├── .gitignore ├── LICENSE ├── README.md ├── assets ├── rcp_arch.jpg └── rcp_arch_compressed.jpg ├── configs ├── test │ ├── rcp_test.yaml │ └── rcp_test_kitti.yaml └── train │ ├── rcp_self_ft.yaml │ ├── rcp_self_pre.yaml │ ├── rcp_sup_ft.yaml │ └── rcp_sup_pre.yaml ├── data ├── __init__.py ├── exp_utils.py ├── loaders │ ├── KITTI_mapping.txt │ ├── ModelNet40.py │ ├── __init__.py │ ├── flyingthings3d_subset.py │ └── kitti.py ├── preprocess │ ├── IO.py │ ├── flyingthings3d_utils.py │ ├── kitti_utils.py │ ├── process_flyingthings3d_subset.py │ ├── process_kitti.py │ └── python_pfm.py ├── transforms │ ├── __init__.py │ ├── functional.py │ └── transforms.py └── utils │ └── pr_utils.py ├── lib └── pointnet2 │ ├── pointnet2_modules.py │ ├── pointnet2_utils.py │ ├── pytorch_utils.py │ ├── setup.py │ └── src │ ├── ball_query.cpp │ ├── ball_query_gpu.cu │ ├── ball_query_gpu.h │ ├── cuda_utils.h │ ├── group_points.cpp │ ├── group_points_gpu.cu │ ├── group_points_gpu.h │ ├── interpolate.cpp │ ├── interpolate_gpu.cu │ ├── interpolate_gpu.h │ ├── pointnet2_api.cpp │ ├── sampling.cpp │ ├── sampling_gpu.cu │ └── sampling_gpu.h ├── losses ├── __init__.py ├── common_losses.py ├── supervised_losses.py └── unsupervised_losses.py ├── models ├── SF_RCP.py ├── __init__.py └── common.py ├── requirements.txt ├── run.py ├── scene_flow.py └── utils ├── __init__.py ├── calib_cam_to_cam ├── 000000.txt ├── 000001.txt ├── 000002.txt ├── 000003.txt ├── 000004.txt ├── 000005.txt ├── 000006.txt ├── 000007.txt ├── 000008.txt ├── 000009.txt ├── 000010.txt ├── 000011.txt ├── 000012.txt ├── 000013.txt ├── 000014.txt ├── 000015.txt ├── 000016.txt ├── 000017.txt ├── 000018.txt ├── 000019.txt ├── 000020.txt ├── 000021.txt ├── 000022.txt ├── 000023.txt ├── 000024.txt ├── 000025.txt ├── 000026.txt ├── 000027.txt ├── 000028.txt ├── 000029.txt ├── 000030.txt ├── 000031.txt ├── 000032.txt ├── 000033.txt ├── 000034.txt ├── 000035.txt ├── 000036.txt ├── 000037.txt ├── 000038.txt ├── 000039.txt ├── 000040.txt ├── 000041.txt ├── 000042.txt ├── 000043.txt ├── 000044.txt ├── 000045.txt ├── 000046.txt ├── 000047.txt ├── 000048.txt ├── 000049.txt ├── 000050.txt ├── 000051.txt ├── 000052.txt ├── 000053.txt ├── 000054.txt ├── 000055.txt ├── 000056.txt ├── 000057.txt ├── 000058.txt ├── 000059.txt ├── 000060.txt ├── 000061.txt ├── 000062.txt ├── 000063.txt ├── 000064.txt ├── 000065.txt ├── 000066.txt ├── 000067.txt ├── 000068.txt ├── 000069.txt ├── 000070.txt ├── 000071.txt ├── 000072.txt ├── 000073.txt ├── 000074.txt ├── 000075.txt ├── 000076.txt ├── 000077.txt ├── 000078.txt ├── 000079.txt ├── 000080.txt ├── 000081.txt ├── 000082.txt ├── 000083.txt ├── 000084.txt ├── 000085.txt ├── 000086.txt ├── 000087.txt ├── 000088.txt ├── 000089.txt ├── 000090.txt ├── 000091.txt ├── 000092.txt ├── 000093.txt ├── 000094.txt ├── 000095.txt ├── 000096.txt ├── 000097.txt ├── 000098.txt ├── 000099.txt ├── 000100.txt ├── 000101.txt ├── 000102.txt ├── 000103.txt ├── 000104.txt ├── 000105.txt ├── 000106.txt ├── 000107.txt ├── 000108.txt ├── 000109.txt ├── 000110.txt ├── 000111.txt ├── 000112.txt ├── 000113.txt ├── 000114.txt ├── 000115.txt ├── 000116.txt ├── 000117.txt ├── 000118.txt ├── 000119.txt ├── 000120.txt ├── 000121.txt ├── 000122.txt ├── 000123.txt ├── 000124.txt ├── 000125.txt ├── 000126.txt ├── 000127.txt ├── 000128.txt ├── 000129.txt ├── 000130.txt ├── 000131.txt ├── 000132.txt ├── 000133.txt ├── 000134.txt ├── 000135.txt ├── 000136.txt ├── 000137.txt ├── 000138.txt ├── 000139.txt ├── 000140.txt ├── 000141.txt ├── 000142.txt ├── 000143.txt ├── 000144.txt ├── 000145.txt ├── 000146.txt ├── 000147.txt ├── 000148.txt ├── 000149.txt ├── 000150.txt ├── 000151.txt ├── 000152.txt ├── 000153.txt ├── 000154.txt ├── 000155.txt ├── 000156.txt ├── 000157.txt ├── 000158.txt ├── 000159.txt ├── 000160.txt ├── 000161.txt ├── 000162.txt ├── 000163.txt ├── 000164.txt ├── 000165.txt ├── 000166.txt ├── 000167.txt ├── 000168.txt ├── 000169.txt ├── 000170.txt ├── 000171.txt ├── 000172.txt ├── 000173.txt ├── 000174.txt ├── 000175.txt ├── 000176.txt ├── 000177.txt ├── 000178.txt ├── 000179.txt ├── 000180.txt ├── 000181.txt ├── 000182.txt ├── 000183.txt ├── 000184.txt ├── 000185.txt ├── 000186.txt ├── 000187.txt ├── 000188.txt ├── 000189.txt ├── 000190.txt ├── 000191.txt ├── 000192.txt ├── 000193.txt ├── 000194.txt ├── 000195.txt ├── 000196.txt ├── 000197.txt ├── 000198.txt └── 000199.txt ├── easydict.py ├── geometry.py ├── metrics.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | venv 2 | .idea 3 | __pycache__/ 4 | .vscode 5 | checkpoints/* 6 | data/datasets/* 7 | lib/pointnet2/__pycache__ 8 | lib/pointnet2/build 9 | lib/pointnet2/dist 10 | lib/pointnet2/pointnet2.egg-info -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Alibaba Research 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /assets/rcp_arch.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlibabaResearch/rcp/64e8d9eac981f26a3918cf6bec71dda0065c6b3d/assets/rcp_arch.jpg -------------------------------------------------------------------------------- /assets/rcp_arch_compressed.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlibabaResearch/rcp/64e8d9eac981f26a3918cf6bec71dda0065c6b3d/assets/rcp_arch_compressed.jpg -------------------------------------------------------------------------------- /configs/test/rcp_test.yaml: -------------------------------------------------------------------------------- 1 | train: False 2 | 3 | task: sceneflow 4 | 5 | model_params: 6 | model_name: 'SF_RCP' 7 | npoint: 8192 8 | use_insrance_norm: False 9 | use_curvature: True 10 | 11 | exp_params: 12 | disable_plots: True 13 | batch_size: 8 14 | num_workers: 32 15 | train_iters: 8 16 | val_iters: 8 17 | test_iters: 8 18 | 19 | data: 20 | overfit_samples: null 21 | train_dataset: null 22 | train_data_root: null 23 | test_dataset: flyingthings3d 24 | test_data_root: /data/datasets/scene_flow/flyingthings3d 25 | num_points: 8192 26 | full: True 27 | allow_less_points: False 28 | 29 | data_process: 30 | DEPTH_THRESHOLD: 35. 31 | NO_CORR: True 32 | IND_DIR: null 33 | 34 | data_augmentation: 35 | aug_together: 36 | degree_range: 0.1745329252 37 | shift_range: 1. 38 | scale_low: 0.95 39 | scale_high: 1.05 40 | jitter_sigma: 0.01 41 | jitter_clip: 0.00 42 | aug_pc2: 43 | degree_range: 0. 44 | xy_shift_range: 0. 45 | deg_max: False 46 | shift_range: 0.2 47 | jitter_sigma: 0.01 48 | jitter_clip: 0.00 49 | val_aug_pc2: null 50 | 51 | loss: 52 | loss_type: 'sv_l1_reg_v1' 53 | w_data: [0.9] 54 | w_smoothness: [0.1] 55 | smoothness_loss_params: 56 | w_knn: 3. 57 | w_ball_q: 1. 58 | knn_loss_params: 59 | k: 16 60 | radius: 0.5 61 | loss_norm: 1 62 | ball_q_loss_params: 63 | k: 24 64 | radius: 0.5 65 | loss_norm: 1 66 | optimizer: 67 | type: 'AdamW' 68 | lr: 0.000125 69 | weight_decay: 0.01 70 | 71 | scheduler: 72 | type: 'MultiStepLR' 73 | milestones: [1, 2, 3] 74 | gamma: 0.5 75 | 76 | trainer_params: 77 | deterministic: False # slower. reproduce. 78 | strategy: 'ddp' 79 | num_nodes: 1 80 | gpus: 8 81 | max_epochs: 90 82 | num_sanity_val_steps: 3 83 | check_val_every_n_epoch: 1 84 | sync_batchnorm: True 85 | 86 | resume_from_checkpoint: None 87 | 88 | logging_params: 89 | ckpt: 90 | save_dir: 'checkpoints/rcp_sup_ft' 91 | top_k: 3 92 | monitor: 'val_epe' 93 | 94 | log: false 95 | -------------------------------------------------------------------------------- /configs/test/rcp_test_kitti.yaml: -------------------------------------------------------------------------------- 1 | train: False 2 | task: "sceneflow" 3 | 4 | model_params: 5 | model_name: 'SF_RCP' 6 | npoint: 8192 7 | use_insrance_norm: False 8 | use_curvature: True 9 | 10 | exp_params: 11 | disable_plots: True 12 | batch_size: 1 13 | num_workers: 32 14 | train_iters: 16 15 | val_iters: 16 16 | test_iters: 16 17 | 18 | data: 19 | overfit_samples: null 20 | train_dataset: null 21 | train_data_root: null 22 | test_dataset: kitti 23 | test_data_root: /data/datasets/scene_flow/kitti 24 | num_points: 8192 25 | full: True 26 | allow_less_points: False 27 | 28 | data_process: 29 | DEPTH_THRESHOLD: 35. 30 | NO_CORR: True 31 | IND_DIR: null 32 | 33 | data_augmentation: 34 | aug_together: 35 | degree_range: 0.1745329252 36 | shift_range: 1. 37 | scale_low: 0.95 38 | scale_high: 1.05 39 | jitter_sigma: 0.01 40 | jitter_clip: 0.00 41 | aug_pc2: 42 | degree_range: 0. 43 | xy_shift_range: 0. 44 | deg_max: False 45 | shift_range: 0.2 46 | jitter_sigma: 0.01 47 | jitter_clip: 0.00 48 | val_aug_pc2: null 49 | 50 | loss: 51 | loss_type: 'sv_l1_reg_v1' 52 | w_data: [0.9] 53 | w_smoothness: [0.1] 54 | smoothness_loss_params: 55 | w_knn: 3. 56 | w_ball_q: 1. 57 | knn_loss_params: 58 | k: 16 59 | radius: 0.5 60 | loss_norm: 1 61 | ball_q_loss_params: 62 | k: 24 63 | radius: 0.5 64 | loss_norm: 1 65 | optimizer: 66 | type: 'AdamW' 67 | lr: 0.000125 68 | weight_decay: 0.01 69 | 70 | scheduler: 71 | type: 'MultiStepLR' 72 | milestones: [1, 2, 3] 73 | gamma: 0.5 74 | 75 | trainer_params: 76 | deterministic: False # slower. reproduce. 77 | strategy: 'ddp' 78 | num_nodes: 1 79 | gpus: 8 80 | max_epochs: 90 81 | num_sanity_val_steps: 3 82 | check_val_every_n_epoch: 1 83 | sync_batchnorm: True 84 | resume_from_checkpoint: None 85 | 86 | logging_params: 87 | ckpt: 88 | save_dir: 'checkpoints/rcp_sup_ft' 89 | top_k: 3 90 | monitor: 'val_epe' 91 | log: false -------------------------------------------------------------------------------- /configs/train/rcp_self_ft.yaml: -------------------------------------------------------------------------------- 1 | train: True 2 | 3 | task: 'sceneflow' 4 | model_params: 5 | model_name: 'SF_RCP' 6 | npoint: 8192 7 | use_insrance_norm: False 8 | use_curvature: True 9 | 10 | exp_params: 11 | pre_trained_weights_checkpoint: None 12 | disable_plots: True 13 | batch_size: 1 14 | num_workers: 32 15 | train_iters: 8 16 | val_iters: 8 17 | loss_iters_w: [0.5, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2] 18 | 19 | data: 20 | overfit_samples: null 21 | train_dataset: flyingthings3d 22 | train_data_root: /data/datasets/scene_flow/flyingthings3d 23 | test_dataset: null 24 | test_data_root: null 25 | num_points: 8192 26 | full: True 27 | allow_less_points: False 28 | 29 | data_process: 30 | DEPTH_THRESHOLD: 35. 31 | NO_CORR: True 32 | IND_DIR: null 33 | 34 | data_augmentation: 35 | aug_together: 36 | degree_range: 0.1745329252 37 | shift_range: 1. 38 | scale_low: 0.95 39 | scale_high: 1.05 40 | jitter_sigma: 0.01 41 | jitter_clip: 0.00 42 | aug_pc2: 43 | degree_range: 0. 44 | xy_shift_range: 0. 45 | deg_max: False 46 | shift_range: 0.2 47 | jitter_sigma: 0.01 48 | jitter_clip: 0.00 49 | val_aug_pc2: null 50 | 51 | loss: 52 | loss_type: 'unsup_l1' 53 | w_data: [0.65, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7] 54 | w_smoothness: [0.35, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3] 55 | w_curvature: [0.1, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01] 56 | smoothness_loss_params: 57 | w_knn: 4. 58 | w_ball_q: 1. 59 | knn_loss_params: 60 | k: 16 61 | radius: 0.25 62 | loss_norm: 1 63 | ball_q_loss_params: 64 | k: 48 65 | radius: 0.5 66 | loss_norm: 1 67 | chamfer_loss_params: 68 | loss_norm: 2 69 | k: 1 70 | optimizer: 71 | type: 'AdamW' 72 | lr: 0.002 73 | weight_decay: 0.01 74 | 75 | scheduler: 76 | type: 'MultiStepLR' 77 | milestones: [3, 6, 9] 78 | gamma: 0.5 79 | 80 | trainer_params: 81 | deterministic: False # slower. reproduce. 82 | strategy: 'ddp' 83 | num_nodes: 1 84 | gpus: 8 85 | max_epochs: 15 86 | num_sanity_val_steps: 3 87 | check_val_every_n_epoch: 1 88 | resume_from_checkpoint: null 89 | sync_batchnorm: True 90 | 91 | logging_params: 92 | ckpt: 93 | save_dir: 'checkpoints/rcp_selfsup_ft' 94 | top_k: 3 95 | monitor: 'val_epe' 96 | 97 | log: false -------------------------------------------------------------------------------- /configs/train/rcp_self_pre.yaml: -------------------------------------------------------------------------------- 1 | train: True 2 | 3 | task: 'sceneflow' 4 | 5 | model_params: 6 | model_name: 'SF_RCP' 7 | npoint: 8192 8 | use_insrance_norm: False 9 | use_curvature: True 10 | 11 | 12 | exp_params: 13 | disable_plots: True 14 | batch_size: 2 15 | num_workers: 16 16 | train_iters: 4 17 | val_iters: 4 18 | loss_iters_w: [0.7, 0.4, 0.4, 0.4] 19 | 20 | data: 21 | overfit_samples: null 22 | train_dataset: flyingthings3d 23 | train_data_root: /data/datasets/scene_flow/flyingthings3d 24 | test_dataset: null 25 | test_data_root: null 26 | num_points: 8192 27 | full: True 28 | allow_less_points: False 29 | 30 | data_process: 31 | DEPTH_THRESHOLD: 35. 32 | NO_CORR: True 33 | IND_DIR: null 34 | 35 | data_augmentation: 36 | aug_together: 37 | degree_range: 0.1745329252 38 | shift_range: 1. 39 | scale_low: 0.95 40 | scale_high: 1.05 41 | jitter_sigma: 0.01 42 | jitter_clip: 0.00 43 | aug_pc2: 44 | degree_range: 0. 45 | xy_shift_range: 0. 46 | deg_max: False 47 | shift_range: 0.2 48 | jitter_sigma: 0.01 49 | jitter_clip: 0.00 50 | val_aug_pc2: null 51 | 52 | loss: 53 | loss_type: 'unsup_l1' 54 | w_data: [0.75, 0.75, 0.75, 0.75] 55 | w_smoothness: [0.25, 0.25, 0.25, 0.25] 56 | w_curvature: [0.1, 0.01, 0.01, 0.01] 57 | 58 | smoothness_loss_params: 59 | w_knn: 3. 60 | w_ball_q: 1. 61 | knn_loss_params: 62 | k: 16 63 | radius: 0.25 64 | loss_norm: 1 65 | ball_q_loss_params: 66 | k: 64 67 | radius: 0.75 68 | loss_norm: 1 69 | chamfer_loss_params: 70 | loss_norm: 2 71 | k: 1 72 | optimizer: 73 | type: 'AdamW' 74 | lr: 0.002 75 | weight_decay: 0.01 76 | 77 | scheduler: 78 | type: 'MultiStepLR' 79 | milestones: [20, 40, 60, 80] 80 | gamma: 0.5 81 | 82 | trainer_params: 83 | deterministic: False # slower. reproduce. 84 | strategy: 'ddp' 85 | num_nodes: 1 86 | gpus: 8 87 | max_epochs: 90 88 | num_sanity_val_steps: 3 89 | check_val_every_n_epoch: 2 90 | resume_from_checkpoint: null 91 | sync_batchnorm: True 92 | 93 | logging_params: 94 | ckpt: 95 | save_dir: 'checkpoints/rcp_selfsup' 96 | top_k: 3 97 | monitor: 'val_epe' 98 | 99 | log: false -------------------------------------------------------------------------------- /configs/train/rcp_sup_ft.yaml: -------------------------------------------------------------------------------- 1 | train: True 2 | 3 | task: "sceneflow" 4 | 5 | model_params: 6 | model_name: 'SF_RCP' 7 | npoint: 8192 8 | use_insrance_norm: False 9 | use_curvature: True 10 | 11 | exp_params: 12 | pre_trained_weights_checkpoint: None 13 | 14 | disable_plots: True 15 | batch_size: 1 16 | num_workers: 32 17 | train_iters: 8 18 | val_iters: 8 19 | loss_iters_w: [0.5, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2] 20 | 21 | data: 22 | overfit_samples: null 23 | train_dataset: flyingthings3d 24 | train_data_root: /data/datasets/scene_flow/flyingthings3d 25 | test_dataset: null 26 | test_data_root: null 27 | num_points: 8192 28 | full: True 29 | allow_less_points: False 30 | 31 | data_process: 32 | DEPTH_THRESHOLD: 35. 33 | NO_CORR: True 34 | IND_DIR: null 35 | 36 | data_augmentation: 37 | aug_together: 38 | degree_range: 0.1745329252 39 | shift_range: 1. 40 | scale_low: 0.95 41 | scale_high: 1.05 42 | jitter_sigma: 0.01 43 | jitter_clip: 0.00 44 | aug_pc2: 45 | degree_range: 0. 46 | xy_shift_range: 0. 47 | deg_max: False 48 | shift_range: 0.2 49 | jitter_sigma: 0.01 50 | jitter_clip: 0.00 51 | val_aug_pc2: null 52 | 53 | loss: 54 | loss_type: 'sv_l1_reg_v1' 55 | w_data: [0.95, 0.98, 0.99, 0.99, 0.99, 0.99, 0.99, 0.99] 56 | w_smoothness: [0.05, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01] 57 | w_curvature: [0.1, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01] 58 | smoothness_loss_params: 59 | w_knn: 4. 60 | w_ball_q: 1. 61 | knn_loss_params: 62 | k: 16 63 | radius: 0.25 64 | loss_norm: 1 65 | ball_q_loss_params: 66 | k: 48 67 | radius: 0.75 68 | loss_norm: 1 69 | 70 | optimizer: 71 | type: 'AdamW' 72 | lr: 0.000125 73 | weight_decay: 0.01 74 | 75 | scheduler: 76 | type: 'MultiStepLR' 77 | milestones: [1, 2, 3] 78 | gamma: 0.5 79 | 80 | trainer_params: 81 | deterministic: False # slower. reproduce. 82 | strategy: 'ddp' 83 | num_nodes: 1 84 | gpus: 8 85 | max_epochs: 5 86 | num_sanity_val_steps: 3 87 | check_val_every_n_epoch: 1 88 | resume_from_checkpoint: null 89 | sync_batchnorm: True 90 | 91 | logging_params: 92 | ckpt: 93 | save_dir: 'checkpoints/rcp_sup_ft' 94 | top_k: 3 95 | monitor: 'val_epe' 96 | 97 | log: false -------------------------------------------------------------------------------- /configs/train/rcp_sup_pre.yaml: -------------------------------------------------------------------------------- 1 | train: True 2 | 3 | task: 'sceneflow' 4 | 5 | model_params: 6 | model_name: 'SF_RCP' 7 | npoint: 8192 8 | use_insrance_norm: False 9 | use_curvature: True 10 | 11 | exp_params: 12 | disable_plots: True 13 | batch_size: 2 14 | num_workers: 16 15 | train_iters: 4 16 | val_iters: 4 17 | loss_iters_w: [0.7, 0.4, 0.4, 0.4] 18 | 19 | data: 20 | overfit_samples: null 21 | train_dataset: flyingthings3d 22 | train_data_root: /data/datasets/scene_flow/flyingthings3d 23 | test_dataset: flyingthings3d 24 | test_data_root: /data/datasets/scene_flow/flyingthings3d 25 | test_dataset: null 26 | test_data_root: null 27 | num_points: 8192 28 | full: True 29 | allow_less_points: False 30 | 31 | data_process: 32 | DEPTH_THRESHOLD: 35. 33 | NO_CORR: True 34 | IND_DIR: null 35 | 36 | data_augmentation: 37 | aug_together: 38 | degree_range: 0.1745329252 39 | shift_range: 1. 40 | scale_low: 0.95 41 | scale_high: 1.05 42 | jitter_sigma: 0.01 43 | jitter_clip: 0.00 44 | aug_pc2: 45 | degree_range: 0. 46 | xy_shift_range: 0. 47 | deg_max: False 48 | shift_range: 0.2 49 | jitter_sigma: 0.01 50 | jitter_clip: 0.00 51 | val_aug_pc2: null 52 | 53 | loss: 54 | loss_type: 'sv_l1_reg_v1' 55 | w_data: [0.9, 0.99, 0.99, 0.99] 56 | w_smoothness: [0.1, 0.01, 0.01, 0.01] 57 | w_curvature: [0.1, 0.01, 0.01, 0.01] 58 | smoothness_loss_params: 59 | w_knn: 3. 60 | w_ball_q: 1. 61 | knn_loss_params: 62 | k: 16 63 | radius: 0.5 64 | loss_norm: 1 65 | ball_q_loss_params: 66 | k: 24 67 | radius: 0.5 68 | loss_norm: 1 69 | optimizer: 70 | type: 'AdamW' 71 | lr: 0.002 72 | weight_decay: 0.01 73 | 74 | scheduler: 75 | type: 'MultiStepLR' 76 | milestones: [20, 40, 60, 80] 77 | gamma: 0.5 78 | 79 | 80 | trainer_params: 81 | deterministic: False # slower. reproduce. 82 | strategy: 'ddp' 83 | num_nodes: 1 84 | gpus: 8 85 | max_epochs: 90 86 | num_sanity_val_steps: 3 87 | check_val_every_n_epoch: 2 88 | sync_batchnorm: True 89 | 90 | logging_params: 91 | ckpt: 92 | save_dir: 'checkpoints/rcp_sup' 93 | top_k: 3 94 | monitor: 'val_epe' 95 | 96 | log: false -------------------------------------------------------------------------------- /data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlibabaResearch/rcp/64e8d9eac981f26a3918cf6bec71dda0065c6b3d/data/__init__.py -------------------------------------------------------------------------------- /data/loaders/__init__.py: -------------------------------------------------------------------------------- 1 | # from .flyingthings3d_subset import * 2 | # from .kitti import * -------------------------------------------------------------------------------- /data/preprocess/flyingthings3d_utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def next_pixel2pc(flow, disparity, save_path=None, f=-1050., cx=479.5, cy=269.5): 5 | height, width = disparity.shape 6 | 7 | BASELINE = 1.0 8 | depth = -1. * f * BASELINE / disparity 9 | 10 | x = ((np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) - cx + flow[..., 0]) * -1. / disparity)[:, 11 | :, None] 12 | y = ((np.tile(np.arange(height, dtype=np.float32)[:, None], (1, width)) - cy + flow[..., 1]) * 1. / disparity)[:, 13 | :, None] 14 | pc = np.concatenate((x, y, depth[:, :, None]), axis=-1) 15 | 16 | if save_path is not None: 17 | np.save(save_path, pc) 18 | return pc 19 | 20 | 21 | def pixel2pc(disparity, save_path=None, f=-1050., cx=479.5, cy=269.5): 22 | height, width = disparity.shape 23 | 24 | BASELINE = 1.0 25 | depth = -1. * f * BASELINE / disparity 26 | 27 | x = ((np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) - cx) * -1. / disparity)[:, :, None] 28 | y = ((np.tile(np.arange(height, dtype=np.float32)[:, None], (1, width)) - cy) * 1. / disparity)[:, :, None] 29 | pc = np.concatenate((x, y, depth[:, :, None]), axis=-1) 30 | 31 | if save_path is not None: 32 | np.save(save_path, pc) 33 | return pc -------------------------------------------------------------------------------- /data/preprocess/kitti_utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import png 3 | 4 | 5 | def pixel2xyz(depth, P_rect, px=None, py=None): 6 | assert P_rect[0,1] == 0 7 | assert P_rect[1,0] == 0 8 | assert P_rect[2,0] == 0 9 | assert P_rect[2,1] == 0 10 | assert P_rect[0,0] == P_rect[1,1] 11 | focal_length_pixel = P_rect[0,0] 12 | 13 | height, width = depth.shape[:2] 14 | if px is None: 15 | px = np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) 16 | if py is None: 17 | py = np.tile(np.arange(height, dtype=np.float32)[:, None], (1, width)) 18 | const_x = P_rect[0,2] * depth + P_rect[0,3] 19 | const_y = P_rect[1,2] * depth + P_rect[1,3] 20 | 21 | x = ((px * (depth + P_rect[2,3]) - const_x) / focal_length_pixel) [:, :, None] 22 | y = ((py * (depth + P_rect[2,3]) - const_y) / focal_length_pixel) [:, :, None] 23 | pc = np.concatenate((x, y, depth[:, :, None]), axis=-1) 24 | 25 | pc[..., :2] *= -1. 26 | return pc 27 | 28 | 29 | def load_uint16PNG(fpath): 30 | reader = png.Reader(fpath) 31 | pngdata = reader.read() 32 | px_array = np.vstack( map(np.uint16, pngdata[2]) ) 33 | if pngdata[3]['planes'] == 3: 34 | width, height = pngdata[:2] 35 | px_array = px_array.reshape(height, width, 3) 36 | return px_array 37 | 38 | 39 | def load_disp(fpath): 40 | # A 0 value indicates an invalid pixel (ie, no 41 | # ground truth exists, or the estimation algorithm didn't produce an estimate 42 | # for that pixel). 43 | array = load_uint16PNG(fpath) 44 | valid = array > 0 45 | disp = array.astype(np.float32) / 256.0 46 | disp[np.logical_not(valid)] = -1. 47 | return disp, valid 48 | 49 | 50 | def load_op_flow(fpath): 51 | array = load_uint16PNG(fpath) 52 | valid = array[..., -1] == 1 53 | array = array.astype(np.float32) 54 | flow = (array[..., :-1] - 2**15) / 64. 55 | return flow, valid 56 | 57 | 58 | def disp_2_depth(disparity, valid_disp, FOCAL_LENGTH_PIXEL): 59 | BASELINE = 0.54 60 | depth = FOCAL_LENGTH_PIXEL * BASELINE / (disparity + 1e-5) 61 | depth[np.logical_not(valid_disp)] = -1. 62 | return depth 63 | -------------------------------------------------------------------------------- /data/preprocess/process_kitti.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import os.path as osp 4 | from multiprocessing import Pool 5 | 6 | from kitti_utils import * 7 | 8 | calib_root = './utils/calib_cam_to_cam/' 9 | data_root = sys.argv[1] 10 | disp1_root = osp.join(data_root, 'training/disp_occ_0') 11 | disp2_root = osp.join(data_root, 'training/disp_occ_1') 12 | op_flow_root = osp.join(data_root, 'training/flow_occ') 13 | 14 | save_path = sys.argv[2] 15 | 16 | 17 | def process_one_frame(idx): 18 | sidx = '{:06d}'.format(idx) 19 | 20 | calib_path = osp.join(calib_root, sidx + '.txt') 21 | with open(calib_path) as fd: 22 | lines = fd.readlines() 23 | assert len([line for line in lines if line.startswith('P_rect_02')]) == 1 24 | P_rect_left = \ 25 | np.array([float(item) for item in 26 | [line for line in lines if line.startswith('P_rect_02')][0].split()[1:]], 27 | dtype=np.float32).reshape(3, 4) 28 | 29 | assert P_rect_left[0, 0] == P_rect_left[1, 1] 30 | focal_length_pixel = P_rect_left[0, 0] 31 | 32 | disp1_path = osp.join(disp1_root, sidx + '_10.png') 33 | disp1, valid_disp1 = load_disp(disp1_path) 34 | depth1 = disp_2_depth(disp1, valid_disp1, focal_length_pixel) 35 | pc1 = pixel2xyz(depth1, P_rect_left) 36 | 37 | disp2_path = osp.join(disp2_root, sidx + '_10.png') 38 | disp2, valid_disp2 = load_disp(disp2_path) 39 | depth2 = disp_2_depth(disp2, valid_disp2, focal_length_pixel) 40 | 41 | valid_disp = np.logical_and(valid_disp1, valid_disp2) 42 | 43 | op_flow, valid_op_flow = load_op_flow(osp.join(op_flow_root, '{:06d}_10.png'.format(idx))) 44 | vertical = op_flow[..., 1] 45 | horizontal = op_flow[..., 0] 46 | height, width = op_flow.shape[:2] 47 | 48 | px2 = np.zeros((height, width), dtype=np.float32) 49 | py2 = np.zeros((height, width), dtype=np.float32) 50 | 51 | for i in range(height): 52 | for j in range(width): 53 | if valid_op_flow[i, j] and valid_disp[i, j]: 54 | try: 55 | dx = horizontal[i, j] 56 | dy = vertical[i, j] 57 | except: 58 | print('error, i,j:', i, j, 'hor and ver:', horizontal[i, j], vertical[i, j]) 59 | continue 60 | 61 | px2[i, j] = j + dx 62 | py2[i, j] = i + dy 63 | 64 | pc2 = pixel2xyz(depth2, P_rect_left, px=px2, py=py2) 65 | 66 | final_mask = np.logical_and(valid_disp, valid_op_flow) 67 | 68 | valid_pc1 = pc1[final_mask] 69 | valid_pc2 = pc2[final_mask] 70 | 71 | truenas_path = osp.join(save_path, '{:06d}'.format(idx)) 72 | os.makedirs(truenas_path, exist_ok=True) 73 | np.save(osp.join(truenas_path, 'pc1.npy'), valid_pc1) 74 | np.save(osp.join(truenas_path, 'pc2.npy'), valid_pc2) 75 | 76 | 77 | pool = Pool(4) 78 | indices = range(200) 79 | pool.map(process_one_frame, indices) 80 | pool.close() 81 | pool.join() 82 | -------------------------------------------------------------------------------- /data/preprocess/python_pfm.py: -------------------------------------------------------------------------------- 1 | import re 2 | import numpy as np 3 | import sys 4 | 5 | 6 | def readPFM(file): 7 | file = open(file, 'rb') 8 | 9 | color = None 10 | width = None 11 | height = None 12 | scale = None 13 | endian = None 14 | 15 | header = file.readline().rstrip() 16 | if header == 'PF': 17 | color = True 18 | elif header == 'Pf': 19 | color = False 20 | else: 21 | raise Exception('Not a PFM file.') 22 | 23 | dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline()) 24 | if dim_match: 25 | width, height = map(int, dim_match.groups()) 26 | else: 27 | raise Exception('Malformed PFM header.') 28 | 29 | scale = float(file.readline().rstrip()) 30 | if scale < 0: # little-endian 31 | endian = '<' 32 | scale = -scale 33 | else: 34 | endian = '>' # big-endian 35 | 36 | data = np.fromfile(file, endian + 'f') 37 | shape = (height, width, 3) if color else (height, width) 38 | 39 | data = np.reshape(data, shape) 40 | data = np.flipud(data) 41 | return data, scale 42 | 43 | 44 | def writePFM(file, image, scale=1): 45 | file = open(file, 'wb') 46 | 47 | color = None 48 | 49 | if image.dtype.name != 'float32': 50 | raise Exception('Image dtype must be float32.') 51 | 52 | image = np.flipud(image) 53 | 54 | if len(image.shape) == 3 and image.shape[2] == 3: # color image 55 | color = True 56 | elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1: # greyscale 57 | color = False 58 | else: 59 | raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.') 60 | 61 | file.write('PF\n' if color else 'Pf\n') 62 | file.write('%d %d\n' % (image.shape[1], image.shape[0])) 63 | 64 | endian = image.dtype.byteorder 65 | 66 | if endian == '<' or endian == '=' and sys.byteorder == 'little': 67 | scale = -scale 68 | 69 | file.write('%f\n' % scale) 70 | 71 | image.tofile(file) 72 | 73 | -------------------------------------------------------------------------------- /data/transforms/__init__.py: -------------------------------------------------------------------------------- 1 | from .transforms import * 2 | -------------------------------------------------------------------------------- /data/transforms/functional.py: -------------------------------------------------------------------------------- 1 | """ 2 | Borrowed from HPLFlowNet 3 | Date: May 2020 4 | 5 | @inproceedings{HPLFlowNet, 6 | title={HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for 7 | Scene Flow Estimation on Large-scale Point Clouds}, 8 | author={Gu, Xiuye and Wang, Yijie and Wu, Chongruo and Lee, Yong Jae and Wang, Panqu}, 9 | booktitle={Computer Vision and Pattern Recognition (CVPR), 2019 IEEE International Conference on}, 10 | year={2019} 11 | } 12 | """ 13 | 14 | import torch 15 | 16 | def to_tensor(array): 17 | """Convert a 2D `numpy.ndarray`` to tensor, do transpose first. 18 | 19 | See ``ToTensor`` for more details. 20 | 21 | Args: 22 | array (numpy.ndarray): Image to be converted to tensor. 23 | 24 | Returns: 25 | Tensor: Converted image. 26 | """ 27 | assert len(array.shape) == 2 28 | array = array.transpose((1, 0)) 29 | 30 | return torch.from_numpy(array) 31 | 32 | 33 | -------------------------------------------------------------------------------- /lib/pointnet2/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | from torch.utils.cpp_extension import BuildExtension, CUDAExtension 3 | 4 | setup( 5 | name='pointnet2', 6 | ext_modules=[ 7 | CUDAExtension('pointnet2_cuda', [ 8 | 'src/pointnet2_api.cpp', 9 | 10 | 'src/ball_query.cpp', 11 | 'src/ball_query_gpu.cu', 12 | 'src/group_points.cpp', 13 | 'src/group_points_gpu.cu', 14 | 'src/interpolate.cpp', 15 | 'src/interpolate_gpu.cu', 16 | 'src/sampling.cpp', 17 | 'src/sampling_gpu.cu', 18 | ], 19 | extra_compile_args={'cxx': ['-g'], 20 | 'nvcc': ['-O2']}) 21 | ], 22 | cmdclass={'build_ext': BuildExtension} 23 | ) 24 | -------------------------------------------------------------------------------- /lib/pointnet2/src/ball_query.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include "ball_query_gpu.h" 7 | 8 | extern THCState *state; 9 | 10 | //#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") 11 | //#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x, " must be contiguous ") 12 | #define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") 13 | #define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") 14 | #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) 15 | 16 | int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, 17 | at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor) { 18 | CHECK_INPUT(new_xyz_tensor); 19 | CHECK_INPUT(xyz_tensor); 20 | const float *new_xyz = new_xyz_tensor.data(); 21 | const float *xyz = xyz_tensor.data(); 22 | int *idx = idx_tensor.data(); 23 | 24 | // cudaStream_t stream = THCState_getCurrentStream(state); 25 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 26 | ball_query_kernel_launcher_fast(b, n, m, radius, nsample, new_xyz, xyz, idx, stream); 27 | return 1; 28 | } -------------------------------------------------------------------------------- /lib/pointnet2/src/ball_query_gpu.cu: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | 5 | #include "ball_query_gpu.h" 6 | #include "cuda_utils.h" 7 | 8 | 9 | __global__ void ball_query_kernel_fast(int b, int n, int m, float radius, int nsample, 10 | const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) { 11 | // new_xyz: (B, M, 3) 12 | // xyz: (B, N, 3) 13 | // output: 14 | // idx: (B, M, nsample) 15 | int bs_idx = blockIdx.y; 16 | int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; 17 | if (bs_idx >= b || pt_idx >= m) return; 18 | 19 | new_xyz += bs_idx * m * 3 + pt_idx * 3; 20 | xyz += bs_idx * n * 3; 21 | idx += bs_idx * m * nsample + pt_idx * nsample; 22 | 23 | float radius2 = radius * radius; 24 | float new_x = new_xyz[0]; 25 | float new_y = new_xyz[1]; 26 | float new_z = new_xyz[2]; 27 | 28 | int cnt = 0; 29 | for (int k = 0; k < n; ++k) { 30 | float x = xyz[k * 3 + 0]; 31 | float y = xyz[k * 3 + 1]; 32 | float z = xyz[k * 3 + 2]; 33 | float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); 34 | if (d2 < radius2){ 35 | if (cnt == 0){ 36 | for (int l = 0; l < nsample; ++l) { 37 | idx[l] = k; 38 | } 39 | } 40 | idx[cnt] = k; 41 | ++cnt; 42 | if (cnt >= nsample) break; 43 | } 44 | } 45 | } 46 | 47 | 48 | void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, \ 49 | const float *new_xyz, const float *xyz, int *idx, cudaStream_t stream) { 50 | // new_xyz: (B, M, 3) 51 | // xyz: (B, N, 3) 52 | // output: 53 | // idx: (B, M, nsample) 54 | 55 | cudaError_t err; 56 | 57 | dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) 58 | dim3 threads(THREADS_PER_BLOCK); 59 | 60 | ball_query_kernel_fast<<>>(b, n, m, radius, nsample, new_xyz, xyz, idx); 61 | // cudaDeviceSynchronize(); // for using printf in kernel function 62 | err = cudaGetLastError(); 63 | if (cudaSuccess != err) { 64 | fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); 65 | exit(-1); 66 | } 67 | } -------------------------------------------------------------------------------- /lib/pointnet2/src/ball_query_gpu.h: -------------------------------------------------------------------------------- 1 | #ifndef _BALL_QUERY_GPU_H 2 | #define _BALL_QUERY_GPU_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, 10 | at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor); 11 | 12 | void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, 13 | const float *xyz, const float *new_xyz, int *idx, cudaStream_t stream); 14 | 15 | #endif 16 | -------------------------------------------------------------------------------- /lib/pointnet2/src/cuda_utils.h: -------------------------------------------------------------------------------- 1 | #ifndef _CUDA_UTILS_H 2 | #define _CUDA_UTILS_H 3 | 4 | #include 5 | 6 | #define TOTAL_THREADS 1024 7 | #define THREADS_PER_BLOCK 256 8 | #define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) 9 | 10 | inline int opt_n_threads(int work_size) { 11 | const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); 12 | 13 | return max(min(1 << pow_2, TOTAL_THREADS), 1); 14 | } 15 | #endif 16 | -------------------------------------------------------------------------------- /lib/pointnet2/src/group_points.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include "group_points_gpu.h" 7 | 8 | extern THCState *state; 9 | 10 | 11 | int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, 12 | at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) { 13 | 14 | float *grad_points = grad_points_tensor.data(); 15 | const int *idx = idx_tensor.data(); 16 | const float *grad_out = grad_out_tensor.data(); 17 | 18 | // cudaStream_t stream = THCState_getCurrentStream(state); 19 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 20 | 21 | group_points_grad_kernel_launcher_fast(b, c, n, npoints, nsample, grad_out, idx, grad_points, stream); 22 | return 1; 23 | } 24 | 25 | 26 | int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, 27 | at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) { 28 | 29 | const float *points = points_tensor.data(); 30 | const int *idx = idx_tensor.data(); 31 | float *out = out_tensor.data(); 32 | 33 | // cudaStream_t stream = THCState_getCurrentStream(state); 34 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 35 | group_points_kernel_launcher_fast(b, c, n, npoints, nsample, points, idx, out, stream); 36 | return 1; 37 | } -------------------------------------------------------------------------------- /lib/pointnet2/src/group_points_gpu.h: -------------------------------------------------------------------------------- 1 | #ifndef _GROUP_POINTS_GPU_H 2 | #define _GROUP_POINTS_GPU_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | 10 | int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, 11 | at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor); 12 | 13 | void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 14 | const float *points, const int *idx, float *out, cudaStream_t stream); 15 | 16 | int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, 17 | at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor); 18 | 19 | void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, 20 | const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream); 21 | 22 | #endif 23 | -------------------------------------------------------------------------------- /lib/pointnet2/src/interpolate.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include "interpolate_gpu.h" 10 | 11 | extern THCState *state; 12 | 13 | 14 | void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, 15 | at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) { 16 | const float *unknown = unknown_tensor.data(); 17 | const float *known = known_tensor.data(); 18 | float *dist2 = dist2_tensor.data(); 19 | int *idx = idx_tensor.data(); 20 | 21 | // cudaStream_t stream = THCState_getCurrentStream(state); 22 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 23 | three_nn_kernel_launcher_fast(b, n, m, unknown, known, dist2, idx, stream); 24 | } 25 | 26 | void knn_wrapper_fast(int b, int n, int m, int k, at::Tensor unknown_tensor, 27 | at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) { 28 | const float *unknown = unknown_tensor.data(); 29 | const float *known = known_tensor.data(); 30 | float *dist2 = dist2_tensor.data(); 31 | int *idx = idx_tensor.data(); 32 | 33 | // cudaStream_t stream = THCState_getCurrentStream(state); 34 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 35 | knn_kernel_launcher_fast(b, n, m, k, unknown, known, dist2, idx, stream); 36 | } 37 | 38 | 39 | void three_interpolate_wrapper_fast(int b, int c, int m, int n, 40 | at::Tensor points_tensor, 41 | at::Tensor idx_tensor, 42 | at::Tensor weight_tensor, 43 | at::Tensor out_tensor) { 44 | 45 | const float *points = points_tensor.data(); 46 | const float *weight = weight_tensor.data(); 47 | float *out = out_tensor.data(); 48 | const int *idx = idx_tensor.data(); 49 | 50 | // cudaStream_t stream = THCState_getCurrentStream(state); 51 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 52 | three_interpolate_kernel_launcher_fast(b, c, m, n, points, idx, weight, out, stream); 53 | } 54 | 55 | void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m, 56 | at::Tensor grad_out_tensor, 57 | at::Tensor idx_tensor, 58 | at::Tensor weight_tensor, 59 | at::Tensor grad_points_tensor) { 60 | 61 | const float *grad_out = grad_out_tensor.data(); 62 | const float *weight = weight_tensor.data(); 63 | float *grad_points = grad_points_tensor.data(); 64 | const int *idx = idx_tensor.data(); 65 | 66 | // cudaStream_t stream = THCState_getCurrentStream(state); 67 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 68 | three_interpolate_grad_kernel_launcher_fast(b, c, n, m, grad_out, idx, weight, grad_points, stream); 69 | } -------------------------------------------------------------------------------- /lib/pointnet2/src/interpolate_gpu.h: -------------------------------------------------------------------------------- 1 | #ifndef _INTERPOLATE_GPU_H 2 | #define _INTERPOLATE_GPU_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | 9 | 10 | void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, 11 | at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor); 12 | 13 | void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown, 14 | const float *known, float *dist2, int *idx, cudaStream_t stream); 15 | 16 | void knn_wrapper_fast(int b, int n, int m, int k, at::Tensor unknown_tensor, 17 | at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor); 18 | 19 | void knn_kernel_launcher_fast(int b, int n, int m, int k, const float *unknown, 20 | const float *known, float *dist2, int *idx, cudaStream_t stream); 21 | 22 | 23 | void three_interpolate_wrapper_fast(int b, int c, int m, int n, at::Tensor points_tensor, 24 | at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor); 25 | 26 | void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, 27 | const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream); 28 | 29 | 30 | void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m, at::Tensor grad_out_tensor, 31 | at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor); 32 | 33 | void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, 34 | const int *idx, const float *weight, float *grad_points, cudaStream_t stream); 35 | 36 | #endif 37 | -------------------------------------------------------------------------------- /lib/pointnet2/src/pointnet2_api.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | 4 | #include "ball_query_gpu.h" 5 | #include "group_points_gpu.h" 6 | #include "sampling_gpu.h" 7 | #include "interpolate_gpu.h" 8 | 9 | 10 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 11 | m.def("ball_query_wrapper", &ball_query_wrapper_fast, "ball_query_wrapper_fast"); 12 | 13 | m.def("group_points_wrapper", &group_points_wrapper_fast, "group_points_wrapper_fast"); 14 | m.def("group_points_grad_wrapper", &group_points_grad_wrapper_fast, "group_points_grad_wrapper_fast"); 15 | 16 | m.def("gather_points_wrapper", &gather_points_wrapper_fast, "gather_points_wrapper_fast"); 17 | m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper_fast, "gather_points_grad_wrapper_fast"); 18 | 19 | m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper"); 20 | 21 | m.def("knn_wrapper", &knn_wrapper_fast, "knn_wrapper_fast"); 22 | m.def("three_nn_wrapper", &three_nn_wrapper_fast, "three_nn_wrapper_fast"); 23 | m.def("three_interpolate_wrapper", &three_interpolate_wrapper_fast, "three_interpolate_wrapper_fast"); 24 | m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_fast, "three_interpolate_grad_wrapper_fast"); 25 | } 26 | -------------------------------------------------------------------------------- /lib/pointnet2/src/sampling.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | 6 | #include "sampling_gpu.h" 7 | 8 | extern THCState *state; 9 | 10 | 11 | int gather_points_wrapper_fast(int b, int c, int n, int npoints, 12 | at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor){ 13 | const float *points = points_tensor.data(); 14 | const int *idx = idx_tensor.data(); 15 | float *out = out_tensor.data(); 16 | 17 | // cudaStream_t stream = THCState_getCurrentStream(state); 18 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 19 | gather_points_kernel_launcher_fast(b, c, n, npoints, points, idx, out, stream); 20 | return 1; 21 | } 22 | 23 | 24 | int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, 25 | at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) { 26 | 27 | const float *grad_out = grad_out_tensor.data(); 28 | const int *idx = idx_tensor.data(); 29 | float *grad_points = grad_points_tensor.data(); 30 | 31 | // cudaStream_t stream = THCState_getCurrentStream(state); 32 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 33 | gather_points_grad_kernel_launcher_fast(b, c, n, npoints, grad_out, idx, grad_points, stream); 34 | return 1; 35 | } 36 | 37 | 38 | int furthest_point_sampling_wrapper(int b, int n, int m, 39 | at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) { 40 | 41 | const float *points = points_tensor.data(); 42 | float *temp = temp_tensor.data(); 43 | int *idx = idx_tensor.data(); 44 | 45 | // cudaStream_t stream = THCState_getCurrentStream(state); 46 | cudaStream_t stream = at::cuda::getCurrentCUDAStream(); 47 | furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx, stream); 48 | return 1; 49 | } 50 | -------------------------------------------------------------------------------- /lib/pointnet2/src/sampling_gpu.h: -------------------------------------------------------------------------------- 1 | #ifndef _SAMPLING_GPU_H 2 | #define _SAMPLING_GPU_H 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | 9 | int gather_points_wrapper_fast(int b, int c, int n, int npoints, 10 | at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor); 11 | 12 | void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, 13 | const float *points, const int *idx, float *out, cudaStream_t stream); 14 | 15 | 16 | int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, 17 | at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor); 18 | 19 | void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, 20 | const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream); 21 | 22 | 23 | int furthest_point_sampling_wrapper(int b, int n, int m, 24 | at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor); 25 | 26 | void furthest_point_sampling_kernel_launcher(int b, int n, int m, 27 | const float *dataset, float *temp, int *idxs, cudaStream_t stream); 28 | 29 | #endif 30 | -------------------------------------------------------------------------------- /losses/__init__.py: -------------------------------------------------------------------------------- 1 | from losses.supervised_losses import SupervisedL1Loss, SupervisedL2Loss, SupervisedL1RegLoss, SupervisedL1RegLossV1 2 | from losses.unsupervised_losses import UnSupervisedL1Loss 3 | from losses.common_losses import get_loss_weights 4 | 5 | losses_dict = { 6 | 'sv_l1': SupervisedL1Loss, 7 | 'sv_l2': SupervisedL2Loss, 8 | 'sv_l1_reg': SupervisedL1RegLoss, 9 | 'sv_l1_reg_v1': SupervisedL1RegLossV1, 10 | 'unsup_l1': UnSupervisedL1Loss, 11 | } -------------------------------------------------------------------------------- /losses/unsupervised_losses.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.nn import Module, MSELoss, L1Loss 3 | from .common_losses import SmoothnessLoss, ChamferLoss 4 | 5 | 6 | class UnSupervisedL1Loss(Module): 7 | def __init__(self, w_data, w_smoothness, smoothness_loss_params, chamfer_loss_params, **kwargs): 8 | super(UnSupervisedL1Loss, self).__init__() 9 | self.data_loss = ChamferLoss(**chamfer_loss_params) 10 | self.smoothness_loss = SmoothnessLoss(**smoothness_loss_params) 11 | self.w_data = w_data 12 | self.w_smoothness = w_smoothness 13 | 14 | def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor, seq_len=None, i=0) -> torch.Tensor: 15 | if len(self.w_data) == 1: 16 | w_data = self.w_data[0] 17 | w_smoothness = self.w_smoothness[0] 18 | else: 19 | w_data = self.w_data[i] 20 | w_smoothness = self.w_smoothness[i] 21 | loss = (w_data * self.data_loss(pc_source, pc_target, pred_flow)) + (w_smoothness * self.smoothness_loss(pc_source, pred_flow)) 22 | return loss -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlibabaResearch/rcp/64e8d9eac981f26a3918cf6bec71dda0065c6b3d/models/__init__.py -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pytorch-lightning==1.5.3 2 | torch==1.7.1 3 | numpy==1.20.3 4 | scipy==1.7.3 -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AlibabaResearch/rcp/64e8d9eac981f26a3918cf6bec71dda0065c6b3d/utils/__init__.py -------------------------------------------------------------------------------- /utils/calib_cam_to_cam/000000.txt: -------------------------------------------------------------------------------- 1 | calib_time: 09-Jan-2012 13:57:47 2 | corner_dist: 9.950000e-02 3 | S_00: 1.392000e+03 5.120000e+02 4 | K_00: 9.842439e+02 0.000000e+00 6.900000e+02 0.000000e+00 9.808141e+02 2.331966e+02 0.000000e+00 0.000000e+00 1.000000e+00 5 | D_00: -3.728755e-01 2.037299e-01 2.219027e-03 1.383707e-03 -7.233722e-02 6 | R_00: 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 7 | T_00: 2.573699e-16 -1.059758e-16 1.614870e-16 8 | S_rect_00: 1.242000e+03 3.750000e+02 9 | R_rect_00: 9.999239e-01 9.837760e-03 -7.445048e-03 -9.869795e-03 9.999421e-01 -4.278459e-03 7.402527e-03 4.351614e-03 9.999631e-01 10 | P_rect_00: 1,1+02 0.000000e+00 6.095593e+02 0.000000e+00 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 11 | S_01: 1.392000e+03 5.120000e+02 12 | K_01: 9.895267e+02 0.000000e+00 7.020000e+02 0.000000e+00 9.878386e+02 2.455590e+02 0.000000e+00 0.000000e+00 1.000000e+00 13 | D_01: -3.644661e-01 1.790019e-01 1.148107e-03 -6.298563e-04 -5.314062e-02 14 | R_01: 9.993513e-01 1.860866e-02 -3.083487e-02 -1.887662e-02 9.997863e-01 -8.421873e-03 3.067156e-02 8.998467e-03 9.994890e-01 15 | T_01: -5.370000e-01 4.822061e-03 -1.252488e-02 16 | S_rect_01: 1.242000e+03 3.750000e+02 17 | R_rect_01: 9.996878e-01 -8.976826e-03 2.331651e-02 8.876121e-03 9.999508e-01 4.418952e-03 -2.335503e-02 -4.210612e-03 9.997184e-01 18 | P_rect_01: 7.215377e+02 0.000000e+00 6.095593e+02 -3.875744e+02 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 19 | S_02: 1.392000e+03 5.120000e+02 20 | K_02: 9.597910e+02 0.000000e+00 6.960217e+02 0.000000e+00 9.569251e+02 2.241806e+02 0.000000e+00 0.000000e+00 1.000000e+00 21 | D_02: -3.691481e-01 1.968681e-01 1.353473e-03 5.677587e-04 -6.770705e-02 22 | R_02: 9.999758e-01 -5.267463e-03 -4.552439e-03 5.251945e-03 9.999804e-01 -3.413835e-03 4.570332e-03 3.389843e-03 9.999838e-01 23 | T_02: 5.956621e-02 2.900141e-04 2.577209e-03 24 | S_rect_02: 1.242000e+03 3.750000e+02 25 | R_rect_02: 9.998817e-01 1.511453e-02 -2.841595e-03 -1.511724e-02 9.998853e-01 -9.338510e-04 2.827154e-03 9.766976e-04 9.999955e-01 26 | P_rect_02: 7.215377e+02 0.000000e+00 6.095593e+02 4.485728e+01 0.000000e+00 7.215377e+02 1.728540e+02 2.163791e-01 0.000000e+00 0.000000e+00 1.000000e+00 2.745884e-03 27 | S_03: 1.392000e+03 5.120000e+02 28 | K_03: 9.037596e+02 0.000000e+00 6.957519e+02 0.000000e+00 9.019653e+02 2.242509e+02 0.000000e+00 0.000000e+00 1.000000e+00 29 | D_03: -3.639558e-01 1.788651e-01 6.029694e-04 -3.922424e-04 -5.382460e-02 30 | R_03: 9.995599e-01 1.699522e-02 -2.431313e-02 -1.704422e-02 9.998531e-01 -1.809756e-03 2.427880e-02 2.223358e-03 9.997028e-01 31 | T_03: -4.731050e-01 5.551470e-03 -5.250882e-03 32 | S_rect_03: 1.242000e+03 3.750000e+02 33 | R_rect_03: 9.998321e-01 -7.193136e-03 1.685599e-02 7.232804e-03 9.999712e-01 -2.293585e-03 -1.683901e-02 2.415116e-03 9.998553e-01 34 | P_rect_03: 7.215377e+02 0.000000e+00 6.095593e+02 -3.395242e+02 0.000000e+00 7.215377e+02 1.728540e+02 2.199936e+00 0.000000e+00 0.000000e+00 1.000000e+00 2.729905e-03 35 | -------------------------------------------------------------------------------- /utils/calib_cam_to_cam/000001.txt: -------------------------------------------------------------------------------- 1 | calib_time: 09-Jan-2012 13:57:47 2 | corner_dist: 9.950000e-02 3 | S_00: 1.392000e+03 5.120000e+02 4 | K_00: 9.842439e+02 0.000000e+00 6.900000e+02 0.000000e+00 9.808141e+02 2.331966e+02 0.000000e+00 0.000000e+00 1.000000e+00 5 | D_00: -3.728755e-01 2.037299e-01 2.219027e-03 1.383707e-03 -7.233722e-02 6 | R_00: 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 7 | T_00: 2.573699e-16 -1.059758e-16 1.614870e-16 8 | S_rect_00: 1.242000e+03 3.750000e+02 9 | R_rect_00: 9.999239e-01 9.837760e-03 -7.445048e-03 -9.869795e-03 9.999421e-01 -4.278459e-03 7.402527e-03 4.351614e-03 9.999631e-01 10 | P_rect_00: 7.215377e+02 0.000000e+00 6.095593e+02 0.000000e+00 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 11 | S_01: 1.392000e+03 5.120000e+02 12 | K_01: 9.895267e+02 0.000000e+00 7.020000e+02 0.000000e+00 9.878386e+02 2.455590e+02 0.000000e+00 0.000000e+00 1.000000e+00 13 | D_01: -3.644661e-01 1.790019e-01 1.148107e-03 -6.298563e-04 -5.314062e-02 14 | R_01: 9.993513e-01 1.860866e-02 -3.083487e-02 -1.887662e-02 9.997863e-01 -8.421873e-03 3.067156e-02 8.998467e-03 9.994890e-01 15 | T_01: -5.370000e-01 4.822061e-03 -1.252488e-02 16 | S_rect_01: 1.242000e+03 3.750000e+02 17 | R_rect_01: 9.996878e-01 -8.976826e-03 2.331651e-02 8.876121e-03 9.999508e-01 4.418952e-03 -2.335503e-02 -4.210612e-03 9.997184e-01 18 | P_rect_01: 7.215377e+02 0.000000e+00 6.095593e+02 -3.875744e+02 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 19 | S_02: 1.392000e+03 5.120000e+02 20 | K_02: 9.597910e+02 0.000000e+00 6.960217e+02 0.000000e+00 9.569251e+02 2.241806e+02 0.000000e+00 0.000000e+00 1.000000e+00 21 | D_02: -3.691481e-01 1.968681e-01 1.353473e-03 5.677587e-04 -6.770705e-02 22 | R_02: 9.999758e-01 -5.267463e-03 -4.552439e-03 5.251945e-03 9.999804e-01 -3.413835e-03 4.570332e-03 3.389843e-03 9.999838e-01 23 | T_02: 5.956621e-02 2.900141e-04 2.577209e-03 24 | S_rect_02: 1.242000e+03 3.750000e+02 25 | R_rect_02: 9.998817e-01 1.511453e-02 -2.841595e-03 -1.511724e-02 9.998853e-01 -9.338510e-04 2.827154e-03 9.766976e-04 9.999955e-01 26 | P_rect_02: 7.215377e+02 0.000000e+00 6.095593e+02 4.485728e+01 0.000000e+00 7.215377e+02 1.728540e+02 2.163791e-01 0.000000e+00 0.000000e+00 1.000000e+00 2.745884e-03 27 | S_03: 1.392000e+03 5.120000e+02 28 | K_03: 9.037596e+02 0.000000e+00 6.957519e+02 0.000000e+00 9.019653e+02 2.242509e+02 0.000000e+00 0.000000e+00 1.000000e+00 29 | D_03: -3.639558e-01 1.788651e-01 6.029694e-04 -3.922424e-04 -5.382460e-02 30 | R_03: 9.995599e-01 1.699522e-02 -2.431313e-02 -1.704422e-02 9.998531e-01 -1.809756e-03 2.427880e-02 2.223358e-03 9.997028e-01 31 | T_03: -4.731050e-01 5.551470e-03 -5.250882e-03 32 | S_rect_03: 1.242000e+03 3.750000e+02 33 | R_rect_03: 9.998321e-01 -7.193136e-03 1.685599e-02 7.232804e-03 9.999712e-01 -2.293585e-03 -1.683901e-02 2.415116e-03 9.998553e-01 34 | P_rect_03: 7.215377e+02 0.000000e+00 6.095593e+02 -3.395242e+02 0.000000e+00 7.215377e+02 1.728540e+02 2.199936e+00 0.000000e+00 0.000000e+00 1.000000e+00 2.729905e-03 35 | -------------------------------------------------------------------------------- /utils/calib_cam_to_cam/000002.txt: -------------------------------------------------------------------------------- 1 | calib_time: 09-Jan-2012 13:57:47 2 | corner_dist: 9.950000e-02 3 | S_00: 1.392000e+03 5.120000e+02 4 | K_00: 9.842439e+02 0.000000e+00 6.900000e+02 0.000000e+00 9.808141e+02 2.331966e+02 0.000000e+00 0.000000e+00 1.000000e+00 5 | D_00: -3.728755e-01 2.037299e-01 2.219027e-03 1.383707e-03 -7.233722e-02 6 | R_00: 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 7 | T_00: 2.573699e-16 -1.059758e-16 1.614870e-16 8 | S_rect_00: 1.242000e+03 3.750000e+02 9 | R_rect_00: 9.999239e-01 9.837760e-03 -7.445048e-03 -9.869795e-03 9.999421e-01 -4.278459e-03 7.402527e-03 4.351614e-03 9.999631e-01 10 | P_rect_00: 7.215377e+02 0.000000e+00 6.095593e+02 0.000000e+00 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 11 | S_01: 1.392000e+03 5.120000e+02 12 | K_01: 9.895267e+02 0.000000e+00 7.020000e+02 0.000000e+00 9.878386e+02 2.455590e+02 0.000000e+00 0.000000e+00 1.000000e+00 13 | D_01: -3.644661e-01 1.790019e-01 1.148107e-03 -6.298563e-04 -5.314062e-02 14 | R_01: 9.993513e-01 1.860866e-02 -3.083487e-02 -1.887662e-02 9.997863e-01 -8.421873e-03 3.067156e-02 8.998467e-03 9.994890e-01 15 | T_01: -5.370000e-01 4.822061e-03 -1.252488e-02 16 | S_rect_01: 1.242000e+03 3.750000e+02 17 | R_rect_01: 9.996878e-01 -8.976826e-03 2.331651e-02 8.876121e-03 9.999508e-01 4.418952e-03 -2.335503e-02 -4.210612e-03 9.997184e-01 18 | P_rect_01: 7.215377e+02 0.000000e+00 6.095593e+02 -3.875744e+02 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 19 | S_02: 1.392000e+03 5.120000e+02 20 | K_02: 9.597910e+02 0.000000e+00 6.960217e+02 0.000000e+00 9.569251e+02 2.241806e+02 0.000000e+00 0.000000e+00 1.000000e+00 21 | D_02: -3.691481e-01 1.968681e-01 1.353473e-03 5.677587e-04 -6.770705e-02 22 | R_02: 9.999758e-01 -5.267463e-03 -4.552439e-03 5.251945e-03 9.999804e-01 -3.413835e-03 4.570332e-03 3.389843e-03 9.999838e-01 23 | T_02: 5.956621e-02 2.900141e-04 2.577209e-03 24 | S_rect_02: 1.242000e+03 3.750000e+02 25 | R_rect_02: 9.998817e-01 1.511453e-02 -2.841595e-03 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27 | S_03: 1.392000e+03 5.120000e+02 28 | K_03: 9.037596e+02 0.000000e+00 6.957519e+02 0.000000e+00 9.019653e+02 2.242509e+02 0.000000e+00 0.000000e+00 1.000000e+00 29 | D_03: -3.639558e-01 1.788651e-01 6.029694e-04 -3.922424e-04 -5.382460e-02 30 | R_03: 9.995599e-01 1.699522e-02 -2.431313e-02 -1.704422e-02 9.998531e-01 -1.809756e-03 2.427880e-02 2.223358e-03 9.997028e-01 31 | T_03: -4.731050e-01 5.551470e-03 -5.250882e-03 32 | S_rect_03: 1.242000e+03 3.750000e+02 33 | R_rect_03: 9.998321e-01 -7.193136e-03 1.685599e-02 7.232804e-03 9.999712e-01 -2.293585e-03 -1.683901e-02 2.415116e-03 9.998553e-01 34 | P_rect_03: 7.215377e+02 0.000000e+00 6.095593e+02 -3.395242e+02 0.000000e+00 7.215377e+02 1.728540e+02 2.199936e+00 0.000000e+00 0.000000e+00 1.000000e+00 2.729905e-03 35 | -------------------------------------------------------------------------------- /utils/easydict.py: -------------------------------------------------------------------------------- 1 | class EasyDict(dict): 2 | """ 3 | Get attributes 4 | >>> d = EasyDict({'foo':3}) 5 | >>> d['foo'] 6 | 3 7 | >>> d.foo 8 | 3 9 | >>> d.bar 10 | Traceback (most recent call last): 11 | ... 12 | AttributeError: 'EasyDict' object has no attribute 'bar' 13 | >>> #Works recursively 14 | >>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y':2}}) 15 | >>> isinstance(d.bar, dict) 16 | True 17 | >>> d.bar.x 18 | 1 19 | >>> EasyDict({}) 20 | {} 21 | >>> EasyDict(d={}) 22 | {} 23 | >>> EasyDict(None) 24 | {} 25 | >>> d = {'a': 1} 26 | >>> EasyDict(**d) 27 | {'a': 1} 28 | >>> d = EasyDict() 29 | >>> d.foo = 3 30 | >>> d.foo 31 | 3 32 | >>> d.bar = {'prop': 'value'} 33 | >>> d.bar.prop 34 | 'value' 35 | >>> d 36 | {'foo': 3, 'bar': {'prop': 'value'}} 37 | >>> d.bar.prop = 'newer' 38 | >>> d.bar.prop 39 | 'newer' 40 | >>> d = EasyDict({'foo':0, 'bar':[{'x':1, 'y':2}, {'x':3, 'y':4}]}) 41 | >>> isinstance(d.bar, list) 42 | True 43 | >>> from operator import attrgetter 44 | >>> list(map(attrgetter('x'), d.bar)) 45 | [1, 3] 46 | >>> list(map(attrgetter('y'), d.bar)) 47 | [2, 4] 48 | >>> d = EasyDict() 49 | >>> list(d.keys()) 50 | [] 51 | >>> d = EasyDict(foo=3, bar=dict(x=1, y=2)) 52 | >>> d.foo 53 | 3 54 | >>> d.bar.x 55 | 1 56 | >>> o = EasyDict({'clean':True}) 57 | >>> list(o.items()) 58 | [('clean', True)] 59 | >>> #And like a class 60 | >>> class Flower(EasyDict): 61 | ... power = 1 62 | ... 63 | >>> f = Flower() 64 | >>> f.power 65 | 1 66 | >>> f = Flower({'height': 12}) 67 | >>> f.height 68 | 12 69 | >>> f['power'] 70 | 1 71 | >>> sorted(f.keys()) 72 | ['height', 'power'] 73 | """ 74 | def __init__(self, d=None, **kwargs): 75 | if d is None: 76 | d = {} 77 | if kwargs: 78 | d.update(**kwargs) 79 | for k, v in d.items(): 80 | setattr(self, k, v) 81 | # Class attributes 82 | for k in self.__class__.__dict__.keys(): 83 | if not (k.startswith('__') and k.endswith('__')): 84 | setattr(self, k, getattr(self, k)) 85 | 86 | def __setattr__(self, name, value): 87 | if isinstance(value, (list, tuple)): 88 | value = [self.__class__(x) 89 | if isinstance(x, dict) else x for x in value] 90 | elif isinstance(value, dict) and not isinstance(value, self.__class__): 91 | value = self.__class__(value) 92 | super(EasyDict, self).__setattr__(name, value) 93 | super(EasyDict, self).__setitem__(name, value) 94 | 95 | __setitem__ = __setattr__ 96 | 97 | 98 | if __name__ == "__main__": 99 | import doctest 100 | doctest.testmod() 101 | -------------------------------------------------------------------------------- /utils/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import torch 4 | 5 | class Unbuffered: 6 | def __init__(self, stream, filename): 7 | self.stream = stream 8 | assert os.path.exists(os.path.dirname(filename)) 9 | self.file = open(filename, "w") 10 | 11 | def write(self, data): 12 | self.stream.write(data) 13 | self.stream.flush() 14 | self.file.write(data) # Write the data of stdout here to a text file as wel 15 | 16 | def flush(self): 17 | pass 18 | 19 | def __exit__(self, *args): 20 | self.file.close() 21 | 22 | def close(self): 23 | self.file.close() 24 | 25 | def debugger_mode(): 26 | import sys 27 | gettrace = getattr(sys, 'gettrace', None) 28 | if gettrace is None: 29 | return False 30 | elif gettrace(): 31 | return True 32 | 33 | 34 | def get_num_workers(num_workers): 35 | if debugger_mode(): 36 | return 0 37 | else: 38 | return num_workers 39 | 40 | def convert_dict_to_device(input, device): 41 | input_new = {} 42 | for key, value in input.items(): 43 | input_new[key] = torch.tensor(value, dtype=torch.float32, device=device) 44 | return input_new 45 | 46 | def detach_tensor(x): 47 | if isinstance(x, torch.Tensor): 48 | x = x.detach().cpu().numpy() 49 | return x 50 | 51 | 52 | def setup_seed(seed): 53 | torch.backends.cudnn.deterministic = True 54 | torch.manual_seed(seed) 55 | torch.cuda.manual_seed_all(seed) 56 | np.random.seed(seed) 57 | 58 | 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