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opentelemetry-proto==1.27.0
|
| 283 |
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PyQt5==5.15.11
|
| 284 |
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PyQt5-Qt5==5.15.18
|
| 285 |
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pyarrow==23.0.1
|
| 286 |
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jedi==0.19.2
|
| 287 |
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sympy==1.13.1
|
| 288 |
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ray==2.54.1
|
| 289 |
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jupyterlab==4.5.6
|
| 290 |
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transformers==4.46.3
|
| 291 |
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pandas==2.3.3
|
| 292 |
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plotly==6.7.0
|
| 293 |
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nodejs-wheel-binaries==24.14.1
|
| 294 |
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torch==2.5.0+cu124
|
| 295 |
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importlib_metadata==8.7.1
|
| 296 |
+
jaraco.context==6.1.0
|
| 297 |
+
jaraco.functools==4.4.0
|
| 298 |
+
more-itertools==10.8.0
|
| 299 |
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packaging==26.0
|
| 300 |
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platformdirs==4.4.0
|
| 301 |
+
tomli==2.4.0
|
| 302 |
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wheel==0.46.3
|
| 303 |
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zipp==3.23.0
|
| 304 |
+
autocommand==2.2.2
|
| 305 |
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backports.tarfile==1.2.0
|
| 306 |
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jaraco.text==4.0.0
|
wandb/run-20260519_041525-rmnehalb/files/wandb-metadata.json
ADDED
|
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"os": "Linux-5.15.0-176-generic-x86_64-with-glibc2.35",
|
| 3 |
+
"python": "CPython 3.10.20",
|
| 4 |
+
"startedAt": "2026-05-19T04:15:25.383386Z",
|
| 5 |
+
"args": [
|
| 6 |
+
"logdir=outputs/local_training/adapt_finetune"
|
| 7 |
+
],
|
| 8 |
+
"program": "/home/divyanshu/carla/lead-adapt/lead/training/train.py",
|
| 9 |
+
"codePath": "lead/training/train.py",
|
| 10 |
+
"codePathLocal": "lead/training/train.py",
|
| 11 |
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"git": {
|
| 12 |
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"remote": "https://github.com/Driver-Behaviour-Modeling/lead-adapt.git",
|
| 13 |
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"commit": "9301824552093e71d34fd4f7f3c73d86ce6ad972"
|
| 14 |
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},
|
| 15 |
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"email": "divyanshu_div@outlook.com",
|
| 16 |
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"root": "outputs/local_training/adapt_finetune",
|
| 17 |
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"host": "047f74d76241",
|
| 18 |
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"executable": "/home/divyanshu/miniconda3/envs/lead/bin/python3",
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| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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| 25 |
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| 26 |
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| 28 |
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| 29 |
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"memory": {
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| 30 |
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"total": "1081814089728"
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| 31 |
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| 32 |
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"gpu_nvidia": [
|
| 33 |
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{
|
| 34 |
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| 35 |
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| 36 |
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|
| 38 |
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"uuid": "GPU-45ed8fd7-e8d9-c54f-035d-dc6c397436ea"
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| 39 |
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},
|
| 40 |
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{
|
| 41 |
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"name": "NVIDIA H100 PCIe",
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| 42 |
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"memoryTotal": "85520809984",
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| 43 |
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| 44 |
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2026-05-19 04:15:25,391 INFO MainThread:872082 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
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config: {'target_dataset': <TargetDataset.CARLA_LEADERBOARD2_3CAMERAS: 1>, 'num_available_cameras': 3, 'used_cameras': [True, True, True], 'num_used_cameras': 3, 'pixels_per_meter': 4.0, 'min_x_meter': -32, 'max_x_meter': 64, 'min_y_meter': -40, 'max_y_meter': 40, 'lidar_width_pixel': 384, 'lidar_height_pixel': 320, 'lidar_width_meter': 96, 'lidar_height_meter': 80, 'model_type': 'adapt', 'visualize_dataset': False, 'visualize_failed_scenarios': False, 'load_bev_3rd_person_images': False, 'seed': 0, 'wandb_id': None, 'wandb_resume': 'never', 'wandb_project_name': 'lead_posttrain', 'description': 'An example experiment description.', 'visualize_training': True, 'id': 'Experiment 1', 'load_file': '/home/divyanshu/carla/lead-adapt/outputs/local_training/posttrain/model_0019.pth', 'continue_failed_training': False, 'epoch_checkpoints_keep': [], 'use_training_session_cache': True, 'use_persistent_cache': True, 'force_rebuild_data_cache': False, 'carla_cache_path': ('1152', '384', '-32', '64', '-40', '40', 'True', 'True', 'True', 'True', 'False', '10'), 'training_session_cache_path': '/tmp/divyanshu', 'carla_root': 'data/carla_leaderboard2', 'carla_data': 'data/carla_leaderboard2/data', 'logdir': 'outputs/local_training/adapt_finetune', 'training_png_compression_level': 6, 'vehicle_min_num_lidar_points': 1, 'vehicle_min_num_visible_pixels': 1, 'pedestrian_min_num_lidar_points': 5, 'pedestrian_min_num_visible_pixels': 15, 'parking_vehicle_min_num_lidar_points': 3, 'parking_vehicle_min_num_visible_pixels': 5, 'grad_scaler_init_scale': 1024, 'grad_scaler_growth_factor': 2, 'grad_scaler_backoff_factor': 0.5, 'grad_scaler_growth_interval': 256, 'grad_scaler_max_grad_scale': 65536, 'sync_batchnorm': False, 'epochs': 5, 'batch_size': 128, 'torch_float_type': torch.bfloat16, 'use_mixed_precision_training': True, 'need_grad_scaler': False, 'use_zero_redundancy': False, 'save_model_checkpoint': True, 'is_pretraining': False, 'prefetch_factor': 16, 'compile': True, 'channel_last': True, 'lr': 1e-05, 'skip_first': 8, 'skip_last': 8, 'radar_detection': False, 'use_radar_detection': False, 'num_radar_points_per_sensor': 75, 'num_radar_queries': 20, 'radar_hidden_dim_tokenizer': 1024, 'radar_token_dim': 256, 'radar_tf_dim_ff': 1024, 'radar_dropout': 0.1, 'radar_num_heads': 8, 'radar_num_layers': 4, 'radar_hidden_dim_decoder': 1024, 'radar_classification_loss_weight': 1.0, 'radar_regression_loss_weight': 5.0, 'num_radar_sensors': 4, 'force_rebuild_bucket': False, 'randomize_route_order': False, 'hold_out_town13_routes': False, 'carla_bucket_collection': <class 'lead.data_buckets.full_posttrain_bucket_collection.FullPosttrainBucketCollection'>, 'bucket_collection_path': 'data/carla_leaderboard2/buckets', 'use_sensor_perburtation': True, 'use_sensor_perburtation_prob': 0.5, 'use_color_aug': True, 'use_color_aug_prob': 0.2, 'weight_decay': 0.01, 'use_cosine_annealing_with_restarts': True, 'use_rgb': True, 'use_depth': True, 'use_lidar': True, 'training_used_lidar_steps': 10, 'min_z': -4, 'max_z': 4, 'hist_max_per_pixel': 5, 'lidar_vert_anchors': 10, 'lidar_horz_anchors': 12, 'detect_boxes': True, 'data_bb_static_types_white_list': ['static.prop.constructioncone', 'static.prop.trafficwarning'], 'bb_confidence_threshold': 0.3, 'max_num_bbs': 90, 'num_dir_bins': 12, 'top_k_center_keypoints': 100, 'center_net_max_pooling_kernel': 3, 'bb_input_channel': 64, 'car_open_door_extra_width': 1.2, 'num_bb_classes': 8, 'use_discrete_command': True, 'discrete_command_dim': 6, 'use_noisy_tp': False, 'use_kalman_filter_for_gps': True, 'use_velocity': True, 'max_speed': 25.0, 'use_acceleration': False, 'max_acceleration': 10.0, 'use_previous_tp': True, 'use_next_tp': True, 'use_tp': True, 'target_points_normalization_constants': [[200.0, 50.0]], 'tp_pop_distance': 3.25, 'use_past_positions': False, 'use_past_speeds': False, 'num_past_samples_used': 0, 'transfuser_num_bev_cross_attention_layers': 6, 'transfuser_num_bev_cross_attention_heads': 8, 'transfuser_token_dim': 256, 'predict_target_speed': True, 'predict_spatial_path': True, 'predict_temporal_spatial_waypoints': True, 'use_planning_decoder': False, 'use_adapt_decoder': True, 'kdisks_vocab_path': 'lead/adapt/codebooks/kdisks_carla.pkl', 'use_kdisks': True, 'kdisks_heading_weight': 1.0, 'normalize_kinematics': False, 'use_soft_ce': False, 'soft_ce_sigma': 0.1, 'soft_ce_min_prob': 1e-06, 'kinematic_vocab_size': 4096, 'kinematic_embed_dim': 256, 'decoder_ffn_dim': 1024, 'decoder_num_heads': 8, 'decoder_dropout': 0.1, 'decoder_num_layers': 6, 'max_sequence_length': 64, 'frechet_gamma': 0.1, 'num_history_poses': 5, 'use_history_poses': True, 'target_speed_classes': [0.0, 4.0, 8.0, 10.0, 13.88888888, 16.0, 17.77777777, 20.0], 'target_speeds': [0.0, 4.0, 8.0, 10.0, 13.88888888, 16.0, 17.77777777, 20.0], 'smooth_route': True, 'num_route_points_smoothing': 20, 'num_route_points_prediction': 10, 'max_distance_future_waypoint': 10.0, 'num_way_points_prediction': 8, 'waypoints_spacing': 5, 'horizontal_fov_reduction': 0, 'crop_height': 0, 'carla_crop_height_type': <CarlaImageCroppingType.NONE: 3>, 'image_width_before_camera_subselection': 1152, 'final_image_width': 1152, 'final_image_height': 384, 'img_vert_anchors': 12, 'img_horz_anchors': 36, 'freeze_backbone': False, 'image_architecture': 'resnet34', 'lidar_architecture': 'resnet34', 'LTF': False, 'block_exp': 4, 'n_layer': 2, 'n_head': 4, 'embd_pdrop': 0.1, 'resid_pdrop': 0.1, 'attn_pdrop': 0.1, 'gpt_linear_layer_init_mean': 0.0, 'gpt_linear_layer_init_std': 0.02, 'gpt_layer_norm_init_weight': 1.0, 'use_semantic': True, 'num_semantic_classes': 10, 'perspective_downsample_factor': 1, 'deconv_channel_num_0': 128, 'deconv_channel_num_1': 64, 'deconv_channel_num_2': 32, 'deconv_scale_factor_0': 4, 'deconv_scale_factor_1': 8, 'use_bev_semantic': True, 'num_bev_semantic_classes': 13, 'scale_pedestrian_bev_semantic_size': 2.5, 'pedestrian_bev_min_extent': 0.4, 'bev_features_chanels': 64, 'bev_down_sample_factor': 4, 'bev_upsample_factor': 2, 'use_carla_data': True, 'carla_num_samples': -1, 'use_navsim_data': False, 'navsim_data_root': 'data/navsim_training_cache/navtrain', 'navsim_num_samples': -1, 'schedule_carla_num_samples': False, 'use_waymo_e2e_data': False, 'waymo_e2e_num_training_samples': -1, 'waymo_e2e_training_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_training', 'waymo_e2e_val_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_val_rfm', 'waymo_e2e_test_data_root': 'data/waymo_open_dataset_end_to_end_camera_v_1_0_0_test_submission', 'waymo_e2e_subsample_factor': 5, 'navsim_num_bev_semantic_classes': 7, 'navsim_num_bb_classes': 1, 'mixed_data_training': False, 'carla_leaderboard_mode': True, 'log_scalars_frequency': 1, 'log_images_frequency': 100, 'log_wandb': True, 'rank': 0, 'world_size': 2, 'local_rank': 0, 'device': device(type='cuda', index=0), 'assigned_cpu_cores': 8, 'workers_per_cpu_cores': 1, 'debug_mode': True, 'use_radars': False, '_loaded_config': {'target_dataset': 1, 'num_available_cameras': 3, 'used_cameras': [True, True, True], 'num_used_cameras': 3, 'pixels_per_meter': 4.0, 'min_x_meter': -32, 'max_x_meter': 64, 'min_y_meter': -40, 'max_y_meter': 40, 'lidar_width_pixel': 384, 'lidar_height_pixel': 320, 'lidar_width_meter': 96, 'lidar_height_meter': 80, 'model_type': 'adapt', 'visualize_dataset': False, 'visualize_failed_scenarios': False, 'load_bev_3rd_person_images': False, 'seed': 0, 'wandb_id': None, 'wandb_resume': 'never', 'wandb_project_name': 'lead_posttrain', 'description': 'An example experiment description.', 'visualize_training': True, 'id': 'Experiment 1', 'load_file': '/home/divyanshu/carla/lead-adapt/outputs/local_training/posttrain/model_0019.pth', 'continue_failed_training': False, 'epoch_checkpoints_keep': [], 'use_training_session_cache': True, 'use_persistent_cache': True, 'force_rebuild_data_cache': False, 'carla_cache_path': ['1152', '384', 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