Repository: huang-yh/SelfOcc Branch: main Commit: 1658c2afd464 Files: 181 Total size: 9.5 MB Directory structure: gitextract_ciq3vmv7/ ├── .gitignore ├── LICENSE ├── README.md ├── assets/ │ └── .placeholder ├── config/ │ ├── _base_/ │ │ ├── dataset_v1.py │ │ ├── optimizer.py │ │ └── schedule.py │ ├── kitti/ │ │ ├── kitti_novel_depth.py │ │ └── kitti_occ.py │ ├── kitti_raw/ │ │ └── kitti_raw_depth.py │ ├── nuscenes/ │ │ ├── nuscenes_depth.py │ │ ├── nuscenes_novel_depth.py │ │ ├── nuscenes_occ.py │ │ └── nuscenes_occ_bev.py │ └── openseed/ │ └── openseed_swint_lang.yaml ├── dataset/ │ ├── __init__.py │ ├── dataset_one_frame_eval.py │ ├── dataset_one_frame_sweeps_dist.py │ ├── dataset_one_frame_sweeps_dist_vis.py │ ├── dataset_wrapper_temporal.py │ ├── dataset_wrapper_vis.py │ ├── kitti/ │ │ ├── helpers.py │ │ ├── io_data.py │ │ ├── kitti_dataset_eval.py │ │ ├── kitti_dataset_one_frame.py │ │ ├── params.py │ │ └── semantic-kitti.yaml │ ├── kitti_raw/ │ │ ├── kitti_raw_dataset.py │ │ ├── kitti_raw_dataset_stereo.py │ │ ├── orb-slam_poses/ │ │ │ ├── 2011_09_26/ │ │ │ │ ├── 2011_09_26_drive_0001_sync.txt │ │ │ │ ├── 2011_09_26_drive_0002_sync.txt │ │ │ │ ├── 2011_09_26_drive_0005_sync.txt │ │ │ │ ├── 2011_09_26_drive_0009_sync.txt │ │ │ │ ├── 2011_09_26_drive_0011_sync.txt │ │ │ │ ├── 2011_09_26_drive_0013_sync.txt │ │ │ │ ├── 2011_09_26_drive_0014_sync.txt │ │ │ │ ├── 2011_09_26_drive_0015_sync.txt │ │ │ │ ├── 2011_09_26_drive_0017_sync.txt │ │ │ │ ├── 2011_09_26_drive_0018_sync.txt │ │ │ │ ├── 2011_09_26_drive_0019_sync.txt │ │ │ │ ├── 2011_09_26_drive_0020_sync.txt │ │ │ │ ├── 2011_09_26_drive_0022_sync.txt │ │ │ │ ├── 2011_09_26_drive_0023_sync.txt │ │ │ │ ├── 2011_09_26_drive_0027_sync.txt │ │ │ │ ├── 2011_09_26_drive_0028_sync.txt │ │ │ │ ├── 2011_09_26_drive_0029_sync.txt │ │ │ │ ├── 2011_09_26_drive_0032_sync.txt │ │ │ │ ├── 2011_09_26_drive_0035_sync.txt │ │ │ │ ├── 2011_09_26_drive_0036_sync.txt │ │ │ │ ├── 2011_09_26_drive_0039_sync.txt │ │ │ │ ├── 2011_09_26_drive_0046_sync.txt │ │ │ │ ├── 2011_09_26_drive_0048_sync.txt │ │ │ │ ├── 2011_09_26_drive_0051_sync.txt │ │ │ │ ├── 2011_09_26_drive_0052_sync.txt │ │ │ │ ├── 2011_09_26_drive_0056_sync.txt │ │ │ │ ├── 2011_09_26_drive_0057_sync.txt │ │ │ │ ├── 2011_09_26_drive_0059_sync.txt │ │ │ │ ├── 2011_09_26_drive_0060_sync.txt │ │ │ │ ├── 2011_09_26_drive_0061_sync.txt │ │ │ │ ├── 2011_09_26_drive_0064_sync.txt │ │ │ │ ├── 2011_09_26_drive_0070_sync.txt │ │ │ │ ├── 2011_09_26_drive_0079_sync.txt │ │ │ │ ├── 2011_09_26_drive_0084_sync.txt │ │ │ │ ├── 2011_09_26_drive_0086_sync.txt │ │ │ │ ├── 2011_09_26_drive_0087_sync.txt │ │ │ │ ├── 2011_09_26_drive_0091_sync.txt │ │ │ │ ├── 2011_09_26_drive_0093_sync.txt │ │ │ │ ├── 2011_09_26_drive_0095_sync.txt │ │ │ │ ├── 2011_09_26_drive_0096_sync.txt │ │ │ │ ├── 2011_09_26_drive_0101_sync.txt │ │ │ │ ├── 2011_09_26_drive_0104_sync.txt │ │ │ │ ├── 2011_09_26_drive_0106_sync.txt │ │ │ │ ├── 2011_09_26_drive_0113_sync.txt │ │ │ │ └── 2011_09_26_drive_0117_sync.txt │ │ │ ├── 2011_09_28/ │ │ │ │ ├── 2011_09_28_drive_0001_sync.txt │ │ │ │ └── 2011_09_28_drive_0002_sync.txt │ │ │ ├── 2011_09_29/ │ │ │ │ ├── 2011_09_29_drive_0004_sync.txt │ │ │ │ ├── 2011_09_29_drive_0026_sync.txt │ │ │ │ └── 2011_09_29_drive_0071_sync.txt │ │ │ ├── 2011_09_30/ │ │ │ │ ├── 2011_09_30_drive_0016_sync.txt │ │ │ │ ├── 2011_09_30_drive_0018_sync.txt │ │ │ │ ├── 2011_09_30_drive_0020_sync.txt │ │ │ │ ├── 2011_09_30_drive_0027_sync.txt │ │ │ │ ├── 2011_09_30_drive_0028_sync.txt │ │ │ │ ├── 2011_09_30_drive_0033_sync.txt │ │ │ │ └── 2011_09_30_drive_0034_sync.txt │ │ │ └── 2011_10_03/ │ │ │ ├── 2011_10_03_drive_0027_sync.txt │ │ │ ├── 2011_10_03_drive_0034_sync.txt │ │ │ ├── 2011_10_03_drive_0042_sync.txt │ │ │ └── 2011_10_03_drive_0047_sync.txt │ │ └── splits/ │ │ ├── eigen/ │ │ │ └── test_files.txt │ │ ├── eigen_zhou/ │ │ │ ├── test_files.txt │ │ │ ├── train_files.txt │ │ │ └── val_files.txt │ │ └── tulsiani/ │ │ ├── test_files.txt │ │ ├── train_files.txt │ │ └── val_files.txt │ ├── loading.py │ ├── sampler.py │ ├── transform_3d.py │ └── utils.py ├── docs/ │ ├── get_started.md │ ├── installation.md │ ├── prepare_data.md │ └── visualization.md ├── eval_depth.py ├── eval_iou.py ├── eval_iou_kitti.py ├── eval_novel_depth.py ├── eval_novel_depth_kitti.py ├── examine_sweeps.py ├── generate_videos.py ├── loss/ │ ├── __init__.py │ ├── base_loss.py │ ├── edge_loss_3d_ms.py │ ├── eikonal_loss.py │ ├── multi_loss.py │ ├── reproj_loss_mono_multi_new.py │ ├── reproj_loss_mono_multi_new_combine.py │ ├── rgb_loss_ms.py │ ├── second_grad_loss.py │ └── sparsity_loss.py ├── model/ │ ├── __init__.py │ ├── backbone/ │ │ ├── __init__.py │ │ └── unet2d.py │ ├── encoder/ │ │ ├── __init__.py │ │ ├── base_encoder.py │ │ ├── bevformer/ │ │ │ ├── __init__.py │ │ │ ├── attention/ │ │ │ │ ├── __init__.py │ │ │ │ └── image_cross_attention.py │ │ │ ├── bevformer_encoder.py │ │ │ ├── bevformer_encoder_layer.py │ │ │ ├── bevformer_pos_embed.py │ │ │ ├── mappings.py │ │ │ ├── mappings_old.py │ │ │ └── utils.py │ │ └── tpvformer/ │ │ ├── __init__.py │ │ ├── attention/ │ │ │ ├── __init__.py │ │ │ ├── cross_view_hybrid_attention.py │ │ │ └── image_cross_attention.py │ │ ├── modules/ │ │ │ ├── __init__.py │ │ │ ├── camera_se_net.py │ │ │ ├── split_fpn.py │ │ │ └── split_norm.py │ │ ├── tpvformer_encoder.py │ │ ├── tpvformer_encoder_layer.py │ │ ├── tpvformer_pos_embed.py │ │ └── utils.py │ ├── head/ │ │ ├── __init__.py │ │ ├── base_head.py │ │ ├── nerfacc_head/ │ │ │ ├── bev_nerf.py │ │ │ ├── estimator.py │ │ │ ├── img2lidar.py │ │ │ ├── nerfacc_head.py │ │ │ ├── ray_sampler.py │ │ │ └── rendering.py │ │ ├── neus_head/ │ │ │ └── neus_head.py │ │ └── utils/ │ │ └── sh_render.py │ ├── lifter/ │ │ ├── __init__.py │ │ ├── base_lifter.py │ │ ├── bev_query_lifter.py │ │ ├── tpv_pos_lifter.py │ │ └── tpv_query_lifter.py │ ├── neck/ │ │ ├── __init__.py │ │ └── identity_neck.py │ └── segmentor/ │ ├── __init__.py │ ├── base_segmentor.py │ └── 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction ### [Paper](https://arxiv.org/pdf/2311.12754) | [Project Page](https://huang-yh.github.io/SelfOcc/) > SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction, CVPR 2024 > [Yuanhui Huang](https://scholar.google.com/citations?hl=zh-CN&user=LKVgsk4AAAAJ)*, [Wenzhao Zheng](https://wzzheng.net/)\* $\dagger$, [Borui Zhang](https://boruizhang.site/), [Jie Zhou](https://scholar.google.com/citations?user=6a79aPwAAAAJ&hl=en&authuser=1), [Jiwen Lu](http://ivg.au.tsinghua.edu.cn/Jiwen_Lu/)$\ddagger$ \* Equal contribution $\dagger$ Project leader $\ddagger$ Corresponding author SelfOcc empowers 3D autonomous driving world models (e.g., [OccWorld](https://github.com/wzzheng/OccWorld)) with scalable 3D representations, paving the way for **interpretable end-to-end large driving models**. ## News - **[2024/9/5]** Check out our recent [GaussianFormer](https://github.com/huang-yh/GaussianFormer) for object-centric 3D occupancy prediction. - **[2024/4/30]** Visualization code for 2D, 3D and video is released. - **[2024/2/26]** SelfOcc is accepted to CVPR 2024! - **[2023/12/16]** Training code release. - **[2023/11/28]** Evaluation code release. - **[2023/11/20]** Paper released on [arXiv](https://arxiv.org/abs/2311.12754). - **[2023/11/20]** Demo release. ## Demo ### Trained using only video sequences and poses: ![demo](./assets/iou.gif) ### Trained using an additional off-the-shelf 2D segmentor ([OpenSeeD](https://github.com/IDEA-Research/OpenSeeD)): ![demo](./assets/miou.gif) ![legend](./assets/legend.png) ### More demo videos can be downloaded [here](https://cloud.tsinghua.edu.cn/d/640283b528f7436193a4/). ## Overview ![overview](./assets/overview.png) - We first transform the images into the 3D space (e.g., bird's eye view, [tri-perspective view](https://github.com/wzzheng/TPVFormer)) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. - Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. - SelfOcc produces high-quality depth and achieves state-of-the-art results on **novel depth synthesis**, **monocular depth estimation**, and **surround-view depth estimation** on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. ## Results ## Getting Started ### Installation Follow detailed instructions in [Installation](docs/installation.md). ### Preparing Dataset Follow detailed instructions in [Prepare Dataset](docs/prepare_data.md). We also provide our code for synchronizing sweep data according to keyframe samples. ### Run [23/12/16 Update] Please update the timm package to 0.9.2 to run the training script. #### 3D Occupancy Prediction Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/831c104c82a244e9878a/) and put it under out/nuscenes/occ/ ```bash # train python train.py --py-config config/nuscenes/nuscenes_occ.py --work-dir out/nuscenes/occ_train --depth-metric # eval python eval_iou.py --py-config config/nuscenes/nuscenes_occ.py --work-dir out/nuscenes/occ --resume-from out/nuscenes/occ/model_state_dict.pth --occ3d --resolution 0.4 --sem --use-mask --scene-size 4 ``` #### Novel Depth Synthesis Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/2d217cd298a34ed19039/) and put it under out/nuscenes/novel_depth/ ```bash # train python train.py --py-config config/nuscenes/nuscenes_novel_depth.py --work-dir out/nuscenes/novel_depth_train --depth-metric # evak python eval_novel_depth.py --py-config config/nuscenes/nuscenes_novel_depth.py --work-dir out/nuscenes/novel_depth --resume-from out/nuscenes/novel_depth/model_state_dict.pth ``` #### Depth Estimation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/1a722b9139234542ae1e/) and put it under out/nuscenes/depth/ ```bash # train python train.py --py-config config/nuscenes/nuscenes_depth.py --work-dir out/nuscenes/depth_train --depth-metric # eval python eval_depth.py --py-config config/nuscenes/nuscenes_depth.py --work-dir out/nuscenes/depth --resume-from out/nuscenes/depth/model_state_dict.pth --depth-metric --batch 90000 ``` Note that evaluating at a resolution (450\*800) of 1:2 against the raw image (900\*1600) takes about 90 min, because we batchify rays for rendering due to GPU memory limit. You can change the rendering resolution by the variable *NUM_RAYS* in utils/config_tools.py More details on more datasets are detailed in [Run and Eval](docs/get_started.md). ### Visualization Follow detailed instructions in [Visualization](docs/visualization.md). ## Related Projects Our code is based on [TPVFormer](https://github.com/wzzheng/TPVFormer) and [PointOcc](https://github.com/wzzheng/PointOcc). Also thanks to these excellent open-sourced repos: [SurroundOcc](https://github.com/weiyithu/SurroundOcc) [OccFormer](https://github.com/zhangyp15/OccFormer) [BEVFormer](https://github.com/fundamentalvision/BEVFormer) A pioneering work on object-centric 3D occupancy prediction: [GaussianFormer](https://github.com/huang-yh/GaussianFormer). ## Citation If you find this project helpful, please consider citing the following paper: ``` @article{huang2023self, title={SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction}, author={Huang, Yuanhui and Zheng, Wenzhao and Zhang, Borui and Zhou, Jie and Lu, Jiwen }, journal={arXiv preprint arXiv:2311.12754}, year={2023} } ``` ================================================ FILE: assets/.placeholder ================================================ ================================================ FILE: config/_base_/dataset_v1.py ================================================ data_path = 'data/nuscenes/' train_wrapper_config = dict( phase='train', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) val_wrapper_config = dict( phase='val', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) nusc = dict( version = 'v1.0-trainval', dataroot = data_path ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) ================================================ FILE: config/_base_/optimizer.py ================================================ optimizer = dict( optimizer=dict( type='AdamW', lr=2e-5, weight_decay=0.0001 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) grad_max_norm = 35 ================================================ FILE: config/_base_/schedule.py ================================================ max_epochs = 12 print_freq = 50 ================================================ FILE: config/kitti/kitti_novel_depth.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] img_size = [370, 1216] num_rays = [55, 190] amp = False max_epochs = 24 warmup_iters = 1000 num_cams = 1 optimizer = dict( optimizer=dict( type='AdamW', lr=1e-3, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/kitti/' train_dataset_config = dict( _delete_=True, type='Kitti_One_Frame', split = 'train', root = data_path, preprocess_root = data_path + 'preprocess', cur_prob = 0.5, crop_size = img_size, strict = True, prev_prob = 0., choose_nearest = True, ) val_dataset_config = dict( _delete_=True, type='Kitti_One_Frame', split = 'val', root = data_path, preprocess_root = data_path + 'preprocess', cur_prob = 1.0, crop_size = img_size, strict = False, prev_prob = 0.5, choose_nearest = True, return_depth = True ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=1.0, pad_img_size=[384, 1216], pad_scale_rate=[1.038, 1.0], photometric_aug=dict( use_swap_channel=False, ), img_norm_cfg=dict( mean=[124.16, 116.74, 103.94], std=[58.624, 57.344, 57.6], to_rgb=True) ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=1.0, pad_img_size=[384, 1216], pad_scale_rate=[1.038, 1.0], photometric_aug=dict( use_swap_channel=False, ), img_norm_cfg=dict( mean=[124.16, 116.74, 103.94], std=[58.624, 57.344, 57.6], to_rgb=True) ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNew', weight=1.0, no_ssim=False, img_size=img_size, ray_resize=num_rays, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), dict( type='RGBLossMS', weight=0.1, img_size=img_size, no_ssim=False, ray_resize=num_rays, input_dict={ 'ms_colors': 'ms_colors', 'ms_rays': 'ms_rays', 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='SecondGradLoss', weight=0.01), # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', # ms_accs='ms_accs', ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', second_grad='second_grad', # uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[256, 0], h_range=[51.2, 0], h_half=True, w_size=[128, 0], w_range=[25.6, 0], w_half=False, d_size=[32, 0], d_range=[-2.0, 4.4, 4.4] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 256 tpv_w = 1 + 2 * 128 tpv_z = 1 + 32 + 0 point_cloud_range = [-25.6, 0.0, -2.0, 25.6, 51.2, 4.4] num_points_cross = [48, 48, 8] num_points_self = 12 self_cross_layer = dict( type='TPVFormerLayer', attn_cfgs=[ dict( type='CrossViewHybridAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=3, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='TPVCrossAttention', embed_dims=_dim_, num_cams=num_cams, dropout=0.1, batch_first=True, num_heads=num_heads, num_levels=4, num_points=num_points_cross) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='TPVQueryLifter', tpv_h=tpv_h, tpv_w=tpv_w, tpv_z=tpv_z, dim=_dim_), encoder=dict( type='TPVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, embed_dims=_dim_, num_cams=num_cams, num_feature_levels=4, positional_encoding=dict( type='TPVPositionalEncoding', num_freqs=[12] * 3, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=[num_points_self] * 3, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, return_uniform_sdf=False, return_second_grad=True, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=img_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=3, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=True)) ================================================ FILE: config/kitti/kitti_occ.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] img_size = [352, 1216] num_rays = [55, 190] amp = False max_epochs = 24 warmup_iters = 1000 num_cams = 1 optimizer = dict( optimizer=dict( type='AdamW', lr=1e-3, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/kitti/' train_dataset_config = dict( _delete_=True, type='Kitti_One_Frame', split = 'train', root = data_path, preprocess_root = data_path + 'preprocess', frames_interval=0.4, sequence_distance=[10, 40], cur_prob = 0.333, crop_size = img_size, strict = True, prev_prob = 0.2, choose_nearest = True, ) val_dataset_config = dict( _delete_=True, type='Kitti_One_Frame', split = 'val', root = data_path, preprocess_root = data_path + 'preprocess', frames_interval=0.4, sequence_distance=[10, 40], cur_prob = 1.0, crop_size = img_size, strict = False, prev_prob = 0.2, choose_nearest = True, return_depth = True ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=1.0, photometric_aug=dict( use_swap_channel=False, ), img_norm_cfg=dict( mean=[124.16, 116.74, 103.94], std=[58.624, 57.344, 57.6], to_rgb=True) ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=1.0, photometric_aug=dict( use_swap_channel=False, ), img_norm_cfg=dict( mean=[124.16, 116.74, 103.94], std=[58.624, 57.344, 57.6], to_rgb=True) ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNew', weight=1.0, no_ssim=False, img_size=img_size, ray_resize=num_rays, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), dict( type='RGBLossMS', weight=0.1, img_size=img_size, no_ssim=False, ray_resize=num_rays, input_dict={ 'ms_colors': 'ms_colors', 'ms_rays': 'ms_rays', 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='SecondGradLoss', weight=0.1), dict( type='SoftSparsityLoss', weight=0.005, input_dict={ 'density': 'uniform_sdf'}) # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', ms_accs='ms_accs', ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', second_grad='second_grad', uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[256, 0], h_range=[51.2, 0], h_half=True, w_size=[128, 0], w_range=[25.6, 0], w_half=False, d_size=[32, 0], d_range=[-2.0, 4.4, 4.4] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 256 tpv_w = 1 + 2 * 128 tpv_z = 1 + 32 + 0 point_cloud_range = [-25.6, 0.0, -2.0, 25.6, 51.2, 4.4] num_points_cross = [48, 48, 8] num_points_self = 12 self_cross_layer = dict( type='TPVFormerLayer', attn_cfgs=[ dict( type='CrossViewHybridAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=3, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='TPVCrossAttention', embed_dims=_dim_, num_cams=num_cams, dropout=0.1, batch_first=True, num_heads=num_heads, num_levels=4, num_points=num_points_cross) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='TPVQueryLifter', tpv_h=tpv_h, tpv_w=tpv_w, tpv_z=tpv_z, dim=_dim_), encoder=dict( type='TPVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, embed_dims=_dim_, num_cams=num_cams, num_feature_levels=4, positional_encoding=dict( type='TPVPositionalEncoding', num_freqs=[12] * 3, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=[num_points_self] * 3, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, use_compact_2nd_grad=True, return_uniform_sdf=True, return_second_grad=True, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=img_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=3, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=True)) ================================================ FILE: config/kitti_raw/kitti_raw_depth.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] img_size = [370, 1216] num_rays = [44, 152] amp = False max_epochs = 24 warmup_iters = 1000 num_cams = 1 multisteplr = True multisteplr_config = dict( decay_t = [4976 * 18], decay_rate = 0.1, warmup_t = warmup_iters, warmup_lr_init = 1e-6, t_in_epochs = False ) optimizer = dict( optimizer=dict( type='AdamW', lr=1e-4, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/kitti_raw/' train_dataset_config = dict( _delete_=True, type = 'Kitti_Raw_Stereo', root = data_path, pose_path = 'dataset/kitti_raw/orb-slam_poses', split_path = 'dataset/kitti_raw/splits/eigen_zhou/train_files.txt', frames_interval=1.0, sequence_distance=5.0, cur_prob=1.0, crop_size=img_size, strict=True, return_depth=False, prev_prob=0, choose_nearest = True, eval_depth = 80, include_stereo = True ) val_dataset_config = dict( _delete_=True, type = 'Kitti_Raw_Stereo', root = data_path, pose_path = 'dataset/kitti_raw/orb-slam_poses', split_path = 'dataset/kitti_raw/splits/eigen_zhou/test_files.txt', frames_interval=1.0, sequence_distance=5.0, cur_prob=1.0, crop_size=img_size, strict=False, return_depth=True, prev_prob=0.5, choose_nearest = True, eval_depth = 80, include_stereo = True ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=0.84, pad_img_size=[320, 1024], pad_scale_rate=[0.8649, 0.8421], photometric_aug=dict( use_swap_channel=False, ), img_norm_cfg=dict( mean=[124.16, 116.74, 103.94], std=[58.624, 57.344, 57.6], to_rgb=True) ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=0.84, pad_img_size=[320, 1024], pad_scale_rate=[0.8649, 0.8421], photometric_aug=dict( use_swap_channel=False, ), img_norm_cfg=dict( mean=[124.16, 116.74, 103.94], std=[58.624, 57.344, 57.6], to_rgb=True) ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNew', weight=1.0, no_ssim=False, img_size=img_size, ray_resize=num_rays, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), # dict( # type='RGBLossMS', # weight=0.1, # img_size=img_size, # no_ssim=False, # ray_resize=num_rays, # input_dict={ # 'ms_colors': 'ms_colors', # 'ms_rays': 'ms_rays', # 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='EdgeLoss3DMS', weight=0.01, img_size=img_size, ray_resize=num_rays, ) # dict( # type='SecondGradLoss', # weight=0.01), # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', # ms_accs='ms_accs', # ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', # second_grad='second_grad', # uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[256, 0], h_range=[51.2, 0], h_half=True, w_size=[128, 0], w_range=[25.6, 0], w_half=False, d_size=[32, 0], d_range=[-2.0, 4.4, 4.4] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 256 tpv_w = 1 + 2 * 128 tpv_z = 1 + 32 + 0 point_cloud_range = [-25.6, 0.0, -2.0, 25.6, 51.2, 4.4] num_points_cross = [48, 48, 8] num_points_self = 12 self_cross_layer = dict( type='TPVFormerLayer', attn_cfgs=[ dict( type='CrossViewHybridAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=3, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='TPVCrossAttention', embed_dims=_dim_, num_cams=num_cams, dropout=0.1, batch_first=True, num_heads=num_heads, num_levels=4, num_points=num_points_cross) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='TPVQueryLifter', tpv_h=tpv_h, tpv_w=tpv_w, tpv_z=tpv_z, dim=_dim_), encoder=dict( type='TPVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, embed_dims=_dim_, num_cams=num_cams, num_feature_levels=4, positional_encoding=dict( type='TPVPositionalEncoding', num_freqs=[12] * 3, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=[num_points_self] * 3, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, return_uniform_sdf=False, return_second_grad=False, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=img_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=0, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=True)) ================================================ FILE: config/nuscenes/nuscenes_depth.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] # img_size = [768, 1600] img_size = [896, 1600] crop_size = [900, 1600] num_rays = [48, 100] amp = False max_epochs = 24 warmup_iters = 1000 multisteplr = True multisteplr_config = dict( decay_t = [3516 * 18], decay_rate = 0.1, warmup_t = warmup_iters, warmup_lr_init = 1e-6, t_in_epochs = False ) optimizer = dict( optimizer=dict( type='AdamW', lr=1e-4, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/nuscenes/' train_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_train_sweeps.pkl', crop_size = crop_size, input_img_crop_size = img_size, min_dist = 0.4, max_dist = 6.0, strict = True, return_depth = False, eval_depth = 80, cur_prob = 1.0, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ) val_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_val_sweeps.pkl', crop_size = crop_size, input_img_crop_size = img_size, min_dist = 0.4, max_dist = 6.0, strict = False, return_depth = True, eval_depth = 80, cur_prob = 1, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ), # pad_img_size=[416, 864], # random_scale=[0.92, 1.08], # pad_scale_rate=[0.5417, 0.54], # use_flip=True ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ), # pad_img_size=[416, 864], # pad_scale_rate=[0.5417, 0.54], ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNewCombine', weight=1.0, no_ssim=False, img_size=crop_size, ray_resize=num_rays, no_automask=False, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), # dict( # type='RGBLossMS', # weight=0.1, # img_size=img_size, # no_ssim=False, # ray_resize=num_rays, # input_dict={ # 'ms_colors': 'ms_colors', # 'ms_rays': 'ms_rays', # 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='EdgeLoss3DMS', weight=0.01, img_size=crop_size, ray_resize=num_rays, ), # dict( # type='SecondGradLoss', # weight=0.01), # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', # ms_accs='ms_accs', # ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', # second_grad='second_grad', # uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[128, 0], h_range=[51.2, 0], h_half=False, w_size=[128, 0], w_range=[51.2, 0], w_half=False, d_size=[30, 0], d_range=[-4.0, 5.0, 5.0] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 2 * 128 tpv_w = 1 + 2 * 128 tpv_z = 1 + 30 point_cloud_range = [-51.2, -51.2, -4.0, 51.2, 51.2, 5.0] num_points_cross = [48, 48, 8] num_points_self = 12 self_cross_layer = dict( type='TPVFormerLayer', attn_cfgs=[ dict( type='CrossViewHybridAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=3, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='TPVCrossAttention', embed_dims=_dim_, num_cams=6, dropout=0.1, batch_first=True, num_heads=num_heads, num_levels=4, num_points=num_points_cross) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='TPVQueryLifter', tpv_h=tpv_h, tpv_w=tpv_w, tpv_z=tpv_z, dim=_dim_), encoder=dict( type='TPVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # camera_aware=True, # camera_aware_mid_channels=96, embed_dims=_dim_, num_cams=6, num_feature_levels=4, positional_encoding=dict( type='TPVPositionalEncoding', num_freqs=[12] * 3, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=[num_points_self] * 3, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, return_uniform_sdf=False, return_second_grad=False, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=crop_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=0, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=True)) ================================================ FILE: config/nuscenes/nuscenes_novel_depth.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] img_size = [768, 1600] num_rays = [48, 100] amp = False max_epochs = 24 warmup_iters = 1000 multisteplr = True multisteplr_config = dict( decay_t = [3516 * 18], decay_rate = 0.1, warmup_t = warmup_iters, warmup_lr_init = 1e-6, t_in_epochs = False ) optimizer = dict( optimizer=dict( type='AdamW', lr=1e-4, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/nuscenes/' train_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_train_sweeps.pkl', crop_size = img_size, min_dist = 0.4, max_dist = 10.0, strict = True, return_depth = False, eval_depth = 80, cur_prob = 0.333, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ) val_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_val_sweeps.pkl', crop_size = img_size, min_dist = 0.4, max_dist = 10.0, strict = False, return_depth = True, eval_depth = 80, cur_prob = 1, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNewCombine', weight=1.0, no_ssim=False, img_size=img_size, ray_resize=num_rays, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), dict( type='RGBLossMS', weight=0.1, img_size=img_size, no_ssim=False, ray_resize=num_rays, input_dict={ 'ms_colors': 'ms_colors', 'ms_rays': 'ms_rays', 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='SecondGradLoss', weight=0.01), # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', # ms_accs='ms_accs', ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', second_grad='second_grad', # uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[128, 0], h_range=[51.2, 0], h_half=False, w_size=[128, 0], w_range=[51.2, 0], w_half=False, d_size=[30, 0], d_range=[-4.0, 5.0, 5.0] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 2 * 128 tpv_w = 1 + 2 * 128 tpv_z = 1 + 30 point_cloud_range = [-51.2, -51.2, -4.0, 51.2, 51.2, 5.0] num_points_cross = [48, 48, 8] num_points_self = 12 self_cross_layer = dict( type='TPVFormerLayer', attn_cfgs=[ dict( type='CrossViewHybridAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=3, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='TPVCrossAttention', embed_dims=_dim_, num_cams=6, dropout=0.1, batch_first=True, num_heads=num_heads, num_levels=4, num_points=num_points_cross) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='TPVQueryLifter', tpv_h=tpv_h, tpv_w=tpv_w, tpv_z=tpv_z, dim=_dim_), encoder=dict( type='TPVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, embed_dims=_dim_, num_cams=6, num_feature_levels=4, positional_encoding=dict( type='TPVPositionalEncoding', num_freqs=[12] * 3, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=[num_points_self] * 3, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, return_uniform_sdf=False, return_second_grad=True, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=img_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=3, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=True)) ================================================ FILE: config/nuscenes/nuscenes_occ.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] img_size = [768, 1600] num_rays = [48, 100] amp = False max_epochs = 12 warmup_iters = 1000 sem = True multisteplr = True multisteplr_config = dict( decay_t = [3516 * 9], decay_rate = 0.1, warmup_t = warmup_iters, warmup_lr_init = 1e-6, t_in_epochs = False ) optimizer = dict( optimizer=dict( type='AdamW', lr=1e-4, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/nuscenes/' train_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_train_sweeps.pkl', crop_size = img_size, min_dist = 0.4, max_dist = 30.0, strict = True, return_depth = False, eval_depth = 80, cur_prob = 0.333, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ego_centric=True, ) val_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_val_sweeps.pkl', crop_size = img_size, min_dist = 0.4, max_dist = 30.0, strict = False, return_depth = True, eval_depth = 80, cur_prob = 1, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ego_centric=True ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNewCombine', weight=1.0, no_ssim=False, img_size=img_size, ray_resize=num_rays, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), dict( type='RGBLossMS', weight=0.1, img_size=img_size, no_ssim=False, ray_resize=num_rays, input_dict={ 'ms_colors': 'ms_colors', 'ms_rays': 'ms_rays', 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='SecondGradLoss', weight=0.01), dict( type='SemCELossMS', weight=0.1, img_size=img_size, ray_resize=num_rays, input_dict={ 'sem': 'sem', 'metas': 'metas', 'ms_rays': 'ms_rays'}), # dict( # type='AdaptiveSparsityLoss', # weight=0.001, # slack=4.0, # input_dict={ # 'sdfs': 'sdfs', # 'ts': 'ts', # 'ms_depths': 'ms_depths'}) # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', # ms_accs='ms_accs', ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', second_grad='second_grad', sem='sem', # sdfs='sample_sdf' # uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[128, 0], h_range=[40.0, 0], h_half=False, w_size=[128, 0], w_range=[40.0, 0], w_half=False, d_size=[24, 0], d_range=[-1.0, 5.4, 5.4] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 2 * 128 tpv_w = 1 + 2 * 128 tpv_z = 1 + 24 point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4] num_points_cross = [48, 48, 8] num_points_self = 12 self_cross_layer = dict( type='TPVFormerLayer', attn_cfgs=[ dict( type='CrossViewHybridAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=3, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='TPVCrossAttention', embed_dims=_dim_, num_cams=6, dropout=0.1, batch_first=True, num_heads=num_heads, num_levels=4, num_points=num_points_cross) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='TPVQueryLifter', tpv_h=tpv_h, tpv_w=tpv_w, tpv_z=tpv_z, dim=_dim_), encoder=dict( type='TPVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, embed_dims=_dim_, num_cams=6, num_feature_levels=4, positional_encoding=dict( type='TPVPositionalEncoding', num_freqs=[12] * 3, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=[num_points_self] * 3, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, return_uniform_sdf=False, return_second_grad=True, return_sem=True, return_sample_sdf=False, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=img_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=24, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=True)) ================================================ FILE: config/nuscenes/nuscenes_occ_bev.py ================================================ _base_ = [ '../_base_/dataset_v1.py', '../_base_/optimizer.py', '../_base_/schedule.py', ] img_size = [768, 1600] num_rays = [48, 100] amp = False max_epochs = 12 warmup_iters = 1000 sem = True multisteplr = True multisteplr_config = dict( decay_t = [3516 * 9], decay_rate = 0.1, warmup_t = warmup_iters, warmup_lr_init = 1e-6, t_in_epochs = False ) optimizer = dict( optimizer=dict( type='AdamW', lr=1e-4, weight_decay=0.01, # eps=1e-4 ), paramwise_cfg=dict( custom_keys={ 'img_backbone': dict(lr_mult=0.1),} ), ) data_path = 'data/nuscenes/' train_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_train_sweeps.pkl', crop_size = img_size, min_dist = 0.4, max_dist = 30.0, strict = True, return_depth = False, eval_depth = 80, cur_prob = 0.333, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ego_centric=True, ) val_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Sweeps_Dist', data_path = data_path, imageset = 'data/nuscenes_infos_val_sweeps.pkl', crop_size = img_size, min_dist = 0.4, max_dist = 30.0, strict = False, return_depth = True, eval_depth = 80, cur_prob = 1, prev_prob = 0.5, choose_nearest = True, ref_sensor = 'CAM_FRONT', composite_prev_next=True, sensor_mus=[0.5, 0.5], sensor_sigma=0.5, ego_centric=True ) train_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='train', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) val_wrapper_config = dict( type='tpvformer_dataset_nuscenes_temporal', phase='val', scale_rate=0.5, photometric_aug=dict( use_swap_channel=False, ) ) train_loader = dict( batch_size = 1, shuffle = True, num_workers = 1, ) val_loader = dict( batch_size = 1, shuffle = False, num_workers = 1, ) loss = dict( type='MultiLoss', loss_cfgs=[ dict( type='ReprojLossMonoMultiNewCombine', weight=1.0, no_ssim=False, img_size=img_size, ray_resize=num_rays, input_dict={ 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays', # 'deltas': 'deltas' }), dict( type='RGBLossMS', weight=0.1, img_size=img_size, no_ssim=False, ray_resize=num_rays, input_dict={ 'ms_colors': 'ms_colors', 'ms_rays': 'ms_rays', 'gt_imgs': 'color_imgs'}), dict( type='EikonalLoss', weight=0.1,), dict( type='SecondGradLoss', weight=0.01), dict( type='SemLossMS', weight=0.1, img_size=img_size, ray_resize=num_rays, input_dict={ 'sem': 'sem', 'metas': 'metas', 'ms_rays': 'ms_rays'}), dict( type='SoftSparsityLoss', weight=0.001, input_dict={ 'density': 'uniform_sdf'}) # dict( # type='SparsityLoss', # weight=0.001, # scale=0.1, # input_dict={ # 'density': 'uniform_sdf'}), ]) loss_input_convertion = dict( ms_depths='ms_depths', ms_rays='ms_rays', # ms_accs='ms_accs', ms_colors='ms_colors', ray_indices='ray_indices', weights='weights', ts='ts', eik_grad='eik_grad', second_grad='second_grad', sem='sem', # sdfs='sample_sdf' uniform_sdf='uniform_sdf', # deltas='deltas' ) load_from = '' _dim_ = 96 _ffn_dim_ = 2 * _dim_ num_heads = 6 mapping_args = dict( nonlinear_mode='linear', h_size=[128, 0], h_range=[40.0, 0], h_half=False, w_size=[128, 0], w_range=[40.0, 0], w_half=False, d_size=[24, 0], d_range=[-1.0, 5.4, 5.4] ) # bev_inner = 160 # bev_outer = 1 # range_inner = 80.0 # range_outer = 1.0 # nonlinear_mode = 'linear_upscale' # z_inner = 20 # z_outer = 10 # z_ranges = [-4.0, 4.0, 12.0] tpv_h = 1 + 2 * 128 tpv_w = 1 + 2 * 128 tpv_z = 1 + 24 point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4] num_points_cross = 8 num_points_self = 12 self_cross_layer = dict( type='BEVFormerLayer', attn_cfgs=[ dict( type='MultiScaleDeformableAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=1, num_points=num_points_self, dropout=0.1, batch_first=True), dict( type='BEVCrossAttention', embed_dims=_dim_, num_cams=6, dropout=0.1, batch_first=True, deformable_attention=dict( type='BEVDeformableAttention', embed_dims=_dim_, num_heads=num_heads, num_levels=4, num_points=num_points_cross, dropout=0.1, batch_first=True)) ], feedforward_channels=_ffn_dim_, ffn_dropout=0.1, operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm') ) model = dict( type='TPVSegmentor', img_backbone_out_indices=[0, 1, 2, 3], img_backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=0, norm_eval=False, style='pytorch', pretrained='./ckpts/resnet50-0676ba61.pth'), img_neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=_dim_, start_level=0, add_extra_convs='on_output', num_outs=4, relu_before_extra_convs=True), lifter=dict( type='BEVQueryLifter', bev_h=tpv_h, bev_w=tpv_w, dim=_dim_), encoder=dict( type='BEVFormerEncoder', # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # camera_aware=True, # camera_aware_mid_channels=96, embed_dims=_dim_, num_cams=6, num_feature_levels=4, positional_encoding=dict( type='BEVPositionalEncoding', num_freqs=12, embed_dims=_dim_, tot_range=point_cloud_range), num_points_cross=num_points_cross, num_points_self=num_points_self, transformerlayers=[ self_cross_layer, self_cross_layer, self_cross_layer, self_cross_layer], num_layers=4), head=dict( type='NeuSHead', roi_aabb=point_cloud_range, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=256, num_samples_importance=0, num_up_sample_steps=0, base_variance=4, beta_init=0.1, beta_max=0.195, total_iters=3516*11, beta_hand_tune=False, use_numerical_gradients=False, sample_gradient=True, return_uniform_sdf=True, return_second_grad=True, return_sem=True, return_sample_sdf=False, # rays args ray_sample_mode='cellular', # fixed, cellular ray_number=num_rays, # 192 * 400 ray_img_size=img_size, ray_upper_crop=0, # img2lidar args trans_kw='temImg2lidar', novel_view=None, # render args render_bkgd='random', # bev nerf # bev_inner=bev_inner, # bev_outer=bev_outer, # range_inner=range_inner, # range_outer=range_outer, # nonlinear_mode=nonlinear_mode, # z_inner=z_inner, # z_outer=z_outer, # z_ranges=z_ranges, mapping_args=mapping_args, # mlp decoder embed_dims=_dim_, color_dims=24, density_layers=2, sh_deg=0, sh_act='relu', two_split=False, tpv=False)) ================================================ FILE: config/openseed/openseed_swint_lang.yaml ================================================ # -------------------------------------------------------- # X-Decoder -- Generalized Decoding for Pixel, Image, and Language # ------------------------------------------------------------------------ # OpenSeeD # Copyright (c) 2023 IDEA. All Rights Reserved. # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ # Modified from MaskDINO https://github.com/IDEA-Research/MaskDINO by Feng Li and Hao Zhang. # ------------------------------------------------------------------------ ################## # Task settings ################## WEIGHT: '' PORT: 53711 VERBOSE: true OUTPUT_DIR: '../../data/output/test' # misc LOADER: JOINT: True KEY_DATASET: 'coco' # model MODEL: NAME: openseed_model HEAD: openseed_head MASK_ON: false KEYPOINT_ON: false LOAD_PROPOSALS: false DIM_PROJ: 512 BACKBONE_DIM: 768 BACKGROUND: False WEIGHTS: '' TEXT: ARCH: encoder NAME: transformer TOKENIZER: clip CONTEXT_LENGTH: 18 # 18 WIDTH: 512 HEADS: 8 LAYERS: 12 AUTOGRESSIVE: True BACKBONE: NAME: swin PRETRAINED: 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' LOAD_PRETRAINED: true SWIN: PRETRAIN_IMG_SIZE: 224 PATCH_SIZE: 4 EMBED_DIM: 96 DEPTHS: [ 2, 2, 6, 2 ] NUM_HEADS: [ 3, 6, 12, 24 ] WINDOW_SIZE: 7 MLP_RATIO: 4.0 QKV_BIAS: true QK_SCALE: ~ DROP_RATE: 0.0 ATTN_DROP_RATE: 0.0 DROP_PATH_RATE: 0.3 APE: false PATCH_NORM: true USE_CHECKPOINT: false OUT_FEATURES: [ 'res2', 'res3', 'res4', 'res5' ] ENCODER: NAME: encoder_deform IGNORE_VALUE: 255 NUM_CLASSES: 133 LOSS_WEIGHT: 1.0 CONVS_DIM: 256 MASK_DIM: 256 NORM: "GN" IN_FEATURES: [ "res2", "res3", "res4", "res5" ] DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: [ "res3", "res4", "res5" ] COMMON_STRIDE: 4 TRANSFORMER_ENC_LAYERS: 6 TOTAL_NUM_FEATURE_LEVELS: 4 NUM_FEATURE_LEVELS: 3 FEATURE_ORDER: "low2high" DECODER: NAME: openseed_decoder TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" MASK: True BOX: True GROUNDING: ENABLED: False MAX_LEN: 5 TEXT_WEIGHT: 2.0 CLASS_WEIGHT: 0.5 CAPTION: ENABLED: False PHRASE_PROB: 0.0 SIM_THRES: 0.95 CAPTIONING: ENABLED: False STEP: 50 RETRIEVAL: ENABLED: False DIM_IMG: 768 ENSEMBLE: True OPENIMAGE: ENABLED: False NEGATIVE_SAMPLES: 5 GROUNDING: ENABLED: False MAX_LEN: 5 DEEP_SUPERVISION: True NO_OBJECT_WEIGHT: 0.1 CLASS_WEIGHT: 4.0 MASK_WEIGHT: 5.0 DICE_WEIGHT: 5.0 BOX_WEIGHT: 5.0 GIOU_WEIGHT: 2.0 COST_CLASS_WEIGHT: 4.0 COST_DICE_WEIGHT: 5.0 COST_MASK_WEIGHT: 5.0 COST_BOX_WEIGHT: 5.0 COST_GIOU_WEIGHT: 2.0 HIDDEN_DIM: 256 NUM_OBJECT_QUERIES: 300 NHEADS: 8 DROPOUT: 0.0 DIM_FEEDFORWARD: 2048 ENC_LAYERS: 0 PRE_NORM: False ENFORCE_INPUT_PROJ: False SIZE_DIVISIBILITY: 32 DEC_LAYERS: 9 # 9 decoder layers, add one for the loss on learnable query TRAIN_NUM_POINTS: 12544 OVERSAMPLE_RATIO: 3.0 IMPORTANCE_SAMPLE_RATIO: 0.75 TWO_STAGE: True INITIALIZE_BOX_TYPE: 'no' DN: seg DN_NOISE_SCALE: 0.4 DN_NUM: 100 INITIAL_PRED: True LEARN_TGT: False TOTAL_NUM_FEATURE_LEVELS: 4 SEMANTIC_CE_LOSS: False PANO_BOX_LOSS: False COCO: True O365: False TEST: SEMANTIC_ON: True INSTANCE_ON: True PANOPTIC_ON: True OVERLAP_THRESHOLD: 0.8 OBJECT_MASK_THRESHOLD: 0.25 SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE: false TEST_FOUCUS_ON_BOX: False PANO_TRANSFORM_EVAL: True PANO_TEMPERATURE: 0.06 TEST: EVAL_PERIOD: 5000 PRECISE_BN: NUM_ITER: 1 ENABLED: False AUG: ENABLED: False COCO: INPUT: MIN_SIZE_TEST: 800 MAX_SIZE_TEST: 1333 IMAGE_SIZE: 1024 MIN_SCALE: 0.1 MAX_SCALE: 2.0 DATASET_MAPPER_NAME: "coco_panoptic_lsj" IGNORE_VALUE: 255 COLOR_AUG_SSD: False SIZE_DIVISIBILITY: 32 RANDOM_FLIP: "horizontal" MASK_FORMAT: "polygon" FORMAT: "RGB" CROP: ENABLED: True DATASET: DATASET: 'coco' TEST: DETECTIONS_PER_IMAGE: 100 NAME: coco_eval IOU_TYPE: ['bbox', 'segm'] USE_MULTISCALE: false BATCH_SIZE_TOTAL: 8 MODEL_FILE: '' AUG: ENABLED: False TRAIN: BATCH_SIZE_TOTAL: 16 BATCH_SIZE_PER_GPU: 2 SHUFFLE: true DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 2 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: True VLP: INPUT: IMAGE_SIZE: 224 DATASET_MAPPER_NAME: "vlpretrain" IGNORE_VALUE: 255 COLOR_AUG_SSD: False SIZE_DIVISIBILITY: 32 MASK_FORMAT: "polygon" FORMAT: "RGB" CROP: ENABLED: True TRAIN: BATCH_SIZE_TOTAL: 2 BATCH_SIZE_PER_GPU: 2 TEST: BATCH_SIZE_TOTAL: 256 DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 16 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: True INPUT: PIXEL_MEAN: [123.675, 116.280, 103.530] PIXEL_STD: [58.395, 57.120, 57.375] DATASETS: TRAIN: ["coco_2017_train_panoptic"] TEST: ["coco_2017_val_panoptic_with_sem_seg"] CLASS_CONCAT: false SIZE_DIVISIBILITY: 32 PROPOSAL_FILES_TRAIN: [] DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 16 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: True # Detectron2 training config for optimizer and lr scheduler SOLVER: BASE_LR_END: 0.0 MOMENTUM: 0.9 NESTEROV: False CHECKPOINT_PERIOD: 5000 IMS_PER_BATCH: 16 REFERENCE_WORLD_SIZE: 0 BIAS_LR_FACTOR: 1.0 WEIGHT_DECAY_BIAS: None # original BASE_LR: 0.0001 STEPS: [327778, 355092] MAX_ITER: 368750 GAMMA: 0.1 WARMUP_FACTOR: 1.0 WARMUP_ITERS: 10 WARMUP_METHOD: "linear" WEIGHT_DECAY: 0.05 OPTIMIZER: "ADAMW" LR_SCHEDULER_NAME: "WarmupMultiStepLR" LR_MULTIPLIER: backbone: 0.1 lang_encoder: 0.1 WEIGHT_DECAY_NORM: 0.0 WEIGHT_DECAY_EMBED: 0.0 CLIP_GRADIENTS: ENABLED: True CLIP_TYPE: "full_model" CLIP_VALUE: 0.01 NORM_TYPE: 2.0 AMP: ENABLED: True # Evaluation Dataset ADE20K: INPUT: MIN_SIZE_TRAIN: [320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152, 1216, 1280] MIN_SIZE_TRAIN_SAMPLING: "choice" MIN_SIZE_TEST: 640 MAX_SIZE_TRAIN: 2560 MAX_SIZE_TEST: 2560 MASK_FORMAT: "polygon" CROP: ENABLED: True TYPE: "absolute" SIZE: [640, 640] SINGLE_CATEGORY_MAX_AREA: 1.0 IGNORE_VALUE: 255 COLOR_AUG_SSD: True SIZE_DIVISIBILITY: 640 # used in dataset mapper DATASET_MAPPER_NAME: "mask_former_panoptic" FORMAT: "RGB" DATASET: DATASET: 'ade' TRAIN: ASPECT_RATIO_GROUPING: true BATCH_SIZE_TOTAL: 16 BATCH_SIZE_PER_GPU: 2 SHUFFLE: true TEST: DETECTIONS_PER_IMAGE: 100 NAME: coco_eval IOU_TYPE: ['bbox', 'segm'] USE_MULTISCALE: false BATCH_SIZE_TOTAL: 8 MODEL_FILE: '' AUG: ENABLED: False DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 8 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: True REF: INPUT: PIXEL_MEAN: [123.675, 116.280, 103.530] PIXEL_STD: [58.395, 57.120, 57.375] MIN_SIZE_TEST: 512 MAX_SIZE_TEST: 1024 FORMAT: "RGB" DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 0 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: False TEST: BATCH_SIZE_TOTAL: 8 SUN: INPUT: PIXEL_MEAN: [123.675, 116.280, 103.530] PIXEL_STD: [58.395, 57.120, 57.375] MIN_SIZE_TEST: 512 MAX_SIZE_TEST: 1024 DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 0 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: False TEST: BATCH_SIZE_TOTAL: 8 SCAN: INPUT: PIXEL_MEAN: [123.675, 116.280, 103.530] PIXEL_STD: [58.395, 57.120, 57.375] MIN_SIZE_TEST: 512 MAX_SIZE_TEST: 1024 DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 0 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: False TEST: BATCH_SIZE_TOTAL: 8 BDD: INPUT: PIXEL_MEAN: [123.675, 116.280, 103.530] PIXEL_STD: [58.395, 57.120, 57.375] MIN_SIZE_TEST: 800 MAX_SIZE_TEST: 1333 DATALOADER: FILTER_EMPTY_ANNOTATIONS: False NUM_WORKERS: 0 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: False TEST: BATCH_SIZE_TOTAL: 8 CITY: INPUT: MIN_SIZE_TRAIN: [ 512, 614, 716, 819, 921, 1024, 1126, 1228, 1331, 1433, 1536, 1638, 1740, 1843, 1945, 2048 ] MIN_SIZE_TRAIN_SAMPLING: "choice" MIN_SIZE_TEST: 1024 MAX_SIZE_TRAIN: 4096 MAX_SIZE_TEST: 2048 CROP: ENABLED: True TYPE: "absolute" SIZE: [ 512, 1024 ] SINGLE_CATEGORY_MAX_AREA: 1.0 IGNORE_VALUE: 255 COLOR_AUG_SSD: True SIZE_DIVISIBILITY: -1 FORMAT: "RGB" DATASET_MAPPER_NAME: "mask_former_panoptic" MASK_FORMAT: "polygon" TEST: EVAL_PERIOD: 5000 BATCH_SIZE_TOTAL: 1 AUG: ENABLED: False MIN_SIZES: [ 512, 768, 1024, 1280, 1536, 1792 ] MAX_SIZE: 4096 FLIP: True DATALOADER: FILTER_EMPTY_ANNOTATIONS: True NUM_WORKERS: 2 LOAD_PROPOSALS: False SAMPLER_TRAIN: "TrainingSampler" ASPECT_RATIO_GROUPING: True TRAIN: ASPECT_RATIO_GROUPING: true BATCH_SIZE_TOTAL: 2 BATCH_SIZE_PER_GPU: 2 SHUFFLE: true ================================================ FILE: dataset/__init__.py ================================================ from mmengine.registry import Registry OPENOCC_DATASET = Registry('openocc_dataset') OPENOCC_DATAWRAPPER = Registry('openocc_datawrapper') from .dataset_one_frame_sweeps_dist import nuScenes_One_Frame_Sweeps_Dist from .dataset_one_frame_sweeps_dist_vis import nuScenes_One_Frame_Sweeps_Dist_Vis from .dataset_wrapper_vis import tpvformer_dataset_nuscenes_vis from .dataset_one_frame_eval import nuScenes_One_Frame_Eval from .dataset_wrapper_temporal import tpvformer_dataset_nuscenes_temporal, custom_collate_fn_temporal from .sampler import CustomDistributedSampler from .kitti.kitti_dataset_eval import Kitti_Novel_View_Eval from .kitti_raw.kitti_raw_dataset import Kitti_Raw from .kitti_raw.kitti_raw_dataset_stereo import Kitti_Raw_Stereo from .kitti.kitti_dataset_one_frame import Kitti_One_Frame from torch.utils.data.distributed import DistributedSampler from torch.utils.data.dataloader import DataLoader def get_dataloader( train_dataset_config, val_dataset_config, train_wrapper_config, val_wrapper_config, train_loader, val_loader, nusc=dict( version='v1.0-trainval', dataroot='data/nuscenes'), dist=False, iter_resume=False, train_sampler_config=dict( shuffle=True, drop_last=True), val_sampler_config=dict( shuffle=False, drop_last=False), val_only=False, ): # if nusc is not None: # from nuscenes import NuScenes # nusc = NuScenes(**nusc) if val_only: val_dataset = OPENOCC_DATASET.build( val_dataset_config, default_args={'nusc': nusc}) val_wrapper = OPENOCC_DATAWRAPPER.build( val_wrapper_config, default_args={'in_dataset': val_dataset}) val_sampler = None if dist: val_sampler = DistributedSampler(val_wrapper, **val_sampler_config) val_dataset_loader = DataLoader( dataset=val_wrapper, batch_size=val_loader["batch_size"], collate_fn=custom_collate_fn_temporal, shuffle=False, sampler=val_sampler, num_workers=val_loader["num_workers"], pin_memory=True) return None, val_dataset_loader train_dataset = OPENOCC_DATASET.build( train_dataset_config, default_args={'nusc': nusc}) val_dataset = OPENOCC_DATASET.build( val_dataset_config, default_args={'nusc': nusc}) train_wrapper = OPENOCC_DATAWRAPPER.build( train_wrapper_config, default_args={'in_dataset': train_dataset}) val_wrapper = OPENOCC_DATAWRAPPER.build( val_wrapper_config, default_args={'in_dataset': val_dataset}) train_sampler = val_sampler = None if dist: if iter_resume: train_sampler = CustomDistributedSampler(train_wrapper, **train_sampler_config) else: train_sampler = DistributedSampler(train_wrapper, **train_sampler_config) val_sampler = DistributedSampler(val_wrapper, **val_sampler_config) train_dataset_loader = DataLoader( dataset=train_wrapper, batch_size=train_loader["batch_size"], collate_fn=custom_collate_fn_temporal, shuffle=False if dist else train_loader["shuffle"], sampler=train_sampler, num_workers=train_loader["num_workers"], pin_memory=True) val_dataset_loader = DataLoader( dataset=val_wrapper, batch_size=val_loader["batch_size"], collate_fn=custom_collate_fn_temporal, shuffle=False, sampler=val_sampler, num_workers=val_loader["num_workers"], pin_memory=True) return train_dataset_loader, val_dataset_loader ================================================ FILE: dataset/dataset_one_frame_eval.py ================================================ import os, numpy as np, pickle, random from mmcv.image.io import imread from pyquaternion import Quaternion from copy import deepcopy from . import OPENOCC_DATASET if 'HFAI' in os.environ: hfai = True from dataset.loading import LoadMultiViewImageFromFilesHF, \ LoadPtsFromFilesHF else: hfai = False @OPENOCC_DATASET.register_module() class nuScenes_One_Frame_Eval: def __init__( self, data_path, imageset='train', nusc=None, crop_size=[768, 1600], cam_types=None, eval_depth=80, scene_name=None, **kwargs): with open(imageset, 'rb') as f: data = pickle.load(f) self.nusc_infos = data['infos'] if scene_name is not None: assert isinstance(scene_name, str) and scene_name.startswith('scene-') selected_indices = data['scene_info'][scene_name] self.selected_indices = list(range(selected_indices[0], selected_indices[1] + 1)) else: self.selected_indices = list(range(len(self.nusc_infos))) self.data_path = data_path self.nusc = nusc self.crop_size = crop_size self.cam_types = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT' ] if cam_types is None else cam_types self.eval_depth = eval_depth if hfai: self.img_loader = LoadMultiViewImageFromFilesHF( to_float32=True, file_client_args=dict( backend='ffrecord', fname=data_path+'CAM', filename2idx='img_fname2idx.pkl')) self.pts_loader = LoadPtsFromFilesHF( to_float32=True, file_client_args=dict( backend='ffrecord', fname=data_path+'LIDAR', filename2idx='pts_fname2idx.pkl')) else: self.img_loader = self.pts_loader = None self.lidar2cam_rect = np.eye(4) def __len__(self): 'Denotes the total number of samples' return len(self.selected_indices) def get_depth_from_lidar(self, lidar_path, lidar2img, image_size): # lidar2img: N, 4, 4 scan = np.fromfile(lidar_path, dtype=np.float32) scan = scan.reshape((-1, 5))[:, :4] scan[:, 3] = 1.0 # points_hcoords = scan[scan[:, 0] > 0, :] points_hcoords = np.expand_dims(scan.T, 0) # 1, 4, n img_points = np.transpose(lidar2img @ points_hcoords, (0, 2, 1)) # N, n, 4 depth = img_points[..., 2] # N, n img_points = img_points[..., :2] # N, n, 2 mask = (depth < self.eval_depth) & (depth > 1e-3) # get points with depth < max_sample_depth img_points = img_points / np.expand_dims(depth, axis=2) # scale 2D points img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] # img_points = np.round(img_points).astype(int) mask = mask & (img_points[..., 0] > 0) & \ (img_points[..., 1] > 0) & \ (img_points[..., 0] < 1) & \ (img_points[..., 1] < 1) return img_points, depth, mask def __getitem__(self, index): #### 2. get self, prev, next infos for the stem, and also temp_depth info while True: index = self.selected_indices[index] info = deepcopy(self.nusc_infos[index]) if len(info['nice_neighbor_prev']) == 0 and len(info['nice_neighbor_next']) ==0: index = np.random.randint(len(self)) continue break #### 3. prepare img_metas imgs_info = self.get_data_info(info) img_metas = { 'input_imgs_path': imgs_info['img_filename'], 'lidar2img': imgs_info['lidar2img'], 'img2lidar': imgs_info['img2lidar'], 'ego2lidar': imgs_info['ego2lidar'], 'token': info['token'], 'timestamp': info['timestamp'], 'intrinsic': imgs_info['cam_intrinsic']} anchor_imgs = [] # list[list[array]] anchor_depth_locs = [] # list[array] anchor_depth_gts = [] # list[array] anchor_depth_masks = [] # list[array] temImg2lidars = [] # list[list[array]] anchor_frame_dist = \ info['prev_dists'] + info['next_dists'] # list[float] for anchor in info['nice_neighbor_prev'] + info['nice_neighbor_next']: anchor_info = deepcopy(self.nusc_infos[anchor]) anchor_imgs_info = self.get_data_info_temporal(info, anchor_info) anchor_img_path = anchor_imgs_info['image_paths'] temImg2lidar = anchor_imgs_info['temImg2lidar'] anchor_img = self.read_surround_imgs(anchor_img_path) depth_loc, depth_gt, depth_mask = self.get_depth_from_lidar( anchor_info['lidar_path'], img_metas['lidar2img'], self.crop_size) anchor_imgs.append(anchor_img) anchor_depth_locs.append(depth_loc) anchor_depth_gts.append(depth_gt) anchor_depth_masks.append(depth_mask) temImg2lidars.append(temImg2lidar) img_metas.update({ 'depth_loc': anchor_depth_locs, 'depth_gt': anchor_depth_gts, 'depth_mask': anchor_depth_masks, 'temImg2lidars': temImg2lidars, 'frame_dists': anchor_frame_dist }) #### 4. read imgs input_imgs = self.read_surround_imgs(img_metas['input_imgs_path']) data_tuple = (input_imgs, anchor_imgs, img_metas) return data_tuple def read_surround_imgs(self, img_paths): if hfai: imgs = self.img_loader.load(img_paths) else: imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:self.crop_size[0], :self.crop_size[1], :] for img in imgs] return imgs def get_data_info_temporal(self, info, info_tem): image_paths = [] temImg2lidars = [] lidar2ego_r = Quaternion(info['lidar2ego_rotation']).rotation_matrix lidar2ego = np.eye(4) lidar2ego[:3, :3] = lidar2ego_r lidar2ego[:3, 3] = np.array(info['lidar2ego_translation']).T ego2global_r = Quaternion(info['ego2global_rotation']).rotation_matrix ego2global = np.eye(4) ego2global[:3, :3] = ego2global_r ego2global[:3, 3] = np.array(info['ego2global_translation']).T lidar2global = ego2global @ lidar2ego global2lidar = np.linalg.inv(lidar2global) for cam_type_tem, cam_info_tem in info_tem['cams'].items(): image_paths.append(cam_info_tem['data_path']) temIntrinsic = cam_info_tem['cam_intrinsic'] temImg2temSensor = np.eye(4) temImg2temSensor[:3, :3] = temIntrinsic temImg2temSensor = np.linalg.inv(temImg2temSensor) temSensor2temEgo_r = Quaternion(cam_info_tem['sensor2ego_rotation']).rotation_matrix temSensor2temEgo = np.eye(4) temSensor2temEgo[:3, :3] = temSensor2temEgo_r temSensor2temEgo[:3, 3] = np.array(cam_info_tem['sensor2ego_translation']).T temEgo2global_r = Quaternion(cam_info_tem['ego2global_rotation']).rotation_matrix temEgo2global = np.eye(4) temEgo2global[:3, :3] = temEgo2global_r temEgo2global[:3, 3] = np.array(cam_info_tem['ego2global_translation']).T temImg2global = temEgo2global @ temSensor2temEgo @ temImg2temSensor temImg2lidar = global2lidar @ temImg2global temImg2lidars.append(temImg2lidar) out_dict = dict( image_paths=image_paths, temImg2lidar=np.asarray(temImg2lidars) ) return out_dict def get_data_info(self, info): image_paths = [] lidar2img_rts = [] img2lidar_rts = [] cam_intrinsics = [] lidar2ego_r = Quaternion(info['lidar2ego_rotation']).rotation_matrix lidar2ego = np.eye(4) lidar2ego[:3, :3] = lidar2ego_r lidar2ego[:3, 3] = np.array(info['lidar2ego_translation']).T ego2lidar = np.linalg.inv(lidar2ego) for cam_type, cam_info in info['cams'].items(): image_paths.append(cam_info['data_path']) lidar2cam_r = np.linalg.inv(cam_info['sensor2lidar_rotation']) lidar2cam_t = cam_info['sensor2lidar_translation'] @ lidar2cam_r.T lidar2cam_rt = np.eye(4) lidar2cam_rt[:3, :3] = lidar2cam_r.T lidar2cam_rt[3, :3] = -lidar2cam_t intrinsic = cam_info['cam_intrinsic'] viewpad = np.eye(4) viewpad[:intrinsic.shape[0], :intrinsic.shape[1]] = intrinsic lidar2img_rt = (viewpad @ lidar2cam_rt.T) img2lidar_rt = np.linalg.inv(lidar2img_rt) cam_intrinsics.append(viewpad) lidar2img_rts.append(lidar2img_rt) img2lidar_rts.append(img2lidar_rt) input_dict =dict( img_filename=image_paths, lidar2img=np.asarray(lidar2img_rts), img2lidar=np.asarray(img2lidar_rts), cam_intrinsic=np.asarray(cam_intrinsics), ego2lidar=ego2lidar) return input_dict ================================================ FILE: dataset/dataset_one_frame_sweeps_dist.py ================================================ import os, numpy as np, random, mmengine, math from mmcv.image.io import imread from pyquaternion import Quaternion from copy import deepcopy from . import OPENOCC_DATASET if 'HFAI' in os.environ: hfai = True from dataset.loading import LoadMultiViewImageFromFilesHF, \ LoadPtsFromFilesHF else: hfai = False def get_xyz(pose_dict): return np.array(pose_dict['translation']) def get_img2global(calib_dict, pose_dict): cam2img = np.eye(4) cam2img[:3, :3] = np.asarray(calib_dict['camera_intrinsic']) img2cam = np.linalg.inv(cam2img) cam2ego = np.eye(4) cam2ego[:3, :3] = Quaternion(calib_dict['rotation']).rotation_matrix cam2ego[:3, 3] = np.asarray(calib_dict['translation']).T ego2global = np.eye(4) ego2global[:3, :3] = Quaternion(pose_dict['rotation']).rotation_matrix ego2global[:3, 3] = np.asarray(pose_dict['translation']).T img2global = ego2global @ cam2ego @ img2cam return img2global def get_lidar2global(calib_dict, pose_dict): lidar2ego = np.eye(4) lidar2ego[:3, :3] = Quaternion(calib_dict['rotation']).rotation_matrix lidar2ego[:3, 3] = np.asarray(calib_dict['translation']).T ego2global = np.eye(4) ego2global[:3, :3] = Quaternion(pose_dict['rotation']).rotation_matrix ego2global[:3, 3] = np.asarray(pose_dict['translation']).T lidar2global = ego2global @ lidar2ego return lidar2global @OPENOCC_DATASET.register_module() class nuScenes_One_Frame_Sweeps_Dist: def __init__( self, data_path, imageset='train', crop_size=[768, 1600], input_img_crop_size=None, min_dist=0.4, max_dist=10.0, strict=True, return_depth=False, eval_depth=80, cur_prob=1.0, prev_prob=0.5, choose_nearest=False, ref_sensor='CAM_FRONT', composite_prev_next=False, sensor_mus=[3.0, 0.5], sensor_sigma=0.5, ego_centric=False, **kwargs): data = mmengine.load(imageset) self.scene_infos = data['infos'] self.keyframes = data['metadata'] self.data_path = data_path self.crop_size = crop_size self.input_img_crop_size = crop_size if input_img_crop_size is None else input_img_crop_size self.strict = strict self.return_depth = return_depth self.eval_depth = eval_depth self.cur_prob = cur_prob self.prev_prob = prev_prob self.choose_nearest = choose_nearest self.composite_prev_next = composite_prev_next self.sensor_mus = { 'CAM_FRONT': sensor_mus[0], 'CAM_FRONT_RIGHT': sensor_mus[1], 'CAM_FRONT_LEFT': sensor_mus[1], 'CAM_BACK': sensor_mus[0], 'CAM_BACK_LEFT': sensor_mus[1], 'CAM_BACK_RIGHT': sensor_mus[1] } self.sensor_sigma = sensor_sigma self.sensor_types = ['CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'] self.ego_centric = ego_centric if hfai: self.img_loader = LoadMultiViewImageFromFilesHF( to_float32=True, file_client_args=dict( backend='ffrecord', fname=data_path+'CAM', filename2idx='img_fname2idx.pkl')) self.pts_loader = LoadPtsFromFilesHF( to_float32=True, file_client_args=dict( backend='ffrecord', fname=data_path+'LIDAR', filename2idx='pts_fname2idx.pkl')) else: self.img_loader = self.pts_loader = None self.lidar2cam_rect = np.eye(4) #### collect temporal information for scene_idx, (scene_token, scene_samples) in enumerate(self.scene_infos.items()): if (scene_idx + 1) % 50 == 0: print(f'One_Frame_Sweeps: processed {scene_idx + 1} scenes.') length = len(scene_samples) for i, sample in enumerate(scene_samples): curr_xyz = get_xyz(sample['data'][ref_sensor]['pose']) prev_samples, next_samples = [], [] prev_dists, next_dists = [], [] for j in range(i - 1, -1, -1): temp_xyz = get_xyz(scene_samples[j]['data'][ref_sensor]['pose']) temp_dist = np.linalg.norm(curr_xyz - temp_xyz) if temp_dist > max_dist: break if temp_dist > min_dist: prev_samples.append((scene_token, j)) prev_dists.append(temp_dist) for j in range(i + 1, length, 1): temp_xyz = get_xyz(scene_samples[j]['data'][ref_sensor]['pose']) temp_dist = np.linalg.norm(curr_xyz - temp_xyz) if temp_dist > max_dist: break if temp_dist > min_dist: next_samples.append((scene_token, j)) next_dists.append(temp_dist) if not strict: prev_samples.append((scene_token, i)) prev_dists.append(0.) next_samples.append((scene_token, i)) next_dists.append(0.) sample.update({ 'prev_samples': prev_samples, 'prev_dists': prev_dists, 'next_samples': next_samples, 'next_dists': next_dists,}) def __len__(self): 'Denotes the total number of samples' # return len(self.nusc_infos) return len(self.keyframes) def get_depth_from_lidar(self, lidar_path, lidar2img, image_size): # lidar2img: N, 4, 4 scan = np.fromfile(os.path.join(self.data_path, lidar_path), dtype=np.float32) scan = scan.reshape((-1, 5))[:, :4] scan[:, 3] = 1.0 # points_hcoords = scan[scan[:, 0] > 0, :] points_hcoords = np.expand_dims(scan.T, 0) # 1, 4, n img_points = np.transpose(lidar2img @ points_hcoords, (0, 2, 1)) # N, n, 4 depth = img_points[..., 2] # N, n img_points = img_points[..., :2] # N, n, 2 # mask = (depth < self.eval_depth) & (depth > 1e-3) # get points with depth < max_sample_depth mask = (depth < self.eval_depth) & (depth > 1.0) # get points with depth < max_sample_depth img_points = img_points / np.expand_dims(depth, axis=2) # scale 2D points img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] # img_points = np.round(img_points).astype(int) mask = mask & (img_points[..., 0] > 0) & \ (img_points[..., 1] > 0) & \ (img_points[..., 0] < 1) & \ (img_points[..., 1] < 1) return img_points, depth, mask def composite_dict(self, anchor_info): datas = [] for prefix in ['prev_', 'next_']: data = dict() dists = np.asarray(anchor_info[prefix + 'dists']) for sensor_type in self.sensor_types: mu = self.sensor_mus[sensor_type] sigma = self.sensor_sigma probs = 1 / math.sqrt(2 * math.pi) / sigma * np.exp(-1 / (2 * sigma * sigma) * ((dists - mu) ** 2)) probs = probs / np.sum(probs) idx = np.random.choice(len(dists), p=probs) scene_token, sample_idx = anchor_info[prefix + 'samples'][idx] data.update({sensor_type: self.scene_infos[scene_token][sample_idx]['data'][sensor_type]}) datas.append(data) return {'data': datas[0]}, {'data': datas[1]} def __getitem__(self, index): #### 1. get color, temporal_depth choice if necessary if random.random() < self.cur_prob: temporal_supervision = 'curr' elif random.random() < self.prev_prob: temporal_supervision = 'prev' else: temporal_supervision = 'next' #### 2. get self, prev, next infos for the stem, and also temp_depth info while True: scene_token, index = self.keyframes[index] info = deepcopy(self.scene_infos[scene_token][index]) if temporal_supervision == 'curr': anchor_info = deepcopy(info) elif temporal_supervision == 'prev': if len(info['prev_samples']) == 0: index = np.random.randint(len(self)) continue anchor_scene_token, anchor_info_id = info['prev_samples'][np.random.randint(len(info['prev_samples']))] # anchor_scene_token, anchor_info_id = np.random.choice(info['prev_samples']) assert anchor_scene_token == scene_token and anchor_info_id <= index anchor_info = deepcopy(self.scene_infos[scene_token][anchor_info_id]) else: if len(info['next_samples']) == 0: index = np.random.randint(len(self)) continue anchor_scene_token, anchor_info_id = info['next_samples'][np.random.randint(len(info['next_samples']))] # anchor_scene_token, anchor_info_id = np.random.choice(info['next_samples']) assert anchor_scene_token == scene_token and anchor_info_id >= index anchor_info = deepcopy(self.scene_infos[scene_token][anchor_info_id]) if len(anchor_info['prev_samples']) == 0 or \ len(anchor_info['next_samples']) == 0: index = np.random.randint(len(self)) continue if self.composite_prev_next: anchor_prev, anchor_next = self.composite_dict(anchor_info) else: if self.choose_nearest: anchor_prev_scene_token, anchor_prev_idx = anchor_info['prev_samples'][0] anchor_next_scene_token, anchor_next_idx = anchor_info['next_samples'][0] else: anchor_prev_scene_token, anchor_prev_idx = anchor_info['prev_samples'][np.random.randint(len(anchor_info['prev_samples']))] anchor_next_scene_token, anchor_next_idx = anchor_info['next_samples'][np.random.randint(len(anchor_info['next_samples']))] # anchor_prev_scene_token, anchor_prev_idx = np.random.choice(anchor_info['prev_samples']) # anchor_next_scene_token, anchor_next_idx = np.random.choice(anchor_info['next_samples']) assert anchor_prev_scene_token == scene_token and \ anchor_next_scene_token == scene_token anchor_prev = deepcopy(self.scene_infos[scene_token][anchor_prev_idx]) anchor_next = deepcopy(self.scene_infos[scene_token][anchor_next_idx]) break #### 3. prepare img_metas imgs_info = self.get_data_info(info) anchor_dict = self.get_data_info_anchor(info, anchor_info) prev_dict = self.get_data_info_temporal(anchor_info, anchor_prev) next_dict = self.get_data_info_temporal(anchor_info, anchor_next) img_metas = { 'input_imgs_path': imgs_info['img_filename'], 'curr_imgs_path': anchor_dict['image_paths'], 'prev_imgs_path': prev_dict['image_paths'], 'next_imgs_path': next_dict['image_paths'], 'lidar2img': imgs_info['lidar2img'], 'img2lidar': imgs_info['img2lidar'], 'intrinsic': imgs_info['cam_intrinsic'], 'cam2ego': imgs_info['cam2ego'], 'temImg2lidar': anchor_dict['temImg2lidar'], 'ego2lidar': imgs_info['ego2lidar'], 'token': info['token'], 'timestamp': info['timestamp'], 'img2prevImg': prev_dict['img2temImg'], 'img2nextImg': next_dict['img2temImg'],} if self.return_depth: depth_loc, depth_gt, depth_mask = self.get_depth_from_lidar( info['data']['LIDAR_TOP']['filename'], img_metas['lidar2img'], self.crop_size) img_metas.update({ 'depth_loc': depth_loc, 'depth_gt': depth_gt, 'depth_mask': depth_mask}) if self.ego_centric: ego2lidar = img_metas['ego2lidar'] lidar2ego = np.linalg.inv(ego2lidar) ego2img = img_metas['lidar2img'] @ ego2lidar[None, ...] img2ego = lidar2ego[None, ...] @ img_metas['img2lidar'] temImg2ego = lidar2ego[None, ...] @ img_metas['temImg2lidar'] img_metas.update({ 'lidar2img': ego2img, 'img2lidar': img2ego, 'temImg2lidar': temImg2ego, 'ego2lidar': np.eye(4)}) #### 4. read imgs input_imgs = self.read_surround_imgs(img_metas['input_imgs_path'], self.input_img_crop_size) curr_imgs = self.read_surround_imgs(img_metas['curr_imgs_path'], self.crop_size) prev_imgs = self.read_surround_imgs(img_metas['prev_imgs_path'], self.crop_size) next_imgs = self.read_surround_imgs(img_metas['next_imgs_path'], self.crop_size) data_tuple = ( [input_imgs, curr_imgs, prev_imgs, next_imgs], img_metas) return data_tuple def read_surround_imgs(self, img_paths, crop_size): if hfai: imgs = self.img_loader.load(img_paths) else: imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:crop_size[0], :crop_size[1], :] for img in imgs] return imgs def get_data_info_temporal(self, info, info_tem): image_paths = [] img2temImgs = [] for cam_type in self.sensor_types: cam_info_tem = info_tem['data'][cam_type] cam_info = info['data'][cam_type] image_paths.append(os.path.join(self.data_path, cam_info_tem['filename'])) temImg2global = get_img2global(cam_info_tem['calib'], cam_info_tem['pose']) img2global = get_img2global(cam_info['calib'], cam_info['pose']) img2temImg = np.linalg.inv(temImg2global) @ img2global img2temImgs.append(img2temImg) out_dict = dict( image_paths=image_paths, img2temImg=np.asarray(img2temImgs)) return out_dict def get_data_info_anchor(self, info, info_tem): image_paths = [] temImg2lidars = [] lidar2global = get_lidar2global(info['data']['LIDAR_TOP']['calib'], info['data']['LIDAR_TOP']['pose']) for cam_type in self.sensor_types: cam_info_tem = info_tem['data'][cam_type] image_paths.append(os.path.join(self.data_path, cam_info_tem['filename'])) temImg2global = get_img2global(cam_info_tem['calib'], cam_info_tem['pose']) temImg2lidar = np.linalg.inv(lidar2global) @ temImg2global temImg2lidars.append(temImg2lidar) out_dict = dict( image_paths=image_paths, temImg2lidar=np.asarray(temImg2lidars)) return out_dict def get_data_info(self, info): image_paths = [] lidar2img_rts = [] img2lidar_rts = [] cam_intrinsics = [] cam2ego_rts = [] lidar2ego_r = Quaternion(info['data']['LIDAR_TOP']['calib']['rotation']).rotation_matrix lidar2ego = np.eye(4) lidar2ego[:3, :3] = lidar2ego_r lidar2ego[:3, 3] = np.array(info['data']['LIDAR_TOP']['calib']['translation']).T ego2lidar = np.linalg.inv(lidar2ego) lidar2global = get_lidar2global(info['data']['LIDAR_TOP']['calib'], info['data']['LIDAR_TOP']['pose']) for cam_type in self.sensor_types: image_paths.append(os.path.join(self.data_path, info['data'][cam_type]['filename'])) img2global = get_img2global(info['data'][cam_type]['calib'], info['data'][cam_type]['pose']) lidar2img = np.linalg.inv(img2global) @ lidar2global img2lidar = np.linalg.inv(lidar2global) @ img2global cam2ego_r = Quaternion(info['data'][cam_type]['calib']['rotation']).rotation_matrix cam2ego = np.eye(4) cam2ego[:3, :3] = cam2ego_r cam2ego[:3, 3] = np.array(info['data'][cam_type]['calib']['translation']).T intrinsic = info['data'][cam_type]['calib']['camera_intrinsic'] viewpad = np.eye(4) viewpad[:3, :3] = intrinsic lidar2img_rts.append(lidar2img) img2lidar_rts.append(img2lidar) cam_intrinsics.append(viewpad) cam2ego_rts.append(cam2ego) input_dict =dict( img_filename=image_paths, lidar2img=np.asarray(lidar2img_rts), img2lidar=np.asarray(img2lidar_rts), cam_intrinsic=np.asarray(cam_intrinsics), ego2lidar=ego2lidar, cam2ego=np.asarray(cam2ego_rts)) return input_dict # def get_data_info(self, info): # image_paths = [] # lidar2img_rts = [] # img2lidar_rts = [] # cam_intrinsics = [] # cam2ego_rts = [] # lidar2ego_r = Quaternion(info['data']['LIDAR_TOP']['calib']['rotation']).rotation_matrix # lidar2ego = np.eye(4) # lidar2ego[:3, :3] = lidar2ego_r # lidar2ego[:3, 3] = np.array(info['data']['LIDAR_TOP']['calib']['translation']).T # ego2lidar = np.linalg.inv(lidar2ego) # for cam_type in self.sensor_types: # image_paths.append(os.path.join(self.data_path, info['data'][cam_type]['filename'])) # cam2ego_r = Quaternion(info['data'][cam_type]['calib']['rotation']).rotation_matrix # cam2ego = np.eye(4) # cam2ego[:3, :3] = cam2ego_r # cam2ego[:3, 3] = np.array(info['data'][cam_type]['calib']['translation']).T # ego2cam = np.linalg.inv(cam2ego) # lidar2cam = ego2cam @ lidar2ego # intrinsic = info['data'][cam_type]['calib']['camera_intrinsic'] # viewpad = np.eye(4) # viewpad[:3, :3] = intrinsic # lidar2img = viewpad @ lidar2cam # img2lidar = np.linalg.inv(lidar2img) # lidar2img_rts.append(lidar2img) # img2lidar_rts.append(img2lidar) # cam_intrinsics.append(viewpad) # cam2ego_rts.append(cam2ego) # input_dict =dict( # img_filename=image_paths, # lidar2img=np.asarray(lidar2img_rts), # img2lidar=np.asarray(img2lidar_rts), # cam_intrinsic=np.asarray(cam_intrinsics), # ego2lidar=ego2lidar, # cam2ego=np.asarray(cam2ego_rts)) # return input_dict if __name__ == '__main__': import torch dataset = nuScenes_One_Frame_Sweeps( 'data/nuscenes', 'data/nuscenes_infos_val_temporal.pkl', ) batch = dataset[300] imgs, img_metas, points = batch curr_imgs, prev_imgs, next_imgs = imgs def list2tensor(imgs): imgs = np.asarray(imgs) imgs = torch.from_numpy(imgs) return imgs curr_imgs = list2tensor(curr_imgs) prev_imgs = list2tensor(prev_imgs) next_imgs = list2tensor(next_imgs) print('img metas contains keys: ', list(img_metas.keys())) l2i = list2tensor(img_metas['lidar2img']) img2prevImg = list2tensor(img_metas['img2prevImg']) img2nextImg = list2tensor(img_metas['img2nextImg']) def get_diagonal(trans): trans = trans.reshape(6, 6, 4, 4) trans = torch.diagonal(trans, 0, 0, 1) trans = trans.permute(2, 0, 1) return trans img2prevImg = get_diagonal(img2prevImg) img2nextImg = get_diagonal(img2nextImg) def filter_invalid_pts(pts): mask = torch.norm(pts, dim=-1) mask = mask > 2 pts = pts[mask] return pts points = torch.from_numpy(points) points = filter_invalid_pts(points) points = torch.cat([ points, torch.ones_like(points[:, :1]) ], dim=1) curr_pts_uv = torch.matmul( l2i.unsqueeze(1).to(points.dtype), points.reshape(1, -1, 4, 1)) prev_pts_uv = torch.matmul( img2prevImg.unsqueeze(1).to(points.dtype), curr_pts_uv) next_pts_uv = torch.matmul( img2nextImg.unsqueeze(1).to(points.dtype), curr_pts_uv) curr_pts_uv = curr_pts_uv.squeeze(-1) prev_pts_uv = prev_pts_uv.squeeze(-1) next_pts_uv = next_pts_uv.squeeze(-1) def get_pixel(uv, eps=1e-5): uv = uv[..., :2] / torch.maximum( uv[..., 2:3], torch.ones_like(uv[..., :1] * eps)) return uv curr_pts_uv = get_pixel(curr_pts_uv) prev_pts_uv = get_pixel(prev_pts_uv) next_pts_uv = get_pixel(next_pts_uv) mask1 = torch.logical_and( curr_pts_uv[0, :, 0] > 600, curr_pts_uv[0, :, 0] < 1000) mask2 = torch.logical_and( curr_pts_uv[0, :, 1] > 400, curr_pts_uv[0, :, 1] < 500) mask = torch.logical_and(mask1, mask2) curr_pts_uv = curr_pts_uv[:, mask, :] prev_pts_uv = prev_pts_uv[:, mask, :] next_pts_uv = next_pts_uv[:, mask, :] def filter_invalid_points(uv): uv[..., 0] = torch.clamp(uv[..., 0], 0, 1600) uv[..., 1] = torch.clamp(uv[..., 1], 0, 900) return uv curr_pts_uv = filter_invalid_points(curr_pts_uv) prev_pts_uv = filter_invalid_points(prev_pts_uv) next_pts_uv = filter_invalid_points(next_pts_uv) import matplotlib.pyplot as plt def saveimg(i, imgs, uv, name): plt.imshow(imgs[i] / 256) plt.scatter(uv[i][:, 0], uv[i][:, 1], s=2) plt.savefig(f'{name}_{i}.jpg') plt.cla() for i in range(6): saveimg(i, curr_imgs, curr_pts_uv, 'curr') saveimg(i, prev_imgs, prev_pts_uv, 'prev') saveimg(i, next_imgs, next_pts_uv, 'next') pass ================================================ FILE: dataset/dataset_one_frame_sweeps_dist_vis.py ================================================ import os, numpy as np, random, mmengine, math from mmcv.image.io import imread from pyquaternion import Quaternion from copy import deepcopy from . import OPENOCC_DATASET def get_xyz(pose_dict): return np.array(pose_dict['translation']) def get_img2global(calib_dict, pose_dict): cam2img = np.eye(4) cam2img[:3, :3] = np.asarray(calib_dict['camera_intrinsic']) img2cam = np.linalg.inv(cam2img) cam2ego = np.eye(4) cam2ego[:3, :3] = Quaternion(calib_dict['rotation']).rotation_matrix cam2ego[:3, 3] = np.asarray(calib_dict['translation']).T ego2global = np.eye(4) ego2global[:3, :3] = Quaternion(pose_dict['rotation']).rotation_matrix ego2global[:3, 3] = np.asarray(pose_dict['translation']).T img2global = ego2global @ cam2ego @ img2cam return img2global def get_lidar2global(calib_dict, pose_dict): lidar2ego = np.eye(4) lidar2ego[:3, :3] = Quaternion(calib_dict['rotation']).rotation_matrix lidar2ego[:3, 3] = np.asarray(calib_dict['translation']).T ego2global = np.eye(4) ego2global[:3, :3] = Quaternion(pose_dict['rotation']).rotation_matrix ego2global[:3, 3] = np.asarray(pose_dict['translation']).T lidar2global = ego2global @ lidar2ego return lidar2global @OPENOCC_DATASET.register_module() class nuScenes_One_Frame_Sweeps_Dist_Vis: def __init__( self, data_path, imageset='train', crop_size=[768, 1600], return_depth=False, eval_depth=80, ego_centric=False, scene_token=None, **kwargs): assert scene_token is not None data = mmengine.load(imageset) self.scene_infos = data['infos'] assert scene_token in self.scene_infos self.infos = self.scene_infos[scene_token] self.data_path = data_path self.crop_size = crop_size self.return_depth = return_depth self.eval_depth = eval_depth self.sensor_types = ['CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'] self.ego_centric = ego_centric self.img_loader = self.pts_loader = None self.lidar2cam_rect = np.eye(4) def __len__(self): 'Denotes the total number of samples' return len(self.infos) def __getitem__(self, index): info = deepcopy(self.infos[index]) imgs_info = self.get_data_info(info) img_metas = { 'input_imgs_path': imgs_info['img_filename'], 'lidar2img': imgs_info['lidar2img'], 'img2lidar': imgs_info['img2lidar'], 'intrinsic': imgs_info['cam_intrinsic'], 'cam2ego': imgs_info['cam2ego'], 'ego2lidar': imgs_info['ego2lidar'], # 'token': info['token'], 'timestamp': info['timestamp'], 'cam_positions': imgs_info['cam_positions'], 'focal_positions': imgs_info['focal_positions'] } if self.ego_centric: ego2lidar = img_metas['ego2lidar'] lidar2ego = np.linalg.inv(ego2lidar) ego2img = img_metas['lidar2img'] @ ego2lidar[None, ...] img2ego = lidar2ego[None, ...] @ img_metas['img2lidar'] img_metas.update({ 'lidar2img': ego2img, 'img2lidar': img2ego}) #### 4. read imgs input_imgs = self.read_surround_imgs(img_metas['input_imgs_path']) data_tuple = (input_imgs, img_metas) return data_tuple def read_surround_imgs(self, img_paths): imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:self.crop_size[0], :self.crop_size[1], :] for img in imgs] return imgs def get_data_info(self, info): f = 0.0055 image_paths = [] lidar2img_rts = [] img2lidar_rts = [] cam_intrinsics = [] cam2ego_rts = [] cam_positions = [] focal_positions = [] lidar2ego_r = Quaternion(info['data']['LIDAR_TOP']['calib']['rotation']).rotation_matrix lidar2ego = np.eye(4) lidar2ego[:3, :3] = lidar2ego_r lidar2ego[:3, 3] = np.array(info['data']['LIDAR_TOP']['calib']['translation']).T ego2lidar = np.linalg.inv(lidar2ego) lidar2global = get_lidar2global(info['data']['LIDAR_TOP']['calib'], info['data']['LIDAR_TOP']['pose']) for cam_type in self.sensor_types: image_paths.append(os.path.join(self.data_path, info['data'][cam_type]['filename'])) img2global = get_img2global(info['data'][cam_type]['calib'], info['data'][cam_type]['pose']) lidar2img = np.linalg.inv(img2global) @ lidar2global img2lidar = np.linalg.inv(lidar2global) @ img2global cam2ego_r = Quaternion(info['data'][cam_type]['calib']['rotation']).rotation_matrix cam2ego = np.eye(4) cam2ego[:3, :3] = cam2ego_r cam2ego[:3, 3] = np.array(info['data'][cam_type]['calib']['translation']).T intrinsic = info['data'][cam_type]['calib']['camera_intrinsic'] viewpad = np.eye(4) viewpad[:3, :3] = intrinsic cam_position = img2lidar @ viewpad @ np.array([0., 0., 0., 1.]).reshape([4, 1]) cam_positions.append(cam_position.flatten()[:3]) focal_position = img2lidar @ viewpad @ np.array([0., 0., f, 1.]).reshape([4, 1]) focal_positions.append(focal_position.flatten()[:3]) lidar2img_rts.append(lidar2img) img2lidar_rts.append(img2lidar) cam_intrinsics.append(viewpad) cam2ego_rts.append(cam2ego) input_dict =dict( img_filename=image_paths, lidar2img=np.asarray(lidar2img_rts), img2lidar=np.asarray(img2lidar_rts), cam_intrinsic=np.asarray(cam_intrinsics), ego2lidar=ego2lidar, cam2ego=np.asarray(cam2ego_rts), cam_positions=cam_positions, focal_positions=focal_positions) return input_dict ================================================ FILE: dataset/dataset_wrapper_temporal.py ================================================ import numpy as np, torch from torch.utils import data from dataset.transform_3d import PadMultiViewImage, NormalizeMultiviewImage, \ PhotoMetricDistortionMultiViewImage, RandomScaleImageMultiViewImage, \ RandomFlip import torch.nn.functional as F from copy import deepcopy from mmengine import MMLogger logger = MMLogger.get_instance('selfocc') from . import OPENOCC_DATAWRAPPER # img_norm_cfg = dict( # mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) @OPENOCC_DATAWRAPPER.register_module() class tpvformer_dataset_nuscenes_temporal(data.Dataset): def __init__( self, in_dataset, phase='train', scale_rate=1, photometric_aug=dict( use_swap_channel=False, ), use_temporal_aug=False, temporal_aug_list=[], img_norm_cfg=img_norm_cfg, supervision_img_size=None, supervision_scale_rate=None, use_flip=False, ref_focal_len=None, pad_img_size=None, random_scale=None, pad_scale_rate=None, ): 'Initialization' self.point_cloud_dataset = in_dataset self.scale_rate = scale_rate self.use_temporal_aug = use_temporal_aug if use_temporal_aug: assert len(temporal_aug_list) > 0 self.temporal_aug_list = temporal_aug_list photometric = PhotoMetricDistortionMultiViewImage(**photometric_aug) logger.info('using photometric augmentation: '+ str(photometric_aug)) train_transforms = [ photometric, NormalizeMultiviewImage(**img_norm_cfg), PadMultiViewImage(size_divisor=32, size=pad_img_size) ] val_transforms = [ NormalizeMultiviewImage(**img_norm_cfg), PadMultiViewImage(size_divisor=32, size=pad_img_size) ] if scale_rate != 1 or ref_focal_len is not None or random_scale is not None or pad_scale_rate is not None: train_transforms.insert(2, RandomScaleImageMultiViewImage([scale_rate], ref_focal_len, random_scale, pad_scale_rate)) val_transforms.insert(1, RandomScaleImageMultiViewImage([scale_rate], ref_focal_len, pad_scale_rate=pad_scale_rate)) if use_flip: train_transforms.append(RandomFlip(0.5)) if phase == 'train': self.transforms = train_transforms else: self.transforms = val_transforms if use_temporal_aug: self.temporal_transforms = [ NormalizeMultiviewImage(**img_norm_cfg), PadMultiViewImage(size_divisor=4)] if supervision_scale_rate != 1: self.temporal_transforms.insert(1, RandomScaleImageMultiViewImage([supervision_scale_rate])) self.supervision_img_size = supervision_img_size def __len__(self): return len(self.point_cloud_dataset) def to_tensor(self, imgs): imgs = np.stack(imgs).astype(np.float32) imgs = torch.from_numpy(imgs) imgs = imgs.permute(0, 3, 1, 2) return imgs def __getitem__(self, index): data = self.point_cloud_dataset[index] if len(data) == 2: return self.deal_with_length2_dataset(data) elif len(data) == 3: return self.deal_with_length3_dataset(data) def deal_with_length3_dataset(self, data): input_imgs, anchor_imgs, img_metas = data img_metas['img_shape'] = input_imgs[0].shape[:2] # deal with img augmentations input_imgs, imgs_dict = forward_aug(input_imgs, img_metas, self.transforms) input_imgs = self.to_tensor(input_imgs) # img_metas['img_shape'] = imgs_dict['img_shape'] img_metas['scale_rate'] = self.scale_rate # if 'focal_ratios' in imgs_dict: # img_metas['focal_ratios'] = imgs_dict['focal_ratios'] if 'focal_ratios_x' in imgs_dict: img_metas['focal_ratios_x'] = imgs_dict['focal_ratios_x'] if 'focal_ratios_y' in imgs_dict: img_metas['focal_ratios_y'] = imgs_dict['focal_ratios_y'] img_metas['flip'] = imgs_dict.get('flip', False) anchor_imgs = np.asarray(anchor_imgs).astype(np.float32) anchor_imgs = torch.from_numpy(anchor_imgs) anchor_imgs = anchor_imgs.permute(0, 1, 4, 2, 3) data_tuple = (input_imgs, anchor_imgs / 256., img_metas) return data_tuple def deal_with_length2_dataset(self, data): imgs, img_metas = data if len(imgs) == 4: input_imgs, curr_imgs, prev_imgs, next_imgs = imgs color_imgs = deepcopy(curr_imgs) elif len(imgs) == 3: curr_imgs, prev_imgs, next_imgs = imgs input_imgs = deepcopy(curr_imgs) color_imgs = deepcopy(curr_imgs) elif len(imgs) == 5: input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs = imgs img_metas['img_shape'] = input_imgs[0].shape[:2] # deal with img augmentations input_imgs, imgs_dict = forward_aug(input_imgs, img_metas, self.transforms) input_imgs = self.to_tensor(input_imgs) curr_aug = prev_aug = next_aug = None if 'curr_imgs' in self.temporal_aug_list: curr_aug, _ = forward_aug(curr_imgs, {}, self.temporal_transforms) curr_aug = self.to_tensor(curr_aug) if 'prev_imgs' in self.temporal_aug_list: prev_aug, _ = forward_aug(prev_imgs, {}, self.temporal_transforms) prev_aug = self.to_tensor(prev_aug) if 'next_imgs' in self.temporal_aug_list: next_aug, _ = forward_aug(next_imgs, {}, self.temporal_transforms) next_aug = self.to_tensor(next_aug) curr_imgs = self.to_tensor(curr_imgs) prev_imgs = self.to_tensor(prev_imgs) next_imgs = self.to_tensor(next_imgs) color_imgs = self.to_tensor(color_imgs) if self.supervision_img_size is not None: curr_imgs = F.interpolate(curr_imgs, size=self.supervision_img_size, mode='bilinear', align_corners=True) prev_imgs = F.interpolate(prev_imgs, size=self.supervision_img_size, mode='bilinear', align_corners=True) next_imgs = F.interpolate(next_imgs, size=self.supervision_img_size, mode='bilinear', align_corners=True) # img_metas['img_shape'] = imgs_dict['img_shape'] img_metas['scale_rate'] = self.scale_rate # if 'focal_ratios' in imgs_dict: # img_metas['focal_ratios'] = imgs_dict['focal_ratios'] if 'focal_ratios_x' in imgs_dict: img_metas['focal_ratios_x'] = imgs_dict['focal_ratios_x'] if 'focal_ratios_y' in imgs_dict: img_metas['focal_ratios_y'] = imgs_dict['focal_ratios_y'] img_metas['flip'] = imgs_dict.get('flip', False) data_tuple = (input_imgs, curr_imgs / 256., prev_imgs / 256., next_imgs / 256., color_imgs / 256., img_metas, curr_aug, prev_aug, next_aug) return data_tuple def custom_collate_fn_temporal(data): data_tuple = [] for i, item in enumerate(data[0]): if isinstance(item, torch.Tensor): data_tuple.append(torch.stack([d[i] for d in data])) elif isinstance(item, (dict, str)): data_tuple.append([d[i] for d in data]) elif item is None: data_tuple.append(None) else: raise NotImplementedError return data_tuple def forward_aug(imgs, metas, transforms): imgs_dict = { 'img': imgs, 'metas': metas, } for t in transforms: imgs_dict = t(imgs_dict) aug_imgs = imgs_dict['img'] return aug_imgs, imgs_dict ================================================ FILE: dataset/dataset_wrapper_vis.py ================================================ import numpy as np, torch from torch.utils import data from dataset.transform_3d import PadMultiViewImage, NormalizeMultiviewImage, \ RandomScaleImageMultiViewImage from mmengine import MMLogger logger = MMLogger.get_instance('selfocc') from . import OPENOCC_DATAWRAPPER img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) @OPENOCC_DATAWRAPPER.register_module() class tpvformer_dataset_nuscenes_vis(data.Dataset): def __init__( self, in_dataset, scale_rate=1, img_norm_cfg=img_norm_cfg, pad_img_size=None, pad_scale_rate=None, **kwargs ): 'Initialization' self.point_cloud_dataset = in_dataset self.scale_rate = scale_rate val_transforms = [ NormalizeMultiviewImage(**img_norm_cfg), PadMultiViewImage(size_divisor=32, size=pad_img_size)] if scale_rate != 1 or pad_scale_rate is not None: val_transforms.insert(1, RandomScaleImageMultiViewImage([scale_rate], pad_scale_rate=pad_scale_rate)) self.transforms = val_transforms def __len__(self): return len(self.point_cloud_dataset) def to_tensor(self, imgs): imgs = np.stack(imgs).astype(np.float32) imgs = torch.from_numpy(imgs) imgs = imgs.permute(0, 3, 1, 2) return imgs def __getitem__(self, index): data = self.point_cloud_dataset[index] return self.deal_with_length2_dataset(data) def deal_with_length2_dataset(self, data): input_imgs, img_metas = data img_metas['img_shape'] = input_imgs[0].shape[:2] # deal with img augmentations input_imgs, imgs_dict = forward_aug(input_imgs, img_metas, self.transforms) input_imgs = self.to_tensor(input_imgs) img_metas['scale_rate'] = self.scale_rate if 'focal_ratios_x' in imgs_dict: img_metas['focal_ratios_x'] = imgs_dict['focal_ratios_x'] if 'focal_ratios_y' in imgs_dict: img_metas['focal_ratios_y'] = imgs_dict['focal_ratios_y'] img_metas['flip'] = imgs_dict.get('flip', False) data_tuple = (input_imgs, img_metas,) return data_tuple def custom_collate_fn_temporal(data): data_tuple = [] for i, item in enumerate(data[0]): if isinstance(item, torch.Tensor): data_tuple.append(torch.stack([d[i] for d in data])) elif isinstance(item, (dict, str)): data_tuple.append([d[i] for d in data]) elif item is None: data_tuple.append(None) else: raise NotImplementedError return data_tuple def forward_aug(imgs, metas, transforms): imgs_dict = { 'img': imgs, 'metas': metas, } for t in transforms: imgs_dict = t(imgs_dict) aug_imgs = imgs_dict['img'] return aug_imgs, imgs_dict ================================================ FILE: dataset/kitti/helpers.py ================================================ import numpy as np # import scenerf.data.utils.fusion as fusion # import open3d as o3d from PIL import Image # def make_open3d_point_cloud(xyz, color=None): # pcd = o3d.geometry.PointCloud() # pcd.points = o3d.utility.Vector3dVector(xyz) # if color is not None: # if len(color) != len(xyz): # color = np.tile(color, (len(xyz), 1)) # pcd.colors = o3d.utility.Vector3dVector(color) # return pcd def apply_transform(pts, T): """ cam_ptx_from: B, 3 """ ones = np.ones((pts.shape[0], 1)) homo_pts = np.concatenate([pts, ones], axis=1) homo_cam_pts_to = (T @ homo_pts.T).T cam_pts_to = homo_cam_pts_to[:, :3] return cam_pts_to def dump_xyz(P): return P[0:3, 3] def read_rgb(path): img = Image.open(path).convert("RGB") # PIL to numpy img = np.array(img, dtype=np.float32, copy=False) / 255.0 img = img[:370, :1220, :] # crop image return img def read_poses(path): # Read and parse the poses poses = [] with open(path, 'r') as f: lines = f.readlines() for line in lines: T_w_cam0 = np.fromstring(line, dtype=float, sep=' ') T_w_cam0 = T_w_cam0.reshape(3, 4) T_w_cam0 = np.vstack((T_w_cam0, [0, 0, 0, 1])) poses.append(T_w_cam0) return poses def read_calib(calib_path): """ Modify from https://github.com/utiasSTARS/pykitti/blob/d3e1bb81676e831886726cc5ed79ce1f049aef2c/pykitti/utils.py#L68 :param calib_path: Path to a calibration text file. :return: dict with calibration matrices. """ calib_all = {} with open(calib_path, "r") as f: for line in f.readlines(): if line == "\n": break key, value = line.split(":", 1) calib_all[key] = np.array([float(x) for x in value.split()]) # reshape matrices calib_out = {} # 3x4 projection matrix for left camera calib_out["P2"] = calib_all["P2"].reshape(3, 4) calib_out["Tr"] = np.identity(4) # 4x4 matrix calib_out["Tr"][:3, :4] = calib_all["Tr"].reshape(3, 4) T2 = np.eye(4) T2[0, 3] = calib_out["P2"][0, 3] / calib_out["P2"][0, 0] calib_out["T_cam0_2_cam2"] = T2 return calib_out # def compute_transformation( # lidar_path_source, lidar_path_infer, lidar_path_target, # pose_source, pose_infer, pose_target, # T_velo_2_cam2, T_cam0_2_cam2): # pts_velo_source = np.fromfile(lidar_path_source, dtype=np.float32).reshape((-1, 4))[:, :3] # pts_velo_infer = np.fromfile(lidar_path_infer, dtype=np.float32).reshape((-1, 4))[:, :3] # pts_velo_target = np.fromfile(lidar_path_target, dtype=np.float32).reshape((-1, 4))[:, :3] # pts_cam2_source = apply_transform(pts_velo_source, T_velo_2_cam2) # pts_cam2_infer = apply_transform(pts_velo_infer, T_velo_2_cam2) # pts_cam2_target = apply_transform(pts_velo_target, T_velo_2_cam2) # T_cam2_2_cam0 = np.linalg.inv(T_cam0_2_cam2) # T_source2infer = T_cam0_2_cam2 @ np.linalg.inv(pose_infer) @ pose_source @ T_cam2_2_cam0 # T_source2target = T_cam0_2_cam2 @ np.linalg.inv(pose_target) @ pose_source @ T_cam2_2_cam0 # pts_cam2_source2infer = apply_transform(pts_cam2_source, T_source2infer) # pts_cam2_source2target = apply_transform(pts_cam2_source, T_source2target) # pcd_source2infer = make_open3d_point_cloud(pts_cam2_source2infer).voxel_down_sample(voxel_size=0.05) # pcd_source2target = make_open3d_point_cloud(pts_cam2_source2target).voxel_down_sample(voxel_size=0.05) # pcd_infer = make_open3d_point_cloud(pts_cam2_infer).voxel_down_sample(voxel_size=0.05) # pcd_target = make_open3d_point_cloud(pts_cam2_target).voxel_down_sample(voxel_size=0.05) # reg_source2infer = o3d.pipelines.registration.registration_icp( # pcd_source2infer, pcd_infer, 0.2, np.eye(4), # o3d.pipelines.registration.TransformationEstimationPointToPoint(), # o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=200)) # reg_source2target = o3d.pipelines.registration.registration_icp( # pcd_source2target, pcd_target, 0.2, np.eye(4), # o3d.pipelines.registration.TransformationEstimationPointToPoint(), # o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=200)) # return { # "T_source2infer": T_source2infer @ reg_source2infer.transformation, # "T_source2target": T_source2target @ reg_source2target.transformation # } # def vox2pix(cam_E, cam_K, # vox_origin, voxel_size, # img_W, img_H, # scene_size): # """ # compute the 2D projection of voxels centroids # Parameters: # ---------- # cam_E: 4x4 # =camera pose in case of NYUv2 dataset # =Transformation from camera to lidar coordinate in case of SemKITTI # cam_K: 3x3 # camera intrinsics # vox_origin: (3,) # world(NYU)/lidar(SemKITTI) cooridnates of the voxel at index (0, 0, 0) # img_W: int # image width # img_H: int # image height # scene_size: (3,) # scene size in meter: (51.2, 51.2, 6.4) for SemKITTI and (4.8, 4.8, 2.88) for NYUv2 # Returns # ------- # projected_pix: (N, 2) # Projected 2D positions of voxels # fov_mask: (N,) # Voxels mask indice voxels inside image's FOV # sensor_distance: (N,) # Voxels'distance to the sensor in meter # """ # # Compute the x, y, z bounding of the scene in meter # vol_bnds = np.zeros((3,2)) # vol_bnds[:,0] = vox_origin # vol_bnds[:,1] = vox_origin + np.array(scene_size) # # Compute the voxels centroids in lidar cooridnates # vol_dim = np.ceil((vol_bnds[:,1]- vol_bnds[:,0])/ voxel_size).copy(order='C').astype(int) # xv, yv, zv = np.meshgrid( # range(vol_dim[0]), # range(vol_dim[1]), # range(vol_dim[2]), # indexing='ij' # ) # vox_coords = np.concatenate([ # xv.reshape(1,-1), # yv.reshape(1,-1), # zv.reshape(1,-1) # ], axis=0).astype(int).T # # Project voxels'centroid from lidar coordinates to camera coordinates # cam_pts = fusion.TSDFVolume.vox2world(vox_origin, vox_coords, voxel_size) # cam_pts = fusion.rigid_transform(cam_pts, cam_E) # # Project camera coordinates to pixel positions # projected_pix = fusion.TSDFVolume.cam2pix(cam_pts, cam_K) # pix_x, pix_y = projected_pix[:, 0], projected_pix[:, 1] # # Eliminate pixels outside view frustum # sensor_distance = cam_pts[:, 2] # fov_mask = np.logical_and(pix_x >= 0, # np.logical_and(pix_x < img_W, # np.logical_and(pix_y >= 0, # np.logical_and(pix_y < img_H, # sensor_distance > 0)))) # return projected_pix, fov_mask, sensor_distance ================================================ FILE: dataset/kitti/io_data.py ================================================ """ Most of the code in this file is taken from https://github.com/cv-rits/LMSCNet/blob/main/LMSCNet/data/io_data.py """ import numpy as np import yaml import imageio def unpack(compressed): ''' given a bit encoded voxel grid, make a normal voxel grid out of it. ''' uncompressed = np.zeros(compressed.shape[0] * 8, dtype=np.uint8) uncompressed[::8] = compressed[:] >> 7 & 1 uncompressed[1::8] = compressed[:] >> 6 & 1 uncompressed[2::8] = compressed[:] >> 5 & 1 uncompressed[3::8] = compressed[:] >> 4 & 1 uncompressed[4::8] = compressed[:] >> 3 & 1 uncompressed[5::8] = compressed[:] >> 2 & 1 uncompressed[6::8] = compressed[:] >> 1 & 1 uncompressed[7::8] = compressed[:] & 1 return uncompressed def img_normalize(img, mean, std): img = img.astype(np.float32) / 255.0 img = img - mean img = img / std return img def pack(array): """ convert a boolean array into a bitwise array. """ array = array.reshape((-1)) #compressing bit flags. # yapf: disable compressed = array[::8] << 7 | array[1::8] << 6 | array[2::8] << 5 | array[3::8] << 4 | array[4::8] << 3 | array[5::8] << 2 | array[6::8] << 1 | array[7::8] # yapf: enable return np.array(compressed, dtype=np.uint8) def get_grid_coords(dims, resolution): ''' :param dims: the dimensions of the grid [x, y, z] (i.e. [256, 256, 32]) :return coords_grid: is the center coords of voxels in the grid ''' # The sensor in centered in X (we go to dims/2 + 1 for the histogramdd) g_xx = np.arange(-dims[0]/2, dims[0]/2 + 1) # The sensor is in Y=0 (we go to dims + 1 for the histogramdd) g_yy = np.arange(0, dims[1] + 1) # The sensor is in Z=1.73. I observed that the ground was to voxel levels above the grid bottom, so Z pose is at 10 # if bottom voxel is 0. If we want the sensor to be at (0, 0, 0), then the bottom in z is -10, top is 22 # (we go to 22 + 1 for the histogramdd) # ATTENTION.. Is 11 for old grids.. 10 for new grids (v1.1) (https://github.com/PRBonn/semantic-kitti-api/issues/49) sensor_pose = 10 g_zz = np.arange(0 - sensor_pose, dims[2] - sensor_pose + 1) # Obtaining the grid with coords... xx, yy, zz = np.meshgrid(g_xx[:-1], g_yy[:-1], g_zz[:-1]) coords_grid = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T coords_grid = coords_grid.astype(np.float) coords_grid = (coords_grid * resolution) + resolution/2 temp = np.copy(coords_grid) temp[:, 0] = coords_grid[:, 1] temp[:, 1] = coords_grid[:, 0] coords_grid = np.copy(temp) return coords_grid, g_xx, g_yy, g_zz def _get_remap_lut(config_path): ''' remap_lut to remap classes of semantic kitti for training... :return: ''' dataset_config = yaml.safe_load(open(config_path, 'r')) # make lookup table for mapping maxkey = max(dataset_config['learning_map'].keys()) # +100 hack making lut bigger just in case there are unknown labels remap_lut = np.zeros((maxkey + 100), dtype=np.int32) remap_lut[list(dataset_config['learning_map'].keys())] = list(dataset_config['learning_map'].values()) # in completion we have to distinguish empty and invalid voxels. # Important: For voxels 0 corresponds to "empty" and not "unlabeled". remap_lut[remap_lut == 0] = 255 # map 0 to 'invalid' remap_lut[0] = 0 # only 'empty' stays 'empty'. return remap_lut def get_inv_map(): ''' remap_lut to remap classes of semantic kitti for training... :return: ''' config_path = "./semantic-kitti.yaml" dataset_config = yaml.safe_load(open(config_path, 'r')) # make lookup table for mapping inv_map = np.zeros(20, dtype=np.int32) inv_map[list(dataset_config['learning_map_inv'].keys())] = list(dataset_config['learning_map_inv'].values()) return inv_map def _read_SemKITTI(path, dtype, do_unpack): bin = np.fromfile(path, dtype=dtype) # Flattened array if do_unpack: bin = unpack(bin) return bin def _read_label_SemKITTI(path): label = _read_SemKITTI(path, dtype=np.uint16, do_unpack=False).astype(np.float32) return label def _read_invalid_SemKITTI(path): invalid = _read_SemKITTI(path, dtype=np.uint8, do_unpack=True) return invalid def _read_occluded_SemKITTI(path): occluded = _read_SemKITTI(path, dtype=np.uint8, do_unpack=True) return occluded def _read_occupancy_SemKITTI(path): occupancy = _read_SemKITTI(path, dtype=np.uint8, do_unpack=True).astype(np.float32) return occupancy def _read_rgb_SemKITTI(path): rgb = np.asarray(imageio.imread(path)) return rgb def _read_pointcloud_SemKITTI(path): 'Return pointcloud semantic kitti with remissions (x, y, z, intensity)' pointcloud = _read_SemKITTI(path, dtype=np.float32, do_unpack=False) pointcloud = pointcloud.reshape((-1, 4)) return pointcloud def _read_calib_SemKITTI(calib_path): """ :param calib_path: Path to a calibration text file. :return: dict with calibration matrices. """ calib_all = {} with open(calib_path, 'r') as f: for line in f.readlines(): if line == '\n': break key, value = line.split(':', 1) calib_all[key] = np.array([float(x) for x in value.split()]) # reshape matrices calib_out = {} calib_out['P2'] = calib_all['P2'].reshape(3, 4) # 3x4 projection matrix for left camera calib_out['Tr'] = np.identity(4) # 4x4 matrix calib_out['Tr'][:3, :4] = calib_all['Tr'].reshape(3, 4) return calib_out def get_remap_lut(path): ''' remap_lut to remap classes of semantic kitti for training... :return: ''' dataset_config = yaml.safe_load(open(path, 'r')) # make lookup table for mapping maxkey = max(dataset_config['learning_map'].keys()) # +100 hack making lut bigger just in case there are unknown labels remap_lut = np.zeros((maxkey + 100), dtype=np.int32) remap_lut[list(dataset_config['learning_map'].keys())] = list(dataset_config['learning_map'].values()) # in completion we have to distinguish empty and invalid voxels. # Important: For voxels 0 corresponds to "empty" and not "unlabeled". remap_lut[remap_lut == 0] = 255 # map 0 to 'invalid' remap_lut[0] = 0 # only 'empty' stays 'empty'. return remap_lut def data_augmentation_3Dflips(flip, data): # The .copy() is done to avoid negative strides of the numpy array caused by the way numpy manages the data # into memory. This gives errors when trying to pass the array to torch sensors.. Solution seen in: # https://discuss.pytorch.org/t/torch-from-numpy-not-support-negative-strides/3663 # Dims -> {XZY} # Flipping around the X axis... if np.isclose(flip, 1): data = np.flip(data, axis=0).copy() # Flipping around the Y axis... if np.isclose(flip, 2): data = np.flip(data, 2).copy() # Flipping around the X and the Y axis... if np.isclose(flip, 3): data = np.flip(np.flip(data, axis=0), axis=2).copy() return data def get_cmap_semanticKITTI20(): colors = np.array([ # [0 , 0 , 0, 255], [100, 150, 245, 255], [100, 230, 245, 255], [30, 60, 150, 255], [80, 30, 180, 255], [100, 80, 250, 255], [255, 30, 30, 255], [255, 40, 200, 255], [150, 30, 90, 255], [255, 0, 255, 255], [255, 150, 255, 255], [75, 0, 75, 255], [175, 0, 75, 255], [255, 200, 0, 255], [255, 120, 50, 255], [0, 175, 0, 255], [135, 60, 0, 255], [150, 240, 80, 255], [255, 240, 150, 255], [255, 0, 0, 255]]).astype(np.uint8) return colors ================================================ FILE: dataset/kitti/kitti_dataset_eval.py ================================================ import glob import os import time import numpy as np import torch # from torchvision import transforms from dataset.kitti.helpers import dump_xyz, read_calib, read_poses, read_rgb from dataset.kitti.params import val_error_frames # from . import io_data as SemanticKittiIO from copy import deepcopy from mmcv.image.io import imread from .. import OPENOCC_DATASET if 'HFAI' in os.environ: hfai = True from dataset.loading import LoadMultiViewImageFromFilesHF, LoadPtsFromFilesHF else: hfai = False @OPENOCC_DATASET.register_module() class Kitti_Novel_View_Eval: def __init__( self, split, root, preprocess_root, frames_interval=0.4, sequence_distance=10, n_sources=1, eval_depth=80, sequences=None, selected_frames=None, crop_size=[370, 1220], **kwargs, ): self.root = root self.preprocess_root = preprocess_root self.depth_preprocess_root = os.path.join(preprocess_root, "depth") self.transform_preprocess_root = os.path.join(preprocess_root, "transform") self.n_classes = 20 self.n_sources = n_sources self.eval_depth = eval_depth self.transxy = [ [0, -1., 0, 0], [1., 0, 0, 0], [0, 0, 1., 0], [0, 0, 0, 1.]] self.transxy = np.array(self.transxy) splits = { "train": ["00", "01", "02", "03", "04", "05", "06", "07", "09", "10"], "val": ["08"], "test": ["11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21"], } self.split = split if sequences is not None: self.sequences = sequences else: self.sequences = splits[split] self.output_scale = 1 self.scene_size = (51.2, 51.2, 6.4) self.vox_origin = np.array([0, -25.6, -2]) self.frames_interval = frames_interval self.sequence_distance = sequence_distance self.voxel_size = 0.2 # 0.2m self.img_W = crop_size[1] self.img_H = crop_size[0] start_time = time.time() self.scans = [] self.frame2scan = {} for sequence in self.sequences: pose_path = os.path.join(self.root, "dataset", "poses", sequence + ".txt") gt_global_poses = read_poses(pose_path) calib = read_calib( os.path.join(self.root, "dataset", "sequences", sequence, "calib.txt") ) P = calib["P2"] T_cam0_2_cam2 = calib['T_cam0_2_cam2'] T_cam2_2_cam0 = np.linalg.inv(T_cam0_2_cam2) T_velo_2_cam = T_cam0_2_cam2 @ calib["Tr"] if split == "val": glob_path = os.path.join( self.root, "dataset", "sequences", sequence, "voxels", "*.bin" ) else: glob_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", "*.png" ) dists_glob_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", "*.png" ) seq_img_paths = glob.glob(glob_path) dists_seq_img_paths = glob.glob(dists_glob_path) max_length = 0 min_length = 50 paired_dists = {} # dist_step = 1 if split == 'train' else 5 dist_step = 1 for seq_img_path in dists_seq_img_paths: filename = os.path.basename(seq_img_path) frame_id = os.path.splitext(filename)[0] prev_frame_id = "{:06d}".format(int(frame_id) - dist_step) img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", prev_frame_id + ".png") if not os.path.exists(img_path): paired_dists[frame_id] = 0. else: curr_pose = gt_global_poses[int(frame_id)] prev_pose = gt_global_poses[int(prev_frame_id)] prev_xyz = dump_xyz(prev_pose) curr_xyz = dump_xyz(curr_pose) rel_distance = np.sqrt( (prev_xyz[0] - curr_xyz[0]) ** 2 + (prev_xyz[2] - curr_xyz[2]) ** 2) paired_dists[frame_id] = rel_distance for seq_img_path in seq_img_paths: filename = os.path.basename(seq_img_path) frame_id = os.path.splitext(filename)[0] current_img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", frame_id + ".png") current_lid_path = os.path.join( self.root, "dataset", "sequences", sequence, "velodyne", frame_id + ".bin") prev_frame_ids, next_frame_ids = [], [] prev_img_paths, next_img_paths = [], [] prev_lid_paths, next_lid_paths = [], [] prev_poses, next_poses = [gt_global_poses[int(frame_id)]], [gt_global_poses[int(frame_id)]] prev_dists, next_dists = [], [] # if self.split == 'train': # pos_step = 1 # neg_step = -1 # else: # pos_step = 5 # neg_step = -5 pos_step = 1 neg_step = -1 # deal with prev step = -1 cnt = 0 dist = 0. while True: # cnt += step cnt += neg_step rel_frame_id = "{:06d}".format(int(frame_id) + cnt) img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", rel_frame_id + ".png") if not os.path.exists(img_path): break prev_pose = prev_poses[-1] prev_xyz = dump_xyz(prev_pose) current_xyz = dump_xyz(gt_global_poses[int(rel_frame_id)]) tmp_dist = np.sqrt( (prev_xyz[0] - current_xyz[0]) ** 2 + (prev_xyz[2] - current_xyz[2]) ** 2) # tmp_dist = paired_dists["{:06d}".format(int(rel_frame_id) + pos_step)] dist += tmp_dist # if dist < frames_interval: if tmp_dist < frames_interval: continue if dist > sequence_distance: break if split == "val" and rel_frame_id in val_error_frames: continue prev_frame_ids.append(rel_frame_id) prev_img_paths.append(img_path) lidar_path = os.path.join( self.root, "dataset", "sequences", sequence, "velodyne", rel_frame_id + ".bin") prev_lid_paths.append(lidar_path) prev_poses.append(gt_global_poses[int(rel_frame_id)]) prev_dists.append(dist) # deal with next step = 1 cnt = 0 dist = 0. while True: # cnt += step cnt += pos_step rel_frame_id = "{:06d}".format(int(frame_id) + cnt) img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", rel_frame_id + ".png") if not os.path.exists(img_path): break prev_pose = next_poses[-1] prev_xyz = dump_xyz(prev_pose) current_xyz = dump_xyz(gt_global_poses[int(rel_frame_id)]) tmp_dist = np.sqrt( (prev_xyz[0] - current_xyz[0]) ** 2 + (prev_xyz[2] - current_xyz[2]) ** 2) # tmp_dist = paired_dists[rel_frame_id] dist += tmp_dist # if dist < frames_interval: if tmp_dist < frames_interval: continue if dist > sequence_distance: break if split == "val" and rel_frame_id in val_error_frames: continue next_frame_ids.append(rel_frame_id) next_img_paths.append(img_path) lidar_path = os.path.join( self.root, "dataset", "sequences", sequence, "velodyne", rel_frame_id + ".bin") next_lid_paths.append(lidar_path) next_poses.append(gt_global_poses[int(rel_frame_id)]) next_dists.append(dist) # ignore error frame if split == "val" and frame_id in val_error_frames: continue is_included = len(next_frame_ids) > 0 if selected_frames is None else frame_id in selected_frames if is_included: # prev_next_len = len(prev_poses) + len(next_poses) prev_next_len = len(next_frame_ids) if prev_next_len > max_length: max_length = prev_next_len if prev_next_len < min_length: min_length = prev_next_len self.frame2scan.update({str(sequence) + "_" + frame_id: len(self.scans)}) self.scans.append( { "frame_id": frame_id, "sequence": sequence, "img_path": current_img_path, "lid_path": current_lid_path, "pose": gt_global_poses[int(frame_id)], "prev_img_paths": prev_img_paths, "prev_lid_paths": prev_lid_paths, "next_img_paths": next_img_paths, "next_lid_paths": next_lid_paths, "T_velo_2_cam": T_velo_2_cam, "P": P, "T_cam0_2_cam2": T_cam0_2_cam2, "T_cam2_2_cam0": T_cam2_2_cam0, "prev_poses": prev_poses[1:], "next_poses": next_poses[1:], "prev_dists": prev_dists, "next_dists": next_dists, "prev_frame_ids": prev_frame_ids, "next_frame_ids": next_frame_ids } ) print(sequence, min_length, max_length) # self.to_tensor_normalized = transforms.Compose( # [ # transforms.ToTensor(), # transforms.Normalize( # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] # ), # ] # ) # self.to_tensor = transforms.Compose([ # transforms.ToTensor() # ]) print("Preprocess time: --- %s seconds ---" % (time.time() - start_time)) def get_depth_from_lidar(self, lidar_path, lidar2img, image_size): # lidar2img: N, 4, 4 scan = np.fromfile(lidar_path, dtype=np.float32) scan = scan.reshape((-1, 4)) scan[:, 3] = 1.0 # points_hcoords = scan[scan[:, 0] > 0, :] points_hcoords = np.expand_dims(self.transxy @ scan.T, 0) # 1, 4, n img_points = np.transpose(lidar2img @ points_hcoords, (0, 2, 1)) # N, n, 4 depth = img_points[..., 2] # N, n img_points = img_points[..., :2] # N, n, 2 mask = (depth < self.eval_depth) & (depth > 1e-3) # get points with depth < max_sample_depth img_points = img_points / np.expand_dims(depth, axis=2) # scale 2D points img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] # img_points = np.round(img_points).astype(int) mask = mask & (img_points[..., 0] > 0) & \ (img_points[..., 1] > 0) & \ (img_points[..., 0] < 1) & \ (img_points[..., 1] < 1) return img_points, depth, mask def __len__(self): return len(self.scans) def prepare_temImg2lidar(self, scan, anchor): intrinsic = np.eye(4) intrinsic[:3, :3] = scan['P'][:3, :3] temImg2lidar = self.transxy @ np.linalg.inv(scan['T_velo_2_cam']) @ \ scan['T_cam0_2_cam2'] @ \ np.linalg.inv(scan['pose']) @ \ scan['next_poses'][anchor] @ \ np.linalg.inv(scan['T_cam0_2_cam2']) @ \ np.linalg.inv(intrinsic) return [temImg2lidar] def prepare_img_metas(self, scan): img_metas = {} img_metas.update({ 'input_imgs_path': [scan['img_path']]}) intrinsic = np.eye(4) intrinsic[:3, :3] = scan['P'][:3, :3] lidar2img = intrinsic @ scan['T_velo_2_cam'] @ np.linalg.inv(self.transxy) img2lidar = np.linalg.inv(lidar2img) img_metas.update({ 'lidar2img': np.expand_dims(lidar2img, axis=0), 'img2lidar': [img2lidar], 'token': scan['frame_id'], 'sequence': scan['sequence'] }) return img_metas def read_surround_imgs(self, img_paths): if hfai: imgs = self.img_loader.load(img_paths) else: imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:self.img_H, :self.img_W, :] for img in imgs] return imgs def __getitem__(self, index): #### 1. get color, temporal_depth choice if necessary #### 2. get self, prev, next infos for the stem, and also temp_depth info scan = deepcopy(self.scans[index]) img_metas = self.prepare_img_metas(scan) anchor_imgs = [] # list[list[array]] anchor_depth_locs = [] # list[array] anchor_depth_gts = [] # list[array] anchor_depth_masks = [] # list[array] temImg2lidars = [] # list[list[array]] anchor_frame_dist = scan['next_dists'] # list[float] for anchor in range(len(scan['next_frame_ids'])): anchor_img_path = scan['next_img_paths'][anchor] anchor_lid_path = scan['next_lid_paths'][anchor] temImg2lidar = self.prepare_temImg2lidar(scan, anchor) anchor_img = self.read_surround_imgs([anchor_img_path]) depth_loc, depth_gt, depth_mask = self.get_depth_from_lidar( anchor_lid_path, img_metas['lidar2img'], [self.img_H, self.img_W]) anchor_imgs.append(anchor_img) anchor_depth_locs.append(depth_loc) anchor_depth_gts.append(depth_gt) anchor_depth_masks.append(depth_mask) temImg2lidars.append(temImg2lidar) img_metas.update({ 'depth_loc': anchor_depth_locs, 'depth_gt': anchor_depth_gts, 'depth_mask': anchor_depth_masks, 'temImg2lidars': temImg2lidars, 'frame_dists': anchor_frame_dist }) # read 6 cams input_imgs = self.read_surround_imgs(img_metas['input_imgs_path']) data_tuple = (input_imgs, anchor_imgs, img_metas) return data_tuple # frame_id = scan['frame_id'] # sequence = scan['sequence'] # lidar_paths = scan['lidar_paths'] # rel_frame_ids = scan['rel_frame_ids'] # distances = scan['distances'] # infer_id = 0 # P = scan["P"] # T_velo_2_cam = scan["T_velo_2_cam"] # img_paths = scan["img_paths"] # img_sources = [] # img_input_sources = [] # img_targets = [] # lidar_depths = [] # depths = [] # loc2d_with_depths = [] # T_source2infers = [] # T_source2targets = [] # source_distances = [] # source_frame_ids = [] # n_sources = min(len(distances) - 1, self.n_sources) # for d_id in range(n_sources): # if self.n_sources < len(distances): # source_id = np.random.randint(1, len(distances)) # source_distance = distances[source_id] # else: # source_id = d_id + 1 # source_distance = distances[source_id] # source_distances.append(source_distance) # rel_frame_id = rel_frame_ids[source_id] # source_frame_ids.append(rel_frame_id) # target_id = source_id - 1 # img_input_source = self.to_tensor_normalized(read_rgb(img_paths[source_id])) # img_input_sources.append(img_input_source) # img_source = self.to_tensor(read_rgb(img_paths[source_id])) # img_target = self.to_tensor(read_rgb(img_paths[target_id])) # lidar_path = lidar_paths[source_id] # loc2d_with_depth, lidar_depth, _ = self.get_depth_from_lidar(lidar_path, P, T_velo_2_cam, # (self.img_W, self.img_H)) # if self.n_rays < lidar_depth.shape[0]: # idx = np.random.choice(lidar_depth.shape[0], size=self.n_rays, replace=False) # loc2d_with_depth = loc2d_with_depth[idx, :] # lidar_depth = lidar_depth[idx] # img_sources.append(img_source) # img_targets.append(img_target) # lidar_depths.append(torch.from_numpy(lidar_depth)) # loc2d_with_depths.append(torch.from_numpy(loc2d_with_depth)) # # Get transformation from source to target coord # transform_dir = os.path.join(self.transform_preprocess_root, # "{}_{}_all".format(sequence, self.frames_interval)) # os.makedirs(transform_dir, exist_ok=True) # transform_path = os.path.join(transform_dir, "{}.pkl".format(frame_id)) # if os.path.exists(transform_path): # try: # with open(transform_path, "rb") as input_file: # transform_data = pickle.load(input_file) # except EOFError: # transform_data = {} # else: # transform_data = {} # if '{}'.format(source_id) in transform_data: # T_out_dict = transform_data['{}'.format(source_id)] # else: # poses = scan["poses"] # pose_source = poses[source_id] # pose_infer = poses[infer_id] # pose_target = poses[target_id] # lidar_path_source = lidar_paths[source_id] # lidar_path_target = lidar_paths[target_id] # lidar_path_infer = lidar_paths[infer_id] # T_out_dict = compute_transformation( # lidar_path_source, lidar_path_infer, lidar_path_target, # pose_source, pose_infer, pose_target, # T_velo_2_cam, scan['T_cam0_2_cam2']) # transform_data['{}'.format(source_id)] = T_out_dict # with open(transform_path, "wb") as input_file: # pickle.dump(transform_data, input_file) # print("{}: saved {} to {}".format(frame_id, source_id, transform_path)) # T_source2infer = T_out_dict['T_source2infer'] # T_source2target = T_out_dict['T_source2target'] # T_source2infers.append(torch.from_numpy(T_source2infer).float()) # T_source2targets.append(torch.from_numpy(T_source2target).float()) # data = { # "img_input_sources": img_input_sources, # "source_distances": source_distances, # "source_frame_ids": source_frame_ids, # "img_sources": img_sources, # "img_targets": img_targets, # "lidar_depths": lidar_depths, # "depths": depths, # "loc2d_with_depths": loc2d_with_depths, # "T_source2infers": T_source2infers, # "T_source2targets": T_source2targets, # "frame_id": frame_id, # "sequence": sequence, # "P": P, # "T_velo_2_cam": T_velo_2_cam, # "T_cam2_2_cam0": scan['T_cam2_2_cam0'], # "T_cam0_2_cam2": scan['T_cam0_2_cam2'], # } # scale_3ds = [self.output_scale] # data["scale_3ds"] = scale_3ds # cam_K = P[0:3, 0:3] # data["cam_K"] = cam_K # for scale_3d in scale_3ds: # # compute the 3D-2D mapping # projected_pix, fov_mask, sensor_distance = vox2pix( # T_velo_2_cam, # cam_K, # self.vox_origin, # self.voxel_size * scale_3d, # self.img_W, # self.img_H, # self.scene_size, # ) # data["projected_pix_{}".format(scale_3d)] = projected_pix # data["sensor_distance_{}".format(scale_3d)] = sensor_distance # data["fov_mask_{}".format(scale_3d)] = fov_mask # img_input = read_rgb(img_paths[infer_id]) # img_input = self.to_tensor_normalized(img_input) # data["img_input"] = img_input # label_path = os.path.join( # self.root, "dataset", "sequences", sequence, "voxels", "{}.label".format(frame_id) # ) # invalid_path = os.path.join( # self.root, "dataset", "sequences", sequence, "voxels", "{}.invalid".format(frame_id) # ) # # data['target_1_1'] = self.read_semKITTI_label(label_path, invalid_path) # return data # @staticmethod # def read_semKITTI_label(label_path, invalid_path): # remap_lut = SemanticKittiIO.get_remap_lut("./scenerf/data/semantic_kitti/semantic-kitti.yaml") # LABEL = SemanticKittiIO._read_label_SemKITTI(label_path) # INVALID = SemanticKittiIO._read_invalid_SemKITTI(invalid_path) # LABEL = remap_lut[LABEL.astype(np.uint16)].astype( # np.float32 # ) # Remap 20 classes semanticKITTI SSC # LABEL[ # np.isclose(INVALID, 1) # ] = 255 # Setting to unknown all voxels marked on invalid mask... # LABEL = LABEL.reshape(256,256, 32) # return LABEL # def __len__(self): # return len(self.scans) if __name__ == "__main__": import os, sys print(sys.path) dataset = Kitti_Smart( 'train', root='data/kitti', preprocess_root='data/kitti/preprocess', cur_prob=1.0, crop_size=[352, 1216]) batch = dataset[0] import pdb; pdb.set_trace() ================================================ FILE: dataset/kitti/kitti_dataset_one_frame.py ================================================ import glob, os, time, random import numpy as np import torch # from torchvision import transforms from dataset.kitti.helpers import dump_xyz, read_calib, read_poses, read_rgb from dataset.kitti.params import val_error_frames # from . import io_data as SemanticKittiIO from copy import deepcopy from mmcv.image.io import imread from .. import OPENOCC_DATASET if 'HFAI' in os.environ: hfai = True from dataset.loading import LoadMultiViewImageFromFilesHF, LoadPtsFromFilesHF else: hfai = False @OPENOCC_DATASET.register_module() class Kitti_One_Frame: def __init__( self, split, root, preprocess_root, frames_interval=0.4, sequence_distance=10, n_sources=1, eval_depth=80, sequences=None, selected_frames=None, n_rays=1200, cur_prob=1.0, crop_size=[370, 1220], strict=True, return_depth=False, prev_prob=0.5, choose_nearest=False, return_sem=False, sem_path=None, **kwargs, ): self.root = root self.preprocess_root = preprocess_root self.depth_preprocess_root = os.path.join(preprocess_root, "depth") self.transform_preprocess_root = os.path.join(preprocess_root, "transform") self.n_classes = 20 self.n_sources = n_sources self.eval_depth = eval_depth self.n_rays = n_rays self.cur_prob = cur_prob self.return_depth = return_depth self.prev_prob = prev_prob self.choose_nearest = choose_nearest self.return_sem = return_sem assert (not return_sem) or os.path.exists(sem_path) self.sem_path = sem_path self.transxy = [ [0, -1., 0, 0], [1., 0, 0, 0], [0, 0, 1., 0], [0, 0, 0, 1.]] self.transxy = np.array(self.transxy) splits = { "train": ["00", "01", "02", "03", "04", "05", "06", "07", "09", "10"], "val": ["08"], "test": ["11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21"], } self.split = split if sequences is not None: self.sequences = sequences else: self.sequences = splits[split] self.output_scale = 1 self.scene_size = (51.2, 51.2, 6.4) self.vox_origin = np.array([0, -25.6, -2]) self.frames_interval = frames_interval if not isinstance(sequence_distance, list): sequence_distance = [sequence_distance] * 2 self.sequence_distance = sequence_distance self.strict = strict self.voxel_size = 0.2 # 0.2m self.img_W = crop_size[1] self.img_H = crop_size[0] start_time = time.time() self.scans = [] self.frame2scan = {} for sequence in self.sequences: pose_path = os.path.join(self.root, "dataset", "poses", sequence + ".txt") gt_global_poses = read_poses(pose_path) calib = read_calib( os.path.join(self.root, "dataset", "sequences", sequence, "calib.txt") ) P = calib["P2"] T_cam0_2_cam2 = calib['T_cam0_2_cam2'] T_cam2_2_cam0 = np.linalg.inv(T_cam0_2_cam2) T_velo_2_cam = T_cam0_2_cam2 @ calib["Tr"] if split == "val": glob_path = os.path.join( self.root, "dataset", "sequences", sequence, "voxels", "*.bin" ) else: glob_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", "*.png" ) seq_img_paths = glob.glob(glob_path) max_length = 0 min_length = 50 paired_dists = {} dist_step = 1 if split == 'train' else 5 for seq_img_path in seq_img_paths: filename = os.path.basename(seq_img_path) frame_id = os.path.splitext(filename)[0] prev_frame_id = "{:06d}".format(int(frame_id) - dist_step) img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", prev_frame_id + ".png") if not os.path.exists(img_path): paired_dists[frame_id] = 0. else: curr_pose = gt_global_poses[int(frame_id)] prev_pose = gt_global_poses[int(prev_frame_id)] prev_xyz = dump_xyz(prev_pose) curr_xyz = dump_xyz(curr_pose) rel_distance = np.sqrt( (prev_xyz[0] - curr_xyz[0]) ** 2 + (prev_xyz[2] - curr_xyz[2]) ** 2) paired_dists[frame_id] = rel_distance for seq_img_path in seq_img_paths: filename = os.path.basename(seq_img_path) frame_id = os.path.splitext(filename)[0] current_img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", frame_id + ".png") current_lid_path = os.path.join( self.root, "dataset", "sequences", sequence, "velodyne", frame_id + ".bin") prev_frame_ids, next_frame_ids = [], [] prev_img_paths, next_img_paths = [], [] prev_lid_paths, next_lid_paths = [], [] prev_poses, next_poses = [], [] prev_dists, next_dists = [], [] if self.split == 'train': pos_step = 1 neg_step = -1 else: pos_step = 5 neg_step = -5 # deal with prev step = -1 cnt = 0 dist = 0. while True: # cnt += step cnt += neg_step rel_frame_id = "{:06d}".format(int(frame_id) + cnt) img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", rel_frame_id + ".png") if not os.path.exists(img_path): break dist += paired_dists["{:06d}".format(int(rel_frame_id) + pos_step)] if dist < frames_interval: continue if dist > sequence_distance[0]: break if split == "val" and rel_frame_id in val_error_frames: continue prev_frame_ids.append(rel_frame_id) prev_img_paths.append(img_path) lidar_path = os.path.join( self.root, "dataset", "sequences", sequence, "velodyne", rel_frame_id + ".bin") prev_lid_paths.append(lidar_path) prev_poses.append(gt_global_poses[int(rel_frame_id)]) prev_dists.append(dist) # deal with next step = 1 cnt = 0 dist = 0. while True: # cnt += step cnt += pos_step rel_frame_id = "{:06d}".format(int(frame_id) + cnt) img_path = os.path.join( self.root, "dataset", "sequences", sequence, "image_2", rel_frame_id + ".png") if not os.path.exists(img_path): break dist += paired_dists[rel_frame_id] if dist < frames_interval: continue if dist > sequence_distance[1]: break if split == "val" and rel_frame_id in val_error_frames: continue next_frame_ids.append(rel_frame_id) next_img_paths.append(img_path) lidar_path = os.path.join( self.root, "dataset", "sequences", sequence, "velodyne", rel_frame_id + ".bin") next_lid_paths.append(lidar_path) next_poses.append(gt_global_poses[int(rel_frame_id)]) next_dists.append(dist) # ignore error frame if split == "val" and frame_id in val_error_frames: continue is_included = True if selected_frames is None else frame_id in selected_frames if is_included: prev_next_len = len(prev_poses) + len(next_poses) if prev_next_len > max_length: max_length = prev_next_len if prev_next_len < min_length: min_length = prev_next_len self.frame2scan.update({str(sequence) + "_" + frame_id: len(self.scans)}) if not self.strict: prev_img_paths.append(current_img_path) prev_lid_paths.append(current_lid_path) next_img_paths.append(current_img_path) next_lid_paths.append(current_lid_path) prev_poses.append(curr_pose) next_poses.append(curr_pose) prev_dists.append(0.) next_dists.append(0.) prev_frame_ids.append(frame_id) next_frame_ids.append(frame_id) self.scans.append( { "frame_id": frame_id, "sequence": sequence, "img_path": current_img_path, "lid_path": current_lid_path, "pose": gt_global_poses[int(frame_id)], "prev_img_paths": prev_img_paths, "prev_lid_paths": prev_lid_paths, "next_img_paths": next_img_paths, "next_lid_paths": next_lid_paths, "T_velo_2_cam": T_velo_2_cam, "P": P, "T_cam0_2_cam2": T_cam0_2_cam2, "T_cam2_2_cam0": T_cam2_2_cam0, "prev_poses": prev_poses, "next_poses": next_poses, "prev_dists": prev_dists, "next_dists": next_dists, "prev_frame_ids": prev_frame_ids, "next_frame_ids": next_frame_ids } ) print(sequence, min_length, max_length) # self.to_tensor_normalized = transforms.Compose( # [ # transforms.ToTensor(), # transforms.Normalize( # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] # ), # ] # ) # self.to_tensor = transforms.Compose([ # transforms.ToTensor() # ]) print("Preprocess time: --- %s seconds ---" % (time.time() - start_time)) def get_depth_from_lidar(self, lidar_path, lidar2img, image_size): # lidar2img: N, 4, 4 scan = np.fromfile(lidar_path, dtype=np.float32) scan = scan.reshape((-1, 4)) scan[:, 3] = 1.0 # points_hcoords = scan[scan[:, 0] > 0, :] points_hcoords = np.expand_dims(self.transxy @ scan.T, 0) # 1, 4, n img_points = np.transpose(lidar2img @ points_hcoords, (0, 2, 1)) # N, n, 4 depth = img_points[..., 2] # N, n img_points = img_points[..., :2] # N, n, 2 mask = (depth < self.eval_depth) & (depth > 1e-3) # get points with depth < max_sample_depth img_points = img_points / np.expand_dims(depth, axis=2) # scale 2D points img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] # img_points = np.round(img_points).astype(int) mask = mask & (img_points[..., 0] > 0) & \ (img_points[..., 1] > 0) & \ (img_points[..., 0] < 1) & \ (img_points[..., 1] < 1) return img_points, depth, mask def load_2d_sem_label(self, scan): sequence = scan['sequence'] filename = os.path.basename(scan['img_path']) sem_path = os.path.join(self.sem_path, sequence, 'image_02', filename + '.npy') sem = np.load(sem_path)[None, ...] return sem def __len__(self): return len(self.scans) def prepare_img_metas(self, scan, anchor_scan, anchor_prev, anchor_next): img_metas = {} img_metas.update({ 'input_imgs_path': [scan['img_path']], 'curr_imgs_path': [anchor_scan['img_path']], 'prev_imgs_path': [anchor_scan['prev_img_paths'][anchor_prev]], 'next_imgs_path': [anchor_scan['next_img_paths'][anchor_next]] }) intrinsic = np.eye(4) intrinsic[:3, :3] = scan['P'][:3, :3] lidar2img = intrinsic @ scan['T_velo_2_cam'] @ np.linalg.inv(self.transxy) img2lidar = np.linalg.inv(lidar2img) temImg2lidar = self.transxy @ np.linalg.inv(scan['T_velo_2_cam']) @ \ scan['T_cam0_2_cam2'] @ \ np.linalg.inv(scan['pose']) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) img2prevImg = intrinsic @ \ anchor_scan['T_cam0_2_cam2'] @ \ np.linalg.inv(anchor_scan['prev_poses'][anchor_prev]) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) img2nextImg = intrinsic @ \ anchor_scan['T_cam0_2_cam2'] @ \ np.linalg.inv(anchor_scan['next_poses'][anchor_next]) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) img_metas.update({ 'lidar2img': np.expand_dims(lidar2img, axis=0), 'img2lidar': [img2lidar], 'img2prevImg': [img2prevImg], 'img2nextImg': [img2nextImg], 'token': scan['frame_id'], 'sequence': scan['sequence'], 'temImg2lidar': [temImg2lidar] }) return img_metas def read_surround_imgs(self, img_paths): if hfai: imgs = self.img_loader.load(img_paths) else: imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:self.img_H, :self.img_W, :] for img in imgs] return imgs def __getitem__(self, index): #### 1. get color, temporal_depth choice if necessary if random.random() < self.cur_prob: temporal_supervision = 'curr' elif random.random() < self.prev_prob: temporal_supervision = 'prev' else: temporal_supervision = 'next' #### 2. get self, prev, next infos for the stem, and also temp_depth info while True: scan = deepcopy(self.scans[index]) sequence = scan['sequence'] # scan_id = scan['frame_id'] # day, seq = scan['sequence'] # is_right = scan['is_right'] # stereo_sign = 'r' if is_right else 'l' if temporal_supervision == 'curr': anchor_scan = deepcopy(scan) elif temporal_supervision == 'prev': if len(scan['prev_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_scan_id = np.random.choice(scan['prev_frame_ids']) anchor_scan = deepcopy(self.scans[self.frame2scan[str(sequence) + '_' + anchor_scan_id]]) else: if len(scan['next_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_scan_id = np.random.choice(scan['next_frame_ids']) anchor_scan = deepcopy(self.scans[self.frame2scan[str(sequence) + '_' + anchor_scan_id]]) if len(anchor_scan['prev_frame_ids']) == 0 or len(anchor_scan['next_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_prev = 0 if self.choose_nearest else np.random.randint(len(anchor_scan['prev_frame_ids'])) anchor_next = 0 if self.choose_nearest else np.random.randint(len(anchor_scan['next_frame_ids'])) break img_metas = self.prepare_img_metas( scan, anchor_scan, anchor_prev, anchor_next) if self.return_depth: depth_loc, depth_gt, depth_mask = self.get_depth_from_lidar( scan['lid_path'], img_metas['lidar2img'], [self.img_H, self.img_W]) img_metas.update({ 'depth_loc': depth_loc, 'depth_gt': depth_gt, 'depth_mask': depth_mask}) if self.return_sem: sem = self.load_2d_sem_label(anchor_scan) img_metas.update({'sem': sem}) # read 6 cams input_imgs = self.read_surround_imgs(img_metas['input_imgs_path']) curr_imgs = self.read_surround_imgs(img_metas['curr_imgs_path']) prev_imgs = self.read_surround_imgs(img_metas['prev_imgs_path']) next_imgs = self.read_surround_imgs(img_metas['next_imgs_path']) data_tuple = ([input_imgs, curr_imgs, prev_imgs, next_imgs], img_metas) return data_tuple if __name__ == "__main__": import os, sys print(sys.path) dataset = Kitti_Smart( 'train', root='data/kitti', preprocess_root='data/kitti/preprocess', cur_prob=1.0, crop_size=[352, 1216]) batch = dataset[0] import pdb; pdb.set_trace() ================================================ FILE: dataset/kitti/params.py ================================================ import numpy as np semantic_kitti_class_frequencies = np.array( [ 5.41773033e09, 1.57835390e07, 1.25136000e05, 1.18809000e05, 6.46799000e05, 8.21951000e05, 2.62978000e05, 2.83696000e05, 2.04750000e05, 6.16887030e07, 4.50296100e06, 4.48836500e07, 2.26992300e06, 5.68402180e07, 1.57196520e07, 1.58442623e08, 2.06162300e06, 3.69705220e07, 1.15198800e06, 3.34146000e05, ] ) kitti_class_names = [ "empty", "car", "bicycle", "motorcycle", "truck", "other-vehicle", "person", "bicyclist", "motorcyclist", "road", "parking", "sidewalk", "other-ground", "building", "fence", "vegetation", "trunk", "terrain", "pole", "traffic-sign", ] val_error_frames = [ '000015', "000020", "000025", "000030", "000035", "000040", "000045", "000050", "000055", "000075", "000060", "000000", "000010" ] ================================================ FILE: dataset/kitti/semantic-kitti.yaml ================================================ # This file is covered by the LICENSE file in the root of this project. nbr_classes: 20 grid_dims: [256, 32, 256] # (W, H, D) labels: 0 : "unlabeled" 1 : "outlier" 10: "car" 11: "bicycle" 13: "bus" 15: "motorcycle" 16: "on-rails" 18: "truck" 20: "other-vehicle" 30: "person" 31: "bicyclist" 32: "motorcyclist" 40: "road" 44: "parking" 48: "sidewalk" 49: "other-ground" 50: "building" 51: "fence" 52: "other-structure" 60: "lane-marking" 70: "vegetation" 71: "trunk" 72: "terrain" 80: "pole" 81: "traffic-sign" 99: "other-object" 252: "moving-car" 253: "moving-bicyclist" 254: "moving-person" 255: "moving-motorcyclist" 256: "moving-on-rails" 257: "moving-bus" 258: "moving-truck" 259: "moving-other-vehicle" color_map: # bgr 0 : [0, 0, 0] 1 : [0, 0, 255] 10: [245, 150, 100] 11: [245, 230, 100] 13: [250, 80, 100] 15: [150, 60, 30] 16: [255, 0, 0] 18: [180, 30, 80] 20: [255, 0, 0] 30: [30, 30, 255] 31: [200, 40, 255] 32: [90, 30, 150] 40: [255, 0, 255] 44: [255, 150, 255] 48: [75, 0, 75] 49: [75, 0, 175] 50: [0, 200, 255] 51: [50, 120, 255] 52: [0, 150, 255] 60: [170, 255, 150] 70: [0, 175, 0] 71: [0, 60, 135] 72: [80, 240, 150] 80: [150, 240, 255] 81: [0, 0, 255] 99: [255, 255, 50] 252: [245, 150, 100] 256: [255, 0, 0] 253: [200, 40, 255] 254: [30, 30, 255] 255: [90, 30, 150] 257: [250, 80, 100] 258: [180, 30, 80] 259: [255, 0, 0] content: # as a ratio with the total number of points 0: 0.018889854628292943 1: 0.0002937197336781505 10: 0.040818519255974316 11: 0.00016609538710764618 13: 2.7879693665067774e-05 15: 0.00039838616015114444 16: 0.0 18: 0.0020633612104619787 20: 0.0016218197275284021 30: 0.00017698551338515307 31: 1.1065903904919655e-08 32: 5.532951952459828e-09 40: 0.1987493871255525 44: 0.014717169549888214 48: 0.14392298360372 49: 0.0039048553037472045 50: 0.1326861944777486 51: 0.0723592229456223 52: 0.002395131480328884 60: 4.7084144280367186e-05 70: 0.26681502148037506 71: 0.006035012012626033 72: 0.07814222006271769 80: 0.002855498193863172 81: 0.0006155958086189918 99: 0.009923127583046915 252: 0.001789309418528068 253: 0.00012709999297008662 254: 0.00016059776092534436 255: 3.745553104802113e-05 256: 0.0 257: 0.00011351574470342043 258: 0.00010157861367183268 259: 4.3840131989471124e-05 # classes that are indistinguishable from single scan or inconsistent in # ground truth are mapped to their closest equivalent learning_map: 0 : 0 # "unlabeled" 1 : 0 # "outlier" mapped to "unlabeled" --------------------------mapped 10: 1 # "car" 11: 2 # "bicycle" 13: 5 # "bus" mapped to "other-vehicle" --------------------------mapped 15: 3 # "motorcycle" 16: 5 # "on-rails" mapped to "other-vehicle" ---------------------mapped 18: 4 # "truck" 20: 5 # "other-vehicle" 30: 6 # "person" 31: 7 # "bicyclist" 32: 8 # "motorcyclist" 40: 9 # "road" 44: 10 # "parking" 48: 11 # "sidewalk" 49: 12 # "other-ground" 50: 13 # "building" 51: 14 # "fence" 52: 0 # "other-structure" mapped to "unlabeled" ------------------mapped 60: 9 # "lane-marking" to "road" ---------------------------------mapped 70: 15 # "vegetation" 71: 16 # "trunk" 72: 17 # "terrain" 80: 18 # "pole" 81: 19 # "traffic-sign" 99: 0 # "other-object" to "unlabeled" ----------------------------mapped 252: 1 # "moving-car" to "car" ------------------------------------mapped 253: 7 # "moving-bicyclist" to "bicyclist" ------------------------mapped 254: 6 # "moving-person" to "person" ------------------------------mapped 255: 8 # "moving-motorcyclist" to "motorcyclist" ------------------mapped 256: 5 # "moving-on-rails" mapped to "other-vehicle" --------------mapped 257: 5 # "moving-bus" mapped to "other-vehicle" -------------------mapped 258: 4 # "moving-truck" to "truck" --------------------------------mapped 259: 5 # "moving-other"-vehicle to "other-vehicle" ----------------mapped learning_map_inv: # inverse of previous map 0: 0 # "unlabeled", and others ignored 1: 10 # "car" 2: 11 # "bicycle" 3: 15 # "motorcycle" 4: 18 # "truck" 5: 20 # "other-vehicle" 6: 30 # "person" 7: 31 # "bicyclist" 8: 32 # "motorcyclist" 9: 40 # "road" 10: 44 # "parking" 11: 48 # "sidewalk" 12: 49 # "other-ground" 13: 50 # "building" 14: 51 # "fence" 15: 70 # "vegetation" 16: 71 # "trunk" 17: 72 # "terrain" 18: 80 # "pole" 19: 81 # "traffic-sign" learning_ignore: # Ignore classes 0: True # "unlabeled", and others ignored 1: False # "car" 2: False # "bicycle" 3: False # "motorcycle" 4: False # "truck" 5: False # "other-vehicle" 6: False # "person" 7: False # "bicyclist" 8: False # "motorcyclist" 9: False # "road" 10: False # "parking" 11: False # "sidewalk" 12: False # "other-ground" 13: False # "building" 14: False # "fence" 15: False # "vegetation" 16: False # "trunk" 17: False # "terrain" 18: False # "pole" 19: False # "traffic-sign" split: # sequence numbers train: - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 7 - 9 - 10 valid: - 8 test: - 11 - 12 - 13 - 14 - 15 - 16 - 17 - 18 - 19 - 20 - 21 ================================================ FILE: dataset/kitti_raw/kitti_raw_dataset.py ================================================ import os, time, random from pathlib import Path from copy import deepcopy from collections import Counter import numpy as np from mmcv.image.io import imread from .. import OPENOCC_DATASET BASE_SIZES = { "2011_09_26": (375, 1242), "2011_09_28": (370, 1224), "2011_09_29": (374, 1238), "2011_09_30": (370, 1226), "2011_10_03": (376, 1241), } @OPENOCC_DATASET.register_module() class Kitti_Raw: def __init__( self, root, pose_path, split_path, frames_interval=0.4, sequence_distance=10, n_sources=1, eval_depth=80, eigen_depth=True, cur_prob=1.0, crop_size=[370, 1220], strict=True, return_depth=False, prev_prob=0.5, choose_nearest=False, **kwargs, ): self.root = root self.pose_path = pose_path self.split_path = split_path self.n_sources = n_sources self.eval_depth = eval_depth self.eigen_depth = eigen_depth self.cur_prob = cur_prob self.return_depth = return_depth self.prev_prob = prev_prob self.frames_interval = frames_interval self.sequence_distance = sequence_distance self.strict = strict self.choose_nearest = choose_nearest self.img_W = crop_size[1] self.img_H = crop_size[0] self.transxy = [ [0, -1., 0, 0], [1., 0, 0, 0], [0, 0, 1., 0], [0, 0, 0, 1.]] self.transxy = np.array(self.transxy) self._sequences = self._get_sequences(self.root) self._seq_lengths = {(day, seq): length for day, seq, length in self._sequences} self._calibs = self._load_calibs(self.root) self._poses = self._load_poses(self.pose_path, self._sequences) self._old_poses = self._load_poses('dataset/kitti_raw/orb-slam_poses', self._sequences) self._datapoints = self._load_split(self.split_path) self.length = len(self._datapoints) start_time = time.time() self.scans = [] self.frame2scan = {} max_length = 0 min_length = 50 # for i_sample, sample in enumerate(self._datapoints): for day, seq, length in self._sequences: if len(self._old_poses[(day, seq)]) == 0: continue for frame_id in range(length): for is_right in [False, True]: image_folder = "image_03" if is_right else "image_02" current_img_path = os.path.join(self.root, day, seq, image_folder, "data", f"{frame_id:010d}.png") current_lid_path = os.path.join(self.root, day, seq, "velodyne_points", "data", f"{frame_id:010d}.bin") assert os.path.exists(current_img_path), current_img_path curr_pose = self._poses[(day, seq)][frame_id, :, :] curr_xyz = curr_pose[:3, 3] calib = self._calibs[day] P = calib["P_2"] if not is_right else calib["P_3"] T_cam0_2_cam2 = calib['T_cam0_2_cam2'] if not is_right else calib['T_cam0_2_cam3'] T_cam2_2_cam0 = np.linalg.inv(T_cam0_2_cam2) T_velo_2_img = calib['P_v2cr'] if is_right else calib['P_v2cl'] prev_frame_ids, next_frame_ids = [], [] prev_img_paths, next_img_paths = [], [] prev_lid_paths, next_lid_paths = [], [] prev_poses, next_poses = [], [] prev_dists, next_dists = [], [] pos_step = 1 neg_step = -1 # deal with prev cnt = 0 while True: cnt += neg_step rel_frame_id = frame_id + cnt img_path = os.path.join(self.root, day, seq, image_folder, "data", f"{rel_frame_id:010d}.png") if not os.path.exists(img_path): break temp_pose = self._poses[(day, seq)][rel_frame_id, :, :] temp_xyz = temp_pose[:3, 3] dist = np.linalg.norm(temp_xyz - curr_xyz) if dist < frames_interval: continue if dist > sequence_distance: break prev_frame_ids.append(rel_frame_id) prev_img_paths.append(img_path) lidar_path = os.path.join(self.root, day, seq, "velodyne_points", "data", f"{rel_frame_id:010d}.bin") prev_lid_paths.append(lidar_path) prev_poses.append(temp_pose) prev_dists.append(dist) # deal with next cnt = 0 while True: cnt += pos_step rel_frame_id = frame_id + cnt img_path = os.path.join(self.root, day, seq, image_folder, "data", f"{rel_frame_id:010d}.png") if not os.path.exists(img_path): break temp_pose = self._poses[(day, seq)][rel_frame_id, :, :] temp_xyz = temp_pose[:3, 3] dist = np.linalg.norm(temp_xyz - curr_xyz) if dist < frames_interval: continue if dist > sequence_distance: break next_frame_ids.append(rel_frame_id) next_img_paths.append(img_path) lidar_path = os.path.join(self.root, day, seq, "velodyne_points", "data", f"{rel_frame_id:010d}.bin") next_lid_paths.append(lidar_path) next_poses.append(temp_pose) next_dists.append(dist) prev_next_len = len(prev_poses) + len(next_poses) if prev_next_len > max_length: max_length = prev_next_len if prev_next_len < min_length: min_length = prev_next_len stereo_sign = 'r' if is_right else 'l' self.frame2scan.update({day + '/' + seq + "_" + str(frame_id) + '_' + stereo_sign: len(self.scans)}) if not self.strict: prev_img_paths.append(current_img_path) prev_lid_paths.append(current_lid_path) next_img_paths.append(current_img_path) next_lid_paths.append(current_lid_path) prev_poses.append(curr_pose) next_poses.append(curr_pose) prev_dists.append(0.) next_dists.append(0.) prev_frame_ids.append(frame_id) next_frame_ids.append(frame_id) self.scans.append({ "frame_id": frame_id, "sequence": (day, seq), "img_path": current_img_path, "lid_path": current_lid_path, "pose": curr_pose, "is_right": is_right, "prev_img_paths": prev_img_paths, "prev_lid_paths": prev_lid_paths, "next_img_paths": next_img_paths, "next_lid_paths": next_lid_paths, "T_velo_2_img": T_velo_2_img, "T_velo_2_cam0": calib['P_v2c0'], "T_velo_2_cam": T_cam0_2_cam2 @ calib['P_v2c0'], "P": P, "T_cam0_2_cam2": T_cam0_2_cam2, "T_cam2_2_cam0": T_cam2_2_cam0, "prev_poses": prev_poses, "next_poses": next_poses, "prev_dists": prev_dists, "next_dists": next_dists, "prev_frame_ids": prev_frame_ids, "next_frame_ids": next_frame_ids }) print(min_length, max_length) print("Preprocess time: --- %s seconds ---" % (time.time() - start_time)) @staticmethod def _get_sequences(data_path): all_sequences = [] data_path = Path(data_path) for day in data_path.iterdir(): if not day.is_dir(): continue day_sequences = [seq for seq in day.iterdir() if seq.is_dir()] lengths = [len(list((seq / "image_02" / "data").iterdir())) for seq in day_sequences] day_sequences = [(day.name, seq.name, length) for seq, length in zip(day_sequences, lengths)] all_sequences.extend(day_sequences) return all_sequences @staticmethod def _load_split(split_path): with open(split_path, "r") as f: lines = f.readlines() def split_line(l): segments = l.split(" ") day, sequence = segments[0].split("/") # (day, sequence, id, is_right) return day, sequence, int(segments[1]), segments[2] == "r" return list(map(split_line, lines)) @staticmethod def _load_calibs(data_path): calibs = {} for day in BASE_SIZES.keys(): day_folder = Path(data_path) / day cam_calib_file = day_folder / "calib_cam_to_cam.txt" velo_calib_file = day_folder / "calib_velo_to_cam.txt" cam_calib_file_data = {} with open(cam_calib_file, 'r') as f: for line in f.readlines(): key, value = line.split(':', 1) try: cam_calib_file_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32) except ValueError: pass velo_calib_file_data = {} with open(velo_calib_file, 'r') as f: for line in f.readlines(): key, value = line.split(':', 1) try: velo_calib_file_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32) except ValueError: pass # Create 3x4 projection matrices P_rect_l = np.reshape(cam_calib_file_data['P_rect_02'], (3, 4)) P_rect_l = np.vstack((P_rect_l, np.array([0, 0, 0, 1.0], dtype=np.float32))) P_rect_r = np.reshape(cam_calib_file_data['P_rect_03'], (3, 4)) P_rect_r = np.vstack((P_rect_r, np.array([0, 0, 0, 1.0], dtype=np.float32))) R_rect = np.eye(4, dtype=np.float32) R_rect[:3, :3] = cam_calib_file_data['R_rect_00'].reshape(3, 3) T_v2c = np.hstack((velo_calib_file_data['R'].reshape(3, 3), velo_calib_file_data['T'][..., np.newaxis])) T_v2c = np.vstack((T_v2c, np.array([0, 0, 0, 1.0], dtype=np.float32))) P_v2c0 = R_rect @ T_v2c P_v2cl = P_rect_l @ P_v2c0 P_v2cr = P_rect_r @ P_v2c0 T_cam0_2_cam2 = np.eye(4) T_cam0_2_cam2[0, 3] = P_rect_l[0, 3] / P_rect_l[0, 0] T_cam0_2_cam3 = np.eye(4) T_cam0_2_cam3[0, 3] = P_rect_r[0, 3] / P_rect_r[0, 0] calibs[day] = { "P_v2c0": P_v2c0, "P_v2cl": P_v2cl, "P_v2cr": P_v2cr, "T_cam0_2_cam2": T_cam0_2_cam2, "T_cam0_2_cam3": T_cam0_2_cam3, "P_2": P_rect_l[:3, :3], "P_3": P_rect_r[:3, :3]} return calibs @staticmethod def _load_poses(pose_path, sequences): poses = {} for day, seq, _ in sequences: pose_file = Path(pose_path) / day / f"{seq}.txt" poses_seq = [] try: with open(pose_file, 'r') as f: lines = f.readlines() for line in lines: T_w_cam0 = np.fromstring(line, dtype=float, sep=' ') T_w_cam0 = T_w_cam0.reshape(3, 4) T_w_cam0 = np.vstack((T_w_cam0, [0, 0, 0, 1])) poses_seq.append(T_w_cam0) except FileNotFoundError: pass # print(f'Ground truth poses are not avaialble for sequence {seq}.') poses_seq = np.array(poses_seq, dtype=np.float32) poses[(day, seq)] = poses_seq return poses def get_depth_from_lidar(self, lidar_path, lidar2img, image_size): # lidar2img: N, 4, 4 scan = np.fromfile(lidar_path, dtype=np.float32) scan = scan.reshape((-1, 4)) scan[:, 3] = 1.0 # points_hcoords = scan[scan[:, 0] > 0, :] points_hcoords = np.expand_dims(self.transxy @ scan.T, 0) # 1, 4, n img_points = np.transpose(lidar2img @ points_hcoords, (0, 2, 1)) # N, n, 4 depth = img_points[..., 2] # N, n img_points = img_points[..., :2] # N, n, 2 mask = (depth < self.eval_depth) & (depth > 0.1) # get points with depth < max_sample_depth img_points = img_points / np.expand_dims(depth, axis=2) # scale 2D points img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] # img_points = np.round(img_points).astype(int) mask = mask & (img_points[..., 0] > 0) & \ (img_points[..., 1] > 0) & \ (img_points[..., 0] < 1) & \ (img_points[..., 1] < 1) if self.eigen_depth: mask = mask & (img_points[..., 0] > 0.03594771) & \ (img_points[..., 1] > 0.40810811) & \ (img_points[..., 0] < 0.96405229) & \ (img_points[..., 1] < 0.99189189) return img_points, depth, mask def load_depth(self, lidar_path, lidar2img, image_size): points = np.fromfile(lidar_path, dtype=np.float32).reshape(-1, 4) points[:, 3] = 1.0 points = points[points[:, 0] >= 0, :] # project the points to the camera points = (self.transxy @ points.T).T # n, 4 velo_pts_im = np.dot(lidar2img[0], points.T).T velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., np.newaxis] # check if in bounds # use minus 1 to get the exact same value as KITTI matlab code velo_pts_im[:, 0] = np.round(velo_pts_im[:, 0]) - 1 velo_pts_im[:, 1] = np.round(velo_pts_im[:, 1]) - 1 val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) val_inds = val_inds & (velo_pts_im[:, 0] < image_size[1]) & (velo_pts_im[:, 1] < image_size[0]) velo_pts_im = velo_pts_im[val_inds, :] # project to image depth = np.zeros(image_size) depth[velo_pts_im[:, 1].astype(np.int64), velo_pts_im[:, 0].astype(np.int64)] = velo_pts_im[:, 2] # find the duplicate points and choose the closest depth inds = velo_pts_im[:, 1] * (image_size[1] - 1) + velo_pts_im[:, 0] - 1 dupe_inds = [item for item, count in Counter(inds).items() if count > 1] for dd in dupe_inds: pts = np.where(inds == dd)[0] x_loc = int(velo_pts_im[pts[0], 0]) y_loc = int(velo_pts_im[pts[0], 1]) depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() depth[depth < 0] = 0 if self.eigen_depth: mask = np.logical_and(depth > 1e-3, depth < self.eval_depth) crop = np.array([0.40810811 * image_size[0], 0.99189189 * image_size[0], 0.03594771 * image_size[1], 0.96405229 * image_size[1]]).astype(np.int32) crop_mask = np.zeros(mask.shape) crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1 mask = np.logical_and(mask, crop_mask) depth[~mask] = 0 img_points = np.nonzero(depth) depth = depth[img_points][None, ...] img_points = np.stack((img_points[1], img_points[0]), axis=-1, dtype=np.float32) img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] return img_points[None, ...], depth, np.ones_like(depth) > 0 def __len__(self): return self.length def prepare_img_metas(self, scan, anchor_scan, anchor_prev, anchor_next): img_metas = {} img_metas.update({ 'input_imgs_path': [scan['img_path']], 'curr_imgs_path': [anchor_scan['img_path']], 'prev_imgs_path': [anchor_scan['prev_img_paths'][anchor_prev]], 'next_imgs_path': [anchor_scan['next_img_paths'][anchor_next]] }) intrinsic = np.eye(4) intrinsic[:3, :3] = scan['P'][:3, :3] lidar2img = scan['T_velo_2_img'] @ np.linalg.inv(self.transxy) img2lidar = np.linalg.inv(lidar2img) # img2prevImg = lidar2img @ \ # np.linalg.inv(anchor_scan['T_velo_2_cam0']) @ \ # np.linalg.inv(anchor_scan['prev_poses'][anchor_prev]) @ \ # anchor_scan['pose'] @ \ # anchor_scan['T_velo_2_cam0'] @ \ # img2lidar img2prevImg = intrinsic @ \ anchor_scan['T_cam0_2_cam2'] @ \ np.linalg.inv(anchor_scan['prev_poses'][anchor_prev]) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) # img2nextImg = lidar2img @ \ # np.linalg.inv(anchor_scan['T_velo_2_cam0']) @ \ # np.linalg.inv(anchor_scan['next_poses'][anchor_next]) @ \ # anchor_scan['pose'] @ \ # anchor_scan['T_velo_2_cam0'] @ \ # img2lidar img2nextImg = intrinsic @ \ anchor_scan['T_cam0_2_cam2'] @ \ np.linalg.inv(anchor_scan['next_poses'][anchor_next]) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) # temImg2lidar = np.linalg.inv(scan['T_velo_2_cam0']) @ \ # np.linalg.inv(scan['pose']) @ \ # anchor_scan['pose'] @ \ # anchor_scan['T_velo_2_cam0'] @ \ # img2lidar temImg2lidar = self.transxy @ np.linalg.inv(scan['T_velo_2_cam']) @ \ scan['T_cam0_2_cam2'] @ \ np.linalg.inv(scan['pose']) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) img_metas.update({ 'lidar2img': np.expand_dims(lidar2img, axis=0), 'img2lidar': [img2lidar], 'img2prevImg': [img2prevImg], 'img2nextImg': [img2nextImg], 'token': scan['frame_id'], 'sequence': scan['sequence'], 'temImg2lidar': [temImg2lidar] }) return img_metas def read_surround_imgs(self, img_paths): imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:self.img_H, :self.img_W, :] for img in imgs] return imgs def to_scan_idx(self, index): day, seq, frame_id, is_right = self._datapoints[index] stereo_sign = 'r' if is_right else 'l' key = day + '/' + seq + "_" + str(frame_id) + '_' + stereo_sign return self.frame2scan[key] def __getitem__(self, index): #### 1. get color, temporal_depth choice if necessary if random.random() < self.cur_prob: temporal_supervision = 'curr' elif random.random() < self.prev_prob: temporal_supervision = 'prev' else: temporal_supervision = 'next' #### 2. get self, prev, next infos for the stem, and also temp_depth info while True: scan = deepcopy(self.scans[self.to_scan_idx(index)]) scan_id = scan['frame_id'] day, seq = scan['sequence'] is_right = scan['is_right'] stereo_sign = 'r' if is_right else 'l' if temporal_supervision == 'curr': anchor_scan = deepcopy(scan) elif temporal_supervision == 'prev': if len(scan['prev_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_scan = np.random.choice(scan['prev_frame_ids']) anchor_scan = deepcopy(self.scans[self.frame2scan[day + '/' + seq + "_" + str(anchor_scan) + '_' + stereo_sign]]) else: if len(scan['next_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_scan = np.random.choice(scan['next_frame_ids']) anchor_scan = deepcopy(self.scans[self.frame2scan[day + '/' + seq + "_" + str(anchor_scan) + '_' + stereo_sign]]) if len(anchor_scan['prev_frame_ids']) == 0 or len(anchor_scan['next_frame_ids']) == 0: index = np.random.randint(len(self)) continue if self.choose_nearest: anchor_prev = 0 anchor_next = 0 else: anchor_prev = np.random.randint(len(anchor_scan['prev_frame_ids'])) anchor_next = np.random.randint(len(anchor_scan['next_frame_ids'])) break img_metas = self.prepare_img_metas( scan, anchor_scan, anchor_prev, anchor_next) if self.return_depth: depth_loc, depth_gt, depth_mask = self.get_depth_from_lidar( scan['lid_path'], img_metas['lidar2img'], [self.img_H, self.img_W]) img_metas.update({ 'depth_loc': depth_loc, 'depth_gt': depth_gt, 'depth_mask': depth_mask}) # read 6 cams input_imgs = self.read_surround_imgs(img_metas['input_imgs_path']) curr_imgs = self.read_surround_imgs(img_metas['curr_imgs_path']) prev_imgs = self.read_surround_imgs(img_metas['prev_imgs_path']) next_imgs = self.read_surround_imgs(img_metas['next_imgs_path']) data_tuple = ([input_imgs, curr_imgs, prev_imgs, next_imgs], img_metas) return data_tuple if __name__ == "__main__": import os, sys print(sys.path) data_path = 'data/kitti_raw/' img_size = [352, 1216] dataset = Kitti_Raw( root = data_path, pose_path = 'dataset/kitti_raw/orb-slam_poses', split_path = 'dataset/kitti_raw/splits/eigen_zhou/train_files.txt', frames_interval=0.4, sequence_distance=10, cur_prob=0.333, crop_size=img_size, strict=True, return_depth=False, prev_prob=0.5, choose_nearest = True, ) batch = dataset[0] import pdb; pdb.set_trace() ================================================ FILE: dataset/kitti_raw/kitti_raw_dataset_stereo.py ================================================ import os, time, random from pathlib import Path from copy import deepcopy from collections import Counter import numpy as np from mmcv.image.io import imread from .. import OPENOCC_DATASET BASE_SIZES = { "2011_09_26": (375, 1242), "2011_09_28": (370, 1224), "2011_09_29": (374, 1238), "2011_09_30": (370, 1226), "2011_10_03": (376, 1241), } @OPENOCC_DATASET.register_module() class Kitti_Raw_Stereo: """ Only support current timestamp supervision. """ def __init__( self, root, pose_path, split_path, frames_interval=0.4, sequence_distance=10, n_sources=1, eval_depth=80, eigen_depth=True, cur_prob=1.0, crop_size=[370, 1220], strict=True, return_depth=False, prev_prob=0.5, choose_nearest=False, include_stereo=False, **kwargs, ): self.root = root self.pose_path = pose_path self.split_path = split_path self.n_sources = n_sources self.eval_depth = eval_depth self.eigen_depth = eigen_depth self.cur_prob = cur_prob assert cur_prob == 1. self.return_depth = return_depth self.prev_prob = prev_prob self.frames_interval = frames_interval self.sequence_distance = sequence_distance self.strict = strict self.choose_nearest = choose_nearest self.include_stereo = include_stereo self.img_W = crop_size[1] self.img_H = crop_size[0] self.transxy = [ [0, -1., 0, 0], [1., 0, 0, 0], [0, 0, 1., 0], [0, 0, 0, 1.]] self.transxy = np.array(self.transxy) self._sequences = self._get_sequences(self.root) self._seq_lengths = {(day, seq): length for day, seq, length in self._sequences} self._calibs = self._load_calibs(self.root) self._poses = self._load_poses(self.pose_path, self._sequences) self._old_poses = self._load_poses('dataset/kitti_raw/orb-slam_poses', self._sequences) self._datapoints = self._load_split(self.split_path) self.length = len(self._datapoints) start_time = time.time() self.scans = [] self.frame2scan = {} max_length = 0 min_length = 50 # for i_sample, sample in enumerate(self._datapoints): for day, seq, length in self._sequences: if len(self._old_poses[(day, seq)]) == 0: continue for frame_id in range(length): for is_right in [False, True]: image_folder = "image_03" if is_right else "image_02" current_img_path = os.path.join(self.root, day, seq, image_folder, "data", f"{frame_id:010d}.png") current_lid_path = os.path.join(self.root, day, seq, "velodyne_points", "data", f"{frame_id:010d}.bin") assert os.path.exists(current_img_path), current_img_path curr_pose = self._poses[(day, seq)][frame_id, :, :] curr_xyz = curr_pose[:3, 3] calib = self._calibs[day] P = calib["P_2"] if not is_right else calib["P_3"] T_cam0_2_cam2 = calib['T_cam0_2_cam2'] if not is_right else calib['T_cam0_2_cam3'] T_cam2_2_cam0 = np.linalg.inv(T_cam0_2_cam2) T_cam0_2_cam3 = calib['T_cam0_2_cam2'] if is_right else calib['T_cam0_2_cam3'] T_velo_2_img = calib['P_v2cr'] if is_right else calib['P_v2cl'] prev_frame_ids, next_frame_ids = [], [] prev_img_paths, next_img_paths = [], [] prev_lid_paths, next_lid_paths = [], [] prev_poses, next_poses = [], [] prev_dists, next_dists = [], [] pos_step = 1 neg_step = -1 # deal with prev cnt = 0 while True: cnt += neg_step rel_frame_id = frame_id + cnt img_path = os.path.join(self.root, day, seq, image_folder, "data", f"{rel_frame_id:010d}.png") if not os.path.exists(img_path): break temp_pose = self._poses[(day, seq)][rel_frame_id, :, :] temp_xyz = temp_pose[:3, 3] dist = np.linalg.norm(temp_xyz - curr_xyz) if dist < frames_interval: continue if dist > sequence_distance: break prev_frame_ids.append(rel_frame_id) prev_img_paths.append(img_path) lidar_path = os.path.join(self.root, day, seq, "velodyne_points", "data", f"{rel_frame_id:010d}.bin") prev_lid_paths.append(lidar_path) prev_poses.append(temp_pose) prev_dists.append(dist) # deal with next cnt = 0 while True: cnt += pos_step rel_frame_id = frame_id + cnt img_path = os.path.join(self.root, day, seq, image_folder, "data", f"{rel_frame_id:010d}.png") if not os.path.exists(img_path): break temp_pose = self._poses[(day, seq)][rel_frame_id, :, :] temp_xyz = temp_pose[:3, 3] dist = np.linalg.norm(temp_xyz - curr_xyz) if dist < frames_interval: continue if dist > sequence_distance: break next_frame_ids.append(rel_frame_id) next_img_paths.append(img_path) lidar_path = os.path.join(self.root, day, seq, "velodyne_points", "data", f"{rel_frame_id:010d}.bin") next_lid_paths.append(lidar_path) next_poses.append(temp_pose) next_dists.append(dist) prev_next_len = len(prev_poses) + len(next_poses) if prev_next_len > max_length: max_length = prev_next_len if prev_next_len < min_length: min_length = prev_next_len stereo_sign = 'r' if is_right else 'l' self.frame2scan.update({day + '/' + seq + "_" + str(frame_id) + '_' + stereo_sign: len(self.scans)}) if not self.strict: prev_img_paths.append(current_img_path) prev_lid_paths.append(current_lid_path) next_img_paths.append(current_img_path) next_lid_paths.append(current_lid_path) prev_poses.append(curr_pose) next_poses.append(curr_pose) prev_dists.append(0.) next_dists.append(0.) prev_frame_ids.append(frame_id) next_frame_ids.append(frame_id) self.scans.append({ "frame_id": frame_id, "sequence": (day, seq), "img_path": current_img_path, "lid_path": current_lid_path, "pose": curr_pose, "is_right": is_right, "prev_img_paths": prev_img_paths, "prev_lid_paths": prev_lid_paths, "next_img_paths": next_img_paths, "next_lid_paths": next_lid_paths, "T_velo_2_img": T_velo_2_img, "T_velo_2_cam0": calib['P_v2c0'], "T_velo_2_cam": T_cam0_2_cam2 @ calib['P_v2c0'], "P": P, "T_cam0_2_cam2": T_cam0_2_cam2, "T_cam2_2_cam0": T_cam2_2_cam0, 'T_cam0_2_cam3': T_cam0_2_cam3, "prev_poses": prev_poses, "next_poses": next_poses, "prev_dists": prev_dists, "next_dists": next_dists, "prev_frame_ids": prev_frame_ids, "next_frame_ids": next_frame_ids }) print(min_length, max_length) print("Preprocess time: --- %s seconds ---" % (time.time() - start_time)) @staticmethod def _get_sequences(data_path): all_sequences = [] data_path = Path(data_path) for day in data_path.iterdir(): if not day.is_dir(): continue day_sequences = [seq for seq in day.iterdir() if seq.is_dir()] lengths = [len(list((seq / "image_02" / "data").iterdir())) for seq in day_sequences] day_sequences = [(day.name, seq.name, length) for seq, length in zip(day_sequences, lengths)] all_sequences.extend(day_sequences) return all_sequences @staticmethod def _load_split(split_path): with open(split_path, "r") as f: lines = f.readlines() def split_line(l): segments = l.split(" ") day, sequence = segments[0].split("/") # (day, sequence, id, is_right) return day, sequence, int(segments[1]), segments[2] == "r" return list(map(split_line, lines)) @staticmethod def _load_calibs(data_path): calibs = {} for day in BASE_SIZES.keys(): day_folder = Path(data_path) / day cam_calib_file = day_folder / "calib_cam_to_cam.txt" velo_calib_file = day_folder / "calib_velo_to_cam.txt" cam_calib_file_data = {} with open(cam_calib_file, 'r') as f: for line in f.readlines(): key, value = line.split(':', 1) try: cam_calib_file_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32) except ValueError: pass velo_calib_file_data = {} with open(velo_calib_file, 'r') as f: for line in f.readlines(): key, value = line.split(':', 1) try: velo_calib_file_data[key] = np.array([float(x) for x in value.split()], dtype=np.float32) except ValueError: pass # Create 3x4 projection matrices P_rect_l = np.reshape(cam_calib_file_data['P_rect_02'], (3, 4)) P_rect_l = np.vstack((P_rect_l, np.array([0, 0, 0, 1.0], dtype=np.float32))) P_rect_r = np.reshape(cam_calib_file_data['P_rect_03'], (3, 4)) P_rect_r = np.vstack((P_rect_r, np.array([0, 0, 0, 1.0], dtype=np.float32))) R_rect = np.eye(4, dtype=np.float32) R_rect[:3, :3] = cam_calib_file_data['R_rect_00'].reshape(3, 3) T_v2c = np.hstack((velo_calib_file_data['R'].reshape(3, 3), velo_calib_file_data['T'][..., np.newaxis])) T_v2c = np.vstack((T_v2c, np.array([0, 0, 0, 1.0], dtype=np.float32))) P_v2c0 = R_rect @ T_v2c P_v2cl = P_rect_l @ P_v2c0 P_v2cr = P_rect_r @ P_v2c0 T_cam0_2_cam2 = np.eye(4) T_cam0_2_cam2[0, 3] = P_rect_l[0, 3] / P_rect_l[0, 0] T_cam0_2_cam3 = np.eye(4) T_cam0_2_cam3[0, 3] = P_rect_r[0, 3] / P_rect_r[0, 0] calibs[day] = { "P_v2c0": P_v2c0, "P_v2cl": P_v2cl, "P_v2cr": P_v2cr, "T_cam0_2_cam2": T_cam0_2_cam2, "T_cam0_2_cam3": T_cam0_2_cam3, "P_2": P_rect_l[:3, :3], "P_3": P_rect_r[:3, :3]} return calibs @staticmethod def _load_poses(pose_path, sequences): poses = {} for day, seq, _ in sequences: pose_file = Path(pose_path) / day / f"{seq}.txt" poses_seq = [] try: with open(pose_file, 'r') as f: lines = f.readlines() for line in lines: T_w_cam0 = np.fromstring(line, dtype=float, sep=' ') T_w_cam0 = T_w_cam0.reshape(3, 4) T_w_cam0 = np.vstack((T_w_cam0, [0, 0, 0, 1])) poses_seq.append(T_w_cam0) except FileNotFoundError: pass # print(f'Ground truth poses are not avaialble for sequence {seq}.') poses_seq = np.array(poses_seq, dtype=np.float32) poses[(day, seq)] = poses_seq return poses def get_depth_from_lidar(self, lidar_path, lidar2img, image_size): # lidar2img: N, 4, 4 scan = np.fromfile(lidar_path, dtype=np.float32) scan = scan.reshape((-1, 4)) scan[:, 3] = 1.0 # points_hcoords = scan[scan[:, 0] > 0, :] points_hcoords = np.expand_dims(self.transxy @ scan.T, 0) # 1, 4, n img_points = np.transpose(lidar2img @ points_hcoords, (0, 2, 1)) # N, n, 4 depth = img_points[..., 2] # N, n img_points = img_points[..., :2] # N, n, 2 mask = (depth < self.eval_depth) & (depth > 0.1) # get points with depth < max_sample_depth img_points = img_points / np.expand_dims(depth, axis=2) # scale 2D points img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] # img_points = np.round(img_points).astype(int) mask = mask & (img_points[..., 0] > 0) & \ (img_points[..., 1] > 0) & \ (img_points[..., 0] < 1) & \ (img_points[..., 1] < 1) if self.eigen_depth: mask = mask & (img_points[..., 0] > 0.03594771) & \ (img_points[..., 1] > 0.40810811) & \ (img_points[..., 0] < 0.96405229) & \ (img_points[..., 1] < 0.99189189) return img_points, depth, mask def load_depth(self, lidar_path, lidar2img, image_size): points = np.fromfile(lidar_path, dtype=np.float32).reshape(-1, 4) points[:, 3] = 1.0 points = points[points[:, 0] >= 0, :] # project the points to the camera points = (self.transxy @ points.T).T # n, 4 velo_pts_im = np.dot(lidar2img[0], points.T).T velo_pts_im[:, :2] = velo_pts_im[:, :2] / velo_pts_im[:, 2][..., np.newaxis] # check if in bounds # use minus 1 to get the exact same value as KITTI matlab code velo_pts_im[:, 0] = np.round(velo_pts_im[:, 0]) - 1 velo_pts_im[:, 1] = np.round(velo_pts_im[:, 1]) - 1 val_inds = (velo_pts_im[:, 0] >= 0) & (velo_pts_im[:, 1] >= 0) val_inds = val_inds & (velo_pts_im[:, 0] < image_size[1]) & (velo_pts_im[:, 1] < image_size[0]) velo_pts_im = velo_pts_im[val_inds, :] # project to image depth = np.zeros(image_size) depth[velo_pts_im[:, 1].astype(np.int64), velo_pts_im[:, 0].astype(np.int64)] = velo_pts_im[:, 2] # find the duplicate points and choose the closest depth inds = velo_pts_im[:, 1] * (image_size[1] - 1) + velo_pts_im[:, 0] - 1 dupe_inds = [item for item, count in Counter(inds).items() if count > 1] for dd in dupe_inds: pts = np.where(inds == dd)[0] x_loc = int(velo_pts_im[pts[0], 0]) y_loc = int(velo_pts_im[pts[0], 1]) depth[y_loc, x_loc] = velo_pts_im[pts, 2].min() depth[depth < 0] = 0 if self.eigen_depth: mask = np.logical_and(depth > 1e-3, depth < self.eval_depth) crop = np.array([0.40810811 * image_size[0], 0.99189189 * image_size[0], 0.03594771 * image_size[1], 0.96405229 * image_size[1]]).astype(np.int32) crop_mask = np.zeros(mask.shape) crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1 mask = np.logical_and(mask, crop_mask) depth[~mask] = 0 img_points = np.nonzero(depth) depth = depth[img_points][None, ...] img_points = np.stack((img_points[1], img_points[0]), axis=-1, dtype=np.float32) img_points[..., 0] = img_points[..., 0] / image_size[1] img_points[..., 1] = img_points[..., 1] / image_size[0] return img_points[None, ...], depth, np.ones_like(depth) > 0 def __len__(self): return self.length def prepare_img_metas(self, scan, anchor_scan, anchor_prev, anchor_next): img_metas = {} img_metas.update({ 'input_imgs_path': [scan['img_path']], 'curr_imgs_path': [anchor_scan['img_path']], 'prev_imgs_path': [anchor_scan['prev_img_paths'][anchor_prev]], 'next_imgs_path': [anchor_scan['next_img_paths'][anchor_next]] }) intrinsic = np.eye(4) intrinsic[:3, :3] = scan['P'][:3, :3] lidar2img = scan['T_velo_2_img'] @ np.linalg.inv(self.transxy) img2lidar = np.linalg.inv(lidar2img) # img2prevImg = lidar2img @ \ # np.linalg.inv(anchor_scan['T_velo_2_cam0']) @ \ # np.linalg.inv(anchor_scan['prev_poses'][anchor_prev]) @ \ # anchor_scan['pose'] @ \ # anchor_scan['T_velo_2_cam0'] @ \ # img2lidar img2prevImg = intrinsic @ \ anchor_scan['T_cam0_2_cam2'] @ \ np.linalg.inv(anchor_scan['prev_poses'][anchor_prev]) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) # img2nextImg = lidar2img @ \ # np.linalg.inv(anchor_scan['T_velo_2_cam0']) @ \ # np.linalg.inv(anchor_scan['next_poses'][anchor_next]) @ \ # anchor_scan['pose'] @ \ # anchor_scan['T_velo_2_cam0'] @ \ # img2lidar img2nextImg = intrinsic @ \ anchor_scan['T_cam0_2_cam2'] @ \ np.linalg.inv(anchor_scan['next_poses'][anchor_next]) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) # temImg2lidar = np.linalg.inv(scan['T_velo_2_cam0']) @ \ # np.linalg.inv(scan['pose']) @ \ # anchor_scan['pose'] @ \ # anchor_scan['T_velo_2_cam0'] @ \ # img2lidar temImg2lidar = self.transxy @ np.linalg.inv(scan['T_velo_2_cam']) @ \ scan['T_cam0_2_cam2'] @ \ np.linalg.inv(scan['pose']) @ \ anchor_scan['pose'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(intrinsic) img_metas.update({ 'lidar2img': np.expand_dims(lidar2img, axis=0), 'img2lidar': [img2lidar], 'img2prevImg': [img2prevImg], 'img2nextImg': [img2nextImg], 'token': scan['frame_id'], 'sequence': scan['sequence'], 'temImg2lidar': [temImg2lidar], 'intrinsic': intrinsic }) return img_metas def read_surround_imgs(self, img_paths): imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:self.img_H, :self.img_W, :] for img in imgs] return imgs def to_scan_idx(self, index): day, seq, frame_id, is_right = self._datapoints[index] stereo_sign = 'r' if is_right else 'l' key = day + '/' + seq + "_" + str(frame_id) + '_' + stereo_sign return self.frame2scan[key] def append_self(self, flag, obj): obj[flag + '_img_paths'].append(obj['img_path']) obj[flag + '_lid_paths'].append(obj['lid_path']) obj[flag + '_poses'].append(obj['pose']) obj[flag + '_dists'].append(0.) obj[flag + '_frame_ids'].append(obj['frame_id']) return def __getitem__(self, index): #### 1. get color, temporal_depth choice if necessary if random.random() < self.cur_prob: temporal_supervision = 'curr' elif random.random() < self.prev_prob: temporal_supervision = 'prev' else: temporal_supervision = 'next' #### 2. get self, prev, next infos for the stem, and also temp_depth info while True: scan = deepcopy(self.scans[self.to_scan_idx(index)]) scan_id = scan['frame_id'] day, seq = scan['sequence'] is_right = scan['is_right'] stereo_sign = 'r' if is_right else 'l' if temporal_supervision == 'curr': anchor_scan = deepcopy(scan) elif temporal_supervision == 'prev': if len(scan['prev_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_scan = np.random.choice(scan['prev_frame_ids']) anchor_scan = deepcopy(self.scans[self.frame2scan[day + '/' + seq + "_" + str(anchor_scan) + '_' + stereo_sign]]) else: if len(scan['next_frame_ids']) == 0: index = np.random.randint(len(self)) continue anchor_scan = np.random.choice(scan['next_frame_ids']) anchor_scan = deepcopy(self.scans[self.frame2scan[day + '/' + seq + "_" + str(anchor_scan) + '_' + stereo_sign]]) if len(anchor_scan['prev_frame_ids']) == 0 and len(anchor_scan['next_frame_ids']) == 0: index = np.random.randint(len(self)) continue if len(anchor_scan['prev_frame_ids']) == 0: self.append_self('prev', anchor_scan) target_sign = 'ns' elif len(anchor_scan['next_frame_ids']) == 0: self.append_self('next', anchor_scan) target_sign = 'ps' else: if random.random() < 0.333: target_sign = 'pn' elif random.random() < 0.5: target_sign = 'ps' else: target_sign = 'ns' if self.choose_nearest: anchor_prev = 0 anchor_next = 0 else: anchor_prev = np.random.randint(len(anchor_scan['prev_frame_ids'])) anchor_next = np.random.randint(len(anchor_scan['next_frame_ids'])) break img_metas = self.prepare_img_metas( scan, anchor_scan, anchor_prev, anchor_next) if target_sign == 'ns': anchor_img_path = img_metas['curr_imgs_path'][0] if stereo_sign == 'l': img_metas['prev_imgs_path'] = [anchor_img_path.replace('image_02', 'image_03')] else: img_metas['prev_imgs_path'] = [anchor_img_path.replace('image_03', 'image_02')] img_metas['img2prevImg'] = [ img_metas['intrinsic'] @ \ anchor_scan['T_cam0_2_cam3'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(img_metas['intrinsic'])] elif target_sign == 'ps': anchor_img_path = img_metas['curr_imgs_path'][0] if stereo_sign == 'l': img_metas['next_imgs_path'] = [anchor_img_path.replace('image_02', 'image_03')] else: img_metas['next_imgs_path'] = [anchor_img_path.replace('image_03', 'image_02')] img_metas['img2nextImg'] = [ img_metas['intrinsic'] @ \ anchor_scan['T_cam0_2_cam3'] @ \ anchor_scan['T_cam2_2_cam0'] @ \ np.linalg.inv(img_metas['intrinsic'])] if self.return_depth: depth_loc, depth_gt, depth_mask = self.get_depth_from_lidar( scan['lid_path'], img_metas['lidar2img'], [self.img_H, self.img_W]) img_metas.update({ 'depth_loc': depth_loc, 'depth_gt': depth_gt, 'depth_mask': depth_mask}) # read 6 cams input_imgs = self.read_surround_imgs(img_metas['input_imgs_path']) curr_imgs = self.read_surround_imgs(img_metas['curr_imgs_path']) prev_imgs = self.read_surround_imgs(img_metas['prev_imgs_path']) next_imgs = self.read_surround_imgs(img_metas['next_imgs_path']) data_tuple = ([input_imgs, curr_imgs, prev_imgs, next_imgs], img_metas) return data_tuple if __name__ == "__main__": import os, sys print(sys.path) data_path = 'data/kitti_raw/' img_size = [352, 1216] dataset = Kitti_Raw( root = data_path, pose_path = 'dataset/kitti_raw/orb-slam_poses', split_path = 'dataset/kitti_raw/splits/eigen_zhou/train_files.txt', frames_interval=0.4, sequence_distance=10, cur_prob=0.333, crop_size=img_size, strict=True, return_depth=False, prev_prob=0.5, choose_nearest = True, ) batch = dataset[0] import pdb; pdb.set_trace() ================================================ FILE: dataset/kitti_raw/orb-slam_poses/2011_09_26/2011_09_26_drive_0001_sync.txt ================================================ 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 0.999998152 -0.000745813 0.001759061 -0.001589443 0.000747153 0.999999404 -0.000761480 0.004017271 -0.001758492 0.000762793 0.999998152 1.387179852 0.999991238 -0.000752704 0.004113252 0.002357255 0.000763507 0.999996245 -0.002625279 -0.003071738 -0.004111261 0.002628396 0.999988079 2.786494493 0.999979615 -0.000027886 0.006385359 0.013338886 0.000052994 0.999992251 -0.003931980 -0.002396141 -0.006385200 0.003932238 0.999971867 4.175350189 0.999959469 0.001093276 0.008935491 0.025990222 -0.001066447 0.999994934 -0.003006748 -0.006325660 -0.008938732 0.002997097 0.999955535 5.557537079 0.999926805 0.000968523 0.012058660 0.038517863 -0.000956504 0.999999046 -0.001002438 -0.014480142 -0.012059621 0.000990831 0.999926805 6.954649448 0.999890983 0.001684154 0.014667797 0.058076724 -0.001672495 0.999998271 -0.000807143 -0.017365439 -0.014669131 0.000782523 0.999892116 8.302928925 0.999838412 0.002612882 0.017784417 0.081813574 -0.002570696 0.999993801 -0.002394520 -0.021253463 -0.017790563 0.002348415 0.999838948 9.682145119 0.999775529 0.004142035 0.020778792 0.105194531 -0.004071228 0.999985754 -0.003448821 -0.022940369 -0.020792780 0.003363451 0.999778152 11.027830124 0.999691010 0.005593197 0.024219584 0.137107760 -0.005545462 0.999982536 -0.002037673 -0.029917421 -0.024230558 0.001902735 0.999704599 12.376829147 0.999576151 0.006289756 0.028424915 0.168856785 -0.006314415 0.999979734 0.000777816 -0.028259385 -0.028419448 -0.000956973 0.999595642 13.694996834 0.999447942 0.006229841 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2011_09_26/2011_09_26_drive_0009_sync 0000000431 l 2011_09_26/2011_09_26_drive_0009_sync 0000000432 l 2011_09_26/2011_09_26_drive_0009_sync 0000000433 l 2011_09_26/2011_09_26_drive_0009_sync 0000000434 l 2011_09_26/2011_09_26_drive_0009_sync 0000000435 l 2011_09_26/2011_09_26_drive_0009_sync 0000000436 l 2011_09_26/2011_09_26_drive_0009_sync 0000000437 l 2011_09_26/2011_09_26_drive_0009_sync 0000000438 l 2011_09_26/2011_09_26_drive_0009_sync 0000000439 l 2011_09_26/2011_09_26_drive_0009_sync 0000000440 l 2011_09_26/2011_09_26_drive_0009_sync 0000000441 l 2011_09_26/2011_09_26_drive_0009_sync 0000000442 l 2011_09_26/2011_09_26_drive_0009_sync 0000000443 l 2011_09_26/2011_09_26_drive_0009_sync 0000000444 l 2011_09_26/2011_09_26_drive_0009_sync 0000000445 l 2011_09_26/2011_09_26_drive_0009_sync 0000000446 l ================================================ FILE: dataset/loading.py ================================================ import os.path import mmcv import numpy as np from mmengine import BaseStorageBackend, FileClient from typing import Union from pathlib import Path from ffrecord import FileReader import pickle class LoadMultiViewImageFromFilesHF: """Load multi channel images from a list of separate channel files. Expects results['img_filename'] to be a list of filenames. Args: to_float32 (bool): Whether to convert the img to float32. Defaults to False. color_type (str): Color type of the file. Defaults to 'unchanged'. file_client_args (dict): Config dict of file clients, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py for more details. Defaults to dict(backend='disk'). """ def __init__(self, to_float32=False, color_type='unchanged', file_client_args=None): self.to_float32 = to_float32 self.color_type = color_type self.file_client_args = file_client_args.copy() self.file_client = None def load(self, results): """Call function to load multi-view image from files. Args: results (dict): Result dict containing multi-view image filenames. Returns: dict: The result dict containing the multi-view image data. \ Added keys and values are described below. - filename (str): Multi-view image filenames. - img (np.ndarray): Multi-view image arrays. - img_shape (tuple[int]): Shape of multi-view image arrays. - ori_shape (tuple[int]): Shape of original image arrays. - pad_shape (tuple[int]): Shape of padded image arrays. - scale_factor (float): Scale factor. - img_norm_cfg (dict): Normalization configuration of images. """ if self.file_client is None: self.file_client = FileClient(**self.file_client_args) filename = results # img is of shape (h, w, c, num_views) if self.file_client_args['backend'] == 'ffrecord': img_list = self.file_client.get(filename) else: img_list = [self.file_client.get(name ) for name in filename] img_list = [mmcv.imfrombytes(img, flag=self.color_type) for img in img_list] img = np.stack(img_list, axis=-1) if self.to_float32: img = img.astype(np.float32) # unravel to list, see `DefaultFormatBundle` in formating.py # which will transpose each image separately and then stack into array img_list = [img[..., i] for i in range(img.shape[-1])] return img_list def __call__(self, results): """Call function to load multi-view image from files. Args: results (dict): Result dict containing multi-view image filenames. Returns: dict: The result dict containing the multi-view image data. \ Added keys and values are described below. - filename (str): Multi-view image filenames. - img (np.ndarray): Multi-view image arrays. - img_shape (tuple[int]): Shape of multi-view image arrays. - ori_shape (tuple[int]): Shape of original image arrays. - pad_shape (tuple[int]): Shape of padded image arrays. - scale_factor (float): Scale factor. - img_norm_cfg (dict): Normalization configuration of images. """ if self.file_client is None: self.file_client = FileClient(**self.file_client_args) if isinstance(results, dict): filename = results['img_filename'] else: filename = results # img is of shape (h, w, c, num_views) if self.file_client_args['backend'] == 'ffrecord': img_list = self.file_client.get(filename) else: img_list = [self.file_client.get(name) for name in filename] img_list = [mmcv.imfrombytes(img, flag=self.color_type) for img in img_list] img = np.stack(img_list, axis=-1) if self.to_float32: img = img.astype(np.float32) # unravel to list, see `DefaultFormatBundle` in formating.py # which will transpose each image separately and then stack into array img_list = [img[..., i] for i in range(img.shape[-1])] return img_list class OriLoadMultiViewImageFromFilesHF(object): """Load multi channel images from a list of separate channel files. Expects results['img_filename'] to be a list of filenames. Args: to_float32 (bool): Whether to convert the img to float32. Defaults to False. color_type (str): Color type of the file. Defaults to 'unchanged'. file_client_args (dict): Config dict of file clients, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py for more details. Defaults to dict(backend='disk'). """ def __init__(self, to_float32=False, color_type='unchanged', file_client_args=None): self.to_float32 = to_float32 self.color_type = color_type self.file_client_args = file_client_args.copy() self.file_client = None def __call__(self, results): """Call function to load multi-view image from files. Args: results (dict): Result dict containing multi-view image filenames. Returns: dict: The result dict containing the multi-view image data. \ Added keys and values are described below. - filename (str): Multi-view image filenames. - img (np.ndarray): Multi-view image arrays. - img_shape (tuple[int]): Shape of multi-view image arrays. - ori_shape (tuple[int]): Shape of original image arrays. - pad_shape (tuple[int]): Shape of padded image arrays. - scale_factor (float): Scale factor. - img_norm_cfg (dict): Normalization configuration of images. """ if self.file_client is None: self.file_client = FileClient(**self.file_client_args) filename = results['img_filename'] # img is of shape (h, w, c, num_views) if self.file_client_args['backend'] == 'ffrecord': img_list = self.file_client.get(filename) else: img_list = [self.file_client.get(name) for name in filename] img_list = [mmcv.imfrombytes(img, flag=self.color_type) for img in img_list] img = np.stack(img_list, axis=-1) if self.to_float32: img = img.astype(np.float32) results['filename'] = filename # unravel to list, see `DefaultFormatBundle` in formating.py # which will transpose each image separately and then stack into array results['img'] = [img[..., i] for i in range(img.shape[-1])] results['img_shape'] = img.shape results['ori_shape'] = img.shape # Set initial values for default meta_keys results['pad_shape'] = img.shape results['scale_factor'] = 1.0 num_channels = 1 if len(img.shape) < 3 else img.shape[2] results['img_norm_cfg'] = dict( mean=np.zeros(num_channels, dtype=np.float32), std=np.ones(num_channels, dtype=np.float32), to_rgb=False) return results def __repr__(self): """str: Return a string that describes the module.""" repr_str = self.__class__.__name__ repr_str += f'(to_float32={self.to_float32}, ' repr_str += f"color_type='{self.color_type}')" return repr_str class LoadPtsFromFilesHF: def __init__(self, to_float32=True, file_client_args=None): self.to_float32 = to_float32 self.file_client_args = file_client_args.copy() self.file_client = None def load(self, filename): if self.file_client is None: self.file_client = FileClient(**self.file_client_args) # img is of shape (h, w, c, num_views) if self.file_client_args['backend'] == 'ffrecord': pts = self.file_client.get([filename])[0] else: pts = self.file_client.get(filename) # print(pts) # print(len(pts)) # print(type(pts)) # print(type(pts[0])) tot_len = len(pts) counts = int((tot_len - 159) / 4) pts = np.frombuffer(pts, dtype=np.float32, offset=152, count=counts) # print(pts) # print(len(pts)) # print(type(pts)) # print(type(pts[0])) return pts @FileClient.register_backend('ffrecord') class FFrecordClient(BaseStorageBackend): """FFRecord storage backend.""" _allow_symlink = True def __init__(self, fname, check_data=False, filename2idx='filename2idx.pkl'): assert os.path.exists(fname), f'Pls convert to ffr first! {fname} not exists!' self.reader = FileReader(fname, check_data) filename2idx_file = os.path.join(fname, filename2idx) assert os.path.exists(filename2idx_file), f'Pls convert to ffr first! {filename2idx_file} not exists!' with open(filename2idx_file, 'rb') as handle: filename2idx_file = pickle.load(handle) self.filename2idx_file = {} for k, v in filename2idx_file.items(): self.filename2idx_file[os.path.basename(k)] = v def get(self, filepath: Union[str, Path]) -> bytes: """Read data from a given ``filepath`` with 'rb' mode. Args: filepath (str or Path): Path to read data. Returns: bytes: Expected bytes object. """ single_flag = False if not isinstance(filepath, list): filepath = [filepath] single_flag = True indices = [] for name in filepath: file_basename = os.path.basename(name) idx = self.filename2idx_file[file_basename] indices.append(idx) sample = self.reader.read(indices) if single_flag: sample = sample[0] return sample def get_text(self, filepath: Union[str, Path], encoding: str = 'utf-8') -> str: """Read data from a given ``filepath`` with 'r' mode. Args: filepath (str or Path): Path to read data. encoding (str): The encoding format used to open the ``filepath``. Default: 'utf-8'. Returns: str: Expected text reading from ``filepath``. """ with open(filepath, 'r', encoding=encoding) as f: value_buf = f.read() return value_buf ================================================ FILE: dataset/sampler.py ================================================ import math from typing import TypeVar, Optional, Iterator import torch from torch.utils.data import Sampler, Dataset import torch.distributed as dist T_co = TypeVar('T_co', covariant=True) class CustomDistributedSampler(Sampler[T_co]): r"""Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the original dataset that is exclusive to it. .. note:: Dataset is assumed to be of constant size. Arguments: dataset: Dataset used for sampling. num_replicas (int, optional): Number of processes participating in distributed training. By default, :attr:`rank` is retrieved from the current distributed group. rank (int, optional): Rank of the current process within :attr:`num_replicas`. By default, :attr:`rank` is retrieved from the current distributed group. shuffle (bool, optional): If ``True`` (default), sampler will shuffle the indices. seed (int, optional): random seed used to shuffle the sampler if :attr:`shuffle=True`. This number should be identical across all processes in the distributed group. Default: ``0``. drop_last (bool, optional): if ``True``, then the sampler will drop the tail of the data to make it evenly divisible across the number of replicas. If ``False``, the sampler will add extra indices to make the data evenly divisible across the replicas. Default: ``False``. .. warning:: In distributed mode, calling the :meth:`set_epoch` method at the beginning of each epoch **before** creating the :class:`DataLoader` iterator is necessary to make shuffling work properly across multiple epochs. Otherwise, the same ordering will be always used. Example:: >>> sampler = DistributedSampler(dataset) if is_distributed else None >>> loader = DataLoader(dataset, shuffle=(sampler is None), ... sampler=sampler) >>> for epoch in range(start_epoch, n_epochs): ... if is_distributed: ... sampler.set_epoch(epoch) ... train(loader) """ def __init__(self, dataset: Dataset, num_replicas: Optional[int] = None, rank: Optional[int] = None, shuffle: bool = True, seed: int = 0, drop_last: bool = False, last_iter: int = 0) -> None: if num_replicas is None: if not dist.is_available(): raise RuntimeError("Requires distributed package to be available") num_replicas = dist.get_world_size() if rank is None: if not dist.is_available(): raise RuntimeError("Requires distributed package to be available") rank = dist.get_rank() self.dataset = dataset self.num_replicas = num_replicas self.rank = rank self.epoch = 0 self.drop_last = drop_last # If the dataset length is evenly divisible by # of replicas, then there # is no need to drop any data, since the dataset will be split equally. if self.drop_last and len(self.dataset) % self.num_replicas != 0: # type: ignore # Split to nearest available length that is evenly divisible. # This is to ensure each rank receives the same amount of data when # using this Sampler. self.num_samples = math.ceil( # `type:ignore` is required because Dataset cannot provide a default __len__ # see NOTE in pytorch/torch/utils/data/sampler.py (len(self.dataset) - self.num_replicas) / self.num_replicas # type: ignore ) else: self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore self.total_size = self.num_samples * self.num_replicas self.shuffle = shuffle self.seed = seed self.first_run = True self.last_iter = last_iter def __iter__(self) -> Iterator[T_co]: if self.shuffle: # deterministically shuffle based on epoch and seed g = torch.Generator() g.manual_seed(self.seed + self.epoch) indices = torch.randperm(len(self.dataset), generator=g).tolist() # type: ignore else: indices = list(range(len(self.dataset))) # type: ignore if not self.drop_last: # add extra samples to make it evenly divisible indices += indices[:(self.total_size - len(indices))] else: # remove tail of data to make it evenly divisible. indices = indices[:self.total_size] assert len(indices) == self.total_size # subsample indices = indices[self.rank:self.total_size:self.num_replicas] if not self.first_run: assert len(indices) == self.num_samples else: indices = indices[self.last_iter:] self.last_iter = 0 self.first_run = False return iter(indices) def __len__(self) -> int: return self.num_samples def set_epoch(self, epoch: int) -> None: r""" Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas use a different random ordering for each epoch. Otherwise, the next iteration of this sampler will yield the same ordering. Arguments: epoch (int): Epoch number. """ self.epoch = epoch def set_last_iter(self, last_iter: int): self.last_iter = last_iter ================================================ FILE: dataset/transform_3d.py ================================================ from typing import Any import numpy as np from numpy import random import mmcv # from mmcv.parallel import DataContainer as DC # from mmdet3d.datasets.pipelines import DefaultFormatBundle3D class PadMultiViewImage(object): """Pad the multi-view image. There are two padding modes: (1) pad to a fixed size and (2) pad to the minimum size that is divisible by some number. Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor", Args: size (tuple, optional): Fixed padding size. size_divisor (int, optional): The divisor of padded size. pad_val (float, optional): Padding value, 0 by default. """ def __init__(self, size=None, size_divisor=None, pad_val=0): self.size = size self.size_divisor = size_divisor self.pad_val = pad_val # only one of size and size_divisor should be valid assert size is not None or size_divisor is not None if size is not None: self.size_divisor = size_divisor = None assert size is None or size_divisor is None def _pad_img(self, results): """Pad images according to ``self.size``.""" if self.size is not None: padded_img = [mmcv.impad( img, shape=self.size, pad_val=self.pad_val) for img in results['img']] if 'extra_img' in results: padded_extra_img = [mmcv.impad( img, shape=self.size, pad_val=self.pad_val) for img in results['extra_img']] if 'ori_img' in results: padded_ori_img = [mmcv.impad( img, shape=self.size, pad_val=self.pad_val) for img in results['ori_img']] elif self.size_divisor is not None: padded_img = [mmcv.impad_to_multiple( img, self.size_divisor, pad_val=self.pad_val) for img in results['img']] if 'extra_img' in results: padded_extra_img = [mmcv.impad_to_multiple( img, self.size_divisor, pad_val=self.pad_val) for img in results['extra_img']] if 'ori_img' in results: padded_ori_img = [mmcv.impad_to_multiple( img, self.size_divisor, pad_val=self.pad_val) for img in results['ori_img']] # results['ori_shape'] = [img.shape for img in results['img']] results['img'] = padded_img # results['img_shape'] = [img.shape for img in padded_img] # results['pad_shape'] = [img.shape for img in padded_img] results['pad_fixed_size'] = self.size results['pad_size_divisor'] = self.size_divisor if 'extra_img' in results: results['extra_img'] = padded_extra_img if 'ori_img' in results: results['ori_img'] = padded_ori_img def __call__(self, results): """Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline. Returns: dict: Updated result dict. """ self._pad_img(results) return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(size={self.size}, ' repr_str += f'size_divisor={self.size_divisor}, ' repr_str += f'pad_val={self.pad_val})' return repr_str class NormalizeMultiviewImage(object): """Normalize the image. Added key is "img_norm_cfg". Args: mean (sequence): Mean values of 3 channels. std (sequence): Std values of 3 channels. to_rgb (bool): Whether to convert the image from BGR to RGB, default is true. """ def __init__(self, mean, std, to_rgb=True): self.mean = np.array(mean, dtype=np.float32) self.std = np.array(std, dtype=np.float32) self.to_rgb = to_rgb def __call__(self, results): """Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Normalized results, 'img_norm_cfg' key is added into result dict. """ results['img'] = [mmcv.imnormalize(img, self.mean, self.std, self.to_rgb) for img in results['img']] results['img_norm_cfg'] = dict( mean=self.mean, std=self.std, to_rgb=self.to_rgb) return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})' return repr_str class RandomFlip(object): def __init__(self, prob=0.5) -> None: self.prob = prob def __call__(self, results): flip = random.random() < self.prob if flip: results['img'] = [mmcv.imflip(img) for img in results['img']] results['flip'] = flip return results class PhotoMetricDistortionMultiViewImage: """Apply photometric distortion to image sequentially, every transformation is applied with a probability of 0.5. The position of random contrast is in second or second to last. 1. random brightness 2. random contrast (mode 0) 3. convert color from BGR to HSV 4. random saturation 5. random hue 6. convert color from HSV to BGR 7. random contrast (mode 1) 8. randomly swap channels Args: brightness_delta (int): delta of brightness. contrast_range (tuple): range of contrast. saturation_range (tuple): range of saturation. hue_delta (int): delta of hue. brightness_delta = 8 contrast_range = (0.9, 1.1) saturation_range = (0.9, 1.1) hue_delta = 8 use_swap_channel = False """ def __init__(self, brightness_delta=32, contrast_range=(0.8, 1.2), saturation_range=(0.8, 1.2), hue_delta=18, use_swap_channel=True): self.brightness_delta = brightness_delta self.contrast_lower, self.contrast_upper = contrast_range self.saturation_lower, self.saturation_upper = saturation_range self.hue_delta = hue_delta self.use_swap_channel = use_swap_channel def __call__(self, results): """Call function to perform photometric distortion on images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Result dict with images distorted. """ imgs = results['img'] new_imgs = [] for img in imgs: assert img.dtype == np.float32, \ 'PhotoMetricDistortion needs the input image of dtype np.float32,'\ ' please set "to_float32=True" in "LoadImageFromFile" pipeline' # random brightness if random.randint(2): delta = random.uniform(-self.brightness_delta, self.brightness_delta) img += delta # mode == 0 --> do random contrast first # mode == 1 --> do random contrast last mode = random.randint(2) if mode == 1: if random.randint(2): alpha = random.uniform(self.contrast_lower, self.contrast_upper) img *= alpha # convert color from BGR to HSV img = mmcv.bgr2hsv(img) # random saturation if random.randint(2): img[..., 1] *= random.uniform(self.saturation_lower, self.saturation_upper) # random hue if random.randint(2): img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta) img[..., 0][img[..., 0] > 360] -= 360 img[..., 0][img[..., 0] < 0] += 360 # convert color from HSV to BGR img = mmcv.hsv2bgr(img) # random contrast if mode == 0: if random.randint(2): alpha = random.uniform(self.contrast_lower, self.contrast_upper) img *= alpha # randomly swap channels if random.randint(2) and self.use_swap_channel: img = img[..., random.permutation(3)] new_imgs.append(img) results['img'] = new_imgs return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(\nbrightness_delta={self.brightness_delta},\n' repr_str += 'contrast_range=' repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n' repr_str += 'saturation_range=' repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n' repr_str += f'hue_delta={self.hue_delta})' return repr_str # @PIPELINES.register_module() # class CustomCollect3D(object): # """Collect data from the loader relevant to the specific task. # This is usually the last stage of the data loader pipeline. Typically keys # is set to some subset of "img", "proposals", "gt_bboxes", # "gt_bboxes_ignore", "gt_labels", and/or "gt_masks". # The "img_meta" item is always populated. The contents of the "img_meta" # dictionary depends on "meta_keys". By default this includes: # - 'img_shape': shape of the image input to the network as a tuple \ # (h, w, c). Note that images may be zero padded on the \ # bottom/right if the batch tensor is larger than this shape. # - 'scale_factor': a float indicating the preprocessing scale # - 'flip': a boolean indicating if image flip transform was used # - 'filename': path to the image file # - 'ori_shape': original shape of the image as a tuple (h, w, c) # - 'pad_shape': image shape after padding # - 'lidar2img': transform from lidar to image # - 'depth2img': transform from depth to image # - 'cam2img': transform from camera to image # - 'pcd_horizontal_flip': a boolean indicating if point cloud is \ # flipped horizontally # - 'pcd_vertical_flip': a boolean indicating if point cloud is \ # flipped vertically # - 'box_mode_3d': 3D box mode # - 'box_type_3d': 3D box type # - 'img_norm_cfg': a dict of normalization information: # - mean: per channel mean subtraction # - std: per channel std divisor # - to_rgb: bool indicating if bgr was converted to rgb # - 'pcd_trans': point cloud transformations # - 'sample_idx': sample index # - 'pcd_scale_factor': point cloud scale factor # - 'pcd_rotation': rotation applied to point cloud # - 'pts_filename': path to point cloud file. # Args: # keys (Sequence[str]): Keys of results to be collected in ``data``. # meta_keys (Sequence[str], optional): Meta keys to be converted to # ``mmcv.DataContainer`` and collected in ``data[img_metas]``. # Default: ('filename', 'ori_shape', 'img_shape', 'lidar2img', # 'depth2img', 'cam2img', 'pad_shape', 'scale_factor', 'flip', # 'pcd_horizontal_flip', 'pcd_vertical_flip', 'box_mode_3d', # 'box_type_3d', 'img_norm_cfg', 'pcd_trans', # 'sample_idx', 'pcd_scale_factor', 'pcd_rotation', 'pts_filename') # """ # def __init__(self, # keys, # meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img', # 'depth2img', 'cam2img', 'pad_shape', # 'scale_factor', 'flip', 'pcd_horizontal_flip', # 'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d', # 'img_norm_cfg', 'pcd_trans', 'sample_idx', 'prev_idx', 'next_idx', # 'pcd_scale_factor', 'pcd_rotation', 'pts_filename', # 'transformation_3d_flow', 'scene_token', # 'can_bus', # )): # self.keys = keys # self.meta_keys = meta_keys # def __call__(self, results): # """Call function to collect keys in results. The keys in ``meta_keys`` # will be converted to :obj:`mmcv.DataContainer`. # Args: # results (dict): Result dict contains the data to collect. # Returns: # dict: The result dict contains the following keys # - keys in ``self.keys`` # - ``img_metas`` # """ # data = {} # img_metas = {} # for key in self.meta_keys: # if key in results: # img_metas[key] = results[key] # data['img_metas'] = DC(img_metas, cpu_only=True) # for key in self.keys: # data[key] = results[key] # return data # def __repr__(self): # """str: Return a string that describes the module.""" # return self.__class__.__name__ + \ # f'(keys={self.keys}, meta_keys={self.meta_keys})' class RandomScaleImageMultiViewImage(object): """Random scale the image Args: scales """ def __init__(self, scales=[], ref_focal_len=None, random_scale=None, pad_scale_rate=None): self.scales = scales self.ref_focal_len = ref_focal_len if random_scale is not None: assert isinstance(random_scale, list) and len(random_scale) == 2 and ref_focal_len is None # assert random_scale[1] <= 1.0 # TODO if pad_scale_rate is None: pad_scale_rate = [scales[0]] * 2 self.pad_scale_rate = pad_scale_rate self.random_scale = random_scale assert len(self.scales)==1 def __call__(self, results): """Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline. Returns: dict: Updated result dict. """ # rand_ind = np.random.permutation(range(len(self.scales)))[0] if self.ref_focal_len is None: if self.random_scale is not None: focal_ratios = np.random.rand(len(results['img'])) * (self.random_scale[1] - self.random_scale[0]) + self.random_scale[0] rand_scales = [self.scales[0] * focal_ratio for focal_ratio in focal_ratios] results['focal_ratios'] = focal_ratios.tolist() else: rand_scales = self.scales * len(results['img']) else: focal_lens = results['metas']['intrinsic'][:, 0, 0] focal_ratios = [self.ref_focal_len * 1.0 / focal_len for focal_len in focal_lens] rand_scales = [self.scales[0] * focal_ratio for focal_ratio in focal_ratios] results['focal_ratios'] = focal_ratios results['focal_ratios_x'] = [scale / self.pad_scale_rate[1] for scale in rand_scales] results['focal_ratios_y'] = [scale / self.pad_scale_rate[0] for scale in rand_scales] y_size = [int(img.shape[0] * scale) for img, scale in zip(results['img'], rand_scales)] x_size = [int(img.shape[1] * scale) for img, scale in zip(results['img'], rand_scales)] # scale_factor = np.eye(4) # scale_factor[0, 0] *= rand_scale # scale_factor[1, 1] *= rand_scale results['img'] = [mmcv.imresize(img, (x_size[idx], y_size[idx]), return_scale=False) for idx, img in enumerate(results['img'])] # lidar2img = [scale_factor @ l2i for l2i in results['lidar2img']] # results['lidar2img'] = lidar2img # results['img_shape'] = [img.shape for img in results['img']] # results['ori_shape'] = [img.shape for img in results['img']] return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(size={self.scales}, ' return repr_str if __name__ == '__main__': from mmcv.image.io import imread import mmengine, os root = 'data/nuscenes' pkl = mmengine.load('data/nuscenes_infos_val_sweeps.pkl') infos = pkl['infos']['e7ef871f77f44331aefdebc24ec034b7'] img_paths = [] for k, v in infos[0]['data'].items(): if 'CAM' in k: img_paths.append(os.path.join(root, v['filename'])) def read_surround_imgs(img_paths, crop_size=[768, 1600]): imgs = [] for filename in img_paths: imgs.append( imread(filename, 'unchanged').astype(np.float32)) imgs = [img[:crop_size[0], :crop_size[1], :] for img in imgs] return imgs flip_aug = RandomFlip(1.0) imgs = read_surround_imgs(img_paths) img_dict = { 'img': imgs} img_dict = flip_aug(img_dict) pass ================================================ FILE: dataset/utils.py ================================================ import numpy as np def get_rm(angle, axis, deg=False): if deg: angle = np.deg2rad(angle) rm = np.eye(3) if axis == 'x': rm[1, 1] = np.cos(angle) rm[2, 2] = np.cos(angle) rm[1, 2] = - np.sin(angle) rm[2, 1] = np.sin(angle) elif axis == 'y': rm[0, 0] = np.cos(angle) rm[2, 2] = np.cos(angle) rm[0, 2] = np.sin(angle) rm[2, 0] = - np.sin(angle) elif axis == 'z': rm[0, 0] = np.cos(angle) rm[1, 1] = np.cos(angle) rm[0, 1] = - np.sin(angle) rm[1, 0] = np.sin(angle) return rm ================================================ FILE: docs/get_started.md ================================================ # Prerequisites **Please ensure you have prepared the environment and datasets.** [23/12/16 Update] Please update the timm package to 0.9.2 to run the training script. # 3D Occupancy Prediction ## NuScenes ### Training ``` python train.py --py-config config/nuscenes/nuscenes_occ.py --work-dir out/nuscenes/occ_train --depth-metric ``` ### Evaluation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/831c104c82a244e9878a/) and put it under out/nuscenes/occ/ ``` python eval_iou.py --py-config config/nuscenes/nuscenes_occ.py --work-dir out/nuscenes/occ --resume-from out/nuscenes/occ/model_state_dict.pth --occ3d --resolution 0.4 --sem --use-mask --scene-size 4 ``` ## SemanticKITTI ### Training ``` python train.py --py-config config/kitti/kitti_occ.py --work-dir out/kitti/occ_train --depth-metric --dataset kitti ``` ### Evaluation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/3c09a5e8f5b94fa29289/) and put it under out/kitti/occ/ ``` python eval_iou_kitti.py --py-config config/kitti/kitti_occ.py --work-dir out/kitti/occ --resume-from out/kitti/occ/model_state_dict.pth ``` # Novel Depth Synthesis ## NuScenes ### Training ``` python train.py --py-config config/nuscenes/nuscenes_novel_depth.py --work-dir out/nuscenes/novel_depth_train --depth-metric ``` ### Evaluation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/2d217cd298a34ed19039/) and put it under out/nuscenes/novel_depth/ ``` python eval_novel_depth.py --py-config config/nuscenes/nuscenes_novel_depth.py --work-dir out/nuscenes/novel_depth --resume-from out/nuscenes/novel_depth/model_state_dict.pth ``` ## SemanticKITTI ### Training ``` python train.py --py-config config/kitti/kitti_novel_depth.py --work-dir out/kitti/novel_depth_train --depth-metric --dataset kitti ``` ### Evaluation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/7280a44340fd440cba7c/) and put it under out/kitti/novel_depth/ ``` python eval_novel_depth_kitti.py --py-config config/kitti/kitti_novel_depth.py --work-dir out/kitti/novel_depth --resume-from out/kitti/novel_depth/model_state_dict.pth ``` # Depth Estimation ## nuScenes ### Training ``` python train.py --py-config config/nuscenes/nuscenes_depth.py --work-dir out/nuscenes/depth_train --depth-metric ``` ### Evaluation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/1a722b9139234542ae1e/) and put it under out/nuscenes/depth/ ``` python eval_depth.py --py-config config/nuscenes/nuscenes_depth.py --work-dir out/nuscenes/depth --resume-from out/nuscenes/depth/model_state_dict.pth --depth-metric --batch 90000 ``` Note that evaluating at a resolution (450\*800) of 1:2 against the raw image (900\*1600) takes about 90 min, because we batchify rays for rendering due to GPU memory limit. You can change the rendering resolution by the variable *NUM_RAYS* in utils/config_tools.py ## KITTI-2015 ### Training ``` python train.py --py-config config/kitti_raw/kitti_raw_depth.py --work-dir out/kitti_raw/depth_train --depth-metric --dataset kitti ``` ### Evaluation Download model weights [HERE](https://cloud.tsinghua.edu.cn/f/f87f6876569e4fdeb967/) and put it under out/kitti_raw/depth/ ``` python eval_depth.py --py-config config/kitti_raw/kitti_raw_depth.py --work-dir out/kitti_raw/depth --resume-from out/kitti_raw/depth/model_state_dict.pth --depth-metric --dataset kitti_raw ``` ================================================ FILE: docs/installation.md ================================================ # Step-by-step installation instructions The following configuration of conda environment is tested on RTX3090, and we also ran our code on RTX4090 with torch==2.0.0+cu118. **a. Create a conda virtual environment and activate it.** ```shell conda create -n selfocc python=3.8.16 -y conda activate selfocc ``` **b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/).** ```shell pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 # Recommended torch>=1.12.1 ``` **c. Install mmcv, mmdet, mmseg, mmdet3d.** ```shell pip install -U openmim # Following https://mmcv.readthedocs.io/en/latest/get_started/installation.html mim install mmcv==2.0.1 mim install mmdet==3.0.0 # Following https://mmdetection.readthedocs.io/en/latest/get_started.html pip install "mmsegmentation==1.0.0" # Following https://mmsegmentation.readthedocs.io/en/latest/get_started.html mim install "mmdet3d==1.1.1" # Following https://mmdetection3d.readthedocs.io/en/latest/get_started.html ``` **d. Install sdfstudio.** We develop the SDF rendering part of our code based on [sdfstudio](https://github.com/autonomousvision/sdfstudio). Also inside our adapted sdfstudio, we use code from [cuda_gridsample_grad2](https://github.com/AliaksandrSiarohin/cuda-gridsample-grad2) for grid sample implimentation with second order derivative support. Many thanks to them. ```shell git clone --recursive git@github.com:huang-yh/sdfstudio.git cd sdfstudio pip install --upgrade pip setuptools pip install -e . # install tab completion ns-install-cli ``` **e. Install OpenSeeD.** We use the open-vocabulary 2D segmentor [OpenSeeD](https://github.com/IDEA-Research/OpenSeeD) to generate 2D semantic maps for supervision, and install its git repo as a package. Many thanks to them. ```shell git clone git@github.com:huang-yh/OpenSeeD.git python -m pip install 'git+https://github.com/MaureenZOU/detectron2-xyz.git' pip install git+https://github.com/cocodataset/panopticapi.git cd OpenSeeD & python -m pip install -r requirements.txt pip install -e . ``` **f. Install other packages and deal with package versions.** ```shell pip install pillow==8.4.0 typing_extensions==4.8.0 torchmetrics==0.9.3 timm==0.9.2 ``` **g. Clone SelfOcc.** ``` git clone https://github.com/huang-yh/SelfOcc.git ``` **h. Prepare pretrained models.** ```shell cd SelfOcc mkdir ckpts & cd ckpts wget https://download.pytorch.org/models/resnet50-0676ba61.pth wget https://github.com/IDEA-Research/OpenSeeD/releases/download/openseed/model_state_dict_swint_51.2ap.pt ``` ================================================ FILE: docs/prepare_data.md ================================================ ## NuScenes **a. Download nuScenes data.** Download nuScenes V1.0 full dataset data [HERE](https://www.nuscenes.org/download). **b. Download occupancy annotations.** Download the gts.tar.gz of the trainval split of Occ3D-nuScenes [HERE](https://github.com/CVPR2023-3D-Occupancy-Prediction/CVPR2023-3D-Occupancy-Prediction/tree/main#download) and unzip it. **c. Download nuScenes pkl files.** As a self-supervised method, we also include sweep data during training. ```shell # pkl for keyframes and sweep data wget --content-disposition https://cloud.tsinghua.edu.cn/f/ff15569b9e4d4086857e/?dl=1 wget --content-disposition https://cloud.tsinghua.edu.cn/f/fbe2ad8507494953abbe/?dl=1 # pkl for keyframes wget --content-disposition https://cloud.tsinghua.edu.cn/f/07323a7a1c894e768924/?dl=1 ``` Or you can generate the required sweep synchronized pkl files offline. Note that you still need and only need the tokens of keyframe samples of the train and val splits, which we store in additional pkl files (sorry~). Our code takes care of the sweep synchronizing part. ```shell # You can download the pkl files that contain the token of keyframe samples at # train: https://cloud.tsinghua.edu.cn/f/efca1849e0f64551a0c4/?dl=1 # val: https://cloud.tsinghua.edu.cn/f/a4e30d0ed8c945fba1e8/?dl=1 # remember to change the source and target pkl paths at line 9 and 119. python examine_sweeps.py ``` **Folder structure** ``` SelfOcc ├── ... ├── data/ │ ├── nuscenes_infos_train_sweeps.pkl │ ├── nuscenes_infos_val_sweeps.pkl │ ├── nuscenes_infos_val_temporal_v2.pkl │ ├── nuscenes/ │ │ ├── maps/ │ │ ├── samples/ │ │ ├── sweeps/ │ │ ├── v1.0-test/ | | ├── v1.0-trainval/ │ ├── occ3d/ │ │ ├── gts/ │ │ | ├── scene-0001/ │ │ | ├── ... ``` ## SemanticKITTI We follow similar instructions as [SceneRF](https://github.com/astra-vision/SceneRF) to prepare SemanticKITTI. **a. Download calib, rgb, pose and lidar files.** To train and evaluate novel depths synthesis, please download on [KITTI Odometry website](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) the following data: - Odometry data set (calibration files, 1 MB) - Odometry data set (color, 65 GB) - Odometry ground truth poses (4 MB) - Velodyne laser data, 80 GB **b. Download occupancy annotations.** To evaluate 3D occupancy prediction, please download **the SemanticKITTI voxel data (700 MB)** on [Semantic KITTI download website](http://www.semantic-kitti.org/dataset.html). **c. Create preprocess folder.** Create an empty folder to store preprocess data at `data/kitti/preprocess`. **Folder structure** ``` SelfOcc ├── ... ├── data/ │ ├── kitti/ │ │ ├── dataset/ | | | ├── poses/ | | | | ├── 00.txt | | | | ├── ... | | | ├── sequences/ | | | | ├── 00/ | | | | | ├── image_2/ | | | | | ├── image_3/ | | | | | ├── labels/ | | | | | ├── velodyne/ | | | | | ├── voxels/ | | | | | ├── calib.txt | | | | | ├── poses.txt | | | | | ├── times.txt | | | | ├── ... │ │ ├── preprocess/ ``` ## KITTI-2015 We follow similar instructions as [BehindTheScenes](https://github.com/Brummi/BehindTheScenes) to prepare KITTI-2015. To download KITTI, go to https://www.cvlibs.net/datasets/kitti/raw_data.php and create an account. We require all synched+rectified data, as well as the calibrations (using in fact only frames with ego motion). The website also provides scripts for automatic downloading of the different sequences. Following BehindTheScenes, we use the same poses computed from ORB-SLAM3 (can be found under dataset/kitti_raw/orb-slam_poses). **Folder structure** ``` SelfOcc ├── ... ├── data/ │ ├── kitti_raw/ │ │ ├── 2011_09_26/ | | | ├── 2011_09_26_drive_0001_sync/ | | | | ├── image_00/ | | | | ├── image_01/ | | | | ├── image_02/ | | | | ├── image_03/ | | | | ├── oxts/ | | | | ├── velodyne_points/ | | | ├── ... | | | ├── calib_cam_to_cam.txt | | | ├── calib_imu_to_velo.txt | | | ├── calib_velo_to_cam.txt │ │ ├── 2011_09_28/ │ │ ├── 2011_09_29/ │ │ ├── 2011_09_30/ │ │ ├── 2011_10_03/ ``` ================================================ FILE: docs/visualization.md ================================================ # Visualization Guide ## Install packages ``` pip install pyvirtualdisplay mayavi PyQt5 ``` ## 3D Occupancy Prediction ```shell # nuscenes CUDA_VISIBLE_DEVICES=0 python vis_3d.py --py-config config/nuscenes/nuscenes_occ.py --work-dir out/nuscenes/occ --resume-from out/nuscenes/occ/model_state_dict.pth --frame-idx 0 100 200 --cap 4 --model-pred --scene-size 4 --sem --save-rgb # semantickitti CUDA_VISIBLE_DEVICES=0 python vis_3d.py --py-config config/kitti/kitti_occ.py --work-dir out/kitti/occ --resume-from out/kitti/occ/model_state_dict.pth --frame-idx 0 100 200 --cap 4 --dataset kitti --model-pred --save-rgb ``` ## 2D Visualization ```shell CUDA_VISIBLE_DEVICES=0 python vis_pics.py --py-config config/nuscenes/nuscenes_novel_depth.py --work-dir out/nuscenes/novel_depth --resume-from out/nuscenes/novel_depth/model_state_dict.pth --vis-nerf-rgb ms_colors --frame-idx 0 100 200 --num-rays 96 200 ``` ## Video ```shell # download the pkl file which includes lidar pointcloud when processing sweeps cd data wget --content-disposition https://cloud.tsinghua.edu.cn/f/e415321fb5f64c7081d2/?dl=1 cd .. # generate individual images CUDA_VISIBLE_DEVICES=0 python vis_3d_scene.py --py-config config/nuscenes/nuscenes_occ.py --work-dir out/nuscenes/occ --resume-from out/nuscenes/occ/model_state_dict.pth --cap 4 --model-pred --scene-size 4 --sem --save-rgb --scene-name scene-0268 # generate a video python generate_videos.py --scene-dir out/nuscenes/occ/vis_scene/epoch9 --scene-name scene-0268 ``` ================================================ FILE: eval_depth.py ================================================ import time, argparse, os.path as osp, os, sys import torch, numpy as np import torch.distributed as dist import mmcv from mmengine import Config from mmengine.runner import set_random_seed from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") from utils.feat_tools import multi2single_scale from copy import deepcopy from utils.config_tools import modify_for_eval def pass_print(*args, **kwargs): pass def main(local_rank, args): # global settings set_random_seed(args.seed) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, args.dataset) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) if local_rank != 0: import builtins builtins.print = pass_print else: distributed = False if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval_depth_' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval_depth_{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) MMLogger._instance_dict['selfocc'] = logger logger.info(args) logger.info(f'Config:\n{cfg.pretty_text}') import model from dataset import get_dataloader # build model cfg.model.head.return_max_depth = True my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: if cfg.get('syncBN', True): my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) logger.info('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.cuda() raw_model = my_model logger.info('done ddp model') train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed,) # get optimizer, loss, scheduler amp = cfg.get('amp', False) # resume and load cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from logger.info('resume from: ' + cfg.resume_from) logger.info('work dir: ' + args.work_dir) if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) print(raw_model.load_state_dict(ckpt['state_dict'], strict=False)) logger.info(f'successfully resumed from epoch {ckpt["epoch"]}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # eval print_freq = cfg.print_freq my_model.eval() if args.depth_metric: from utils.metric_util import DepthMetric if args.dataset == 'kitti' or args.dataset == 'kitti_raw': camera_names = ['front'] elif args.dataset == 'nuscenes': camera_names = ['front', 'front_right', 'front_left', \ 'back', 'back_left', 'back_right'] depth_metric = DepthMetric( camera_names=camera_names).cuda() depth_metric._reset() if args.save_depth: depth_save_path = os.path.join(args.work_dir, "depth") depth_median_save_path = os.path.join(args.work_dir, "depth_median") depth_max_save_path = os.path.join(args.work_dir, "depth_max") os.makedirs(depth_save_path, exist_ok=True) os.makedirs(depth_median_save_path, exist_ok=True) os.makedirs(depth_max_save_path, exist_ok=True) with torch.no_grad(): for i_iter_val, (input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, \ img_metas, curr_aug, prev_aug, next_aug) in enumerate(val_dataset_loader): input_imgs = input_imgs.cuda() with torch.cuda.amp.autocast(amp): if cfg.get('estimate_pose'): assert curr_aug is not None and prev_aug is not None and next_aug is not None assert img_metas[0]['input_imgs_path'] == img_metas[0]['curr_imgs_path'] curr_aug, prev_aug, next_aug = curr_aug.cuda(), prev_aug.cuda(), next_aug.cuda() pose_dict = my_model(pose_input=[curr_aug, prev_aug, next_aug], metas=img_metas, predict_pose=True) for i_meta, meta in enumerate(img_metas): meta.update(pose_dict[i_meta]) my_model(imgs=input_imgs, metas=img_metas, prepare=True) result_dict = my_model.module.head.render(metas=img_metas, batch=args.batch) if args.flip: input_imgs = torch.flip(input_imgs, dims=[-1]) for meta in img_metas: meta['flip'] = True my_model(imgs=input_imgs, metas=img_metas, prepare=True) result_dict_flip = my_model.module.head.render(metas=img_metas, batch=args.batch) ms_depths_flip = result_dict_flip['ms_depths'][0] result_dict['ms_depths'][0] = (result_dict['ms_depths'][0] + ms_depths_flip) / 2 if 'ms_depths_median' in result_dict_flip: ms_depths_median_flip = result_dict_flip['ms_depths_median'][0] result_dict['ms_depths_median'][0] = (result_dict['ms_depths_median'][0] + ms_depths_median_flip) / 2 ms_depths_max_flip = result_dict_flip['ms_max_depths'][0] result_dict['ms_max_depths'][0] = (result_dict['ms_max_depths'][0] + ms_depths_max_flip) / 2 #### calculate all sorts of depths ms_depths = result_dict['ms_depths'][0] ms_depths = ms_depths.unflatten(-1, cfg.num_rays) if 'ms_depths_median' in result_dict: ms_depths_median = result_dict['ms_depths_median'][0] ms_depths_median = ms_depths_median.unflatten(-1, cfg.num_rays) if 'ms_max_depths' in result_dict: ms_depths_max = result_dict['ms_max_depths'][0] ms_depths_max = ms_depths_max.unflatten(-1, cfg.num_rays) if args.save_depth: # ms_depths: B, N, R for i, (ms_depth, img_meta) in enumerate(zip(ms_depths, img_metas)): token = img_meta['token'] np.save(os.path.join(depth_save_path, token + '.npy'), ms_depth.cpu().numpy()) if 'ms_depths_median' in result_dict: for i, (ms_depth, img_meta) in enumerate(zip(ms_depths_median, img_metas)): token = img_meta['token'] np.save(os.path.join(depth_median_save_path, token + '.npy'), ms_depth.cpu().numpy()) for i, (ms_depth, img_meta) in enumerate(zip(ms_depths_max, img_metas)): token = img_meta['token'] np.save(os.path.join(depth_max_save_path, token + '.npy'), ms_depth.cpu().numpy()) if args.depth_metric: depth_loc = ms_depths.new_tensor(img_metas[0]['depth_loc']) depth_gt = ms_depths.new_tensor(img_metas[0]['depth_gt']) depth_mask = torch.from_numpy(img_metas[0]['depth_mask']).cuda() if args.depth_metric_tgt == 'raw': depth_pred = ms_depths[0] elif args.depth_metric_tgt == 'median': depth_pred = ms_depths_median[0] elif args.depth_metric_tgt == 'max': depth_pred = ms_depths_max[0] depth_metric._after_step(depth_loc, depth_gt, depth_mask, depth_pred) if i_iter_val % print_freq == 0 and local_rank == 0: logger.info('[EVAL] Iter %5d'%(i_iter_val)) if args.depth_metric: depth_metric._after_epoch() if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--save-depth', action='store_true', default=False) parser.add_argument('--depth-metric', action='store_true', default=False) parser.add_argument('--depth-metric-tgt', type=str, default='raw') parser.add_argument('--dataset', type=str, default='nuscenes') parser.add_argument('--batch', type=int, default=0) parser.add_argument('--flip', action='store_true', default=False) args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: eval_iou.py ================================================ import time, argparse, os.path as osp, os import torch, numpy as np import torch.distributed as dist import torch.nn.functional as F import matplotlib.pyplot as plt import matplotlib; matplotlib.use('agg') import mmcv from mmengine import Config from mmengine.runner import set_random_seed from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") # import model from dataset import get_dataloader from utils.config_tools import modify_for_eval from nuscenes import NuScenes def pass_print(*args, **kwargs): pass def read_occ3d_label(metas, nusc): token = metas['token'] scene_token = nusc.get('sample', token)['scene_token'] scene_name = nusc.get('scene', scene_token)['name'] label_file = f'data/occ3d/gts/{scene_name}/{token}/labels.npz' label = np.load(label_file) return label def read_openoccupancy_label(metas, nusc): # import pdb; pdb.set_trace() token = metas['token'] scene_token = nusc.get('sample', token)['scene_token'] lidar_token = nusc.get('sample', token)['data']['LIDAR_TOP'] label_file = f'data/nuScenes-Occupancy/scene_{scene_token}/occupancy/{lidar_token}.npy' label = np.load(label_file) return torch.from_numpy(label[:, :3]) def main(local_rank, args): try: nusc = NuScenes(version='v1.0-trainval', dataroot='data/nuscenes') except: nusc = NuScenes(version='v1.0-trainval', dataroot='data/nuscenes', load_lidar_task=False) # global settings set_random_seed(args.seed) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, 'nuscenes') cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) if local_rank != 0: import builtins builtins.print = pass_print else: distributed = False if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval_iou_' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval_iou_{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) MMLogger._instance_dict['selfocc'] = logger # build model import model my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') my_model = my_model.cuda() raw_model = my_model logger.info('done ddp model') train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed) amp = cfg.get('amp', False) # resume and load cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from logger.info('resume from: ' + cfg.resume_from) logger.info('work dir: ' + args.work_dir) if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) print(raw_model.load_state_dict(ckpt['state_dict'], strict=False)) print(f'successfully resumed from {cfg.resume_from}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # training from utils.metric_util import IoU, MeanIoU, openseed2nuscenes print_freq = cfg.print_freq my_model.eval() iou_metric = MeanIoU([1], 0, ['occupied'], True, 0) iou_metric.reset() if args.sem: miou_metric = MeanIoU( list(range(1, 17)), 0, ['barrier', 'bicycle', 'bus', 'car', 'construction_vehicle', 'motorcycle', 'pedestrian', 'traffic_cone', 'trailer', 'truck', 'driveable_surface', 'other_flat', 'sidewalk', 'terrain', 'manmade', 'vegetation'], True, 0) miou_metric.reset() if args.occ3d: xx = torch.linspace(-40.0, 40.0, 200) yy = torch.linspace(-40.0, 40.0, 200) zz = torch.linspace(-1.0, 5.4, 16) xyz = torch.stack([ xx[:, None, None].expand(-1, 200, 16), yy[None, :, None].expand(200, -1, 16), zz[None, None, :].expand(200, 200, -1), # xx[None, :, None].expand(200, -1, 16), # yy[:, None, None].expand(-1, 200, 16), # zz[None, None, :].expand(200, 200, -1), torch.ones(200, 200, 16) ], dim=-1) # 200, 200, 16, 4 xyz = xyz.cuda() with torch.no_grad(): for i_iter_val, (input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, \ img_metas, curr_aug, prev_aug, next_aug) in enumerate(val_dataset_loader): input_imgs = input_imgs.cuda() with torch.cuda.amp.autocast(amp): if not args.occ3d: point_cloud_range = [-51.2, -51.2, -5, 51.2, 51.2, 3] elif args.scene_size == 0: point_cloud_range = [-51.2, -51.2, -4, 51.2, 51.2, 4] expansion = [102.4, 102.4, 8] elif args.scene_size == 1: point_cloud_range = [-40.0, -40.0, -2.8, 40.0, 40.0, 3.6] expansion = [80.0, 80.0, 6.4] elif args.scene_size == 2: point_cloud_range = [-40.0, -40.0, -3.1, 40.0, 40.0, 3.9] expansion = [80.0, 80.0, 7.0] elif args.scene_size == 3: point_cloud_range = [-40.0, -40.0, -3.2, 40.0, 40.0, 4.0] expansion = [80.0, 80.0, 7.2] elif args.scene_size == 4: point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4] expansion = [80.0, 80.0, 6.4] elif args.scene_size == 5: point_cloud_range = [-51.2, -51.2, -5, 51.2, 51.2, 3] expansion = [102.4, 102.4, 8] elif args.scene_size == 6: point_cloud_range = [-51.2, -51.2, -4, 51.2, 51.2, 5] expansion = [102.4, 102.4, 9] result_dict = my_model( imgs=input_imgs, metas=img_metas, aabb=point_cloud_range, resolution=args.resolution, occ_only=True) if args.density: result_dict['sdf'] = result_dict['sigma'] pred_occ = (result_dict['sdf'] >= args.thresh).to(torch.int) else: pred_occ = (result_dict['sdf'] <= args.thresh).to(torch.int) if args.occ3d: ego2lidar = img_metas[0]['ego2lidar'] ego2lidar = xyz.new_tensor(ego2lidar) # 4, 4 lidar_points = torch.matmul(ego2lidar.unsqueeze(0), xyz.reshape(-1, 4, 1)) lidar_points = lidar_points.squeeze(-1)[:, :3] # 200*200*16, 3 lidar_points[:, 0] = (lidar_points[:, 0] - point_cloud_range[0]) / expansion[0] lidar_points[:, 1] = (lidar_points[:, 1] - point_cloud_range[1]) / expansion[1] lidar_points[:, 2] = (lidar_points[:, 2] - point_cloud_range[2]) / expansion[2] lidar_points = lidar_points.reshape(1, 200, 200, 16, 3) sampled_sdf = F.grid_sample( result_dict['sdf'][None, None, ...], # 1, 1, H, W, D lidar_points[..., [2, 0, 1]] * 2 - 1, mode='bilinear', align_corners=True) # 1, 1, 200, 200, 16 if args.density: pred_occ = (sampled_sdf.squeeze() >= args.thresh).to(torch.int) else: pred_occ = (sampled_sdf.squeeze() <= args.thresh).to(torch.int) pred_occ[..., 12:] = 0 pred_occ[:6, ...] = 0 pred_occ[-6:, ...] = 0 pred_occ[:, :6, :] = 0 pred_occ[:, -6:, :] = 0 pred_occ_iou = pred_occ gt_meta = read_occ3d_label(img_metas[0], nusc) masks = torch.from_numpy(gt_meta['mask_camera']).bool().cuda() gt_occ_raw = torch.from_numpy(gt_meta['semantics']).cuda() gt_occ_raw[gt_occ_raw == 17] = 0 gt_occ_iou = (gt_occ_raw > 0).to(torch.int) if args.sem: gt_occ_miou = gt_occ_raw sem = result_dict['logits'].permute(3, 0, 1, 2) # C, H, W, D sampled_sem = F.grid_sample( sem[None, ...], # 1, C, H, W, D lidar_points[..., [2, 0, 1]] * 2 - 1, mode='bilinear', align_corners=True) # 1, C, 200, 200, 16 sampled_sem = torch.argmax(sampled_sem, dim=1).squeeze() sem = openseed2nuscenes(sampled_sem) pred_occ_miou = pred_occ * sem else: pred_occ[..., -4:] = 0 pred_occ[..., :5] = 0 pred_occ[:6, ...] = 0 pred_occ[-6:, ...] = 0 pred_occ[:, :6, :] = 0 pred_occ[:, -6:, :] = 0 pred_occ_iou = pred_occ gt_occ = read_openoccupancy_label(img_metas[0], nusc).cuda() gt_occ = gt_occ[:, [1, 2, 0]] gt_occ_iou = torch.zeros(512, 512, 40, device=pred_occ.device, dtype=torch.int) gt_occ_iou[gt_occ.transpose(0, 1).tolist()] = 1 masks = None if args.sem: gt_occ_miou = gt_occ sem = result_dict['sem'] sem = openseed2nuscenes(sem) pred_occ_miou = pred_occ * sem if args.save_sem: token = img_metas[0]['token'] scene_token = nusc.get('sample', token)['scene_token'] scene_name = nusc.get('scene', scene_token)['name'] tosave = pred_occ_miou.cpu().numpy().astype(np.uint8) save_path = '{}/{}/'.format(scene_name, token) save_path = osp.join(args.save_sem, save_path) os.makedirs(save_path, exist_ok=True) np.savez_compressed(os.path.join(save_path, 'labels.npz'), semantics=tosave) iou_metric._after_step(pred_occ_iou, gt_occ_iou, masks if args.use_mask else None) if args.sem: miou_metric._after_step(pred_occ_miou, gt_occ_miou, masks if args.use_mask else None) if i_iter_val % print_freq == 0 and local_rank == 0: logger.info('[EVAL] Iter %5d / %5d'%(i_iter_val, len(val_dataset_loader))) iou_miou, iou_iou = iou_metric._after_epoch() logger.info(f"iou_miou: {iou_miou}, iou_iou: {iou_iou}") if args.sem: miou_miou, miou_iou = miou_metric._after_epoch() logger.info(f"miou_miou: {miou_miou}, miou_iou: {miou_iou}") if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--occ3d', action='store_true', default=False) parser.add_argument('--resolution', type=float, default=0.2) parser.add_argument('--thresh', type=float, default=0) parser.add_argument('--sem', action='store_true', default=False) parser.add_argument('--use-mask', action='store_true', default=False) parser.add_argument('--scene-size', type=int, default=0) parser.add_argument('--density', action='store_true', default=False) parser.add_argument('--save-sem', type=str, default='') args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: eval_iou_kitti.py ================================================ import time, argparse, os.path as osp, os import torch, numpy as np import torch.distributed as dist import mmcv from mmengine import Config from mmengine.runner import set_random_seed from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") # import model from dataset import get_dataloader from utils.config_tools import modify_for_eval import dataset.kitti.io_data as SemanticKittiIO KITTI_ROOT = 'data/kitti' def pass_print(*args, **kwargs): pass def read_semantic_kitti(metas): label_path = os.path.join( KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.label".format(metas['token'])) invalid_path = os.path.join( KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.invalid".format(metas['token'])) remap_lut = SemanticKittiIO.get_remap_lut("dataset/kitti/semantic-kitti.yaml") LABEL = SemanticKittiIO._read_label_SemKITTI(label_path) INVALID = SemanticKittiIO._read_invalid_SemKITTI(invalid_path) LABEL = remap_lut[LABEL.astype(np.uint16)].astype( np.float32 ) # Remap 20 classes semanticKITTI SSC LABEL[ np.isclose(INVALID, 1) ] = 255 # Setting to unknown all voxels marked on invalid mask... LABEL = LABEL.reshape(256, 256, 32) return LABEL def main(local_rank, args): # global settings set_random_seed(args.seed) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, 'kitti') cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) if local_rank != 0: import builtins builtins.print = pass_print else: distributed = False if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval_iou_kitti' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval_iou_kitti_{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) MMLogger._instance_dict['selfocc'] = logger # logger.info(f'Config:\n{cfg.pretty_text}') # build model import model from utils.metric_util import cityscapes2semantickitti my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') my_model = my_model.cuda() raw_model = my_model logger.info('done build model') train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed) amp = cfg.get('amp', False) # resume and load cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from logger.info('resume from: ' + cfg.resume_from) logger.info('work dir: ' + args.work_dir) if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) print(raw_model.load_state_dict(ckpt['state_dict'], strict=False)) print(f'successfully resumed from {cfg.resume_from}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # training from utils.metric_util import IoU, MeanIoU from utils.scenerf_metric import SSCMetrics print_freq = cfg.print_freq my_model.eval() iou_metric = IoU() iou_metric.reset() scenerf_metric = SSCMetrics(2) if args.sem: miou_metric = MeanIoU( list(range(1, 20)), 0, ["car", "bicycle", "motorcycle", "truck", "other-vehicle", "person", "bicyclist", "motorcyclist", "road", "parking", "sidewalk", "other-ground", "building", "fence", "vegetation", "trunk", "terrain", "pole", "traffic-sign"], True, 0) miou_metric.reset() max_ds, min_ds = [], [] with torch.no_grad(): for i_iter_val, (input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, \ img_metas, curr_aug, prev_aug, next_aug) in enumerate(val_dataset_loader): input_imgs = input_imgs.cuda() with torch.cuda.amp.autocast(amp): result_dict = my_model( imgs=input_imgs, metas=img_metas, aabb=[-25.6, 0, -2.0, 25.6, 51.2, 4.4], resolution=args.resolution, occ_only=True) pred_occ = (result_dict['sdf'] <= args.thresh).to(torch.int) # gt_occ_raw for scenerf style iou calculation # gt_occ for my own evaluation gt_occ_raw = torch.from_numpy(read_semantic_kitti(img_metas[0])).cuda() gt_occ_raw = torch.flip(gt_occ_raw, [1]) gt_occ = gt_occ_raw.clone() # gt_occ = np.copy(gt_occ_raw) gt_occ[gt_occ == 255] = 0 gt_occ = torch.nonzero(gt_occ) ## post process max_d = gt_occ[:, 2].max() min_d = gt_occ[:, 2].min() # pred_occ[..., (max_d + 1):] = 0 # pred_occ[..., :min_d] = 0 pred_occ[..., 28:] = 0 # pred_occ[0, ...] = 0 pred_occ[-6:, ...] = 0 pred_occ[:, :6, :] = 0 pred_occ[:, -6:, :] = 0 iou_metric._after_step(pred_occ, gt_occ) gt_occ_scenerf = gt_occ_raw.clone() scenerf_metric.add_batch(pred_occ, gt_occ_scenerf) if args.sem: sem = result_dict['sem'] sem = cityscapes2semantickitti(sem) # gt_occ_raw[gt_occ_raw == 255] = 0 pred_miou = pred_occ * sem miou_metric._after_step(pred_miou, gt_occ_raw, gt_occ_raw != 255) max_ds.append(max_d.item()) min_ds.append(min_d.item()) if i_iter_val % print_freq == 0 and local_rank == 0: logger.info('[EVAL] Iter %5d / %5d, max_d %d, min_d %d'%( i_iter_val, len(val_dataset_loader), max_d, min_d)) iou = iou_metric._after_epoch() stats = scenerf_metric.get_stats() if not distributed or dist.get_rank() == 0: logger.info(f'IoU: {iou}') logger.info(f'mean of max_d: {np.mean(max_ds)}') logger.info(f'mean of min_d: {np.mean(min_ds)}') logger.info("========================") logger.info("=========Summary========") logger.info("========================") logger.info("==== Whole Scene ====") logger.info(f"iou: {stats['iou']}, precision: {stats['precision']}, recall: {stats['recall']}") if args.sem: miou_miou, miou_iou = miou_metric._after_epoch() logger.info(f"miou_miou: {miou_miou}, miou_iou: {miou_iou}") if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--resolution', type=float, default=0.2) parser.add_argument('--thresh', type=float, default=0) parser.add_argument('--sem', action='store_true', default=False) args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: eval_novel_depth.py ================================================ import time, argparse, os.path as osp import os, math, pickle import torch, numpy as np import torch.distributed as dist import torch.nn.functional as F import mmcv from mmengine import Config from mmengine.runner import set_random_seed from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") from utils.metric_util import compute_depth_errors, compute_depth_errors_torch from utils.config_tools import modify_for_eval def pass_print(*args, **kwargs): pass def main(local_rank, args): # global settings set_random_seed(args.seed) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, 'nuscenes', True, args) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) if local_rank != 0: import builtins builtins.print = pass_print else: distributed = False if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval_novel_depth_' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval_novel_depth_{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) MMLogger._instance_dict['selfocc'] = logger # logger.info(f'Config:\n{cfg.pretty_text}') logger.info(args) import model from dataset import get_dataloader # build model cfg.model.head.return_max_depth = True my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: if cfg.get('syncBN', True): my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) logger.info('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.cuda() raw_model = my_model logger.info('done ddp model') train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed, val_only=True) # get optimizer, loss, scheduler amp = cfg.get('amp', False) # resume and load cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from logger.info('resume from: ' + cfg.resume_from) logger.info('work dir: ' + args.work_dir) if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) print(raw_model.load_state_dict(ckpt['state_dict'], strict=False)) print(f'successfully resumed from epoch {ckpt["epoch"]}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # eval my_model.eval() with torch.no_grad(): agg_depth_errors = {} n_frames = {} for i_iter_val, (input_imgs, anchor_imgs, img_metas) in enumerate(val_dataset_loader): input_imgs = input_imgs.cuda() anchor_imgs = anchor_imgs.cuda() with torch.cuda.amp.autocast(amp): result_dict = my_model(imgs=input_imgs, metas=img_metas, prepare=True) frame_id = img_metas[0]['token'] save_dir = os.path.join(args.work_dir, "depth_metrics") os.makedirs(save_dir, exist_ok=True) save_filepath = os.path.join(save_dir,"{}.npy".format(frame_id)) if os.path.exists(save_filepath): logger.info(f'{frame_id} already exists.') continue source_distances = img_metas[0]["frame_dists"] loc2d_with_depths = img_metas[0]['depth_loc'] # [N, n, 2] * S lidar_depths = img_metas[0]['depth_gt'] # [N, n] * S lidar_depths_mask = img_metas[0]['depth_mask'] # [N, n] * S for source_id in range(len(source_distances)): source_distance = source_distances[source_id] loc2d_with_depth = input_imgs.new_tensor(loc2d_with_depths[source_id]) lidar_depth = input_imgs.new_tensor(lidar_depths[source_id]) lidar_depth_mask = torch.from_numpy(lidar_depths_mask[source_id]).cuda() num_cams = lidar_depth.shape[0] # gt_depth_infer = lidar_depth[0][lidar_depth_mask[0]] # n img_metas[0].update({ 'render_img2lidar': img_metas[0]['temImg2lidars'][source_id]}) render_out_dict = my_model.module.head.render(metas=img_metas, batch=args.batch) if args.depth_tgt == 'raw': depth_key = 'ms_depths' elif args.depth_tgt == 'max': depth_key = 'ms_max_depths' pred_depth_infer = render_out_dict[depth_key][0] # B, N, R pred_depth_infer = pred_depth_infer.reshape(num_cams, 1, *cfg.num_rays) pred_depth_infer = F.grid_sample( pred_depth_infer, # N, 1, H, W loc2d_with_depth[:, None, ...] * 2 - 1, # N, 1, n, 2 mode='bilinear', padding_mode='border', align_corners=True).reshape(num_cams, -1) # assert len(pred_depth_infer.shape) == 1 # pred_depth_infer = pred_depth_infer[lidar_depth_mask[0]] # assert len(pred_depth_infer) == len(gt_depth_infer) cams_depth_errors = [] for cam in range(num_cams): gt_depth_infer = lidar_depth[cam][lidar_depth_mask[cam]] tmp_depth_infer = pred_depth_infer[cam][lidar_depth_mask[cam]] assert len(tmp_depth_infer) == len(gt_depth_infer) depth_errors = evaluate_depth(gt_depth_infer, tmp_depth_infer) cams_depth_errors.append(depth_errors) # cams_depth_errors = np.asarray(cams_depth_errors) cams_depth_errors = torch.stack(cams_depth_errors, dim=0) k = math.ceil(source_distance) if k not in agg_depth_errors: agg_depth_errors[k] = cams_depth_errors n_frames[k] = torch.ones(1, dtype=torch.int, device=input_imgs.device) else: agg_depth_errors[k] += cams_depth_errors n_frames[k] += 1 # out_dict = { # "depth_errors": agg_depth_errors, # "n_frames": n_frames # } # with open(save_filepath, "wb") as output_file: # pickle.dump(out_dict, output_file) # logger.info("Saved to" + save_filepath) if i_iter_val % 20 == 0: logger.info("=================") logger.info("==== batch {} ====".format(i_iter_val)) logger.info("=================") print_metrics(agg_depth_errors, n_frames, logger) for k in agg_depth_errors: dist.all_reduce(agg_depth_errors[k]) dist.all_reduce(n_frames[k]) logger.info("=================") logger.info("====== TotalS ======") logger.info("=================") print_metrics(agg_depth_errors, n_frames, logger) # logger.info("=================") # logger.info("==== Frame {} ====".format(frame_id)) # logger.info("=================") # print_metrics(agg_depth_errors, n_frames, logger) # if not distributed or dist.get_rank() == 0: # from tqdm import tqdm # train_dataset_loader, val_dataset_loader = get_dataloader( # cfg.train_dataset_config, # cfg.val_dataset_config, # cfg.train_wrapper_config, # cfg.val_wrapper_config, # cfg.train_loader, # cfg.val_loader, # cfg.nusc, # dist=False) # cnt = 0 # agg_depth_errors = {} # agg_n_frames = {} # for i_iter_val, (input_imgs, anchor_imgs, img_metas) in enumerate(tqdm(val_dataset_loader)): # cnt += 1 # frame_id = img_metas[0]['token'] # # sequence = img_metas[0]['sequence'] # save_dir = os.path.join(args.work_dir, "depth_metrics") # save_filepath = os.path.join(save_dir, "{}.npy".format(frame_id)) # with open(save_filepath, "rb") as handle: # data = pickle.load(handle) # depth_errors = data["depth_errors"] # n_frames = data["n_frames"] # for k in depth_errors: # if k not in agg_depth_errors: # agg_depth_errors[k] = depth_errors[k] # agg_n_frames[k] = n_frames[k] # else: # agg_depth_errors[k] += depth_errors[k] # agg_n_frames[k] += n_frames[k] # if cnt % 20 == 0: # logger.info("=================") # logger.info("==== batch {} ====".format(cnt)) # logger.info("=================") # print_metrics(agg_depth_errors, agg_n_frames, logger) # logger.info("=================") # logger.info("====== TotalS ======") # logger.info("=================") # print_metrics(agg_depth_errors, agg_n_frames, logger) def evaluate_depth(gt_depth, pred_depth): depth_errors = [] # depth_error = compute_depth_errors( # gt=gt_depth.reshape(-1).detach().cpu().numpy(), # pred=pred_depth.reshape(-1).detach().cpu().numpy(), # ) depth_error = compute_depth_errors_torch( gt=gt_depth.reshape(-1), pred=pred_depth.reshape(-1), ) # print(depth_error) # depth_errors.append(depth_error) # agg_depth_errors = np.array(depth_errors).sum(0) return torch.stack(depth_error) def print_metrics(agg_depth_errors, n_frames, logger, num_cams=6): logger.info("|cam |distance|abs_rel |sq_rel |rmse |rmse_log|a1 |a2 |a3 |n_frames|") total_depth_errors = None total_frame = 0 for distance in sorted(agg_depth_errors): if total_depth_errors is None: total_depth_errors = agg_depth_errors[distance].clone() else: total_depth_errors = total_depth_errors + agg_depth_errors[distance] for cam in range(num_cams): metric_list = ["abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"] logger.info("|{:04d}|{:08d}|{:02.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:08d}|".format( cam, distance, (agg_depth_errors[distance][cam][0]/n_frames[distance]).item(), (agg_depth_errors[distance][cam][1]/n_frames[distance]).item(), (agg_depth_errors[distance][cam][2]/n_frames[distance]).item(), (agg_depth_errors[distance][cam][3]/n_frames[distance]).item(), (agg_depth_errors[distance][cam][4]/n_frames[distance]).item(), (agg_depth_errors[distance][cam][5]/n_frames[distance]).item(), (agg_depth_errors[distance][cam][6]/n_frames[distance]).item(), n_frames[distance].item() )) total_frame += n_frames[distance] for cam in range(num_cams): logger.info("|{}|{}|{:02.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:08d}|".format( "Pcam ", "All ", (total_depth_errors[cam][0]/total_frame).item(), (total_depth_errors[cam][1]/total_frame).item(), (total_depth_errors[cam][2]/total_frame).item(), (total_depth_errors[cam][3]/total_frame).item(), (total_depth_errors[cam][4]/total_frame).item(), (total_depth_errors[cam][5]/total_frame).item(), (total_depth_errors[cam][6]/total_frame).item(), total_frame.item() )) total_depth_errors = (total_depth_errors / total_frame).mean(0) logger.info("|{}|{}|{:02.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:08d}|".format( "All ", "All ", total_depth_errors[0].item(), total_depth_errors[1].item(), total_depth_errors[2].item(), total_depth_errors[3].item(), total_depth_errors[4].item(), total_depth_errors[5].item(), total_depth_errors[6].item(), total_frame.item() )) if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--depth-tgt', type=str, default='raw') parser.add_argument('--batch', type=int, default=0) parser.add_argument('--num-rays', type=int, nargs='+', default=[96, 200]) args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: eval_novel_depth_kitti.py ================================================ import time, argparse, os.path as osp import os, math, pickle import torch, numpy as np import torch.distributed as dist import torch.nn.functional as F import mmcv from mmengine import Config from mmengine.runner import set_random_seed from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") from utils.metric_util import compute_depth_errors from utils.config_tools import modify_for_eval def pass_print(*args, **kwargs): pass def main(local_rank, args): # global settings set_random_seed(args.seed) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, 'kitti', True) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) if local_rank != 0: import builtins builtins.print = pass_print else: distributed = False if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval_novel_depth_' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval_novel_depth_{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) MMLogger._instance_dict['selfocc'] = logger # logger.info(f'Config:\n{cfg.pretty_text}') logger.info(args) import model from dataset import get_dataloader # build model cfg.model.head.return_max_depth = True my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: if cfg.get('syncBN', True): my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) logger.info('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.cuda() raw_model = my_model logger.info('done ddp model') train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed,) # get optimizer, loss, scheduler amp = cfg.get('amp', False) # resume and load cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from logger.info('resume from: ' + cfg.resume_from) logger.info('work dir: ' + args.work_dir) if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) print(raw_model.load_state_dict(ckpt['state_dict'], strict=False)) print(f'successfully resumed from epoch {ckpt["epoch"]}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # eval my_model.eval() with torch.no_grad(): for i_iter_val, (input_imgs, anchor_imgs, img_metas) in enumerate(val_dataset_loader): input_imgs = input_imgs.cuda() anchor_imgs = anchor_imgs.cuda() with torch.cuda.amp.autocast(amp): result_dict = my_model(imgs=input_imgs, metas=img_metas, prepare=True) frame_id = img_metas[0]['token'] sequence = img_metas[0]['sequence'] save_dir = os.path.join(args.work_dir, "depth_metrics", sequence) os.makedirs(save_dir, exist_ok=True) save_filepath = os.path.join(save_dir,"{}.npy".format(frame_id)) if os.path.exists(save_filepath): continue source_distances = img_metas[0]["frame_dists"] loc2d_with_depths = img_metas[0]['depth_loc'] # [N, n, 2] * S lidar_depths = img_metas[0]['depth_gt'] # [N, n] * S lidar_depths_mask = img_metas[0]['depth_mask'] # [N, n] * S agg_depth_errors = {} n_frames = {} for source_id in range(len(source_distances)): source_distance = source_distances[source_id] loc2d_with_depth = input_imgs.new_tensor(loc2d_with_depths[source_id]) lidar_depth = input_imgs.new_tensor(lidar_depths[source_id]) lidar_depth_mask = torch.from_numpy(lidar_depths_mask[source_id]).cuda() gt_depth_infer = lidar_depth[0][lidar_depth_mask[0]] # n img_metas[0].update({ 'render_img2lidar': img_metas[0]['temImg2lidars'][source_id]}) render_out_dict = my_model.module.head.render(metas=img_metas, batch=args.batch) if args.depth_tgt == 'raw': depth_key = 'ms_depths' elif args.depth_tgt == 'max': depth_key = 'ms_max_depths' pred_depth_infer = render_out_dict[depth_key][0] # B, N, R pred_depth_infer = pred_depth_infer.reshape(1, 1, *cfg.num_rays) pred_depth_infer = F.grid_sample( pred_depth_infer, # 1, 1, H, W loc2d_with_depth[None, ...] * 2 - 1, # 1, 1, n, 2 mode='bilinear', padding_mode='border', align_corners=True).squeeze() # n assert len(pred_depth_infer.shape) == 1 pred_depth_infer = pred_depth_infer[lidar_depth_mask[0]] assert len(pred_depth_infer) == len(gt_depth_infer) depth_errors = evaluate_depth(gt_depth_infer, pred_depth_infer) k = math.ceil(source_distance) if k not in agg_depth_errors: agg_depth_errors[k] = depth_errors n_frames[k] = 1 else: agg_depth_errors[k] += depth_errors n_frames[k] += 1 out_dict = { "depth_errors": agg_depth_errors, "n_frames": n_frames } with open(save_filepath, "wb") as output_file: pickle.dump(out_dict, output_file) logger.info("Saved to" + save_filepath) logger.info("=================") logger.info("==== Frame {} ====".format(frame_id)) logger.info("=================") print_metrics(agg_depth_errors, n_frames, logger) if not distributed or dist.get_rank() == 0: from tqdm import tqdm train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=False) cnt = 0 agg_depth_errors = {} agg_n_frames = {} for i_iter_val, (input_imgs, anchor_imgs, img_metas) in enumerate(tqdm(val_dataset_loader)): cnt += 1 frame_id = img_metas[0]['token'] sequence = img_metas[0]['sequence'] save_dir = os.path.join(args.work_dir, "depth_metrics", sequence) save_filepath = os.path.join(save_dir, "{}.npy".format(frame_id)) with open(save_filepath, "rb") as handle: data = pickle.load(handle) depth_errors = data["depth_errors"] n_frames = data["n_frames"] for k in depth_errors: if k not in agg_depth_errors: agg_depth_errors[k] = depth_errors[k] agg_n_frames[k] = n_frames[k] else: agg_depth_errors[k] += depth_errors[k] agg_n_frames[k] += n_frames[k] if cnt % 20 == 0: logger.info("=================") logger.info("==== batch {} ====".format(cnt)) logger.info("=================") print_metrics(agg_depth_errors, agg_n_frames, logger) logger.info("=================") logger.info("====== TotalS ======") logger.info("=================") print_metrics(agg_depth_errors, agg_n_frames, logger) def evaluate_depth(gt_depth, pred_depth): depth_errors = [] depth_error = compute_depth_errors( gt=gt_depth.reshape(-1).detach().cpu().numpy(), pred=pred_depth.reshape(-1).detach().cpu().numpy(), ) # print(depth_error) depth_errors.append(depth_error) agg_depth_errors = np.array(depth_errors).sum(0) return agg_depth_errors def print_metrics(agg_depth_errors, n_frames, logger): logger.info("|distance|abs_rel |sq_rel |rmse |rmse_log|a1 |a2 |a3 |n_frames|") total_depth_errors = None total_frame = 0 for distance in sorted(agg_depth_errors): if total_depth_errors is None: total_depth_errors = np.copy(agg_depth_errors[distance]) else: total_depth_errors = total_depth_errors + agg_depth_errors[distance] metric_list = ["abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"] logger.info("|{:08d}|{:02.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:08d}|".format( distance, agg_depth_errors[distance][0]/n_frames[distance], agg_depth_errors[distance][1]/n_frames[distance], agg_depth_errors[distance][2]/n_frames[distance], agg_depth_errors[distance][3]/n_frames[distance], agg_depth_errors[distance][4]/n_frames[distance], agg_depth_errors[distance][5]/n_frames[distance], agg_depth_errors[distance][6]/n_frames[distance], n_frames[distance] )) total_frame += n_frames[distance] logger.info("|{}|{:02.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:.6f}|{:08d}|".format( "All ", total_depth_errors[0]/total_frame, total_depth_errors[1]/total_frame, total_depth_errors[2]/total_frame, total_depth_errors[3]/total_frame, total_depth_errors[4]/total_frame, total_depth_errors[5]/total_frame, total_depth_errors[6]/total_frame, total_frame )) if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--depth-tgt', type=str, default='raw') parser.add_argument('--batch', type=int, default=0) args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: examine_sweeps.py ================================================ #### check nuscenes sweeps from nuscenes import NuScenes import mmengine from copy import deepcopy import numpy as np nusc = NuScenes(dataroot='data/nuscenes', version='v1.0-trainval') # pkl = mmengine.load('data/nuscenes_infos_train_temporal_v1.pkl') pkl = mmengine.load('data/nuscenes_infos_val_temporal_v1.pkl') sensor_types = [ 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT', 'LIDAR_TOP'] def gather_sensor(sample_data_token): sample_data = nusc.get('sample_data', sample_data_token) data = deepcopy(sample_data) data.update(dict( pose=nusc.get('ego_pose', data['ego_pose_token']), calib=nusc.get('calibrated_sensor', data['calibrated_sensor_token']))) return data scenes = dict() for sample in pkl['infos']: scene_token = nusc.get('sample', sample['token'])['scene_token'] scene_meta = nusc.get('scene', scene_token) scene_name = scene_meta['name'] if not scene_name in scenes: scenes.update({scene_name: scene_token}) all_infos = dict() meta_data = [] for scene_name, scene_token in scenes.items(): print(f'processing {scene_name}') scene_meta = nusc.get('scene', scene_token) sample_sweep_list = [] sample_token = scene_meta['first_sample_token'] while sample_token: sample = nusc.get('sample', sample_token) sample_dict = deepcopy(sample) data = dict() for sensor_type in sensor_types: data.update({sensor_type: gather_sensor(sample['data'][sensor_type])}) data.update({'LIDAR_TOP': gather_sensor(sample['data']['LIDAR_TOP'])}) sample_dict.update(dict(data=data, is_key_frame=True)) sample_sweep_list.append(sample_dict) meta_data.append((scene_token, len(sample_sweep_list) - 1)) sweeps = dict() for sensor_type in sensor_types: sweeps_sensor = [] sweep_token = data[sensor_type]['next'] while sweep_token: sweep_data = gather_sensor(sweep_token) if sweep_data['is_key_frame']: break sweeps_sensor.append(sweep_data) sweep_token = sweep_data['next'] sweeps.update({sensor_type: sweeps_sensor}) least_length = len(sweeps[sensor_types[0]]) least_sensor_type = sensor_types[0] for sensor_type in sensor_types[1:]: length = len(sweeps[sensor_type]) if length < least_length: least_length = length least_sensor_type = sensor_type if least_length == 0: sample_token = sample['next'] continue ref_timestamps = [v['timestamp'] for v in sweeps[least_sensor_type]] for timestamp in ref_timestamps: sweep_dict = dict(timestamp=timestamp, is_key_frame=False) sweep_dict_data = dict() for sensor_type in sensor_types: intervals = [v['timestamp'] - timestamp for v in sweeps[sensor_type]] nearest = np.argmin(np.abs(intervals)) sweep_dict_data.update({sensor_type: sweeps[sensor_type][nearest]}) sweep_dict.update({'data': sweep_dict_data}) sample_sweep_list.append(sweep_dict) sample_token = sample['next'] all_infos.update({scene_token: sample_sweep_list}) # sample = nusc.get('sample', sample_token) # sweeps = dict() # for sensor_type in sensor_types: # sweeps_sensor = [] # sweep_token = sample['data'][sensor_type] # while sweep_token: # sweep_meta = nusc.get('sample_data', sweep_token) # sweep = deepcopy(sweep_meta) # sweep.update(dict( # pose=nusc.get('ego_pose', sweep_meta['ego_pose_token']), # calib=nusc.get('calibrated_sensor', sweep_meta['calibrated_sensor_token']))) # sweeps_sensor.append(sweep) # sweep_token = sweep_meta['next'] # sweeps.update({sensor_type: sweeps_sensor}) # least_sensor_type = sensor_types[0] # least_length = len(sweeps[least_sensor_type]) # for sensor_type in sensor_types[1:]: # length = len(sweeps[sensor_type]) # if length < least_length: # least_length = length # least_sensor_type = sensor_type # ref_timestamps = [s['timestamp'] for s in sweeps[least_sensor_type]] # import pdb; pdb.set_trace() # mmengine.dump({'infos': all_infos, 'metadata': meta_data}, 'data/nuscenes_infos_train_sweeps_lid.pkl') mmengine.dump({'infos': all_infos, 'metadata': meta_data}, 'data/nuscenes_infos_val_sweeps_lid.pkl') pass ================================================ FILE: generate_videos.py ================================================ import os, os.path as osp import PIL.Image as Image import cv2 import argparse def cat_images(dir, cam_img_size, pred_img_size, pred_img_size2, spacing, img_dir=None): numFrames = len(os.listdir(osp.join(dir, '0'))) // 2 if img_dir is None else len(os.listdir(osp.join(dir, '0'))) cam_imgs = [] pred_imgs = [] for i in range(8): clip_dir = osp.join(dir, str(i)) img_clip_dir = osp.join(dir, str(i)) if img_dir is None else osp.join(img_dir, str(i)) if i < 6: cam_files = [osp.join(img_clip_dir, fn) for fn in os.listdir(img_clip_dir) if fn.startswith('img')] cam_files = sorted(cam_files) cam_imgs.append([ Image.open(fn).resize(cam_img_size, Image.BILINEAR) for fn in cam_files ]) pred_files = [osp.join(clip_dir, fn) for fn in os.listdir(clip_dir) if fn.startswith('vis')] pred_files = sorted(pred_files) pred_imgs.append([ Image.open(fn).resize(cam_img_size, Image.BILINEAR) for fn in pred_files ]) else: pred_files = [osp.join(clip_dir, fn) for fn in os.listdir(clip_dir) if fn.startswith('vis')] pred_files = sorted(pred_files) if i == 6: pred_imgs.append([ Image.open(fn).resize(pred_img_size, Image.BILINEAR) for fn in pred_files ]) else: pred_imgs.append([ Image.open(fn).resize(pred_img_size, Image.BILINEAR).crop([460, 0, 1460, 1080]) for fn in pred_files ]) cam_w, cam_h = cam_img_size pred_w, pred_h = pred_img_size result_w = cam_w * 6 + 5 * spacing result_h = cam_h * 2 + pred_h + 2 * spacing results = [] for i in range(numFrames): result = Image.new(pred_imgs[0][0].mode, (result_w, result_h), (0, 0, 0)) result.paste(cam_imgs[0][i], box=(1*cam_w+1*spacing, 0)) result.paste(cam_imgs[1][i], box=(2*cam_w+2*spacing, 0)) result.paste(cam_imgs[2][i], box=(0, 0)) result.paste(cam_imgs[3][i], box=(1*cam_w+1*spacing, 1*cam_h+1*spacing)) result.paste(cam_imgs[4][i], box=(0, 1*cam_h+1*spacing)) result.paste(cam_imgs[5][i], box=(2*cam_w+2*spacing, 1*cam_h+1*spacing)) result.paste(pred_imgs[0][i], box=(4*cam_w+4*spacing, 0)) result.paste(pred_imgs[1][i], box=(5*cam_w+5*spacing, 0)) result.paste(pred_imgs[2][i], box=(3*cam_w+3*spacing, 0)) result.paste(pred_imgs[3][i], box=(4*cam_w+4*spacing, 1*cam_h+1*spacing)) result.paste(pred_imgs[4][i], box=(3*cam_w+3*spacing, 1*cam_h+1*spacing)) result.paste(pred_imgs[5][i], box=(5*cam_w+5*spacing, 1*cam_h+1*spacing)) result.paste(pred_imgs[6][i], box=(0, 2*cam_h+2*spacing)) result.paste(pred_imgs[7][i], box=(1*pred_w+1*spacing, 2*cam_h+2*spacing)) results.append(result) result_path = osp.join(dir, 'cat') os.makedirs(result_path, exist_ok=True) for i, result in enumerate(results): result.save(osp.join(result_path, f'{i}.png')) return results def get_video(img_path, video_path, fps, size): video = cv2.VideoWriter( video_path, cv2.VideoWriter_fourcc(*"MJPG"), fps, size ) num_imgs = len(os.listdir(img_path)) # img_files = [osp.join(img_path, fn) for fn in img_files] # img_files = sorted(img_files) for i in range(num_imgs): fn = osp.join(img_path, f'{i}.png') img = cv2.imread(fn) video.write(img) video.release() cv2.destroyAllWindows() if __name__ == "__main__": parse = argparse.ArgumentParser('') parse.add_argument('--scene-dir', type=str, default='out/tpv_occupancy/videos', help='directory of the scene outputs') parse.add_argument('--scene-name', type=str, default='scene-0916', nargs='+') parse.add_argument('--img-dir', type=str, default='') args = parse.parse_args() scene_dir = args.scene_dir img_dir = args.img_dir names = args.scene_name dirs = [osp.join(scene_dir, d) for d in names] if img_dir: img_dirs = [osp.join(img_dir, d) for d in names] else: img_dirs = [None] * len(names) cam_img_size = [480, 270] pred_img_size = [1920, 1080] pred_img_size2 = [1000, 1080] spacing = 10 for i, dir in enumerate(dirs): print(f'processing {os.path.basename(dir)}') cat_images(dir, cam_img_size, pred_img_size, pred_img_size2, spacing, img_dirs[i]) get_video( osp.join(dir, 'cat'), osp.join(dir, 'video.avi'), 12, [2930, 1640] ) ================================================ FILE: loss/__init__.py ================================================ from mmengine.registry import Registry OPENOCC_LOSS = Registry('openocc_loss') from .multi_loss import MultiLoss from .rgb_loss_ms import RGBLossMS, SemLossMS, SemCELossMS from .reproj_loss_mono_multi_new import ReprojLossMonoMultiNew from .reproj_loss_mono_multi_new_combine import ReprojLossMonoMultiNewCombine from .edge_loss_3d_ms import EdgeLoss3DMS from .eikonal_loss import EikonalLoss from .sparsity_loss import SparsityLoss, HardSparsityLoss, SoftSparsityLoss, AdaptiveSparsityLoss from .second_grad_loss import SecondGradLoss ================================================ FILE: loss/base_loss.py ================================================ import torch.nn as nn from utils.tb_wrapper import WrappedTBWriter if 'selfocc' in WrappedTBWriter._instance_dict: writer = WrappedTBWriter.get_instance('selfocc') else: writer = None class BaseLoss(nn.Module): """ Base loss class. args: weight: weight of current loss. input_keys: keys for actual inputs to calculate_loss(). Since "inputs" may contain many different fields, we use input_keys to distinguish them. loss_func: the actual loss func to calculate loss. """ def __init__( self, weight=1.0, input_dict={ 'input': 'input'}, **kwargs): super().__init__() self.weight = weight self.input_dict = input_dict self.loss_func = lambda: 0 self.writer = writer # def calculate_loss(self, **kwargs): # return self.loss_func(*[kwargs[key] for key in self.input_keys]) def forward(self, inputs): actual_inputs = {} for input_key, input_val in self.input_dict.items(): actual_inputs.update({input_key: inputs[input_val]}) # return self.weight * self.calculate_loss(**actual_inputs) return self.weight * self.loss_func(**actual_inputs) ================================================ FILE: loss/edge_loss_3d_ms.py ================================================ import torch from .base_loss import BaseLoss from . import OPENOCC_LOSS import torch.nn.functional as F def get_smooth_loss(disp, img): """Computes the smoothness loss for a disparity image The color image is used for edge-aware smoothness """ grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:]) grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :]) grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True) grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True) grad_disp_x *= torch.exp(-grad_img_x) grad_disp_y *= torch.exp(-grad_img_y) return grad_disp_x.mean() + grad_disp_y.mean() @OPENOCC_LOSS.register_module() class EdgeLoss3DMS(BaseLoss): def __init__(self, weight=1.0, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'curr_imgs': 'curr_imgs', 'ms_depths': 'ms_depths', 'ms_rays': 'ms_rays' } else: self.input_dict = input_dict self.img_size = kwargs.get('img_size', [768, 1600]) self.ray_resize = kwargs.get('ray_resize', None) self.use_inf_mask = kwargs.get('use_inf_mask', False) # self.inf_dist = kwargs.get('inf_dist', 1e6) assert self.ray_resize is not None self.loss_func = self.edge_loss def edge_loss(self, curr_imgs, ms_depths, ms_rays, ms_accs=None, max_depths=None): # curr_imgs: B, N, C, H, W # depth: B, N, R # rays: R, 2 if self.use_inf_mask: assert ms_accs is not None and max_depths is not None if not isinstance(ms_rays, list): ms_rays = [ms_rays] * len(ms_depths) bs, num_cams, num_rays = ms_depths[0].shape tot_loss = 0. for scale, (depth, rays) in enumerate(zip(ms_depths, ms_rays)): pixel_curr = rays.clone().reshape(1, 1, num_rays, 2).repeat( bs * num_cams, 1, 1, 1) # bs*N, 1, R, 2 pixel_curr[..., 0] /= self.img_size[1] pixel_curr[..., 1] /= self.img_size[0] pixel_curr = pixel_curr * 2 - 1 rgb_curr = F.grid_sample( curr_imgs.flatten(0, 1), pixel_curr, mode='bilinear', padding_mode='border', align_corners=True) # bs*N, 3, 1, R rgb_curr = rgb_curr.reshape(bs * num_cams, -1, *self.ray_resize) if self.use_inf_mask: depth = depth * ms_accs[scale] + max_depths[scale] * (1 - ms_accs[scale]) depth = depth.reshape(bs * num_cams, 1, *self.ray_resize) mean_depth = depth.mean(2, True).mean(3, True) norm_depth = depth / (mean_depth + 1e-6) smooth_loss = get_smooth_loss(norm_depth, rgb_curr) tot_loss += smooth_loss return tot_loss / len(ms_depths) ================================================ FILE: loss/eikonal_loss.py ================================================ from .base_loss import BaseLoss from . import OPENOCC_LOSS @OPENOCC_LOSS.register_module() class EikonalLoss(BaseLoss): def __init__(self, weight=1.0, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'eik_grad': 'eik_grad', } else: self.input_dict = input_dict self.loss_func = self.eikonal_loss def eikonal_loss(self, eik_grad): grad_theta = eik_grad eikonal_loss = ((grad_theta.norm(2, dim=-1) - 1) ** 2).mean() return eikonal_loss ================================================ FILE: loss/multi_loss.py ================================================ import torch.nn as nn from . import OPENOCC_LOSS from utils.tb_wrapper import WrappedTBWriter if 'selfocc' in WrappedTBWriter._instance_dict: writer = WrappedTBWriter.get_instance('selfocc') else: writer = None @OPENOCC_LOSS.register_module() class MultiLoss(nn.Module): def __init__(self, loss_cfgs): super().__init__() assert isinstance(loss_cfgs, list) self.num_losses = len(loss_cfgs) losses = [] for loss_cfg in loss_cfgs: losses.append(OPENOCC_LOSS.build(loss_cfg)) self.losses = nn.ModuleList(losses) self.iter_counter = 0 def forward(self, inputs): loss_dict = {} tot_loss = 0. for loss_func in self.losses: loss = loss_func(inputs) tot_loss += loss loss_dict.update({ loss_func.__class__.__name__: \ loss.detach().item() }) if writer and self.iter_counter % 10 == 0: writer.add_scalar( f'loss/{loss_func.__class__.__name__}', loss.detach().item(), self.iter_counter) if writer and self.iter_counter % 10 == 0: writer.add_scalar( 'loss/total', tot_loss.detach().item(), self.iter_counter) self.iter_counter += 1 return tot_loss, loss_dict ================================================ FILE: loss/reproj_loss_mono_multi_new.py ================================================ import torch.nn as nn, torch from .base_loss import BaseLoss from . import OPENOCC_LOSS import numpy as np import torch.nn.functional as F class SSIM(nn.Module): """Layer to compute the SSIM loss between a pair of images """ def __init__(self): super(SSIM, self).__init__() self.mu_x_pool = nn.AvgPool2d(3, 1) self.mu_y_pool = nn.AvgPool2d(3, 1) self.sig_x_pool = nn.AvgPool2d(3, 1) self.sig_y_pool = nn.AvgPool2d(3, 1) self.sig_xy_pool = nn.AvgPool2d(3, 1) self.refl = nn.ReflectionPad2d(1) self.C1 = 0.01 ** 2 self.C2 = 0.03 ** 2 def forward(self, x, y): x = self.refl(x) y = self.refl(y) mu_x = self.mu_x_pool(x) mu_y = self.mu_y_pool(y) sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2 sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2 sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2) SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2) return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1) @OPENOCC_LOSS.register_module() class ReprojLossMonoMultiNew(BaseLoss): def __init__(self, weight=1.0, input_dict=None, **kwargs): super().__init__(weight, **kwargs) if input_dict is None: self.input_keys = { 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays'} else: self.input_dict = input_dict self.no_ssim = kwargs.get('no_ssim', False) self.img_size = kwargs.get('img_size', [768, 1600]) self.ray_resize = kwargs.get('ray_resize', None) self.no_automask = kwargs.get('no_automask', False) self.sdf_loss = kwargs.get('sdf_loss', False) self.sdf_loss_weight = kwargs.get('sdf_loss_weight', 0.1) self.dims = kwargs.get('dims', 3) self.no_ssim = self.no_ssim or (self.ray_resize is None) if not self.no_ssim: self.ssim = SSIM() self.loss_func = self.reproj_loss self.iter_counter = 0 def reproj_loss( self, curr_imgs, prev_imgs, next_imgs, ray_indices, weights, ts, metas, ms_rays, deltas=None, sample_sdfs=None): # curr_imgs: B, N, C, H, W # depth: B, N, R # rays: R, 2 # import pdb; pdb.set_trace() if self.sdf_loss: assert sample_sdfs is not None device = ts[0].device bs, num_cams = curr_imgs.shape[:2] num_rays = ms_rays.shape[0] assert bs == 1 # prepare transformation matrices img2prevImg, img2nextImg = [], [] for meta in metas: img2prevImg.append(meta['img2prevImg']) img2nextImg.append(meta['img2nextImg']) def list2tensor(trans): if isinstance(trans[0], (np.ndarray, list)): trans = np.asarray(trans) trans = ts[0].new_tensor(trans) # B, 36(6tem * 6cur), 4, 4 else: trans = torch.stack(trans, dim=0) # trans = trans.reshape(bs, num_cams, num_cams, 1, 4, 4) trans = trans.reshape(bs, num_cams, 1, 4, 4) # trans = trans.transpose(1, 2) return trans img2prevImg = list2tensor(img2prevImg) img2nextImg = list2tensor(img2nextImg) tot_loss = 0. rays = ms_rays for cam, (ray_idx, weight, t) in enumerate(zip(ray_indices, weights, ts)): rays = ms_rays[ray_idx] if deltas is not None: delta = deltas[cam].detach() eps = torch.finfo(delta.dtype).eps weight = weight.clone() weight[delta < eps] = 0. weight = weight / delta.clamp_min(eps) # weight = weight / (delta.detach() + 1e-6) pixel_coords = torch.ones((bs, 1, len(rays), 4), device=device) # B, N, R, 4 pixel_coords[..., :2] = rays.reshape(1, 1, -1, 2) pixel_coords[..., :3] *= t.reshape(1, 1, -1, 1) ## mono specific pixel_coords = pixel_coords.reshape(bs, 1, len(rays), 4, 1) @torch.cuda.amp.autocast(enabled=False) def cal_pixel(trans, coords): trans = trans.float() coords = coords.float() eps = 1e-5 pixel = torch.matmul(trans, coords).squeeze(-1) # bs, N, R, 4 mask = pixel[..., 2] > 0 pixel = pixel[..., :2] / torch.maximum(torch.ones_like(pixel[..., :1]) * eps, pixel[..., 2:3]) mask = mask & (pixel[..., 0] > 0) & (pixel[..., 0] < self.img_size[1]) & \ (pixel[..., 1] > 0) & (pixel[..., 1] < self.img_size[0]) return pixel, mask pixel_prev, prev_mask = cal_pixel(img2prevImg[:, cam:(cam+1), ...], pixel_coords) # bs, N, 1, R, 2 pixel_prev = pixel_prev.unsqueeze(2) pixel_next, next_mask = cal_pixel(img2nextImg[:, cam:(cam+1), ...], pixel_coords) pixel_next = pixel_next.unsqueeze(2) def sample_pixel(pixel, imgs): # imgs: B, N, 3, H, W # pixel: B, N, 1, R, 2 pixel_ = pixel pixel = pixel_.clone() pixel[..., 0] /= self.img_size[1] pixel[..., 1] /= self.img_size[0] pixel = 2 * pixel - 1 pixel_rgb = F.grid_sample( imgs.flatten(0, 1), pixel.flatten(0, 1), align_corners=True) # BN, 3, 1, R pixel_rgb = pixel_rgb.reshape(bs, 1, -1, 1, pixel_rgb.shape[-1]) pixel_rgb = pixel_rgb.permute(0, 3, 1, 2, 4) # B, 1, N, 3, R return pixel_rgb def mask_invalid(mask, weight): new_weight = weight.clone() new_weight[~mask.flatten()] = 0. return new_weight rgb_prev = sample_pixel(pixel_prev, prev_imgs[:, cam:(cam+1), ...]) prev_weight = mask_invalid(prev_mask, weight) rgb_next = sample_pixel(pixel_next, next_imgs[:, cam:(cam+1), ...]) next_weight = mask_invalid(next_mask, weight) def get_acc_weight_mask(rgb_prev, prev_weight, prev_mask): acc_prev_weight = torch.zeros(num_rays, device=rgb_prev.device, dtype=rgb_prev.dtype) acc_prev_weight.index_add_(-1, ray_idx, prev_weight) acc_prev_weight = torch.gather(acc_prev_weight, dim=0, index=ray_idx).clamp_min(torch.finfo(rgb_prev.dtype).eps) prev_weight = prev_weight / acc_prev_weight rgb_prev_new = torch.zeros( (*rgb_prev.shape[:-1], num_rays), device=rgb_prev.device, dtype=rgb_prev.dtype) rgb_prev_new.index_add_(-1, ray_idx, rgb_prev * prev_weight.reshape(1, 1, 1, 1, -1)) acc_prev_mask = torch.zeros(num_rays, device=rgb_prev.device, dtype=rgb_prev.dtype) acc_prev_mask.index_add_(-1, ray_idx, prev_mask.flatten().to(rgb_prev.dtype)) acc_prev_mask = acc_prev_mask == 0 # acc_prev_mask = acc_prev_mask.reshape(1, 1, 1, 1, -1).expand(-1, -1, -1, 3, -1) # rgb_prev_new[acc_prev_mask] = 1e3 return prev_weight, rgb_prev_new, acc_prev_mask prev_weight, rgb_prev_new, acc_prev_mask = get_acc_weight_mask(rgb_prev, prev_weight, prev_mask) next_weight, rgb_next_new, acc_next_mask = get_acc_weight_mask(rgb_next, next_weight, next_mask) pixel_curr = ms_rays.reshape( 1, 1, 1, num_rays, 2).repeat(bs, 1, 1, 1, 1) # bs, N, 1, R, 2 target_curr = sample_pixel(pixel_curr, curr_imgs[:, cam:(cam+1), ...]) # B, 1, N, 3, R target_prev = sample_pixel(pixel_curr, prev_imgs[:, cam:(cam+1), ...]) target_next = sample_pixel(pixel_curr, next_imgs[:, cam:(cam+1), ...]) def compute_reprojection_loss(pred, target): # target = target.reshape( # bs, num_cams, num_cams, 3, num_rays).flatten(0, 2) # B*N*N, 3, R target = target.expand(*pred.shape).flatten(0, 2) pred = pred.flatten(0, 2) abs_diff = torch.abs(target - pred) # B*N_cur*N_tem, 3, R l1_loss = abs_diff.mean(1, True) # B*N_cur*N_tem, 1, R if self.no_ssim: reprojection_loss = l1_loss else: pred_reshape = pred.reshape(pred.shape[0], -1, *self.ray_resize) target_reshape = target.reshape(target.shape[0], -1, *self.ray_resize) ssim_loss = self.ssim(pred_reshape, target_reshape).mean(1, True) ssim_loss = ssim_loss.flatten(2) reprojection_loss = 0.85 * ssim_loss + 0.15 * l1_loss return reprojection_loss def compute_reprojection_loss_fn(pred, target, pred_new, weight, mask): target = target.expand(*pred_new.shape).flatten(0, 2) # BN, 3, R pred = pred.flatten(0, 2) target_ = torch.gather( target, dim=-1, index=ray_idx.reshape(1, 1, -1).expand(-1, self.dims, -1)) abs_diff = torch.abs(target_ - pred) # B*N_cur*N_tem, 3, R l1_loss_ = abs_diff.mean(1, True) # B*N_cur*N_tem, 1, R l1_loss = torch.zeros(*l1_loss_.shape[:-1], num_rays, dtype=l1_loss_.dtype, device=l1_loss_.device) l1_loss.index_add_(dim=-1, index=ray_idx, source=l1_loss_*weight.reshape(1, 1, -1)) if self.no_ssim: reprojection_loss = l1_loss else: pred_reshape = pred_new.reshape(pred.shape[0], -1, *self.ray_resize) target_reshape = target.reshape(target.shape[0], -1, *self.ray_resize) ssim_loss = self.ssim(pred_reshape, target_reshape).mean(1, True) ssim_loss = ssim_loss.flatten(2) reprojection_loss = 0.85 * ssim_loss + 0.15 * l1_loss reprojection_loss[mask.reshape(1, 1, -1)] = 1e3 return reprojection_loss prev_proj_loss = compute_reprojection_loss_fn(rgb_prev, target_curr, rgb_prev_new, prev_weight, acc_prev_mask) # B*N_cur*N_tem, 1, R next_proj_loss = compute_reprojection_loss_fn(rgb_next, target_curr, rgb_next_new, next_weight, acc_next_mask) prev_proj_loss = prev_proj_loss.reshape(bs, 1, num_rays) next_proj_loss = next_proj_loss.reshape(bs, 1, num_rays) prev_proj_loss = prev_proj_loss.unsqueeze(2) next_proj_loss = next_proj_loss.unsqueeze(2) if self.no_automask: proj_loss = torch.cat([ prev_proj_loss, next_proj_loss], dim=2) else: mask_prev_proj_loss = compute_reprojection_loss(target_prev, target_curr) # B*N, 1, R mask_next_proj_loss = compute_reprojection_loss(target_next, target_curr) mask_prev_proj_loss = mask_prev_proj_loss.reshape(bs, 1, 1, num_rays) mask_next_proj_loss = mask_next_proj_loss.reshape(bs, 1, 1, num_rays) proj_loss = torch.cat([ prev_proj_loss, next_proj_loss, mask_prev_proj_loss, mask_next_proj_loss], dim=2) proj_loss, _ = proj_loss.min(dim=2) # bs, N_cur, R if self.sdf_loss: prev_weight_id = prev_weight.reshape(num_rays, -1).argmax(dim=-1, keepdim=True) next_weight_id = next_weight.reshape(num_rays, -1).argmax(dim=-1, keepdim=True) sample_sdf = sample_sdfs[cam].reshape(num_rays, -1) prev_sdf = torch.gather(sample_sdf, -1, prev_weight_id) # R, 1 next_sdf = torch.gather(sample_sdf, -1, next_weight_id) # R, 1 sdfs = torch.cat([prev_sdf, next_sdf, \ torch.zeros(num_rays, 2, device=prev_sdf.device)], dim=-1) # R, 4 sdf = torch.gather(sdfs, -1, _.reshape(num_rays, 1)) # R, 1 sdf_loss = sdf.abs().sum() / (_ <= 1).sum() if self.writer and self.iter_counter % 10 == 0: mask = _ > 1 self.writer.add_scalar(f'masked/{cam}', mask.sum(), self.iter_counter) if self.sdf_loss: self.writer.add_scalar(f'sdf_loss/{cam}', sdf_loss, self.iter_counter) proj_loss_avg = torch.mean(proj_loss) tot_loss = tot_loss + proj_loss_avg if self.sdf_loss: tot_loss = tot_loss + sdf_loss * self.sdf_loss_weight self.iter_counter += 1 return tot_loss / num_cams ================================================ FILE: loss/reproj_loss_mono_multi_new_combine.py ================================================ import torch.nn as nn, torch from .base_loss import BaseLoss from . import OPENOCC_LOSS import numpy as np import torch.nn.functional as F class SSIM(nn.Module): """Layer to compute the SSIM loss between a pair of images """ def __init__(self): super(SSIM, self).__init__() self.mu_x_pool = nn.AvgPool2d(3, 1) self.mu_y_pool = nn.AvgPool2d(3, 1) self.sig_x_pool = nn.AvgPool2d(3, 1) self.sig_y_pool = nn.AvgPool2d(3, 1) self.sig_xy_pool = nn.AvgPool2d(3, 1) self.refl = nn.ReflectionPad2d(1) self.C1 = 0.01 ** 2 self.C2 = 0.03 ** 2 def forward(self, x, y): x = self.refl(x) y = self.refl(y) mu_x = self.mu_x_pool(x) mu_y = self.mu_y_pool(y) sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2 sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2 sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2) SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2) return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1) @OPENOCC_LOSS.register_module() class ReprojLossMonoMultiNewCombine(BaseLoss): def __init__(self, weight=1.0, input_dict=None, **kwargs): super().__init__(weight, **kwargs) if input_dict is None: self.input_keys = { 'curr_imgs': 'curr_imgs', 'prev_imgs': 'prev_imgs', 'next_imgs': 'next_imgs', 'ray_indices': 'ray_indices', 'weights': 'weights', 'ts': 'ts', 'metas': 'metas', 'ms_rays': 'ms_rays'} else: self.input_dict = input_dict self.no_ssim = kwargs.get('no_ssim', False) self.img_size = kwargs.get('img_size', [768, 1600]) self.ray_resize = kwargs.get('ray_resize', None) self.no_automask = kwargs.get('no_automask', False) self.dims = kwargs.get('dims', 3) self.no_ssim = self.no_ssim or (self.ray_resize is None) if not self.no_ssim: self.ssim = SSIM() self.loss_func = self.reproj_loss self.iter_counter = 0 def reproj_loss( self, curr_imgs, prev_imgs, next_imgs, ray_indices, weights, ts, metas, ms_rays, deltas=None): # curr_imgs: B, N, C, H, W # depth: B, N, R # rays: R, 2 device = ts[0].device bs, num_cams = curr_imgs.shape[:2] num_rays = ms_rays.shape[0] assert bs == 1 # prepare transformation matrices img2prevImg, img2nextImg = [], [] for meta in metas: img2prevImg.append(meta['img2prevImg']) img2nextImg.append(meta['img2nextImg']) def list2tensor(trans): if isinstance(trans[0], (np.ndarray, list)): trans = np.asarray(trans) trans = ts[0].new_tensor(trans) # B, 36(6tem * 6cur), 4, 4 else: trans = torch.stack(trans, dim=0) trans = trans.reshape(bs, num_cams, 1, 4, 4) return trans img2prevImg = list2tensor(img2prevImg) img2nextImg = list2tensor(img2nextImg) tot_loss = 0. for cam, (ray_idx, weight, t) in enumerate(zip(ray_indices, weights, ts)): rays = ms_rays[ray_idx] if deltas is not None: delta = deltas[cam].detach() eps = torch.finfo(delta.dtype).eps weight = weight.clone() weight[delta < eps] = 0. weight = weight / delta.clamp_min(eps) pixel_coords = torch.ones((bs, 1, len(rays), 4), device=device) # B, N, R, 4 pixel_coords[..., :2] = rays.reshape(1, 1, -1, 2) pixel_coords[..., :3] *= t.reshape(1, 1, -1, 1) pixel_coords = pixel_coords.unsqueeze(-1) @torch.cuda.amp.autocast(enabled=False) def cal_pixel(trans, coords): trans = trans.float() coords = coords.float() eps = 1e-5 pixel = torch.matmul(trans, coords).squeeze(-1) # bs, N, R, 4 mask = pixel[..., 2] > 0 pixel = pixel[..., :2] / torch.maximum(torch.ones_like(pixel[..., :1]) * eps, pixel[..., 2:3]) mask = mask & (pixel[..., 0] > 0) & (pixel[..., 0] < self.img_size[1]) & \ (pixel[..., 1] > 0) & (pixel[..., 1] < self.img_size[0]) return pixel, mask pixel_prev, prev_mask = cal_pixel(img2prevImg[:, cam:(cam+1), ...], pixel_coords) # bs, N, 1, R, 2 pixel_prev = pixel_prev.unsqueeze(2) # B, N, 1, R, 2 pixel_next, next_mask = cal_pixel(img2nextImg[:, cam:(cam+1), ...], pixel_coords) pixel_next = pixel_next.unsqueeze(2) def sample_pixel(pixel, imgs): # imgs: B, N, 3, H, W # pixel: B, N, 1, R, 2 pixel = pixel.clone() pixel[..., 0] /= self.img_size[1] pixel[..., 1] /= self.img_size[0] pixel = 2 * pixel - 1 pixel_rgb = F.grid_sample( imgs.flatten(0, 1), pixel.flatten(0, 1), mode='bilinear', padding_mode='border', align_corners=True) # BN, 3, 1, R tmp_dim = pixel_rgb.shape[1] pixel_rgb = pixel_rgb.reshape(bs, 1, tmp_dim, pixel_rgb.shape[-1]) pixel_rgb = pixel_rgb.permute(0, 1, 3, 2) # B, N, R, 3 return pixel_rgb def get_diff(x1, x2): return torch.mean(torch.abs(x1 - x2), dim=-1) rgb_prev = sample_pixel(pixel_prev, prev_imgs[:, cam:(cam+1), ...]) rgb_next = sample_pixel(pixel_next, next_imgs[:, cam:(cam+1), ...]) pix_curr = ms_rays.reshape(1, 1, 1, num_rays, 2) # bs, N, 1, r, 2 rgb_curr = sample_pixel(pix_curr, curr_imgs[:, cam:(cam+1), ...]) # B, N, r, 3 rgb_curr_ = torch.gather( rgb_curr, dim=-2, index=ray_idx.reshape(1, 1, -1, 1).repeat(1, 1, 1, self.dims)) # B, N, R, 3 diff_prev = get_diff(rgb_curr_, rgb_prev) # B, N, R diff_next = get_diff(rgb_curr_, rgb_next) diff_prev[~prev_mask] = 0. diff_next[~next_mask] = 0. diff = diff_prev + diff_next cnt = prev_mask.to(torch.float) + next_mask.to(torch.float) general_mask = cnt > 0 cnt = torch.clamp(cnt, 1.0) diff = diff / cnt # B, N, R weight = weight.clone() weight[~general_mask.flatten()] = 0. weight_sum = torch.zeros(num_rays, dtype=weight.dtype, device=device) weight_sum.index_add_(-1, ray_idx, weight) weight_sum = weight_sum.clamp_min(torch.finfo(weight.dtype).eps) # r weight_sum = torch.gather(weight_sum, -1, ray_idx) # R weight = weight / weight_sum # R l1_loss = torch.zeros(num_rays, dtype=diff.dtype, device=device) l1_loss.index_add_(-1, ray_idx, weight * diff.flatten()) # r prev_next_loss = l1_loss if not self.no_ssim: rgb_prev[~prev_mask] = 0. rgb_next[~next_mask] = 0. rgb_combine_ = rgb_prev + rgb_next rgb_combine_ = rgb_combine_ / cnt.unsqueeze(-1) rgb_combine = torch.zeros( # r, 3 num_rays, self.dims, dtype=rgb_combine_.dtype, device=device) rgb_combine.index_add_( 0, ray_idx, rgb_combine_.reshape(-1, self.dims) * weight.unsqueeze(-1)) ssim_loss = self.ssim( rgb_combine.reshape(1, *self.ray_resize, self.dims).permute(0, 3, 1, 2), rgb_curr.reshape(1, *self.ray_resize, self.dims).permute(0, 3, 1, 2)) ssim_loss = ssim_loss.mean(1).flatten() prev_next_loss = 0.15 * prev_next_loss + 0.85 * ssim_loss def compute_reprojection_loss(pred, target): # pred, target: B, N, r, 3 abs_diff = torch.abs(target - pred) l1_loss = abs_diff.mean(-1).flatten() # r if self.no_ssim: reprojection_loss = l1_loss else: pred_reshape = pred.reshape(1, *self.ray_resize, self.dims).permute(0, 3, 1, 2) target_reshape = target.reshape(1, *self.ray_resize, self.dims).permute(0, 3, 1, 2) ssim_loss = self.ssim(pred_reshape, target_reshape).mean(1).flatten() reprojection_loss = 0.85 * ssim_loss + 0.15 * l1_loss return reprojection_loss if not self.no_automask: target_prev = sample_pixel(pix_curr, prev_imgs[:, cam:(cam+1), ...]) target_next = sample_pixel(pix_curr, next_imgs[:, cam:(cam+1), ...]) mask_prev_proj_loss = compute_reprojection_loss(target_prev, rgb_curr) # r mask_next_proj_loss = compute_reprojection_loss(target_next, rgb_curr) ray_filter = torch.zeros(num_rays, dtype=weight.dtype, device=device) ray_filter.index_add_(0, ray_idx, general_mask.flatten().to(weight.dtype)) ray_filter = ray_filter == 0 prev_next_loss[ray_filter] = 1e3 proj_loss = torch.stack([ prev_next_loss, mask_prev_proj_loss, mask_next_proj_loss], dim=-1) proj_loss, _ = torch.min(proj_loss, dim=-1) if self.writer and self.iter_counter % 10 == 0: mask = _ > 0 self.writer.add_scalar(f'masked/{cam}', mask.sum(), self.iter_counter) else: proj_loss = prev_next_loss proj_loss_avg = torch.mean(proj_loss) tot_loss = tot_loss + proj_loss_avg self.iter_counter += 1 return tot_loss / num_cams ================================================ FILE: loss/rgb_loss_ms.py ================================================ import torch.nn as nn, torch from .base_loss import BaseLoss from . import OPENOCC_LOSS import torch.nn.functional as F import numpy as np class SSIM(nn.Module): """Layer to compute the SSIM loss between a pair of images """ def __init__(self): super(SSIM, self).__init__() self.mu_x_pool = nn.AvgPool2d(3, 1) self.mu_y_pool = nn.AvgPool2d(3, 1) self.sig_x_pool = nn.AvgPool2d(3, 1) self.sig_y_pool = nn.AvgPool2d(3, 1) self.sig_xy_pool = nn.AvgPool2d(3, 1) self.refl = nn.ReflectionPad2d(1) self.C1 = 0.01 ** 2 self.C2 = 0.03 ** 2 def forward(self, x, y): x = self.refl(x) y = self.refl(y) mu_x = self.mu_x_pool(x) mu_y = self.mu_y_pool(y) sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2 sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2 sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2) SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2) return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1) @OPENOCC_LOSS.register_module() class RGBLossMS(BaseLoss): def __init__( self, weight=1.0, img_size=None, no_ssim=True, ray_resize=None, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'ms_colors': 'ms_colors', 'ms_rays': 'ms_rays', 'gt_imgs': 'gt_imgs'} else: self.input_dict = input_dict assert img_size is not None self.img_size = img_size self.loss_func = self.rgb_loss self.no_ssim = no_ssim or ray_resize is None self.ray_resize = ray_resize if not self.no_ssim: self.ssim = SSIM() def rgb_loss(self, ms_colors, ms_rays, gt_imgs): # rgb: B, N, R, 3 # rays: R, 2 # curr_imgs: B, N, 3, H, W bs, num_cams = gt_imgs.shape[:2] if not isinstance(ms_rays, list): rays = ms_rays pixels = rays.reshape(1, 1, -1, 2).repeat(bs*num_cams, 1, 1, 1) # B*N, 1, R, 2 pixels[..., 0] /= self.img_size[1] pixels[..., 1] /= self.img_size[0] gt = F.grid_sample( gt_imgs.flatten(0, 1), pixels * 2 - 1, mode='bilinear', padding_mode='zeros', align_corners=True) # B*N, 3, 1, R gt = gt.reshape(bs, num_cams, 3, -1).transpose(-1, -2) # B, N, R, 3 else: raise NotImplementedError tot_loss = 0. for color in ms_colors: loss = torch.abs(color - gt).mean() if not self.no_ssim: color_reshaped = color.reshape(bs*num_cams, *self.ray_resize, 3).permute(0, 3, 1, 2) gt_reshaped = gt.reshape(bs*num_cams, *self.ray_resize, 3).permute(0, 3, 1, 2) ssim_loss = self.ssim(color_reshaped, gt_reshaped).mean() loss = 0.15 * loss + 0.85 * ssim_loss tot_loss += loss return tot_loss / len(ms_colors) @OPENOCC_LOSS.register_module() class SemLossMS(BaseLoss): def __init__( self, weight=1.0, img_size=None, ray_resize=None, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'sem': 'sem', 'metas': 'metas', 'ms_rays': 'ms_rays'} else: self.input_dict = input_dict assert img_size is not None self.img_size = img_size self.loss_func = self.sem_loss self.ray_resize = ray_resize def sem_loss(self, sem, metas, ms_rays): # sem: B, N, R, c # rays: R, 2 # curr_imgs: B, N, 3, H, W gt_imgs = [] for meta in metas: gt_imgs.append(meta['sem']) if isinstance(gt_imgs[0], np.ndarray): gt_imgs = np.asarray(gt_imgs) gt_imgs = sem[0].new_tensor(gt_imgs, dtype=torch.long) # B, N, H, W elif isinstance(gt_imgs[0], torch.Tensor): gt_imgs = torch.stack(gt_imgs).to(sem[0].device) else: raise NotImplementedError num_cls = sem[0].shape[-1] if not isinstance(ms_rays, list): rays = ms_rays.to(torch.long) gt = gt_imgs[:, :, rays[:, 1], rays[:, 0]] # B, N, R gt = F.one_hot(gt, num_classes=num_cls).to(torch.float) # B, N, R, C else: raise NotImplementedError tot_loss = 0. for s in sem: s = torch.clamp(s, 0, 1) loss = F.binary_cross_entropy(s, gt) tot_loss += loss return tot_loss / len(sem) @OPENOCC_LOSS.register_module() class SemCELossMS(BaseLoss): def __init__( self, weight=1.0, img_size=None, ray_resize=None, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'sem': 'sem', 'metas': 'metas', 'ms_rays': 'ms_rays'} else: self.input_dict = input_dict assert img_size is not None self.img_size = img_size self.loss_func = self.sem_loss self.ray_resize = ray_resize def sem_loss(self, sem, metas, ms_rays): # sem: B, N, R, c # rays: R, 2 # curr_imgs: B, N, 3, H, W gt_imgs = [] for meta in metas: gt_imgs.append(meta['sem']) if isinstance(gt_imgs[0], np.ndarray): gt_imgs = np.asarray(gt_imgs) gt_imgs = sem[0].new_tensor(gt_imgs, dtype=torch.long) # B, N, H, W elif isinstance(gt_imgs[0], torch.Tensor): gt_imgs = torch.stack(gt_imgs).to(sem[0].device) else: raise NotImplementedError num_cls = sem[0].shape[-1] if not isinstance(ms_rays, list): rays = ms_rays.to(torch.long) gt = gt_imgs[:, :, rays[:, 1], rays[:, 0]] # B, N, R gt = F.one_hot(gt, num_classes=num_cls).to(torch.float) # B, N, R, C else: raise NotImplementedError tot_loss = 0. for s in sem: s = torch.clamp(s, 1e-6, 1) loss = torch.mean(torch.sum((-1) * torch.log(s) * gt, dim=-1)) # loss = F.binary_cross_entropy(s, gt) tot_loss += loss return tot_loss / len(sem) ================================================ FILE: loss/second_grad_loss.py ================================================ from .base_loss import BaseLoss from . import OPENOCC_LOSS @OPENOCC_LOSS.register_module() class SecondGradLoss(BaseLoss): def __init__(self, weight=1.0, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'second_grad': 'second_grad', } else: self.input_dict = input_dict self.loss_func = self.second_grad_loss def second_grad_loss(self, second_grad): return second_grad.abs().mean() ================================================ FILE: loss/sparsity_loss.py ================================================ from .base_loss import BaseLoss from . import OPENOCC_LOSS import torch, numpy as np @OPENOCC_LOSS.register_module() class SparsityLoss(BaseLoss): """ StreetSurf style. """ def __init__(self, weight=1.0, scale=1.0, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'density': 'density', } else: self.input_dict = input_dict self.scale = scale self.loss_func = self.sparsity_loss def sparsity_loss(self, density): loss = 1.0 / torch.cosh(density / (2.0 * self.scale)) loss = torch.pow(loss, 2).mean() return loss @OPENOCC_LOSS.register_module() class HardSparsityLoss(BaseLoss): def __init__(self, weight=1.0, scale=1.0, thresh=0.2, crop=[[0, 0], [0, 0], [0, 0]], input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'density': 'density', } else: self.input_dict = input_dict self.scale = scale self.thresh = thresh self.crop = np.asarray(crop) self.loss_func = self.hard_sparsity_loss def hard_sigmoid(self, density): return torch.sigmoid((-1) * self.scale * density) def hard_sparsity_loss(self, density): if self.crop[0, 0] > 0: density[:self.crop[0, 0], ...] = 100 if self.crop[0, 1] > 0: density[-self.crop[0, 1]:, ...] = 100 if self.crop[1, 0] > 0: density[:, :self.crop[1, 0], :] = 100 if self.crop[1, 1] > 0: density[:, -self.crop[1, 1]:, :] = 100 if self.crop[2, 0] > 0: density[..., :self.crop[2, 0]] = 100 if self.crop[2, 1] > 0: density[..., -self.crop[2, 1]:] = 100 num_occupied_voxels = self.hard_sigmoid(density).mean() return torch.relu(num_occupied_voxels - self.thresh) @OPENOCC_LOSS.register_module() class SoftSparsityLoss(BaseLoss): def __init__(self, weight=1.0, input_dict=None, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'density': 'density', } else: self.input_dict = input_dict self.loss_func = self.soft_sparsity_loss def soft_sparsity_loss(self, density): return torch.relu(-1 * density).mean() @OPENOCC_LOSS.register_module() class AdaptiveSparsityLoss(BaseLoss): def __init__(self, weight=1, input_dict=None, slack=4.0, **kwargs): super().__init__(weight) if input_dict is None: self.input_dict = { 'sdfs': 'sdfs', 'ts': 'ts', 'ms_depths': 'ms_depths' } else: self.input_dict = input_dict self.slack = slack self.loss_func = self.adaptive_sparsity_loss def adaptive_sparsity_loss(self, sdfs, ts, ms_depths): # ts: [R] * 6 # sdfs: [R] * 6 # ms_depths: B, N, R depths = ms_depths[0] bs, num_cams, num_rays = depths.shape assert bs == 1 ts = torch.stack(ts, dim=0).reshape(bs, num_cams, num_rays, -1) sdfs = torch.stack(sdfs, dim=0).reshape(bs, num_cams, num_rays, -1) mask = ts > (depths + self.slack).unsqueeze(-1) sdfs_behind = sdfs[mask] return torch.relu(-1 * sdfs_behind).mean() ================================================ FILE: model/__init__.py ================================================ from .backbone import * from .neck import * from .lifter import * from .encoder import * from .head import * from .segmentor import * ================================================ FILE: model/backbone/__init__.py ================================================ from mmseg.models.backbones import * from .unet2d import UNet2D ================================================ FILE: model/backbone/unet2d.py ================================================ """ Code adapted from https://github.com/shariqfarooq123/AdaBins/blob/main/models/unet_adaptive_bins.py """ import torch import torch.nn as nn import torch.nn.functional as F import os from mmseg.registry import MODELS class UpSampleBN(nn.Module): def __init__(self, skip_input, output_features): super(UpSampleBN, self).__init__() self._net = nn.Sequential( nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(output_features), nn.LeakyReLU(), nn.Conv2d( output_features, output_features, kernel_size=3, stride=1, padding=1 ), nn.BatchNorm2d(output_features), nn.LeakyReLU(), ) def forward(self, x, concat_with): up_x = F.interpolate( x, size=(concat_with.shape[2], concat_with.shape[3]), mode="bilinear", align_corners=True, ) f = torch.cat([up_x, concat_with], dim=1) return self._net(f) class DecoderBN(nn.Module): def __init__( self, num_features, bottleneck_features, out_feature, use_decoder=True ): super(DecoderBN, self).__init__() features = int(num_features) self.use_decoder = use_decoder self.conv2 = nn.Conv2d( bottleneck_features, features, kernel_size=1, stride=1, padding=1 ) self.out_feature_1_1 = out_feature self.out_feature_1_2 = out_feature self.out_feature_1_4 = out_feature self.out_feature_1_8 = out_feature self.out_feature_1_16 = out_feature self.feature_1_16 = features // 2 self.feature_1_8 = features // 4 self.feature_1_4 = features // 8 self.feature_1_2 = features // 16 self.feature_1_1 = features // 32 if self.use_decoder: # self.resize_output_1_1 = nn.Conv2d( # self.feature_1_1, self.out_feature_1_1, kernel_size=1 # ) self.resize_output_1_2 = nn.Conv2d( self.feature_1_2, self.out_feature_1_2, kernel_size=1 ) self.resize_output_1_4 = nn.Conv2d( self.feature_1_4, self.out_feature_1_4, kernel_size=1 ) self.resize_output_1_8 = nn.Conv2d( self.feature_1_8, self.out_feature_1_8, kernel_size=1 ) self.resize_output_1_16 = nn.Conv2d( self.feature_1_16, self.out_feature_1_16, kernel_size=1 ) self.up16 = UpSampleBN( skip_input=features + 224, output_features=self.feature_1_16 ) self.up8 = UpSampleBN( skip_input=self.feature_1_16 + 80, output_features=self.feature_1_8 ) self.up4 = UpSampleBN( skip_input=self.feature_1_8 + 48, output_features=self.feature_1_4 ) self.up2 = UpSampleBN( skip_input=self.feature_1_4 + 32, output_features=self.feature_1_2 ) # self.up1 = UpSampleBN( # skip_input=self.feature_1_2 + 3, output_features=self.feature_1_1 # ) else: self.resize_output_1_1 = nn.Conv2d(3, out_feature, kernel_size=1) self.resize_output_1_2 = nn.Conv2d(32, out_feature * 2, kernel_size=1) self.resize_output_1_4 = nn.Conv2d(48, out_feature * 4, kernel_size=1) def forward(self, features): x_block0, x_block1, x_block2, x_block3, x_block4 = ( features[4], features[5], features[6], features[8], features[11], ) bs = x_block0.shape[0] x_d0 = self.conv2(x_block4) if self.use_decoder: x_1_16 = self.up16(x_d0, x_block3) x_1_8 = self.up8(x_1_16, x_block2) x_1_4 = self.up4(x_1_8, x_block1) x_1_2 = self.up2(x_1_4, x_block0) # x_1_1 = self.up1(x_1_2, features[0]) # return { # "1_1": self.resize_output_1_1(x_1_1), # "1_2": self.resize_output_1_2(x_1_2), # "1_4": self.resize_output_1_4(x_1_4), # "1_8": self.resize_output_1_8(x_1_8), # "1_16": self.resize_output_1_16(x_1_16), # } return [ # self.resize_output_1_1(x_1_1), self.resize_output_1_2(x_1_2), self.resize_output_1_4(x_1_4), self.resize_output_1_8(x_1_8), self.resize_output_1_16(x_1_16) ] else: x_1_1 = features[0] x_1_2, x_1_4, x_1_8, x_1_16 = ( features[4], features[5], features[6], features[8], ) x_global = features[-1].reshape(bs, 2560, -1).mean(2) return { "1_1": self.resize_output_1_1(x_1_1), "1_2": self.resize_output_1_2(x_1_2), "1_4": self.resize_output_1_4(x_1_4), "global": x_global, } class Encoder(nn.Module): def __init__(self, backend): super(Encoder, self).__init__() self.original_model = backend def forward(self, x): features = [x] for k, v in self.original_model._modules.items(): if k == "blocks": for ki, vi in v._modules.items(): features.append(vi(features[-1])) else: features.append(v(features[-1])) return features @MODELS.register_module() class UNet2D(nn.Module): def __init__(self, out_feature, use_decoder=True): super(UNet2D, self).__init__() basemodel_name = "tf_efficientnet_b7_ns" num_features = 2560 if os.environ.get('PHIGENT', 'false') == 'true': repo = '../gen-efficientnet-pytorch' source = 'local' else: repo = "rwightman/gen-efficientnet-pytorch" source = 'github' basemodel = torch.hub.load( repo, basemodel_name, pretrained=True, source=source) # Remove last layer basemodel.global_pool = nn.Identity() basemodel.classifier = nn.Identity() basemodel.bn2 = nn.Identity() basemodel.act2 = nn.Identity() # Building Encoder-Decoder model backend = basemodel self.use_decoder = use_decoder self.encoder = Encoder(backend) self.decoder = DecoderBN( out_feature=out_feature, use_decoder=use_decoder, bottleneck_features=num_features, num_features=num_features, ) def forward(self, x, **kwargs): encoded_feats = self.encoder(x) unet_out = self.decoder(encoded_feats, **kwargs) return unet_out def get_encoder_params(self): # lr/10 learning rate return self.encoder.parameters() def get_decoder_params(self): # lr learning rate return self.decoder.parameters() if __name__ == '__main__': from mmengine import Config from mmseg.models import build_backbone cfg = Config(dict( type='UNet2D', out_feature=96 )) model = build_backbone(cfg) # model = UNet2D(out_feature=256, use_decoder=True) pass ================================================ FILE: model/encoder/__init__.py ================================================ from .bevformer import * from .tpvformer import * ================================================ FILE: model/encoder/base_encoder.py ================================================ from mmseg.registry import MODELS from mmengine.model import BaseModule @MODELS.register_module() class BaseEncoder(BaseModule): """Further encode 3D representations. image backbone -> neck -> lifter -> encoder -> segmentor """ def __init__(self, init_cfg=None, **kwargs): super().__init__(init_cfg) def forward( self, representation, ms_img_feats=None, metas=None, **kwargs ): pass ================================================ FILE: model/encoder/bevformer/__init__.py ================================================ from .bevformer_encoder import BEVFormerEncoder from .bevformer_pos_embed import BEVPositionalEncoding from .bevformer_encoder_layer import BEVFormerLayer from .attention import * ================================================ FILE: model/encoder/bevformer/attention/__init__.py ================================================ from .image_cross_attention import BEVCrossAttention, BEVDeformableAttention ================================================ FILE: model/encoder/bevformer/attention/image_cross_attention.py ================================================ import warnings import torch, math import torch.nn as nn from mmengine.model import xavier_init, constant_init, BaseModule from mmengine.registry import MODELS from mmcv.cnn.bricks.transformer import build_attention from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttnFunction @MODELS.register_module() class BEVCrossAttention(BaseModule): """ Image cross-attention in TPVFormer. Enable every tpv query to interact with its corresponding area on the image feature plane. """ def __init__(self, embed_dims=256, num_cams=6, dropout=0.1, init_cfg=None, batch_first=True, deformable_attention=dict( type='MSDeformableAttention3D', embed_dims=256, num_levels=4), **kwargs): super().__init__(init_cfg) self.init_cfg = init_cfg self.dropout = nn.Dropout(dropout) self.deformable_attention = build_attention(deformable_attention) self.embed_dims = embed_dims self.num_cams = num_cams self.output_proj = nn.Linear(embed_dims, embed_dims) self.batch_first = batch_first self.init_weight() def init_weight(self): """Default initialization for Parameters of Module.""" xavier_init(self.output_proj, distribution='uniform', bias=0.) # @force_fp32(apply_to=('query', 'key', 'value', 'reference_points_cams')) # @torch.cuda.amp.autocast(enabled=False) def forward(self, query, key, value, residual=None, spatial_shapes=None, reference_points_cams=None, bev_masks=None, level_start_index=None, **kwargs): """Forward Function of Detr3DCrossAtten. Args: query (Tensor): Query of Transformer with shape (bs, num_query, embed_dims). key (Tensor): The key tensor with shape (bs, num_key, embed_dims). value (Tensor): The value tensor with shape (bs, num_key, embed_dims). residual (Tensor): The tensor used for addition, with the same shape as `x`. Default None. If None, `x` will be used. spatial_shapes (Tensor): Spatial shape of features in different level. With shape (num_levels, 2), last dimension represent (h, w). level_start_index (Tensor): The start index of each level. A tensor has shape (num_levels) and can be represented as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...]. Returns: Tensor: forwarded results with shape [num_query, bs, embed_dims]. """ if key is None: key = query if value is None: value = key if residual is None: residual = query bs, num_query, _ = query.size() slots = torch.zeros_like(query) # indexeses = [] # max_lens = [] # queries_rebatches = [] # reference_points_rebatches = [] # for tpv_idx, tpv_mask in enumerate(tpv_masks): indexes = [] for _, mask_per_img in enumerate(bev_masks): index_query_per_img = mask_per_img[0].sum(-1).nonzero().squeeze(-1) indexes.append(index_query_per_img) max_len = max([len(each) for each in indexes]) # max_lens.append(max_len) # indexeses.append(indexes) reference_points_cam = reference_points_cams D = reference_points_cam.size(3) queries_rebatch = query.new_zeros( [bs * self.num_cams, max_len, self.embed_dims]) reference_points_rebatch = reference_points_cam.new_zeros( [bs * self.num_cams, max_len, D, 2]) for i, reference_points_per_img in enumerate(reference_points_cam): for j in range(bs): index_query_per_img = indexes[i] queries_rebatch[j * self.num_cams + i, :len(index_query_per_img)] = query[j, index_query_per_img] reference_points_rebatch[j * self.num_cams + i, :len(index_query_per_img)] = reference_points_per_img[j, index_query_per_img] # queries_rebatches.append(queries_rebatch) # reference_points_rebatches.append(reference_points_rebatch) num_cams, l, bs, embed_dims = key.shape key = key.permute(2, 0, 1, 3).reshape( self.num_cams * bs, l, self.embed_dims) value = value.permute(2, 0, 1, 3).reshape( self.num_cams * bs, l, self.embed_dims) query = self.deformable_attention( query=queries_rebatch, key=key, value=value, reference_points=reference_points_rebatch, spatial_shapes=spatial_shapes, level_start_index=level_start_index,) # for tpv_idx, indexes in enumerate(indexeses): for i, index_query_per_img in enumerate(indexes): for j in range(bs): slots[j, index_query_per_img] += query[j * self.num_cams + i, :len(index_query_per_img)] count = bev_masks.sum(-1) > 0 count = count.permute(1, 2, 0).sum(-1) count = torch.clamp(count, min=1.0) slots = slots / count[..., None] slots = self.output_proj(slots) return self.dropout(slots) + residual from typing import Optional, no_type_check import mmengine from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE from mmcv.ops.multi_scale_deform_attn import multi_scale_deformable_attn_pytorch @MODELS.register_module() class BEVDeformableAttention(BaseModule): """An attention module used in Deformable-Detr. `Deformable DETR: Deformable Transformers for End-to-End Object Detection. `_. Args: embed_dims (int): The embedding dimension of Attention. Default: 256. num_heads (int): Parallel attention heads. Default: 8. num_levels (int): The number of feature map used in Attention. Default: 4. num_points (int): The number of sampling points for each query in each head. Default: 4. im2col_step (int): The step used in image_to_column. Default: 64. dropout (float): A Dropout layer on `inp_identity`. Default: 0.1. batch_first (bool): Key, Query and Value are shape of (batch, n, embed_dim) or (n, batch, embed_dim). Default to False. norm_cfg (dict): Config dict for normalization layer. Default: None. init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. Default: None. value_proj_ratio (float): The expansion ratio of value_proj. Default: 1.0. """ def __init__(self, embed_dims: int = 256, num_heads: int = 8, num_levels: int = 4, num_points: int = 4, im2col_step: int = 64, dropout: float = 0.1, batch_first: bool = False, norm_cfg: Optional[dict] = None, init_cfg: Optional[mmengine.ConfigDict] = None, value_proj_ratio: float = 1.0): super().__init__(init_cfg) if embed_dims % num_heads != 0: raise ValueError(f'embed_dims must be divisible by num_heads, ' f'but got {embed_dims} and {num_heads}') dim_per_head = embed_dims // num_heads self.norm_cfg = norm_cfg self.batch_first = batch_first # you'd better set dim_per_head to a power of 2 # which is more efficient in the CUDA implementation def _is_power_of_2(n): if (not isinstance(n, int)) or (n < 0): raise ValueError( 'invalid input for _is_power_of_2: {} (type: {})'.format( n, type(n))) return (n & (n - 1) == 0) and n != 0 if not _is_power_of_2(dim_per_head): warnings.warn( "You'd better set embed_dims in " 'MultiScaleDeformAttention to make ' 'the dimension of each attention head a power of 2 ' 'which is more efficient in our CUDA implementation.') self.im2col_step = im2col_step self.embed_dims = embed_dims self.num_levels = num_levels self.num_heads = num_heads self.num_points = num_points self.sampling_offsets = nn.Linear( embed_dims, num_heads * num_levels * num_points * 2) self.attention_weights = nn.Linear(embed_dims, num_heads * num_levels * num_points) value_proj_size = int(embed_dims * value_proj_ratio) self.value_proj = nn.Linear(embed_dims, value_proj_size) self.init_weights() def init_weights(self) -> None: """Default initialization for Parameters of Module.""" constant_init(self.sampling_offsets, 0.) device = next(self.parameters()).device thetas = torch.arange( self.num_heads, dtype=torch.float32, device=device) * (2.0 * math.pi / self.num_heads) grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view( self.num_heads, 1, 1, 2).repeat(1, self.num_levels, self.num_points, 1) # for i in range(self.num_points): # grid_init[:, :, i, :] *= i + 1 self.sampling_offsets.bias.data = grid_init.view(-1) constant_init(self.attention_weights, val=0., bias=0.) xavier_init(self.value_proj, distribution='uniform', bias=0.) self._is_init = True @no_type_check def forward(self, query: torch.Tensor, key: Optional[torch.Tensor] = None, value: Optional[torch.Tensor] = None, identity: Optional[torch.Tensor] = None, query_pos: Optional[torch.Tensor] = None, key_padding_mask: Optional[torch.Tensor] = None, reference_points: Optional[torch.Tensor] = None, spatial_shapes: Optional[torch.Tensor] = None, level_start_index: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor: """Forward Function of MultiScaleDeformAttention. Args: query (torch.Tensor): Query of Transformer with shape (num_query, bs, embed_dims). key (torch.Tensor): The key tensor with shape `(num_key, bs, embed_dims)`. value (torch.Tensor): The value tensor with shape `(num_key, bs, embed_dims)`. identity (torch.Tensor): The tensor used for addition, with the same shape as `query`. Default None. If None, `query` will be used. query_pos (torch.Tensor): The positional encoding for `query`. Default: None. key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape [bs, num_key]. reference_points (torch.Tensor): The normalized reference points with shape (bs, num_query, num_levels, 2), all elements is range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area. or (N, Length_{query}, num_levels, 4), add additional two dimensions is (w, h) to form reference boxes. spatial_shapes (torch.Tensor): Spatial shape of features in different levels. With shape (num_levels, 2), last dimension represents (h, w). level_start_index (torch.Tensor): The start index of each level. A tensor has shape ``(num_levels, )`` and can be represented as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...]. Returns: torch.Tensor: forwarded results with shape [num_query, bs, embed_dims]. """ if value is None: value = query if identity is None: identity = query if query_pos is not None: query = query + query_pos if not self.batch_first: # change to (bs, num_query ,embed_dims) query = query.permute(1, 0, 2) value = value.permute(1, 0, 2) bs, num_query, _ = query.shape bs, num_value, _ = value.shape assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value value = self.value_proj(value) if key_padding_mask is not None: value = value.masked_fill(key_padding_mask[..., None], 0.0) value = value.view(bs, num_value, self.num_heads, -1) sampling_offsets = self.sampling_offsets(query).view( bs, num_query, self.num_heads, self.num_levels, self.num_points, 2) attention_weights = self.attention_weights(query).view( bs, num_query, self.num_heads, self.num_levels * self.num_points) attention_weights = attention_weights.softmax(-1) attention_weights = attention_weights.view(bs, num_query, self.num_heads, self.num_levels, self.num_points) if reference_points.shape[-1] == 2: offset_normalizer = torch.stack( [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) sampling_locations = reference_points[:, :, None, None, :, :] \ + sampling_offsets \ / offset_normalizer[None, None, None, :, None, :] elif reference_points.shape[-1] == 4: sampling_locations = reference_points[:, :, None, :, None, :2] \ + sampling_offsets / self.num_points \ * reference_points[:, :, None, :, None, 2:] \ * 0.5 else: raise ValueError( f'Last dim of reference_points must be' f' 2 or 4, but get {reference_points.shape[-1]} instead.') if ((IS_CUDA_AVAILABLE and value.is_cuda) or (IS_MLU_AVAILABLE and value.is_mlu)): output = MultiScaleDeformableAttnFunction.apply( value, spatial_shapes, level_start_index, sampling_locations, attention_weights, self.im2col_step) else: output = multi_scale_deformable_attn_pytorch( value, spatial_shapes, sampling_locations, attention_weights) if not self.batch_first: # (num_query, bs ,embed_dims) output = output.permute(1, 0, 2) return output ================================================ FILE: model/encoder/bevformer/bevformer_encoder.py ================================================ from mmseg.registry import MODELS from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention from mmengine.model import ModuleList import torch.nn as nn, torch, copy from torch.nn.init import normal_ from mmengine.logging import MMLogger logger = MMLogger.get_instance('selfocc') from ..base_encoder import BaseEncoder from .utils import point_sampling from .attention import BEVCrossAttention, BEVDeformableAttention from .mappings import GridMeterMapping @MODELS.register_module() class BEVFormerEncoder(BaseEncoder): def __init__( self, mapping_args: dict, # bev_inner=128, # bev_outer=32, # range_inner=51.2, # range_outer=51.2, # nonlinear_mode='linear_upscale', # z_inner=20, # z_outer=10, # z_ranges=[-5.0, 3.0, 11.0], embed_dims=128, num_cams=6, num_feature_levels=4, positional_encoding=None, num_points_cross=32, num_points_self=16, transformerlayers=None, num_layers=None, init_cfg=None): super().__init__(init_cfg) # self.bev_inner = bev_inner # self.bev_outer = bev_outer # self.range_inner = range_inner # self.range_outer = range_outer # assert nonlinear_mode == 'linear_upscale' # TODO # self.nonlinear_mode = nonlinear_mode # self.z_inner = z_inner # self.z_outer = z_outer # self.z_ranges = z_ranges self.embed_dims = embed_dims self.num_feature_levels = num_feature_levels self.num_cams = num_cams self.mapping = GridMeterMapping( # bev_inner, # bev_outer, # range_inner, # range_outer, # nonlinear_mode, # z_inner, # z_outer, # z_ranges **mapping_args) # bev_size = 2 * (bev_inner + bev_outer) + 1 size_h = self.mapping.size_h size_w = self.mapping.size_w size_d = self.mapping.size_d bev_grid = torch.stack( [torch.arange(size_h, dtype=torch.float).unsqueeze(-1).expand(-1, size_w), torch.arange(size_w, dtype=torch.float).unsqueeze(0).expand(size_h, -1)], dim=-1) bev_meter = self.mapping.grid2meter(bev_grid) positional_encoding.update({'bev_meter': bev_meter}) # positional encoding self.positional_encoding = build_positional_encoding(positional_encoding) self.bev_size = [size_h, size_w] # transformer layers if isinstance(transformerlayers, dict): transformerlayers = [ copy.deepcopy(transformerlayers) for _ in range(num_layers)] else: assert isinstance(transformerlayers, list) and \ len(transformerlayers) == num_layers self.num_layers = num_layers self.layers = ModuleList() for i in range(num_layers): self.layers.append(build_transformer_layer(transformerlayers[i])) self.pre_norm = self.layers[0].pre_norm logger.info('use pre_norm: ' + str(self.pre_norm)) # other learnable embeddings self.level_embeds = nn.Parameter( torch.randn(self.num_feature_levels, self.embed_dims)) self.cams_embeds = nn.Parameter( torch.randn(self.num_cams, self.embed_dims)) # prepare reference points used in image cross-attention and cross-view hybrid-attention self.num_points_cross = num_points_cross self.num_points_self = num_points_self # uniform_d = torch.linspace(0, z_inner + z_outer, num_points_cross) uniform_d = torch.linspace(0, size_d - 1, num_points_cross) bev_3d_grid = torch.cat([ bev_grid.unsqueeze(2).expand(-1, -1, num_points_cross, -1), uniform_d.reshape(1, 1, num_points_cross, 1).expand(size_h, size_w, -1, -1) ], dim=-1) # H, W, D, 3 ref_3d = self.mapping.grid2meter(bev_3d_grid) self.register_buffer('ref_3d', ref_3d.flatten(0, 1).transpose(0, 1), False) bev_grid_normed = bev_grid.clone() bev_grid_normed[..., 0] = bev_grid_normed[..., 0] / (size_h - 1) bev_grid_normed[..., 1] = bev_grid_normed[..., 1] / (size_w - 1) self.register_buffer('ref_2d', bev_grid_normed, False) # H, W, 2 def init_weights(self): """Initialize the transformer weights.""" for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) for m in self.modules(): if isinstance(m, BEVCrossAttention) or \ isinstance(m, MultiScaleDeformableAttention) or \ isinstance(m, BEVDeformableAttention): try: m.init_weight() except AttributeError: m.init_weights() normal_(self.level_embeds) normal_(self.cams_embeds) def forward_layers( self, bev_query, # b, c, h, w key, value, bev_pos=None, # b, h, w, c spatial_shapes=None, level_start_index=None, img_metas=None, **kwargs ): bs = bev_query.shape[0] ref_3d = self.ref_3d.unsqueeze(0).repeat(bs, 1, 1, 1) # bs, p, HW, 3 reference_points_cam, bev_mask = point_sampling(ref_3d, img_metas) # num_cam, bs, hw++, #p, 2 ref_2d = self.ref_2d.unsqueeze(0).repeat(bs, 1, 1, 1) # bs, H, W, 2 ref_2d = ref_2d.reshape(bs, -1, 1, 2) # bs, HW, 1, 2 for lid, layer in enumerate(self.layers): output = layer( bev_query, key, value, bev_pos=bev_pos, ref_2d=ref_2d, spatial_shapes=spatial_shapes, level_start_index=level_start_index, reference_points_cams=reference_points_cam, bev_masks=bev_mask, bev_size=self.bev_size, **kwargs) bev_query = output return bev_query def forward( self, representation, ms_img_feats=None, metas=None, **kwargs ): """Forward function. Args: img_feats (tuple[Tensor]): Features from the upstream network, each is a 5D-tensor with shape (B, N, C, H, W). """ img_feats = ms_img_feats bs = img_feats[0].shape[0] dtype = img_feats[0].dtype device = img_feats[0].device # bev queries and pos embeds bev_queries = representation # bs, HW, C bev_pos = self.positional_encoding().unsqueeze(0).repeat(bs, 1, 1) # bs, HW, C # flatten image features of different scales feat_flatten = [] spatial_shapes = [] for lvl, feat in enumerate(img_feats): bs, num_cam, c, h, w = feat.shape spatial_shape = (h, w) feat = feat.flatten(3).permute(1, 0, 3, 2) # num_cam, bs, hw, c feat = feat + self.cams_embeds[:, None, None, :].to(dtype) feat = feat + self.level_embeds[None, None, lvl:lvl+1, :].to(dtype) spatial_shapes.append(spatial_shape) feat_flatten.append(feat) feat_flatten = torch.cat(feat_flatten, 2) # num_cam, bs, hw++, c spatial_shapes = torch.as_tensor( spatial_shapes, dtype=torch.long, device=device) level_start_index = torch.cat((spatial_shapes.new_zeros( (1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) feat_flatten = feat_flatten.permute(0, 2, 1, 3) # (num_cam, H*W, bs, embed_dims) # forward layers bev_embed = self.forward_layers( bev_queries, feat_flatten, feat_flatten, bev_pos=bev_pos, spatial_shapes=spatial_shapes, level_start_index=level_start_index, img_metas=metas, ) return {'representation': bev_embed} ================================================ FILE: model/encoder/bevformer/bevformer_encoder_layer.py ================================================ import copy, torch from mmengine import ConfigDict from mmengine.model import BaseModule, ModuleList from mmengine.registry import MODELS from mmcv.cnn import build_norm_layer from mmcv.cnn.bricks.transformer import build_feedforward_network, build_attention @MODELS.register_module() class BEVFormerLayer(BaseModule): """Base `TransformerLayer` for vision transformer. It can be built from `mmcv.ConfigDict` and support more flexible customization, for example, using any number of `FFN or LN ` and use different kinds of `attention` by specifying a list of `ConfigDict` named `attn_cfgs`. It is worth mentioning that it supports `prenorm` when you specifying `norm` as the first element of `operation_order`. More details about the `prenorm`: `On Layer Normalization in the Transformer Architecture `_ . Args: attn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )): Configs for `self_attention` or `cross_attention` modules, The order of the configs in the list should be consistent with corresponding attentions in operation_order. If it is a dict, all of the attention modules in operation_order will be built with this config. Default: None. ffn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )): Configs for FFN, The order of the configs in the list should be consistent with corresponding ffn in operation_order. If it is a dict, all of the attention modules in operation_order will be built with this config. operation_order (tuple[str]): The execution order of operation in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). Support `prenorm` when you specifying first element as `norm`. Default：None. norm_cfg (dict): Config dict for normalization layer. Default: dict(type='LN'). init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. Default: None. batch_first (bool): Key, Query and Value are shape of (batch, n, embed_dim) or (n, batch, embed_dim). Default to False. """ def __init__(self, attn_cfgs=None, ffn_cfgs=dict( type='FFN', feedforward_channels=1024, num_fcs=2, ffn_drop=0., act_cfg=dict(type='ReLU', inplace=True)), operation_order=None, norm_cfg=dict(type='LN'), init_cfg=None, batch_first=True, **kwargs): deprecated_args = dict( feedforward_channels='feedforward_channels', ffn_dropout='ffn_drop', ffn_num_fcs='num_fcs') for ori_name, new_name in deprecated_args.items(): if ori_name in kwargs: ffn_cfgs[new_name] = kwargs[ori_name] super().__init__(init_cfg) self.batch_first = batch_first num_attn = operation_order.count('self_attn') + operation_order.count('cross_attn') if isinstance(attn_cfgs, dict): attn_cfgs = [copy.deepcopy(attn_cfgs) for _ in range(num_attn)] else: assert num_attn == len(attn_cfgs), f'The length ' \ f'of attn_cfg {num_attn} is ' \ f'not consistent with the number of attention' \ f'in operation_order {operation_order}.' self.num_attn = num_attn self.operation_order = operation_order self.norm_cfg = norm_cfg self.pre_norm = operation_order[0] == 'norm' self.attentions = ModuleList() index = 0 for operation_name in operation_order: if operation_name in ['self_attn', 'cross_attn']: if 'batch_first' in attn_cfgs[index]: assert self.batch_first == attn_cfgs[index]['batch_first'] else: attn_cfgs[index]['batch_first'] = self.batch_first attention = build_attention(attn_cfgs[index]) # Some custom attentions used as `self_attn` # or `cross_attn` can have different behavior. attention.operation_name = operation_name self.attentions.append(attention) index += 1 self.embed_dims = self.attentions[0].embed_dims self.ffns = ModuleList() num_ffns = operation_order.count('ffn') if isinstance(ffn_cfgs, dict): ffn_cfgs = ConfigDict(ffn_cfgs) if isinstance(ffn_cfgs, dict): ffn_cfgs = [copy.deepcopy(ffn_cfgs) for _ in range(num_ffns)] assert len(ffn_cfgs) == num_ffns for ffn_index in range(num_ffns): if 'embed_dims' not in ffn_cfgs[ffn_index]: ffn_cfgs[ffn_index]['embed_dims'] = self.embed_dims else: assert ffn_cfgs[ffn_index]['embed_dims'] == self.embed_dims self.ffns.append(build_feedforward_network(ffn_cfgs[ffn_index])) self.norms = ModuleList() num_norms = operation_order.count('norm') for _ in range(num_norms): self.norms.append(build_norm_layer(norm_cfg, self.embed_dims)[1]) def forward(self, query, key=None, value=None, bev_pos=None, ref_2d=None, spatial_shapes=None, level_start_index=None, reference_points_cams=None, bev_masks=None, bev_size=None, **kwargs): """ **kwargs contains some specific arguments of attentions. Args: query (Tensor): The input query with shape [num_queries, bs, embed_dims] if self.batch_first is False, else [bs, num_queries embed_dims]. key (Tensor): The key tensor with shape [num_keys, bs, embed_dims] if self.batch_first is False, else [bs, num_keys, embed_dims] . value (Tensor): The value tensor with same shape as `key`. Returns: Tensor: forwarded results with shape [num_queries, bs, embed_dims]. """ # type conversion query = query.type_as(value) if key is not None: key = key.type_as(value) if bev_pos is not None: bev_pos = bev_pos.type_as(value) if ref_2d is not None: ref_2d = ref_2d.type_as(value) if reference_points_cams is not None: reference_points_cams = reference_points_cams.type_as(value) norm_index = 0 attn_index = 0 ffn_index = 0 identity = query for layer in self.operation_order: # cross view hybrid attention if layer == 'self_attn': ss = torch.tensor([bev_size], device=query.device) lsi = torch.tensor([0], device=query.device) query = self.attentions[attn_index]( query, query, query, identity if self.pre_norm else None, query_pos=bev_pos, reference_points=ref_2d, spatial_shapes=ss, level_start_index=lsi, **kwargs) query = query.type_as(value) attn_index += 1 identity = query elif layer == 'norm': query = self.norms[norm_index](query) query = query.type_as(value) norm_index += 1 # image cross attention elif layer == 'cross_attn': # if isinstance(query, (list, tuple)): # query = torch.cat(query, dim=1) query = self.attentions[attn_index]( query, # query.float(), key, # key.float(), value, # value.float(), # identity.float() if self.pre_norm else None, identity if self.pre_norm else None, spatial_shapes=spatial_shapes, level_start_index=level_start_index, # reference_points_cams=reference_points_cams.float(), reference_points_cams=reference_points_cams, bev_masks=bev_masks, **kwargs) query = query.type_as(value) attn_index += 1 identity = query elif layer == 'ffn': ffn = self.ffns[ffn_index] query = ffn(query, identity if self.pre_norm else None) query = query.type_as(value) ffn_index += 1 # query = torch.split(query, [tpv_h*tpv_w, tpv_z*tpv_h, tpv_w*tpv_z], dim=1) return query ================================================ FILE: model/encoder/bevformer/bevformer_pos_embed.py ================================================ import torch, torch.nn as nn from mmengine.model import BaseModule from mmengine.registry import MODELS @MODELS.register_module() class BEVPositionalEncoding(BaseModule): def __init__(self, num_freqs, embed_dims, bev_meter, tot_range, init_cfg=None): super().__init__(init_cfg) if not isinstance(tot_range, list): pc_range = [-1.0 * tot_range, -1.0 * tot_range, 0., tot_range, tot_range, 0.] else: pc_range = tot_range # bev_meter = (bev_meter + tot_range) / 2. / tot_range bev_meter[..., 0] = (bev_meter[..., 0] - pc_range[0]) / (pc_range[3] - pc_range[0]) bev_meter[..., 1] = (bev_meter[..., 1] - pc_range[1]) / (pc_range[4] - pc_range[1]) freqs = torch.arange(-1, num_freqs - 1, dtype=torch.float) freqs = torch.pi * (2 ** freqs) bev_meter_freq = bev_meter.unsqueeze(-1) * freqs[None, None, None, ...] freq_feat = torch.stack([ torch.sin(bev_meter_freq), torch.cos(bev_meter_freq)], dim=-1) freq_feat = freq_feat.flatten(-3).flatten(0, 1) self.register_buffer('freq_feat', freq_feat, False) self.position_layer = nn.Linear(4 * num_freqs, embed_dims) def forward(self): pos = self.position_layer(self.freq_feat) return pos # H, W, C if __name__ == "__main__": import os import sys; sys.path.insert(0, os.path.abspath(os.path.join(os.path.abspath(__file__), os.pardir))) print(sys.path) from mappings import GridMeterMapping from mmcv.cnn.bricks.transformer import build_positional_encoding bev_inner = 2 bev_outer = 2 m = GridMeterMapping( bev_inner=2, bev_outer=2, range_inner=2, range_outer=4, z_inner=2, z_outer=2, z_ranges=[-1., 1., 5.]) bev_size = 2 * (bev_inner + bev_outer) + 1 bev_grid = torch.stack( [torch.arange(bev_size, dtype=torch.float).unsqueeze(-1).expand(-1, bev_size), torch.arange(bev_size, dtype=torch.float).unsqueeze(0).expand(bev_size, -1)], dim=-1) bev_meter = m.grid2meter(bev_grid) positional_encoding = dict( type='BEVPositionalEncoding', num_freqs=3, embed_dims=32, tot_range=6 ) positional_encoding.update({'bev_meter': bev_meter}) # positional encoding positional_encoding = build_positional_encoding(positional_encoding) pass ================================================ FILE: model/encoder/bevformer/mappings.py ================================================ import torch from typing_extensions import Literal class LinearMapping: def __init__( self, h_size=[128, 32], h_range=[51.2, 28.8], h_half=False, w_size=[128, 32], w_range=[51.2, 28.8], w_half=False, d_size=[20, 10], d_range=[-4.0, 4.0, 12.0]): self.h_size = h_size self.h_range = h_range self.h_half = h_half self.w_size = w_size self.w_range = w_range self.w_half = w_half self.d_size = d_size self.d_range = [d_range[1] - d_range[0], d_range[2] - d_range[1]] self.d_start = d_range[0] if h_half: self.h_tot_len = 1 + self.h_size[0] + self.h_size[1] else: self.h_tot_len = 1 + 2 * (self.h_size[0] + self.h_size[1]) if w_half: self.w_tot_len = 1 + self.w_size[0] + self.w_size[1] else: self.w_tot_len = 1 + 2 * (self.w_size[0] + self.w_size[1]) self.d_tot_len = 1 + self.d_size[0] + self.d_size[1] def grid2meter(self, grid): # grid: [..., (h, w, d)] h, w = grid[..., 0], grid[..., 1] if grid.shape[-1] == 3: d = grid[..., 2] else: d = None ## deal with h if not self.h_half: h_ctr = h - (self.h_size[0] + self.h_size[1]) else: h_ctr = h h_abs = torch.abs(h_ctr) if self.h_size[1] == 0: y_abs = h_abs / self.h_size[0] * self.h_range[0] else: y_abs = torch.where( h_abs > self.h_size[0], self.h_range[0] + (h_abs - self.h_size[0]) / \ self.h_size[1] * self.h_range[1], h_abs / self.h_size[0] * self.h_range[0]) y = torch.sign(h_ctr) * y_abs ## deal with w if not self.w_half: w_ctr = w - (self.w_size[0] + self.w_size[1]) else: w_ctr = w w_abs = torch.abs(w_ctr) if self.w_size[1] == 0: x_abs = w_abs / self.w_size[0] * self.w_range[0] else: x_abs = torch.where( w_abs > self.w_size[0], self.w_range[0] + (w_abs - self.w_size[0]) / \ self.w_size[1] * self.w_range[1], w_abs / self.w_size[0] * self.w_range[0]) x = torch.sign(w_ctr) * x_abs ## deal with d if d is not None: d_ctr = d d_abs = torch.abs(d_ctr) if self.d_size[1] == 0: z_abs = d_abs / self.d_size[0] * self.d_range[0] else: z_abs = torch.where( d_abs > self.d_size[0], self.d_range[0] + (d_abs - self.d_size[0]) / \ self.d_size[1] * self.d_range[1], d_abs / self.d_size[0] * self.d_range[0]) z = torch.sign(d_ctr) * z_abs + self.d_start return torch.stack([x, y, z], dim=-1) else: return torch.stack([x, y], dim=-1) def meter2grid(self, meter, normalize=False): x, y, z = meter[..., 0], meter[..., 1], meter[..., 2] ## deal with x x_abs = torch.abs(x) if self.w_size[1] == 0: w_abs = x_abs / self.w_range[0] * self.w_size[0] else: w_abs = torch.where( x_abs > self.w_range[0], self.w_size[0] + (x_abs - self.w_range[0]) / \ self.w_range[1] * self.w_size[1], x_abs / self.w_range[0] * self.w_size[0]) w_ctr = torch.sign(x) * w_abs if not self.w_half: w = w_ctr + self.w_size[0] + self.w_size[1] else: w = w_ctr ## deal with y y_abs = torch.abs(y) if self.h_size[1] == 0: h_abs = y_abs / self.h_range[0] * self.h_size[0] else: h_abs = torch.where( y_abs > self.h_range[0], self.h_size[0] + (y_abs - self.h_range[0]) / \ self.h_range[1] * self.h_size[1], y_abs / self.h_range[0] * self.h_size[0]) h_ctr = torch.sign(y) * h_abs if not self.h_half: h = h_ctr + self.h_size[0] + self.h_size[1] else: h = h_ctr ## deal with z z_ctr = z - self.d_start z_abs = torch.abs(z_ctr) if self.d_size[1] == 0: d_abs = z_abs / self.d_range[0] * self.d_size[0] else: d_abs = torch.where( z_abs > self.d_range[0], self.d_size[0] + (z_abs - self.d_range[0]) / \ self.d_range[1] * self.d_size[1], z_abs / self.d_range[0] * self.d_size[0]) d = torch.sign(z_ctr) * d_abs if normalize: h = h / (self.h_tot_len - 1) w = w / (self.w_tot_len - 1) d = d / (self.d_tot_len - 1) return torch.stack([h, w, d], dim=-1) class GridMeterMapping: def __init__( self, nonlinear_mode: Literal['linear_upscale', 'linear'] = 'linear_upscale', h_size=[128, 32], h_range=[51.2, 28.8], h_half=False, w_size=[128, 32], w_range=[51.2, 28.8], w_half=False, d_size=[20, 10], d_range=[-4.0, 4.0, 12.0] ) -> None: self.nonlinear_mode = nonlinear_mode if nonlinear_mode == 'linear_upscale': assert all([h == w for h, w in zip(h_size, w_size)]) assert all([h == w for h, w in zip(h_range, w_range)]) assert (not h_half) and (not w_half) self.mapping = NonLinearMapping( h_size[0], h_size[1], h_range[0], h_range[1], d_size[0], d_size[1], d_range) self.size_h = self.size_w = self.mapping.bev_size self.size_d = self.mapping.z_size elif nonlinear_mode == 'linear': self.mapping = LinearMapping( h_size, h_range, h_half, w_size, w_range, w_half, d_size, d_range) self.size_h = self.mapping.h_tot_len self.size_w = self.mapping.w_tot_len self.size_d = self.mapping.d_tot_len self.grid2meter = self.mapping.grid2meter self.meter2grid = self.mapping.meter2grid class NonLinearMapping: def __init__( self, bev_inner=128, bev_outer=32, range_inner=51.2, range_outer=51.2, z_inner=20, z_outer=10, z_ranges=[-5.0, 3.0, 11.0], ) -> None: self.bev_inner = bev_inner self.bev_outer = bev_outer self.range_inner = range_inner self.range_outer = range_outer self.z_inner = z_inner self.z_outer = z_outer self.z_ranges = z_ranges self.bev_size = 1 + 2 * (self.bev_inner + self.bev_outer) self.z_size = 1 + self.z_inner + self.z_outer self.hw_unit = range_inner * 1.0 / bev_inner self.increase_unit = (range_outer - bev_outer * self.hw_unit) * 2.0 / bev_outer / (bev_outer + 1) self.z_unit = (z_ranges[1] - z_ranges[0]) * 1.0 / z_inner self.z_increase_unit = (z_ranges[2] - z_ranges[1] - z_outer * self.z_unit) * 2.0 / z_outer / (z_outer + 1) def grid2meter(self, grid): hw = grid[..., :2] hw_center = hw - (self.bev_inner + self.bev_outer) hw_center_abs = torch.abs(hw_center) yx_base_abs = hw_center_abs * self.hw_unit hw_outer = torch.relu(hw_center_abs - self.bev_inner) hw_outer_int = torch.floor(hw_outer) yx_outer_base = hw_outer_int * (hw_outer_int + 1) / 2.0 * self.increase_unit yx_outer_resi = (hw_outer - hw_outer_int) * (hw_outer_int + 1) * self.increase_unit yx_abs = yx_base_abs + yx_outer_base + yx_outer_resi yx = torch.sign(hw_center) * yx_abs if grid.shape[-1] == 3: d = grid[..., 2:3] d_center = d z_base = d_center * self.z_unit d_outer = torch.relu(d_center - self.z_inner) d_outer_int = torch.floor(d_outer) z_outer_base = d_outer_int * (d_outer_int + 1) / 2.0 * self.z_increase_unit z_outer_resi = (d_outer - d_outer_int) * (d_outer_int + 1) * self.z_increase_unit z = z_base + z_outer_base + z_outer_resi + self.z_ranges[0] return torch.cat([yx[..., 1:2], yx[..., 0:1], z], dim=-1) else: return yx[..., [1, 0]] def meter2grid(self, meter, normalize=False): xy = meter[..., :2] xy_abs = torch.abs(xy) wh_base_abs = xy_abs / self.hw_unit wh_base_abs = wh_base_abs.clamp_(max=self.bev_inner) xy_outer_abs = torch.relu(xy_abs - self.range_inner) wh_outer_base = torch.sqrt((1. / 2 + self.hw_unit / self.increase_unit) ** 2 + \ 2 * xy_outer_abs / self.increase_unit) - (1. / 2 + self.hw_unit / self.increase_unit) wh_outer_base = torch.floor(wh_outer_base) xy_outer_resi = xy_outer_abs - wh_outer_base * self.hw_unit - self.increase_unit * wh_outer_base * (wh_outer_base + 1) / 2 wh_outer_resi = xy_outer_resi / (self.hw_unit + (wh_outer_base + 1) * self.increase_unit) wh_center_abs = wh_base_abs + wh_outer_base + wh_outer_resi wh_center = torch.sign(xy) * wh_center_abs wh = wh_center + self.bev_inner + self.bev_outer z = meter[..., 2:3] z_abs = z - self.z_ranges[0] d_base = z_abs / self.z_unit d_base = d_base.clamp_(max=self.z_inner) z_outer = torch.relu(z_abs - (self.z_ranges[1] - self.z_ranges[0])) d_outer_base = torch.sqrt((1. / 2 + self.z_unit / self.z_increase_unit) ** 2 + \ 2 * z_outer / self.z_increase_unit) - (1. / 2 + self.z_unit / self.z_increase_unit) d_outer_base = torch.floor(d_outer_base) z_outer_resi = z_outer - d_outer_base * self.z_unit - self.z_increase_unit * d_outer_base * (d_outer_base + 1) / 2 d_outer_resi = z_outer_resi / (self.z_unit + (d_outer_base + 1) * self.z_increase_unit) d = d_base + d_outer_base + d_outer_resi if normalize: wh = wh / (self.bev_size - 1) d = d / (self.z_size - 1) return torch.cat([wh[..., 1:2], wh[..., 0:1], d], dim=-1) if __name__ == "__main__": # m = GridMeterMapping( # bev_inner=2, # bev_outer=2, # range_inner=2, # range_outer=4, # z_inner=2, # z_outer=2, # z_ranges=[-1., 1., 5.]) m = GridMeterMapping( nonlinear_mode='linear', h_size=[2, 2], h_range=[2, 4], h_half=False, w_size=[2, 2], w_range=[2, 4], w_half=False, d_size=[2, 2], d_range=[-1., 1., 5.] ) grid = [ [4, 0, 0], [0, 4, 1], [4, 4, 2], [5, 6, 4], [1, 0, 1.5], [7.5, 8, 2.5]] print(m.grid2meter(torch.tensor(grid))) meter = [[-6.0000, 0.0000, -1.0000], [ 0.0000, -6.0000, 0.0000], [ 0.0000, 0.0000, 1.0000], [ 2.0000, 1.0000, 5.0000], [-6.0000, -3.6667, 0.5000], [ 6.0000, 4.8333, 1.8333]] print(m.meter2grid(torch.tensor(meter))) pass ================================================ FILE: model/encoder/bevformer/mappings_old.py ================================================ import torch from typing_extensions import Literal class GridMeterMapping: def __init__( self, bev_inner=128, bev_outer=32, range_inner=51.2, range_outer=51.2, nonlinear_mode: Literal['linear_upscale', 'linear'] = 'linear_upscale', z_inner=20, z_outer=10, z_ranges=[-5.0, 3.0, 11.0], ) -> None: self.bev_inner = bev_inner self.bev_outer = bev_outer self.range_inner = range_inner self.range_outer = range_outer # assert nonlinear_mode == 'linear_upscale' # TODO self.nonlinear_mode = nonlinear_mode self.z_inner = z_inner self.z_outer = z_outer self.z_ranges = z_ranges self.hw_unit = range_inner * 1.0 / bev_inner if nonlinear_mode == 'linear': assert bev_outer == 0 and range_outer == 0 else: self.increase_unit = (range_outer - bev_outer * self.hw_unit) * 2.0 / bev_outer / (bev_outer + 1) self.z_unit = (z_ranges[1] - z_ranges[0]) * 1.0 / z_inner self.z_increase_unit = (z_ranges[2] - z_ranges[1] - z_outer * self.z_unit) * 2.0 / z_outer / (z_outer + 1) def grid2meter(self, grid): hw = grid[..., :2] hw_center = hw - (self.bev_inner + self.bev_outer) hw_center_abs = torch.abs(hw_center) yx_base_abs = hw_center_abs * self.hw_unit hw_outer = torch.relu(hw_center_abs - self.bev_inner) hw_outer_int = torch.floor(hw_outer) yx_outer_base = hw_outer_int * (hw_outer_int + 1) / 2.0 * self.increase_unit yx_outer_resi = (hw_outer - hw_outer_int) * (hw_outer_int + 1) * self.increase_unit yx_abs = yx_base_abs + yx_outer_base + yx_outer_resi yx = torch.sign(hw_center) * yx_abs if grid.shape[-1] == 3: d = grid[..., 2:3] d_center = d z_base = d_center * self.z_unit d_outer = torch.relu(d_center - self.z_inner) d_outer_int = torch.floor(d_outer) z_outer_base = d_outer_int * (d_outer_int + 1) / 2.0 * self.z_increase_unit z_outer_resi = (d_outer - d_outer_int) * (d_outer_int + 1) * self.z_increase_unit z = z_base + z_outer_base + z_outer_resi + self.z_ranges[0] return torch.cat([yx[..., 1:2], yx[..., 0:1], z], dim=-1) else: return yx[..., [1, 0]] def meter2grid(self, meter): xy = meter[..., :2] xy_abs = torch.abs(xy) wh_base_abs = xy_abs / self.hw_unit wh_base_abs = wh_base_abs.clamp_(max=self.bev_inner) xy_outer_abs = torch.relu(xy_abs - self.range_inner) wh_outer_base = torch.sqrt((1. / 2 + self.hw_unit / self.increase_unit) ** 2 + \ 2 * xy_outer_abs / self.increase_unit) - (1. / 2 + self.hw_unit / self.increase_unit) wh_outer_base = torch.floor(wh_outer_base) xy_outer_resi = xy_outer_abs - wh_outer_base * self.hw_unit - self.increase_unit * wh_outer_base * (wh_outer_base + 1) / 2 wh_outer_resi = xy_outer_resi / (self.hw_unit + (wh_outer_base + 1) * self.increase_unit) wh_center_abs = wh_base_abs + wh_outer_base + wh_outer_resi wh_center = torch.sign(xy) * wh_center_abs wh = wh_center + self.bev_inner + self.bev_outer z = meter[..., 2:3] z_abs = z - self.z_ranges[0] d_base = z_abs / self.z_unit d_base = d_base.clamp_(max=self.z_inner) z_outer = torch.relu(z_abs - (self.z_ranges[1] - self.z_ranges[0])) d_outer_base = torch.sqrt((1. / 2 + self.z_unit / self.z_increase_unit) ** 2 + \ 2 * z_outer / self.z_increase_unit) - (1. / 2 + self.z_unit / self.z_increase_unit) d_outer_base = torch.floor(d_outer_base) z_outer_resi = z_outer - d_outer_base * self.z_unit - self.z_increase_unit * d_outer_base * (d_outer_base + 1) / 2 d_outer_resi = z_outer_resi / (self.z_unit + (d_outer_base + 1) * self.z_increase_unit) d = d_base + d_outer_base + d_outer_resi return torch.cat([wh[..., 1:2], wh[..., 0:1], d], dim=-1) if __name__ == "__main__": m = GridMeterMapping( bev_inner=2, bev_outer=2, range_inner=2, range_outer=4, z_inner=2, z_outer=2, z_ranges=[-1., 1., 5.]) grid = [ [4, 0, 0], [0, 4, 1], [4, 4, 2], [5, 6, 4], [1, 0, 1.5], [7.5, 8, 2.5]] print(m.grid2meter(torch.tensor(grid))) meter = [[-6.0000, 0.0000, -1.0000], [ 0.0000, -6.0000, 0.0000], [ 0.0000, 0.0000, 1.0000], [ 2.0000, 1.0000, 5.0000], [-6.0000, -3.6667, 0.5000], [ 6.0000, 4.8333, 1.8333]] print(m.meter2grid(torch.tensor(meter))) pass ================================================ FILE: model/encoder/bevformer/utils.py ================================================ import torch import numpy as np def get_cross_view_ref_points(tpv_h, tpv_w, tpv_z, num_points_in_pillar): # ref points generating target: (#query)hw+zh+wz, (#level)3, #p, 2 # generate points for hw and level 1 h_ranges = torch.linspace(0.5, tpv_h-0.5, tpv_h) / tpv_h w_ranges = torch.linspace(0.5, tpv_w-0.5, tpv_w) / tpv_w h_ranges = h_ranges.unsqueeze(-1).expand(-1, tpv_w).flatten() w_ranges = w_ranges.unsqueeze(0).expand(tpv_h, -1).flatten() hw_hw = torch.stack([w_ranges, h_ranges], dim=-1) # hw, 2 hw_hw = hw_hw.unsqueeze(1).expand(-1, num_points_in_pillar[2], -1) # hw, #p, 2 # generate points for hw and level 2 z_ranges = torch.linspace(0.5, tpv_z-0.5, num_points_in_pillar[2]) / tpv_z # #p z_ranges = z_ranges.unsqueeze(0).expand(tpv_h*tpv_w, -1) # hw, #p h_ranges = torch.linspace(0.5, tpv_h-0.5, tpv_h) / tpv_h h_ranges = h_ranges.reshape(-1, 1, 1).expand(-1, tpv_w, num_points_in_pillar[2]).flatten(0, 1) hw_zh = torch.stack([h_ranges, z_ranges], dim=-1) # hw, #p, 2 # generate points for hw and level 3 z_ranges = torch.linspace(0.5, tpv_z-0.5, num_points_in_pillar[2]) / tpv_z # #p z_ranges = z_ranges.unsqueeze(0).expand(tpv_h*tpv_w, -1) # hw, #p w_ranges = torch.linspace(0.5, tpv_w-0.5, tpv_w) / tpv_w w_ranges = w_ranges.reshape(1, -1, 1).expand(tpv_h, -1, num_points_in_pillar[2]).flatten(0, 1) hw_wz = torch.stack([z_ranges, w_ranges], dim=-1) # hw, #p, 2 # generate points for zh and level 1 w_ranges = torch.linspace(0.5, tpv_w-0.5, num_points_in_pillar[1]) / tpv_w w_ranges = w_ranges.unsqueeze(0).expand(tpv_z*tpv_h, -1) h_ranges = torch.linspace(0.5, tpv_h-0.5, tpv_h) / tpv_h h_ranges = h_ranges.reshape(1, -1, 1).expand(tpv_z, -1, num_points_in_pillar[1]).flatten(0, 1) zh_hw = torch.stack([w_ranges, h_ranges], dim=-1) # generate points for zh and level 2 z_ranges = torch.linspace(0.5, tpv_z-0.5, tpv_z) / tpv_z z_ranges = z_ranges.reshape(-1, 1, 1).expand(-1, tpv_h, num_points_in_pillar[1]).flatten(0, 1) h_ranges = torch.linspace(0.5, tpv_h-0.5, tpv_h) / tpv_h h_ranges = h_ranges.reshape(1, -1, 1).expand(tpv_z, -1, num_points_in_pillar[1]).flatten(0, 1) zh_zh = torch.stack([h_ranges, z_ranges], dim=-1) # zh, #p, 2 # generate points for zh and level 3 w_ranges = torch.linspace(0.5, tpv_w-0.5, num_points_in_pillar[1]) / tpv_w w_ranges = w_ranges.unsqueeze(0).expand(tpv_z*tpv_h, -1) z_ranges = torch.linspace(0.5, tpv_z-0.5, tpv_z) / tpv_z z_ranges = z_ranges.reshape(-1, 1, 1).expand(-1, tpv_h, num_points_in_pillar[1]).flatten(0, 1) zh_wz = torch.stack([z_ranges, w_ranges], dim=-1) # generate points for wz and level 1 h_ranges = torch.linspace(0.5, tpv_h-0.5, num_points_in_pillar[0]) / tpv_h h_ranges = h_ranges.unsqueeze(0).expand(tpv_w*tpv_z, -1) w_ranges = torch.linspace(0.5, tpv_w-0.5, tpv_w) / tpv_w w_ranges = w_ranges.reshape(-1, 1, 1).expand(-1, tpv_z, num_points_in_pillar[0]).flatten(0, 1) wz_hw = torch.stack([w_ranges, h_ranges], dim=-1) # generate points for wz and level 2 h_ranges = torch.linspace(0.5, tpv_h-0.5, num_points_in_pillar[0]) / tpv_h h_ranges = h_ranges.unsqueeze(0).expand(tpv_w*tpv_z, -1) z_ranges = torch.linspace(0.5, tpv_z-0.5, tpv_z) / tpv_z z_ranges = z_ranges.reshape(1, -1, 1).expand(tpv_w, -1, num_points_in_pillar[0]).flatten(0, 1) wz_zh = torch.stack([h_ranges, z_ranges], dim=-1) # generate points for wz and level 3 w_ranges = torch.linspace(0.5, tpv_w-0.5, tpv_w) / tpv_w w_ranges = w_ranges.reshape(-1, 1, 1).expand(-1, tpv_z, num_points_in_pillar[0]).flatten(0, 1) z_ranges = torch.linspace(0.5, tpv_z-0.5, tpv_z) / tpv_z z_ranges = z_ranges.reshape(1, -1, 1).expand(tpv_w, -1, num_points_in_pillar[0]).flatten(0, 1) wz_wz = torch.stack([z_ranges, w_ranges], dim=-1) reference_points = torch.cat([ torch.stack([hw_hw, hw_zh, hw_wz], dim=1), torch.stack([zh_hw, zh_zh, zh_wz], dim=1), torch.stack([wz_hw, wz_zh, wz_wz], dim=1) ], dim=0) # hw+zh+wz, 3, #p, 2 return reference_points def get_reference_points(H, W, Z=8, num_points_in_pillar=4, dim='3d', bs=1, device='cpu', dtype=torch.float): """Get the reference points used in image cross-attention and single plane self-attention. Args: H, W: spatial shape of tpv. Z: hight of pillar. D: sample D points uniformly from each pillar. device (obj:`device`): The device where reference_points should be. Returns: Tensor: reference points used in decoder, has \ shape (bs, num_keys, num_levels, 2). """ # reference points in 3D space, used in image cross-attention if dim == '3d': zs = torch.linspace(0.5, Z - 0.5, num_points_in_pillar, dtype=dtype, device=device).view(-1, 1, 1).expand(num_points_in_pillar, H, W) / Z xs = torch.linspace(0.5, W - 0.5, W, dtype=dtype, device=device).view(1, 1, -1).expand(num_points_in_pillar, H, W) / W ys = torch.linspace(0.5, H - 0.5, H, dtype=dtype, device=device).view(1, -1, 1).expand(num_points_in_pillar, H, W) / H ref_3d = torch.stack((xs, ys, zs), -1) ref_3d = ref_3d.permute(0, 3, 1, 2).flatten(2).permute(0, 2, 1) ref_3d = ref_3d[None].repeat(bs, 1, 1, 1) return ref_3d # reference points on 2D tpv plane, used in self attention in tpvformer04 # which is an older version. Now we use get_cross_view_ref_points instead. elif dim == '2d': ref_y, ref_x = torch.meshgrid( torch.linspace( 0.5, H - 0.5, H, dtype=dtype, device=device), torch.linspace( 0.5, W - 0.5, W, dtype=dtype, device=device)) ref_y = ref_y.reshape(-1)[None] / H ref_x = ref_x.reshape(-1)[None] / W ref_2d = torch.stack((ref_x, ref_y), -1) ref_2d = ref_2d.repeat(bs, 1, 1).unsqueeze(2) return ref_2d # This function must use fp32!!! @torch.cuda.amp.autocast(enabled=False) def point_sampling(reference_points, img_metas): reference_points = reference_points.float() lidar2img = [] for img_meta in img_metas: lidar2img.append(img_meta['lidar2img']) if isinstance(lidar2img[0], (np.ndarray, list)): lidar2img = np.asarray(lidar2img) lidar2img = reference_points.new_tensor(lidar2img) # (B, N, 4, 4) else: lidar2img = torch.stack(lidar2img, dim=0) reference_points = torch.cat( (reference_points, torch.ones_like(reference_points[..., :1])), -1) reference_points = reference_points.permute(1, 0, 2, 3) D, B, num_query = reference_points.size()[:3] num_cam = lidar2img.size(1) reference_points = reference_points.view( D, B, 1, num_query, 4, 1) lidar2img = lidar2img.view( 1, B, num_cam, 1, 4, 4) reference_points_cam = torch.matmul( lidar2img.to(torch.float32), reference_points.to(torch.float32)).squeeze(-1) eps = 1e-5 # reference_points_cam[..., 0:2] = reference_points_cam[..., 0:2] * \ # img_metas[0]['scale_rate'] if 'img_augmentation' in img_metas[0] and \ 'post_rots' in img_metas[0]['img_augmentation'] and \ 'post_trans' in img_metas[0]['img_augmentation']: post_rots = [] post_trans = [] for img_meta in img_metas: post_rots.append(img_meta['img_augmentation']['post_rots'].numpy()) post_trans.append(img_meta['img_augmentation']['post_trans'].numpy()) post_rots = np.asarray(post_rots) post_trans = np.asarray(post_trans) post_rots = reference_points.new_tensor(post_rots) post_trans = reference_points.new_tensor(post_trans) reference_points_cam[..., :2] = reference_points_cam[..., :2] / torch.maximum( reference_points_cam[..., 2:3], torch.ones_like(reference_points_cam[..., 2:3]) * eps) # D, B, N, Q, 3, 1 reference_points_cam = reference_points_cam[..., :3].unsqueeze(-1) post_rots = post_rots.view(1, B, num_cam, 1, 3, 3) reference_points_cam = torch.matmul( post_rots.to(torch.float32), reference_points_cam.to(torch.float32)).squeeze(-1) # D, B, N, Q, 3 post_trans = post_trans.view(1, B, num_cam, 1, 3) reference_points_cam = reference_points_cam + post_trans tpv_mask = (reference_points_cam[..., 2:3] > eps) reference_points_cam = reference_points_cam[..., :2] else: tpv_mask = (reference_points_cam[..., 2:3] > eps) reference_points_cam = reference_points_cam[..., 0:2] / torch.maximum( reference_points_cam[..., 2:3], torch.ones_like(reference_points_cam[..., 2:3]) * eps) # reference_points_cam[..., 0] /= img_metas[0]['img_shape'][0][1] # reference_points_cam[..., 1] /= img_metas[0]['img_shape'][0][0] reference_points_cam[..., 0] /= img_metas[0]['img_shape'][1] reference_points_cam[..., 1] /= img_metas[0]['img_shape'][0] # D, B, N, Q, 2 tpv_mask = (tpv_mask & (reference_points_cam[..., 1:2] > 0.0) & (reference_points_cam[..., 1:2] < 1.0) & (reference_points_cam[..., 0:1] < 1.0) & (reference_points_cam[..., 0:1] > 0.0)) tpv_mask = torch.nan_to_num(tpv_mask) reference_points_cam = reference_points_cam.permute(2, 1, 3, 0, 4) # N, B, Q, D, 2 tpv_mask = tpv_mask.permute(2, 1, 3, 0, 4).squeeze(-1) if 'focal_ratios_x' in img_metas[0]: scales_x = np.asarray(img_metas[0]['focal_ratios_x']) scales_x = reference_points.new_tensor(scales_x).view(-1, 1, 1, 1, 1) reference_points_cam[..., :1] = reference_points_cam[..., :1] * scales_x scales_y = np.asarray(img_metas[0]['focal_ratios_y']) scales_y = reference_points.new_tensor(scales_y).view(-1, 1, 1, 1, 1) reference_points_cam[..., 1:] = reference_points_cam[..., 1:] * scales_y return reference_points_cam, tpv_mask ================================================ FILE: model/encoder/tpvformer/__init__.py ================================================ from .tpvformer_encoder import TPVFormerEncoder from .tpvformer_pos_embed import TPVPositionalEncoding from .tpvformer_encoder_layer import TPVFormerLayer from .attention import * from .modules import * ================================================ FILE: model/encoder/tpvformer/attention/__init__.py ================================================ from .image_cross_attention import TPVCrossAttention from .cross_view_hybrid_attention import CrossViewHybridAttention ================================================ FILE: model/encoder/tpvformer/attention/cross_view_hybrid_attention.py ================================================ from typing import Optional, no_type_check from mmcv.ops.multi_scale_deform_attn import \ MultiScaleDeformableAttention, MultiScaleDeformableAttnFunction, \ multi_scale_deformable_attn_pytorch from mmengine.registry import MODELS from mmengine.utils import deprecated_api_warning from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE import torch @MODELS.register_module() class CrossViewHybridAttention(MultiScaleDeformableAttention): @no_type_check @deprecated_api_warning({'residual': 'identity'}, cls_name='MultiScaleDeformableAttention') def forward(self, query: torch.Tensor, key: Optional[torch.Tensor] = None, value: Optional[torch.Tensor] = None, identity: Optional[torch.Tensor] = None, query_pos: Optional[torch.Tensor] = None, key_padding_mask: Optional[torch.Tensor] = None, reference_points: Optional[torch.Tensor] = None, spatial_shapes: Optional[torch.Tensor] = None, level_start_index: Optional[torch.Tensor] = None, **kwargs) -> torch.Tensor: """Forward Function of MultiScaleDeformAttention. Args: query (torch.Tensor): Query of Transformer with shape (num_query, bs, embed_dims). key (torch.Tensor): The key tensor with shape `(num_key, bs, embed_dims)`. value (torch.Tensor): The value tensor with shape `(num_key, bs, embed_dims)`. identity (torch.Tensor): The tensor used for addition, with the same shape as `query`. Default None. If None, `query` will be used. query_pos (torch.Tensor): The positional encoding for `query`. Default: None. key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape [bs, num_key]. reference_points (torch.Tensor): The normalized reference points with shape (bs, num_query, num_levels, 2), all elements is range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area. or (N, Length_{query}, num_levels, 4), add additional two dimensions is (w, h) to form reference boxes. spatial_shapes (torch.Tensor): Spatial shape of features in different levels. With shape (num_levels, 2), last dimension represents (h, w). level_start_index (torch.Tensor): The start index of each level. A tensor has shape ``(num_levels, )`` and can be represented as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...]. Returns: torch.Tensor: forwarded results with shape [num_query, bs, embed_dims]. """ if value is None: value = query if identity is None: identity = query if query_pos is not None: query = query + query_pos if not self.batch_first: # change to (bs, num_query ,embed_dims) query = query.permute(1, 0, 2) value = value.permute(1, 0, 2) bs, num_query, _ = query.shape bs, num_value, _ = value.shape assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value value = self.value_proj(value) if key_padding_mask is not None: value = value.masked_fill(key_padding_mask[..., None], 0.0) value = value.view(bs, num_value, self.num_heads, -1) sampling_offsets = self.sampling_offsets(query).view( bs, num_query, self.num_heads, self.num_levels, self.num_points, 2) attention_weights = self.attention_weights(query).view( bs, num_query, self.num_heads, self.num_levels * self.num_points) attention_weights = attention_weights.softmax(-1) attention_weights = attention_weights.view(bs, num_query, self.num_heads, self.num_levels, self.num_points) if reference_points.shape[-1] == 2: offset_normalizer = torch.stack( [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) ### changed here sampling_locations = reference_points[:, :, None, :, :, :] \ + sampling_offsets \ / offset_normalizer[None, None, None, :, None, :] elif reference_points.shape[-1] == 4: sampling_locations = reference_points[:, :, None, :, None, :2] \ + sampling_offsets / self.num_points \ * reference_points[:, :, None, :, None, 2:] \ * 0.5 else: raise ValueError( f'Last dim of reference_points must be' f' 2 or 4, but get {reference_points.shape[-1]} instead.') if ((IS_CUDA_AVAILABLE and value.is_cuda) or (IS_MLU_AVAILABLE and value.is_mlu)): output = MultiScaleDeformableAttnFunction.apply( value, spatial_shapes, level_start_index, sampling_locations, attention_weights, self.im2col_step) else: output = multi_scale_deformable_attn_pytorch( value, spatial_shapes, sampling_locations, attention_weights) output = self.output_proj(output) if not self.batch_first: # (num_query, bs ,embed_dims) output = output.permute(1, 0, 2) return self.dropout(output) + identity ================================================ FILE: model/encoder/tpvformer/attention/image_cross_attention.py ================================================ from mmengine.model import BaseModule from mmengine.registry import MODELS from mmcv.cnn.bricks.transformer import build_attention @MODELS.register_module() class TPVCrossAttention(BaseModule): def __init__( self, embed_dims=256, num_cams=6, dropout=0.1, init_cfg=None, batch_first=True, num_heads=16, num_levels=4, num_points=[64, 64, 8]): super().__init__(init_cfg) deformable_attn_config_hw = dict( type='BEVCrossAttention', embed_dims=embed_dims, num_cams=num_cams, dropout=dropout, batch_first=batch_first, deformable_attention=dict( type='BEVDeformableAttention', embed_dims=embed_dims, num_heads=num_heads, num_levels=num_levels, num_points=num_points[2], dropout=dropout, batch_first=batch_first)) self.attn_hw = build_attention(deformable_attn_config_hw) deformable_attn_config_zh = dict( type='BEVCrossAttention', embed_dims=embed_dims, num_cams=num_cams, dropout=dropout, batch_first=batch_first, deformable_attention=dict( type='BEVDeformableAttention', embed_dims=embed_dims, num_heads=num_heads, num_levels=num_levels, num_points=num_points[1], dropout=dropout, batch_first=batch_first)) self.attn_zh = build_attention(deformable_attn_config_zh) deformable_attn_config_wz = dict( type='BEVCrossAttention', embed_dims=embed_dims, num_cams=num_cams, dropout=dropout, batch_first=batch_first, deformable_attention=dict( type='BEVDeformableAttention', embed_dims=embed_dims, num_heads=num_heads, num_levels=num_levels, num_points=num_points[0], dropout=dropout, batch_first=batch_first)) self.attn_wz = build_attention(deformable_attn_config_wz) self.attns = [self.attn_hw, self.attn_zh, self.attn_wz] def forward(self, query, key, value, residual=None, spatial_shapes=None, reference_points_cams=None, tpv_masks=None, level_start_index=None, **kwargs): result = [] for i in range(3): out = self.attns[i]( query[i], key, value, residual[i] if residual is not None else None, spatial_shapes=spatial_shapes, level_start_index=level_start_index, reference_points_cams=reference_points_cams[i], bev_masks=tpv_masks[i]) result.append(out) return result ================================================ FILE: model/encoder/tpvformer/modules/__init__.py ================================================ from .split_fpn import MultiPlaneFFN from .split_norm import MultiPlaneNorm from .camera_se_net import CameraAwareSE ================================================ FILE: model/encoder/tpvformer/modules/camera_se_net.py ================================================ import torch, torch.nn as nn import numpy as np class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, drop=0.0): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.drop1 = nn.Dropout(drop) self.fc2 = nn.Linear(hidden_features, out_features) self.drop2 = nn.Dropout(drop) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop1(x) x = self.fc2(x) x = self.drop2(x) return x # class SELayer(nn.Module): # def __init__(self, channels, act_layer=nn.ReLU, gate_layer=nn.Sigmoid): # super().__init__() # self.conv_reduce = nn.Conv2d(channels, channels, 1, bias=True) # self.act1 = act_layer() # self.conv_expand = nn.Conv2d(channels, channels, 1, bias=True) # self.gate = gate_layer() # def forward(self, x, x_se): # x_se = self.conv_reduce(x_se) # x_se = self.act1(x_se) # x_se = self.conv_expand(x_se) # return x * self.gate(x_se) class SELayer(nn.Module): def __init__(self, channels, act_layer=nn.ReLU, gate_layer=nn.Sigmoid): super().__init__() def forward(self, x, x_se): return x * x_se class CameraAwareSE(nn.Module): def __init__( self, in_channels=96, mid_channels=192, out_channles=96): super().__init__() self.in_channels = in_channels self.mid_channels = mid_channels self.out_channels = out_channles self._init_layers() def _init_layers(self): self.bn = nn.BatchNorm1d(16) self.context_mlp = Mlp(16, self.mid_channels, self.mid_channels) self.context_se = SELayer(self.mid_channels) # NOTE: add camera-aware self.context_conv = nn.Conv2d(self.mid_channels, self.out_channels, kernel_size=1, stride=1, padding=0) if self.in_channels == self.mid_channels: self.reduce_conv = nn.Identity() else: self.reduce_conv = nn.Sequential( nn.Conv2d(self.in_channels, self.mid_channels, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(self.mid_channels), nn.ReLU(inplace=True)) def init_weight(self): # nn.init.zeros_(self.context_se.conv_expand.weight) # nn.init.constant_(self.context_se.conv_expand.bias, 10.0) nn.init.zeros_(self.context_mlp.fc2.weight) nn.init.constant_(self.context_mlp.fc2.bias, 10.0) def forward(self, ms_img_feats, metas): intrins, sensor2ego = [], [] for meta in metas: intrins.append(meta['intrinsic']) sensor2ego.append(meta['cam2ego']) intrins = np.asarray(intrins) intrins = ms_img_feats[0].new_tensor(intrins) # bs, N, 4, 4 sensor2ego = np.asarray(sensor2ego) sensor2ego = ms_img_feats[0].new_tensor(sensor2ego)[..., :3, :] batch_size = intrins.shape[0] num_cams = intrins.shape[1] mlp_input = torch.cat( [ torch.stack( [ intrins[..., 0, 0], intrins[..., 1, 1], intrins[..., 0, 2], intrins[..., 1, 2], ], dim=-1, ), sensor2ego.view(batch_size, num_cams, -1), ], -1, ) # bs, N, 16 mlp_input = self.bn(mlp_input.reshape(-1, mlp_input.shape[-1])) context_se = self.context_mlp(mlp_input)[..., None, None] # bs*N, c, 1, 1 context_se = torch.sigmoid(context_se) outputs = [] for i_scale, img_feats in enumerate(ms_img_feats): img_feats = self.reduce_conv(img_feats.flatten(0, 1)) # bs*N, c, h, w img_feats = self.context_se(img_feats, context_se) img_feats = self.context_conv(img_feats) outputs.append(img_feats.unflatten(0, (batch_size, num_cams))) return outputs ================================================ FILE: model/encoder/tpvformer/modules/split_fpn.py ================================================ import torch, torch.nn as nn from mmengine.model import BaseModule from mmengine.registry import MODELS from mmcv.cnn.bricks.transformer import build_feedforward_network @MODELS.register_module() class MultiPlaneFFN(BaseModule): def __init__( self, embed_dims=256, feedforward_channels=1024, num_fcs=2, ffn_drop=0., act_cfg=dict(type='ReLU', inplace=True), init_cfg=None): super().__init__(init_cfg) self.ffn_cfg = dict( type='FFN', embed_dims=embed_dims, feedforward_channels=feedforward_channels, num_fcs=num_fcs, ffn_drop=ffn_drop, act_cfg=act_cfg ) self._init_layers() def _init_layers(self): self.ffns = nn.ModuleList() for i in range(3): self.ffns.append( build_feedforward_network(self.ffn_cfg)) def forward(self, tpv, identity=None): outputs = [] if identity is None: identity = [None] * 3 for i, plane in enumerate(tpv): outputs.append(self.ffns[i](plane, identity[i])) return outputs ================================================ FILE: model/encoder/tpvformer/modules/split_norm.py ================================================ from mmengine.registry import MODELS from mmengine.model import BaseModule from mmcv.cnn import build_norm_layer import torch.nn as nn @MODELS.register_module() class MultiPlaneNorm(BaseModule): def __init__( self, embed_dims=64, norm_cfg=dict(type='LN'), init_cfg=None, **kwargs): super().__init__(init_cfg) self.embed_dim = embed_dims self.norm_cfg = norm_cfg self._init_layers() def _init_layers(self): self.norms = nn.ModuleList() for i in range(3): self.norms.append( build_norm_layer(self.norm_cfg, self.embed_dim)[1]) def forward(self, tpv): outputs = [] for i, plane in enumerate(tpv): outputs.append(self.norms[i](plane)) return outputs ================================================ FILE: model/encoder/tpvformer/tpvformer_encoder.py ================================================ from mmseg.registry import MODELS from mmcv.cnn.bricks.transformer import build_positional_encoding, build_transformer_layer from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention from mmengine.model import ModuleList import torch.nn as nn, torch, copy from torch.nn.init import normal_ from mmengine.logging import MMLogger logger = MMLogger.get_instance('selfocc') from ..base_encoder import BaseEncoder from ..bevformer.utils import point_sampling from .utils import get_cross_view_ref_points from ..bevformer.mappings import GridMeterMapping from ..bevformer.attention import BEVCrossAttention, BEVDeformableAttention from .attention import TPVCrossAttention, CrossViewHybridAttention from .modules import CameraAwareSE @MODELS.register_module() class TPVFormerEncoder(BaseEncoder): def __init__( self, mapping_args: dict, # bev_inner=128, # bev_outer=32, # range_inner=51.2, # range_outer=51.2, # nonlinear_mode='linear_upscale', # z_inner=20, # z_outer=10, # z_ranges=[-5.0, 3.0, 11.0], embed_dims=128, num_cams=6, num_feature_levels=4, positional_encoding=None, num_points_cross=[64, 64, 8], num_points_self=[16, 16, 16], transformerlayers=None, num_layers=None, camera_aware=False, camera_aware_mid_channels=None, init_cfg=None): super().__init__(init_cfg) # self.bev_inner = bev_inner # self.bev_outer = bev_outer # self.range_inner = range_inner # self.range_outer = range_outer # assert nonlinear_mode == 'linear_upscale' # TODO # self.nonlinear_mode = nonlinear_mode # self.z_inner = z_inner # self.z_outer = z_outer # self.z_ranges = z_ranges self.embed_dims = embed_dims self.num_feature_levels = num_feature_levels self.num_cams = num_cams self.camera_aware = camera_aware if camera_aware: if camera_aware_mid_channels is None: camera_aware_mid_channels = embed_dims self.camera_se_net = CameraAwareSE( embed_dims, camera_aware_mid_channels, embed_dims) self.mapping = GridMeterMapping( # bev_inner, # bev_outer, # range_inner, # range_outer, # nonlinear_mode, # z_inner, # z_outer, # z_ranges **mapping_args) size_h = self.mapping.size_h size_w = self.mapping.size_w size_d = self.mapping.size_d hw_grid = torch.stack( [torch.arange(size_h, dtype=torch.float).unsqueeze(-1).expand(-1, size_w), torch.arange(size_w, dtype=torch.float).unsqueeze(0).expand(size_h, -1), torch.zeros(size_h, size_w)], dim=-1) hw_meter = self.mapping.grid2meter(hw_grid)[..., [0, 1]] zh_grid = torch.stack( [torch.arange(size_h, dtype=torch.float).unsqueeze(0).expand(size_d, -1), torch.zeros(size_d, size_h), torch.arange(size_d, dtype=torch.float).unsqueeze(-1).expand(-1, size_h)], dim=-1) zh_meter = self.mapping.grid2meter(zh_grid)[..., [1, 2]] wz_grid = torch.stack( [torch.zeros(size_w, size_d), torch.arange(size_w, dtype=torch.float).unsqueeze(-1).expand(-1, size_d), torch.arange(size_d, dtype=torch.float).unsqueeze(0).expand(size_w, -1)], dim=-1) wz_meter = self.mapping.grid2meter(wz_grid)[..., [0, 2]] positional_encoding.update({'tpv_meters': [hw_meter, zh_meter, wz_meter]}) self.positional_encoding = build_positional_encoding(positional_encoding) self.tpv_size = [size_h, size_w, size_d] # transformer layers if isinstance(transformerlayers, dict): transformerlayers = [ copy.deepcopy(transformerlayers) for _ in range(num_layers)] else: assert isinstance(transformerlayers, list) and \ len(transformerlayers) == num_layers self.num_layers = num_layers self.layers = ModuleList() for i in range(num_layers): self.layers.append(build_transformer_layer(transformerlayers[i])) self.pre_norm = self.layers[0].pre_norm logger.info('use pre_norm: ' + str(self.pre_norm)) # other learnable embeddings self.level_embeds = nn.Parameter( torch.randn(self.num_feature_levels, self.embed_dims)) self.cams_embeds = nn.Parameter( torch.randn(self.num_cams, self.embed_dims)) # prepare reference points used in image cross-attention and cross-view hybrid-attention self.num_points_cross = num_points_cross self.num_points_self = num_points_self uniform_d = torch.linspace(0, size_d - 1, num_points_cross[2]) hw_3d_grid = torch.cat([ hw_grid[..., [0, 1]].unsqueeze(2).expand(-1, -1, num_points_cross[2], -1), uniform_d.reshape(1, 1, -1, 1).expand(size_h, size_w, -1, -1)], dim=-1) ref_3d_hw = self.mapping.grid2meter(hw_3d_grid) # H, W, P0, 3 uniform_w = torch.linspace(0, size_w - 1, num_points_cross[1]) zh_3d_grid = torch.cat([ zh_grid[..., :1].unsqueeze(2).expand(-1, -1, num_points_cross[1], -1), uniform_w.reshape(1, 1, -1, 1).expand(size_d, size_h, -1, -1), zh_grid[..., 2:].unsqueeze(2).expand(-1, -1, num_points_cross[1], -1) ], dim=-1) ref_3d_zh = self.mapping.grid2meter(zh_3d_grid) # Z, H, P1, 3 uniform_h = torch.linspace(0, size_h - 1, num_points_cross[0]) wz_3d_grid = torch.cat([ uniform_h.reshape(1, 1, -1, 1).expand(size_w, size_d, -1, -1), wz_grid[..., [1, 2]].unsqueeze(2).expand(-1, -1, num_points_cross[0], -1) ], dim=-1) ref_3d_wz = self.mapping.grid2meter(wz_3d_grid) # W, Z, P2, 3 self.register_buffer('ref_3d_hw', ref_3d_hw.flatten(0, 1).transpose(0, 1), False) self.register_buffer('ref_3d_zh', ref_3d_zh.flatten(0, 1).transpose(0, 1), False) self.register_buffer('ref_3d_wz', ref_3d_wz.flatten(0, 1).transpose(0, 1), False) cross_view_ref_points = get_cross_view_ref_points(size_h, size_w, size_d, num_points_self) self.register_buffer('cross_view_ref_points', cross_view_ref_points, False) # hw_grid_normed = hw_grid[..., [0, 1]].clone() # hw_grid_normed[..., 0] = hw_grid_normed[..., 0] / (size_h - 1) # hw_grid_normed[..., 1] = hw_grid_normed[..., 1] / (size_w - 1) # zh_grid_normed = zh_grid[..., [2, 0]].clone() # zh_grid_normed[..., 0] = zh_grid_normed[..., 0] / (size_d - 1) # zh_grid_normed[..., 1] = zh_grid_normed[..., 1] / (size_h - 1) # wz_grid_normed = wz_grid[..., [1, 2]].clone() # wz_grid_normed[..., 0] = wz_grid_normed[..., 0] / (size_w - 1) # wz_grid_normed[..., 1] = wz_grid_normed[..., 1] / (size_d - 1) # self.register_buffer('ref_2d_hw', hw_grid_normed, False) # H, W, 2 # self.register_buffer('ref_2d_zh', zh_grid_normed, False) # H, W, 2 # self.register_buffer('ref_2d_wz', wz_grid_normed, False) # H, W, 2 def init_weights(self): """Initialize the transformer weights.""" for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) for m in self.modules(): if isinstance(m, BEVCrossAttention) or \ isinstance(m, MultiScaleDeformableAttention) or \ isinstance(m, BEVDeformableAttention) or \ isinstance(m, TPVCrossAttention) or \ isinstance(m, CrossViewHybridAttention): try: m.init_weight() except AttributeError: m.init_weights() normal_(self.level_embeds) normal_(self.cams_embeds) def forward_layers( self, tpv_query, # b, c, h, w key, value, tpv_pos=None, # b, h, w, c spatial_shapes=None, level_start_index=None, img_metas=None, **kwargs ): bs = tpv_query[0].shape[0] reference_points_cams, tpv_masks = [], [] for ref_3d in [self.ref_3d_hw, self.ref_3d_zh, self.ref_3d_wz]: reference_points_cam, tpv_mask = point_sampling( ref_3d.unsqueeze(0).repeat(bs, 1, 1, 1), img_metas) reference_points_cams.append(reference_points_cam) # num_cam, bs, hw++, #p, 2 tpv_masks.append(tpv_mask) # ref_2d = self.ref_2d.unsqueeze(0).repeat(bs, 1, 1, 1) # bs, H, W, 2 # ref_2d = ref_2d.reshape(bs, -1, 1, 2) # bs, HW, 1, 2 ref_cross_view = self.cross_view_ref_points.clone().unsqueeze( 0).expand(bs, -1, -1, -1, -1) # bs, hw++, 3, #p, 2 for lid, layer in enumerate(self.layers): output = layer( tpv_query, key, value, tpv_pos=tpv_pos, ref_2d=ref_cross_view, spatial_shapes=spatial_shapes, level_start_index=level_start_index, reference_points_cams=reference_points_cams, tpv_masks=tpv_masks, tpv_size=self.tpv_size, **kwargs) tpv_query = output return tpv_query def forward( self, representation, ms_img_feats=None, metas=None, **kwargs ): """Forward function. Args: img_feats (tuple[Tensor]): Features from the upstream network, each is a 5D-tensor with shape (B, N, C, H, W). """ img_feats = ms_img_feats bs = img_feats[0].shape[0] dtype = img_feats[0].dtype device = img_feats[0].device # bev queries and pos embeds tpv_queries = representation # bs, HW, C tpv_pos = self.positional_encoding() tpv_pos = [pos.unsqueeze(0).repeat(bs, 1, 1) for pos in tpv_pos] # add camera awareness if required if self.camera_aware: img_feats = self.camera_se_net(img_feats, metas) # flatten image features of different scales feat_flatten = [] spatial_shapes = [] for lvl, feat in enumerate(img_feats): bs, num_cam, c, h, w = feat.shape spatial_shape = (h, w) feat = feat.flatten(3).permute(1, 0, 3, 2) # num_cam, bs, hw, c feat = feat + self.cams_embeds[:, None, None, :]#.to(dtype) feat = feat + self.level_embeds[None, None, lvl:lvl+1, :]#.to(dtype) spatial_shapes.append(spatial_shape) feat_flatten.append(feat) feat_flatten = torch.cat(feat_flatten, 2) # num_cam, bs, hw++, c spatial_shapes = torch.as_tensor( spatial_shapes, dtype=torch.long, device=device) level_start_index = torch.cat((spatial_shapes.new_zeros( (1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) feat_flatten = feat_flatten.permute(0, 2, 1, 3) # (num_cam, H*W, bs, embed_dims) # forward layers tpv_embed = self.forward_layers( tpv_queries, feat_flatten, feat_flatten, tpv_pos=tpv_pos, spatial_shapes=spatial_shapes, level_start_index=level_start_index, img_metas=metas, ) return {'representation': tpv_embed} ================================================ FILE: model/encoder/tpvformer/tpvformer_encoder_layer.py ================================================ import copy, torch from mmengine import ConfigDict from mmengine.model import BaseModule, ModuleList from mmengine.registry import MODELS from mmcv.cnn import build_norm_layer from mmcv.cnn.bricks.transformer import build_feedforward_network, build_attention @MODELS.register_module() class TPVFormerLayer(BaseModule): """Base `TransformerLayer` for vision transformer. It can be built from `mmcv.ConfigDict` and support more flexible customization, for example, using any number of `FFN or LN ` and use different kinds of `attention` by specifying a list of `ConfigDict` named `attn_cfgs`. It is worth mentioning that it supports `prenorm` when you specifying `norm` as the first element of `operation_order`. More details about the `prenorm`: `On Layer Normalization in the Transformer Architecture `_ . Args: attn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )): Configs for `self_attention` or `cross_attention` modules, The order of the configs in the list should be consistent with corresponding attentions in operation_order. If it is a dict, all of the attention modules in operation_order will be built with this config. Default: None. ffn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )): Configs for FFN, The order of the configs in the list should be consistent with corresponding ffn in operation_order. If it is a dict, all of the attention modules in operation_order will be built with this config. operation_order (tuple[str]): The execution order of operation in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). Support `prenorm` when you specifying first element as `norm`. Default：None. norm_cfg (dict): Config dict for normalization layer. Default: dict(type='LN'). init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. Default: None. batch_first (bool): Key, Query and Value are shape of (batch, n, embed_dim) or (n, batch, embed_dim). Default to False. """ def __init__(self, attn_cfgs=None, ffn_cfgs=dict( type='FFN', feedforward_channels=1024, num_fcs=2, ffn_drop=0., act_cfg=dict(type='ReLU', inplace=True)), operation_order=None, norm_cfg=dict(type='LN'), init_cfg=None, batch_first=True, multi_plane_ffn_norm=False, **kwargs): deprecated_args = dict( feedforward_channels='feedforward_channels', ffn_dropout='ffn_drop', ffn_num_fcs='num_fcs') for ori_name, new_name in deprecated_args.items(): if ori_name in kwargs: ffn_cfgs[new_name] = kwargs[ori_name] super().__init__(init_cfg) self.batch_first = batch_first self.multi_plane_ffn_norm = multi_plane_ffn_norm num_attn = operation_order.count('self_attn') + operation_order.count('cross_attn') if isinstance(attn_cfgs, dict): attn_cfgs = [copy.deepcopy(attn_cfgs) for _ in range(num_attn)] else: assert num_attn == len(attn_cfgs), f'The length ' \ f'of attn_cfg {num_attn} is ' \ f'not consistent with the number of attention' \ f'in operation_order {operation_order}.' self.num_attn = num_attn self.operation_order = operation_order self.norm_cfg = norm_cfg self.pre_norm = operation_order[0] == 'norm' self.attentions = ModuleList() index = 0 for operation_name in operation_order: if operation_name in ['self_attn', 'cross_attn']: if 'batch_first' in attn_cfgs[index]: assert self.batch_first == attn_cfgs[index]['batch_first'] else: attn_cfgs[index]['batch_first'] = self.batch_first attention = build_attention(attn_cfgs[index]) # Some custom attentions used as `self_attn` # or `cross_attn` can have different behavior. attention.operation_name = operation_name self.attentions.append(attention) index += 1 self.embed_dims = self.attentions[0].embed_dims self.ffns = ModuleList() num_ffns = operation_order.count('ffn') if isinstance(ffn_cfgs, dict): ffn_cfgs = ConfigDict(ffn_cfgs) if isinstance(ffn_cfgs, dict): ffn_cfgs = [copy.deepcopy(ffn_cfgs) for _ in range(num_ffns)] assert len(ffn_cfgs) == num_ffns for ffn_index in range(num_ffns): if 'embed_dims' not in ffn_cfgs[ffn_index]: ffn_cfgs[ffn_index]['embed_dims'] = self.embed_dims else: assert ffn_cfgs[ffn_index]['embed_dims'] == self.embed_dims self.ffns.append(build_feedforward_network(ffn_cfgs[ffn_index])) self.norms = ModuleList() num_norms = operation_order.count('norm') for _ in range(num_norms): self.norms.append(build_norm_layer(norm_cfg, self.embed_dims)[1]) def forward(self, query, key=None, value=None, tpv_pos=None, ref_2d=None, spatial_shapes=None, level_start_index=None, reference_points_cams=None, tpv_masks=None, tpv_size=None, **kwargs): """ **kwargs contains some specific arguments of attentions. Args: query (Tensor): The input query with shape [num_queries, bs, embed_dims] if self.batch_first is False, else [bs, num_queries embed_dims]. key (Tensor): The key tensor with shape [num_keys, bs, embed_dims] if self.batch_first is False, else [bs, num_keys, embed_dims] . value (Tensor): The value tensor with same shape as `key`. Returns: Tensor: forwarded results with shape [num_queries, bs, embed_dims]. """ norm_index = 0 attn_index = 0 ffn_index = 0 identity = query device = query[0].device for layer in self.operation_order: # cross view hybrid attention if layer == 'self_attn': ss = torch.tensor([ [tpv_size[0], tpv_size[1]], [tpv_size[2], tpv_size[0]], [tpv_size[1], tpv_size[2]]], device=device) lsi = torch.tensor([ 0, tpv_size[0]*tpv_size[1], tpv_size[0]*tpv_size[1]+tpv_size[2]*tpv_size[0]], device=device) query_ = torch.cat(query, dim=1) query_ = self.attentions[attn_index]( query_, query_, query_, torch.cat(identity, dim=1) if self.pre_norm else None, query_pos=torch.cat(tpv_pos, dim=1), reference_points=ref_2d, spatial_shapes=ss, level_start_index=lsi, **kwargs) query = torch.split(query_, [ tpv_size[0]*tpv_size[1], tpv_size[2]*tpv_size[0], tpv_size[1]*tpv_size[2]], 1) attn_index += 1 identity = query elif layer == 'norm': query_ = query if self.multi_plane_ffn_norm else torch.cat(query, dim=1) query_ = self.norms[norm_index](query_) query = query_ if self.multi_plane_ffn_norm else torch.split(query_, [ tpv_size[0]*tpv_size[1], tpv_size[2]*tpv_size[0], tpv_size[1]*tpv_size[2]], 1) norm_index += 1 # image cross attention elif layer == 'cross_attn': # if isinstance(query, (list, tuple)): # query = torch.cat(query, dim=1) query = self.attentions[attn_index]( query, # query.float(), key, # key.float(), value, # value.float(), # identity.float() if self.pre_norm else None, identity if self.pre_norm else None, spatial_shapes=spatial_shapes, level_start_index=level_start_index, # reference_points_cams=reference_points_cams.float(), reference_points_cams=reference_points_cams, tpv_masks=tpv_masks, **kwargs) attn_index += 1 identity = query elif layer == 'ffn': query_ = query if self.multi_plane_ffn_norm else torch.cat(query, dim=1) identity_ = identity if self.multi_plane_ffn_norm else torch.cat(identity, dim=1) ffn = self.ffns[ffn_index] query_ = ffn(query_, identity_ if self.pre_norm else None) query = query_ if self.multi_plane_ffn_norm else torch.split(query_, [ tpv_size[0]*tpv_size[1], tpv_size[2]*tpv_size[0], tpv_size[1]*tpv_size[2]], 1) ffn_index += 1 return query ================================================ FILE: model/encoder/tpvformer/tpvformer_pos_embed.py ================================================ import torch, torch.nn as nn from mmengine.model import BaseModule from mmengine.registry import MODELS def get_feat_from_meter(num_freqs, meter): freqs = torch.arange(-1, num_freqs - 1, dtype=torch.float) freqs = torch.pi * (2 ** freqs) meter_freq = meter.unsqueeze(-1) * freqs[None, None, None, ...] freq_feat = torch.stack([ torch.sin(meter_freq), torch.cos(meter_freq)], dim=-1) freq_feat = freq_feat.flatten(-3).flatten(0, 1) return freq_feat @MODELS.register_module() class TPVPositionalEncoding(BaseModule): def __init__( self, num_freqs, embed_dims, tpv_meters, tot_range, init_cfg=None): super().__init__(init_cfg) assert isinstance(tot_range, list) and len(tot_range) == 6 pc_range = tot_range hw_meter, zh_meter, wz_meter = tpv_meters hw_meter = hw_meter.clone() hw_meter[..., 0] = (hw_meter[..., 0] - pc_range[0]) / (pc_range[3] - pc_range[0]) hw_meter[..., 1] = (hw_meter[..., 1] - pc_range[1]) / (pc_range[4] - pc_range[1]) hw_freq_feat = get_feat_from_meter(num_freqs[0], hw_meter) zh_meter = zh_meter.clone() zh_meter[..., 0] = (zh_meter[..., 0] - pc_range[1]) / (pc_range[4] - pc_range[1]) zh_meter[..., 1] = (zh_meter[..., 1] - pc_range[2]) / (pc_range[5] - pc_range[2]) zh_freq_feat = get_feat_from_meter(num_freqs[1], zh_meter) wz_meter = wz_meter.clone() wz_meter[..., 0] = (wz_meter[..., 0] - pc_range[0]) / (pc_range[3] - pc_range[0]) wz_meter[..., 1] = (wz_meter[..., 1] - pc_range[2]) / (pc_range[5] - pc_range[2]) wz_freq_feat = get_feat_from_meter(num_freqs[2], wz_meter) self.register_buffer('hw_freq_feat', hw_freq_feat, False) self.register_buffer('zh_freq_feat', zh_freq_feat, False) self.register_buffer('wz_freq_feat', wz_freq_feat, False) self.position_layer_hw = nn.Linear(4 * num_freqs[0], embed_dims) self.position_layer_zh = nn.Linear(4 * num_freqs[1], embed_dims) self.position_layer_wz = nn.Linear(4 * num_freqs[2], embed_dims) def forward(self): pos_hw = self.position_layer_hw(self.hw_freq_feat) pos_zh = self.position_layer_zh(self.zh_freq_feat) pos_wz = self.position_layer_wz(self.wz_freq_feat) return [pos_hw, pos_zh, pos_wz] # H, W, C if __name__ == "__main__": import os import sys; sys.path.insert(0, os.path.abspath(os.path.join(os.path.abspath(__file__), os.pardir))) print(sys.path) from ..bevformer.mappings import GridMeterMapping from mmcv.cnn.bricks.transformer import build_positional_encoding bev_inner = 2 bev_outer = 2 m = GridMeterMapping( bev_inner=2, bev_outer=2, range_inner=2, range_outer=4, z_inner=2, z_outer=2, z_ranges=[-1., 1., 5.]) bev_size = 2 * (bev_inner + bev_outer) + 1 bev_grid = torch.stack( [torch.arange(bev_size, dtype=torch.float).unsqueeze(-1).expand(-1, bev_size), torch.arange(bev_size, dtype=torch.float).unsqueeze(0).expand(bev_size, -1)], dim=-1) bev_meter = m.grid2meter(bev_grid) positional_encoding = dict( type='BEVPositionalEncoding', num_freqs=3, embed_dims=32, tot_range=6 ) positional_encoding.update({'bev_meter': bev_meter}) # positional encoding positional_encoding = build_positional_encoding(positional_encoding) pass ================================================ FILE: model/encoder/tpvformer/utils.py ================================================ import torch import numpy as np def get_cross_view_ref_points(tpv_h, tpv_w, tpv_z, num_points_in_pillar, offset=0): # ref points generating target: (#query)hw+zh+wz, (#level)3, #p, 2 # generate points for hw and level 1 h_ranges = torch.linspace(offset, tpv_h-1+offset, tpv_h) / tpv_h w_ranges = torch.linspace(offset, tpv_w-1+offset, tpv_w) / tpv_w h_ranges = h_ranges.unsqueeze(-1).expand(-1, tpv_w).flatten() w_ranges = w_ranges.unsqueeze(0).expand(tpv_h, -1).flatten() hw_hw = torch.stack([w_ranges, h_ranges], dim=-1) # hw, 2 hw_hw = hw_hw.unsqueeze(1).expand(-1, num_points_in_pillar[2], -1) # hw, #p, 2 # generate points for hw and level 2 z_ranges = torch.linspace(offset, tpv_z-1+offset, num_points_in_pillar[2]) / tpv_z # #p z_ranges = z_ranges.unsqueeze(0).expand(tpv_h*tpv_w, -1) # hw, #p h_ranges = torch.linspace(offset, tpv_h-1+offset, tpv_h) / tpv_h h_ranges = h_ranges.reshape(-1, 1, 1).expand(-1, tpv_w, num_points_in_pillar[2]).flatten(0, 1) hw_zh = torch.stack([h_ranges, z_ranges], dim=-1) # hw, #p, 2 # generate points for hw and level 3 z_ranges = torch.linspace(offset, tpv_z-1+offset, num_points_in_pillar[2]) / tpv_z # #p z_ranges = z_ranges.unsqueeze(0).expand(tpv_h*tpv_w, -1) # hw, #p w_ranges = torch.linspace(offset, tpv_w-1+offset, tpv_w) / tpv_w w_ranges = w_ranges.reshape(1, -1, 1).expand(tpv_h, -1, num_points_in_pillar[2]).flatten(0, 1) hw_wz = torch.stack([z_ranges, w_ranges], dim=-1) # hw, #p, 2 # generate points for zh and level 1 w_ranges = torch.linspace(offset, tpv_w-1+offset, num_points_in_pillar[1]) / tpv_w w_ranges = w_ranges.unsqueeze(0).expand(tpv_z*tpv_h, -1) h_ranges = torch.linspace(offset, tpv_h-1+offset, tpv_h) / tpv_h h_ranges = h_ranges.reshape(1, -1, 1).expand(tpv_z, -1, num_points_in_pillar[1]).flatten(0, 1) zh_hw = torch.stack([w_ranges, h_ranges], dim=-1) # generate points for zh and level 2 z_ranges = torch.linspace(offset, tpv_z-1+offset, tpv_z) / tpv_z z_ranges = z_ranges.reshape(-1, 1, 1).expand(-1, tpv_h, num_points_in_pillar[1]).flatten(0, 1) h_ranges = torch.linspace(offset, tpv_h-1+offset, tpv_h) / tpv_h h_ranges = h_ranges.reshape(1, -1, 1).expand(tpv_z, -1, num_points_in_pillar[1]).flatten(0, 1) zh_zh = torch.stack([h_ranges, z_ranges], dim=-1) # zh, #p, 2 # generate points for zh and level 3 w_ranges = torch.linspace(offset, tpv_w-1+offset, num_points_in_pillar[1]) / tpv_w w_ranges = w_ranges.unsqueeze(0).expand(tpv_z*tpv_h, -1) z_ranges = torch.linspace(offset, tpv_z-1+offset, tpv_z) / tpv_z z_ranges = z_ranges.reshape(-1, 1, 1).expand(-1, tpv_h, num_points_in_pillar[1]).flatten(0, 1) zh_wz = torch.stack([z_ranges, w_ranges], dim=-1) # generate points for wz and level 1 h_ranges = torch.linspace(offset, tpv_h-1+offset, num_points_in_pillar[0]) / tpv_h h_ranges = h_ranges.unsqueeze(0).expand(tpv_w*tpv_z, -1) w_ranges = torch.linspace(offset, tpv_w-1+offset, tpv_w) / tpv_w w_ranges = w_ranges.reshape(-1, 1, 1).expand(-1, tpv_z, num_points_in_pillar[0]).flatten(0, 1) wz_hw = torch.stack([w_ranges, h_ranges], dim=-1) # generate points for wz and level 2 h_ranges = torch.linspace(offset, tpv_h-1+offset, num_points_in_pillar[0]) / tpv_h h_ranges = h_ranges.unsqueeze(0).expand(tpv_w*tpv_z, -1) z_ranges = torch.linspace(offset, tpv_z-1+offset, tpv_z) / tpv_z z_ranges = z_ranges.reshape(1, -1, 1).expand(tpv_w, -1, num_points_in_pillar[0]).flatten(0, 1) wz_zh = torch.stack([h_ranges, z_ranges], dim=-1) # generate points for wz and level 3 w_ranges = torch.linspace(offset, tpv_w-1+offset, tpv_w) / tpv_w w_ranges = w_ranges.reshape(-1, 1, 1).expand(-1, tpv_z, num_points_in_pillar[0]).flatten(0, 1) z_ranges = torch.linspace(offset, tpv_z-1+offset, tpv_z) / tpv_z z_ranges = z_ranges.reshape(1, -1, 1).expand(tpv_w, -1, num_points_in_pillar[0]).flatten(0, 1) wz_wz = torch.stack([z_ranges, w_ranges], dim=-1) reference_points = torch.cat([ torch.stack([hw_hw, hw_zh, hw_wz], dim=1), torch.stack([zh_hw, zh_zh, zh_wz], dim=1), torch.stack([wz_hw, wz_zh, wz_wz], dim=1) ], dim=0) # hw+zh+wz, 3, #p, 2 return reference_points ================================================ FILE: model/head/__init__.py ================================================ import nerfacc from .neus_head.neus_head import NeuSHead ================================================ FILE: model/head/base_head.py ================================================ from mmengine.model import BaseModule from mmseg.models import HEADS @HEADS.register_module() class BaseTaskHead(BaseModule): """Segmentation heads. image backbone -> neck -> lifter -> encoder -> segmentor Predicts semantic labels for voxels (and points for lidar segmentation). """ def __init__(self, init_cfg=None, **kwargs): super().__init__(init_cfg) def forward( self, representation, points=None, **kwargs ): pass ================================================ FILE: model/head/nerfacc_head/bev_nerf.py ================================================ import torch, torch.nn as nn from mmengine.model import BaseModule from ...encoder.bevformer.mappings import GridMeterMapping import torch.nn.functional as F from ..utils.sh_render import SHRender class BEVNeRF(BaseModule): def __init__( self, mapping_args: dict, # bev_inner=128, # bev_outer=32, # range_inner=51.2, # range_outer=51.2, # nonlinear_mode='linear_upscale', # z_inner=20, # z_outer=10, # z_ranges=[-5.0, 3.0, 11.0], # mlp decoder embed_dims=128, color_dims=0, sem_dims=0, density_layers=2, sh_deg=2, sh_act='relu', tpv=False, init_cfg=None): super().__init__(init_cfg) self.mapping = GridMeterMapping( # bev_inner, # bev_outer, # range_inner, # range_outer, # nonlinear_mode, # z_inner, # z_outer, # z_ranges **mapping_args) self.embed_dims = embed_dims self.color_dims = color_dims self.sem_dims = sem_dims self.z_size = self.mapping.size_d self.h_size = self.mapping.size_h self.w_size = self.mapping.size_w self.bev_size = [self.mapping.size_h, self.mapping.size_w] self.density_layers = density_layers self.tpv = tpv self._init_layers() self.color_converter = SHRender self.sh_deg = sh_deg self.sh_act = sh_act self.density_color = None def _init_layers(self): density_net = [] for i in range(self.density_layers - 1): density_net.extend([nn.Softplus(), nn.Linear(self.embed_dims, self.embed_dims)]) if not self.tpv: density_net.extend([nn.Softplus(), nn.Linear(self.embed_dims, (1 + self.color_dims+ self.sem_dims) * self.z_size)]) else: density_net.extend([nn.Softplus(), nn.Linear(self.embed_dims, 1 + self.color_dims + self.sem_dims)]) density_net = nn.Sequential(*density_net) self.density_net = density_net @torch.cuda.amp.autocast(enabled=False) def pre_compute_density_color(self, bev): if not self.tpv: assert bev.dim() == 3 # bev = bev.unflatten(1, (self.bev_size, self.bev_size)) bev = bev.unflatten(1, self.bev_size) density_color = self.density_net(bev).reshape(*bev.shape[:-1], self.z_size, -1) density_color = density_color.permute(0, 4, 1, 2, 3) # bs, C, h, w, d else: tpv_hw, tpv_zh, tpv_wz = bev tpv_hw = tpv_hw.reshape(-1, self.h_size, self.w_size, 1, self.embed_dims) tpv_hw = tpv_hw.expand(-1, -1, -1, self.z_size, -1) tpv_zh = tpv_zh.reshape(-1, self.z_size, self.h_size, 1, self.embed_dims).permute(0, 2, 3, 1, 4) tpv_zh = tpv_zh.expand(-1, -1, self.w_size, -1, -1) tpv_wz = tpv_wz.reshape(-1, self.w_size, self.z_size, 1, self.embed_dims).permute(0, 3, 1, 2, 4) tpv_wz = tpv_wz.expand(-1, self.h_size, -1, -1, -1) tpv = tpv_hw + tpv_zh + tpv_wz density_color = self.density_net(tpv).permute(0, 4, 1, 2, 3) self.density_color = density_color # print(f'type of self.density_color: {self.density_color.dtype}') @torch.cuda.amp.autocast(enabled=False) def query_density(self, x): if self.density_color.dtype == torch.float16: x = x.half() grid = self.mapping.meter2grid(x, True) # grid[..., :2] = grid[..., :2] / (self.bev_size - 1) # grid[..., 2:] = grid[..., 2:] / (self.z_size - 1) grid = 2 * grid - 1 grid = grid.reshape(1, -1, 1, 1, 3) density_color = F.grid_sample( self.density_color, grid[..., [2, 1, 0]], mode='bilinear', align_corners=True) # bs, c, n, 1, 1 density_color = density_color.permute(0, 2, 3, 4, 1).flatten(0, 3) # bs*n, c sigma = density_color[:, :1] return F.softplus(sigma) # F.relu(sigma) @torch.cuda.amp.autocast(enabled=False) def forward(self, x, condition=None): if self.density_color.dtype == torch.float16: x = x.half() condition = condition.half() if condition is not None else None grid = self.mapping.meter2grid(x, True) # grid[..., :2] = grid[..., :2] / (self.bev_size - 1) # grid[..., 2:] = grid[..., 2:] / (self.z_size - 1) grid = 2 * grid - 1 grid = grid.reshape(1, -1, 1, 1, 3) density_color = F.grid_sample( self.density_color, grid[..., [2, 1, 0]], mode='bilinear', align_corners=True) # bs, c, n, 1, 1 density_color = density_color.permute(0, 2, 3, 4, 1).flatten(0, 3) # bs*n, c sigma, sample_colors, sample_sems = density_color[:, :1], \ density_color[:, 1:(1+self.color_dims)], density_color[:, (1+self.color_dims):] if self.color_dims > 0: sample_colors = self.color_converter( None, condition, sample_colors, self.sh_deg, self.sh_act) rgb = sample_colors.reshape(-1, 3) else: rgb = torch.empty((sigma.shape[0], 0), device=sigma.device, dtype=sigma.dtype) if self.sem_dims > 0: sems = torch.softmax(sample_sems, dim=-1) else: sems = torch.empty((sigma.shape[0], 0), device=sigma.device, dtype=sigma.dtype) return rgb, F.softplus(sigma), sems # F.relu(sigma) @torch.cuda.amp.autocast(enabled=False) def forward_geo(self, x): if self.density_color.dtype == torch.float16: x = x.half() grid = self.mapping.meter2grid(x, True) grid = 2 * grid - 1 grid = grid.reshape(1, -1, 1, 1, 3) density_color = F.grid_sample( self.density_color, grid[..., [2, 1, 0]], mode='bilinear', align_corners=True) # bs, c, n, 1, 1 density_color = density_color.permute(0, 2, 3, 4, 1).flatten(0, 3) # bs*n, c sigma, sample_sems = density_color[:, :1], density_color[:, (1+self.color_dims):] if self.sem_dims > 0: sems = torch.softmax(sample_sems, dim=-1) else: sems = torch.empty((sigma.shape[0], 0), device=sigma.device, dtype=sigma.dtype) return F.softplus(sigma), sems # F.relu(sigma) ================================================ FILE: model/head/nerfacc_head/estimator.py ================================================ from nerfacc import OccGridEstimator import torch from torch import Tensor from typing import Callable, Optional, Tuple from nerfacc.grid import traverse_grids class CustomOccGridEstimator(OccGridEstimator): @torch.no_grad() def sampling( self, # rays rays_o: Tensor, # [n_rays, 3] rays_d: Tensor, # [n_rays, 3] # sigma/alpha function for skipping invisible space sigma_fn: Optional[Callable] = None, alpha_fn: Optional[Callable] = None, near_plane: float = 0.0, far_plane: float = 1e10, t_min: Optional[Tensor] = None, # [n_rays] t_max: Optional[Tensor] = None, # [n_rays] # rendering options render_step_size: float = 1e-3, early_stop_eps: float = 1e-4, alpha_thre: float = 0.0, stratified: bool = False, cone_angle: float = 0.0, ) -> Tuple[Tensor, Tensor, Tensor]: """Sampling with spatial skipping. Note: This function is not differentiable to any inputs. Args: rays_o: Ray origins of shape (n_rays, 3). rays_d: Normalized ray directions of shape (n_rays, 3). sigma_fn: Optional. If provided, the marching will skip the invisible space by evaluating the density along the ray with `sigma_fn`. It should be a function that takes in samples {t_starts (N,), t_ends (N,), ray indices (N,)} and returns the post-activation density values (N,). You should only provide either `sigma_fn` or `alpha_fn`. alpha_fn: Optional. If provided, the marching will skip the invisible space by evaluating the density along the ray with `alpha_fn`. It should be a function that takes in samples {t_starts (N,), t_ends (N,), ray indices (N,)} and returns the post-activation opacity values (N,). You should only provide either `sigma_fn` or `alpha_fn`. near_plane: Optional. Near plane distance. Default: 0.0. far_plane: Optional. Far plane distance. Default: 1e10. t_min: Optional. Per-ray minimum distance. Tensor with shape (n_rays). If profided, the marching will start from maximum of t_min and near_plane. t_max: Optional. Per-ray maximum distance. Tensor with shape (n_rays). If profided, the marching will stop by minimum of t_max and far_plane. render_step_size: Step size for marching. Default: 1e-3. early_stop_eps: Early stop threshold for skipping invisible space. Default: 1e-4. alpha_thre: Alpha threshold for skipping empty space. Default: 0.0. stratified: Whether to use stratified sampling. Default: False. cone_angle: Cone angle for linearly-increased step size. 0. means constant step size. Default: 0.0. Returns: A tuple of {LongTensor, Tensor, Tensor}: - **ray_indices**: Ray index of each sample. IntTensor with shape (n_samples). - **t_starts**: Per-sample start distance. Tensor with shape (n_samples,). - **t_ends**: Per-sample end distance. Tensor with shape (n_samples,). Examples: .. code-block:: python >>> ray_indices, t_starts, t_ends = grid.sampling( >>> rays_o, rays_d, render_step_size=1e-3) >>> t_mid = (t_starts + t_ends) / 2.0 >>> sample_locs = rays_o[ray_indices] + t_mid * rays_d[ray_indices] """ near_planes = torch.full_like(rays_o[..., 0], fill_value=near_plane) far_planes = torch.full_like(rays_o[..., 0], fill_value=far_plane) if t_min is not None: near_planes = torch.clamp(near_planes, min=t_min) if t_max is not None: far_planes = torch.clamp(far_planes, max=t_max) if stratified: near_planes += torch.rand_like(near_planes) * render_step_size intervals, samples, _ = traverse_grids( rays_o, rays_d, self.binaries, self.aabbs, near_planes=near_planes, far_planes=far_planes, step_size=render_step_size, cone_angle=cone_angle, ) t_starts = intervals.vals[intervals.is_left] t_ends = intervals.vals[intervals.is_right] ray_indices = samples.ray_indices packed_info = samples.packed_info # # skip invisible space # if (alpha_thre > 0.0 or early_stop_eps > 0.0) and ( # sigma_fn is not None or alpha_fn is not None # ): # alpha_thre = min(alpha_thre, self.occs.mean().item()) # # Compute visibility of the samples, and filter out invisible samples # if sigma_fn is not None: # if t_starts.shape[0] != 0: # sigmas = sigma_fn(t_starts, t_ends, ray_indices) # else: # sigmas = torch.empty((0,), device=t_starts.device) # assert ( # sigmas.shape == t_starts.shape # ), "sigmas must have shape of (N,)! Got {}".format(sigmas.shape) # masks = render_visibility_from_density( # t_starts=t_starts, # t_ends=t_ends, # sigmas=sigmas, # packed_info=packed_info, # early_stop_eps=early_stop_eps, # alpha_thre=alpha_thre, # ) # elif alpha_fn is not None: # if t_starts.shape[0] != 0: # alphas = alpha_fn(t_starts, t_ends, ray_indices) # else: # alphas = torch.empty((0,), device=t_starts.device) # assert ( # alphas.shape == t_starts.shape # ), "alphas must have shape of (N,)! Got {}".format(alphas.shape) # masks = render_visibility_from_alpha( # alphas=alphas, # packed_info=packed_info, # early_stop_eps=early_stop_eps, # alpha_thre=alpha_thre, # ) # ray_indices, t_starts, t_ends = ( # ray_indices[masks], # t_starts[masks], # t_ends[masks], # ) return ray_indices, t_starts, t_ends ================================================ FILE: model/head/nerfacc_head/img2lidar.py ================================================ import torch, torch.nn as nn import numpy as np, os from dataset.utils import get_rm class Img2LiDAR(nn.Module): def __init__( self, trans_kw, trans_kw_eval=None, novel_view=None): super().__init__() if not isinstance(trans_kw, list): trans_kw = [trans_kw] self.two_split = False else: assert trans_kw == ['img2lidar', 'temImg2lidar'] self.two_split = True self.trans_kw = trans_kw self.trans_kw_eval = trans_kw if trans_kw_eval is None else trans_kw_eval self.novel_view = novel_view @torch.cuda.amp.autocast(enabled=False) def forward(self, metas, rays): rays = rays.float() # prepare img2lidar img2lidar = [] # for key in self.trans_kw: # for img_meta in metas: # img2lidar.extend(img_meta[key]) if os.environ.get('eval', 'false') == 'true': trans_kw = self.trans_kw_eval else: trans_kw = self.trans_kw for img_meta in metas: temp = [] for key in trans_kw: temp.extend(img_meta[key]) if isinstance(temp[0], (np.ndarray, list)): temp = np.asarray(temp) else: temp = torch.stack(temp, dim=0) img2lidar.append(temp) if isinstance(img2lidar[0], np.ndarray): img2lidar = np.asarray(img2lidar) # B, N, 4, 4 img2lidar = rays.new_tensor(img2lidar) else: img2lidar = torch.stack(img2lidar, dim=0) if self.novel_view is not None: z_r = self.novel_view[3] rot_mat = rays.new_tensor(get_rm(z_r, 'z', True)) img2lidar[..., :3, :3] = rot_mat.unsqueeze(0).unsqueeze(0) @ \ img2lidar[..., :3, :3] origin = img2lidar[..., :3, 3] # B, N, 3 if self.novel_view is not None: origin[..., 0] = origin[..., 0] + self.novel_view[0] origin[..., 1] = origin[..., 1] + self.novel_view[1] origin[..., 2] = origin[..., 2] + self.novel_view[2] rays = rays.reshape(1, 1, -1, 2) # origin = img2lidar[..., :3, 3] # B, N, 3 rays_pad = torch.cat([ rays, torch.ones_like(rays[..., :1])], dim=-1) # 1, 1, HW, 3 direction = torch.matmul( img2lidar[..., :3, :3].unsqueeze(2), rays_pad.unsqueeze(-1)).squeeze(-1) # B, N, HW, 3 return origin, direction ================================================ FILE: model/head/nerfacc_head/nerfacc_head.py ================================================ import os from ..base_head import BaseTaskHead from .ray_sampler import RaySampler from .img2lidar import Img2LiDAR from .bev_nerf import BEVNeRF from .rendering import custom_rendering from .estimator import CustomOccGridEstimator from mmseg.models import HEADS import nerfacc, torch, collections, math from mmengine.logging import MMLogger logger = MMLogger.get_instance('selfocc') def namedtuple_map(fn, tup): """Apply `fn` to each element of `tup` and cast to `tup`'s namedtuple.""" return type(tup)(*(None if x is None else fn(x) for x in tup)) Rays = collections.namedtuple("Rays", ("origins", "viewdirs")) OCC_THRE = 0.0 @HEADS.register_module() class NeRFAccHead(BaseTaskHead): def __init__( self, # estimator args roi_aabb, resolution, reso=0.4, # rays args ray_sample_mode='fixed', # fixed, cellular ray_number=[192, 400], # 192 * 400 ray_img_size=[768, 1600], ray_upper_crop=0, # img2lidar args trans_kw='img2lidar', # estimator args render_step_size=4e-1, near_plane=0.0, far_plane=1e10, cone_angle: float = 0.0, alpha_thre: float = 0.0, early_stop_eps: float = 1e-4, # render args render_bkgd='white', # bev nerf mapping_args=dict( nonlinear_mode="linear_upscale", h_size=[128, 32], h_range=[51.2, 28.8], h_half=False, w_size=[128, 32], w_range=[51.2, 28.8], w_half=False, d_size=[20, 10], d_range=[-4.0, 4.0, 12.0]), # bev_inner=128, # bev_outer=32, # range_inner=51.2, # range_outer=51.2, # nonlinear_mode='linear_upscale', # z_inner=20, # z_outer=10, # z_ranges=[-5.0, 3.0, 11.0], # mlp decoder embed_dims=128, color_dims=0, sem_dims=0, density_layers=2, sh_deg=2, sh_act='relu', tpv=False, two_split=False, novel_view=None, init_cfg=None, **kwargs): super().__init__(init_cfg, **kwargs) # self.estimator = nerfacc.OccGridEstimator( # roi_aabb=roi_aabb, # resolution=resolution) self.estimator = CustomOccGridEstimator( roi_aabb=roi_aabb, resolution=resolution) self.aabb = roi_aabb self.resolution = resolution self.reso = reso self.ray_sampler = RaySampler( ray_sample_mode=ray_sample_mode, ray_number=ray_number, ray_img_size=ray_img_size, ray_upper_crop=ray_upper_crop) self.ray_sampler_eval = RaySampler( ray_sample_mode='fixed', ray_number=ray_number, ray_img_size=ray_img_size, ray_upper_crop=ray_upper_crop) self.img2lidar = Img2LiDAR( trans_kw=trans_kw, novel_view=novel_view) self.radiance_field = BEVNeRF( # bev_inner, # bev_outer, # range_inner, # range_outer, # nonlinear_mode, # z_inner, # z_outer, # z_ranges, mapping_args, embed_dims, color_dims, sem_dims, density_layers, sh_deg, sh_act, tpv=tpv) self.render_step_size = render_step_size self.near_plane = near_plane self.far_plane = far_plane self.cone_angle = cone_angle self.alpha_thre = alpha_thre self.early_stop_eps = early_stop_eps self.two_split = two_split self.return_sem = sem_dims > 0 # if return_sem: assert color_dims > 3 if render_bkgd == 'white': render_bkgd = torch.ones(3) self.register_buffer('render_bkgd', render_bkgd, False) elif render_bkgd == 'none': self.render_bkgd = None else: self.render_bkgd = render_bkgd def prepare( self, representation, metas=None, **kwargs): # self.model.field.pre_compute_density_color(representation)# , torch.float) self.radiance_field.pre_compute_density_color(representation) return {} def render( self, metas=None, batch=0, **kwargs): amp = 'amp' in os.environ and os.environ['amp'] == 'true' with torch.cuda.amp.autocast(enabled=amp): if os.environ.get('eval', 'false') == 'true': ray_sampler = self.ray_sampler_eval else: ray_sampler = self.ray_sampler rays = ray_sampler() origin, direction = self.img2lidar(metas, rays) bs, num_cams, num_rays = direction.shape[:3] direction_norm = torch.norm(direction, dim=-1, keepdim=True) direction = direction / direction_norm origin, direction = origin.squeeze(0), direction.squeeze(0) origin = origin.unsqueeze(1).repeat(1, direction.shape[1], 1).flatten(0, 1) direction = direction.flatten(0, 1) direction_norm = direction_norm.flatten(0, 2) def occ_eval_fn(x): density = self.radiance_field.query_density(x) # logger.info(f'mean of density: {density.mean()}') return density * self.render_step_size self.estimator._update( step=0, occ_eval_fn=occ_eval_fn, occ_thre=OCC_THRE, ema_decay=0.) if batch > 0: output = { 'rgb': [], 'acc': [], 'depth': [], 'weights': [], 'ts': [], 'sem': [] } chunks = direction.shape[0] * 1.0 / batch chunks = int(math.ceil(chunks)) origins = torch.chunk(origin, chunks) directions = torch.chunk(direction, chunks) direction_norms = torch.chunk(direction_norm, chunks) for i_chunk in range(chunks): # torch.cuda.empty_cache() rgb, acc, depth, sem, n_rendering_samples, ray_indices, weights, ts = self.render_image_with_occgrid( Rays(origins=origins[i_chunk].float(), viewdirs=directions[i_chunk].float())) depth = depth / direction_norms[i_chunk].reshape(-1, 1) output['rgb'].append(rgb) if self.return_sem: output['sem'].append(sem) output['acc'].append(acc) output['depth'].append(depth) rgb = torch.cat(output['rgb']).reshape(bs, num_cams, num_rays, -1) if self.return_sem: sem = torch.cat(output['sem']).reshape(bs, num_cams, num_rays, -1) acc = torch.cat(output['acc']).reshape(bs, num_cams, num_rays) depth = torch.cat(output['depth']).reshape(bs, num_cams, num_rays) else: rgb, acc, depth, sem, n_rendering_samples, ray_indices, weights, ts = self.render_image_with_occgrid( Rays(origins=origin.float(), viewdirs=direction.float())) # turn depth to camera coordinate depth = depth / direction_norm.reshape(-1, 1) rgb = rgb.reshape(bs, num_cams, num_rays, -1) if self.return_sem: sem = sem.reshape(bs, num_cams, num_rays, -1) acc = acc.reshape(bs, num_cams, num_rays) depth = depth.reshape(bs, num_cams, num_rays) outputs = { 'ms_depths': [depth], 'ms_colors': [rgb], 'ms_accs': [acc], 'ms_rays': rays} if self.return_sem: outputs.update({'sem': [sem]}) return outputs @torch.cuda.amp.autocast(enabled=False) def render_image_with_occgrid(self, rays): """Render the pixels of an image.""" rays_shape = rays.origins.shape if len(rays_shape) == 3: height, width, _ = rays_shape num_rays = height * width rays = namedtuple_map( lambda r: r.reshape([num_rays] + list(r.shape[2:])), rays ) else: num_rays, _ = rays_shape def sigma_fn(t_starts, t_ends, ray_indices): t_origins = chunk_rays.origins[ray_indices] t_dirs = chunk_rays.viewdirs[ray_indices] positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 sigmas = self.radiance_field.query_density(positions) return sigmas.squeeze(-1) def rgb_sigma_fn(t_starts, t_ends, ray_indices): t_origins = chunk_rays.origins[ray_indices] t_dirs = chunk_rays.viewdirs[ray_indices] positions = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0 rgbs, sigmas, sems = self.radiance_field(positions, t_dirs) return rgbs, sigmas.squeeze(-1), sems results = [] chunk = torch.iinfo(torch.int32).max for i in range(0, num_rays, chunk): chunk_rays = namedtuple_map(lambda r: r[i : i + chunk], rays) ray_indices, t_starts, t_ends = self.estimator.sampling( chunk_rays.origins, chunk_rays.viewdirs, sigma_fn=sigma_fn, near_plane=self.near_plane, far_plane=self.far_plane, render_step_size=self.render_step_size, stratified=self.radiance_field.training, cone_angle=self.cone_angle, alpha_thre=self.alpha_thre, early_stop_eps=self.early_stop_eps ) rgb, opacity, depth, sem, extras = custom_rendering( t_starts, t_ends, ray_indices, n_rays=chunk_rays.origins.shape[0], rgb_sigma_fn=rgb_sigma_fn, render_bkgd=self.render_bkgd, ) chunk_results = [rgb, opacity, depth, sem, len(t_starts), ray_indices, extras["weights"], extras["ts"]] results.append(chunk_results) colors, opacities, depths, sems, n_rendering_samples, ray_indices, weights, ts = [ torch.cat(r, dim=0) if isinstance(r[0], torch.Tensor) else r for r in zip(*results) ] return ( colors.view((*rays_shape[:-1], -1)), opacities.view((*rays_shape[:-1], -1)), depths.view((*rays_shape[:-1], -1)), sems.view((*rays_shape[:-1], -1)), sum(n_rendering_samples), ray_indices, weights, ts ) # @torch.cuda.amp.autocast(enabled=False) # def forward_occ( # self, # representation, # metas=None, # **kwargs): # rays = self.ray_sampler() # HW, 2 # origin, direction = self.img2lidar(metas, rays) # num_cams, num_rays = direction.shape[1:3] # direction_norm = torch.norm(direction, dim=-1, keepdim=True) # direction = direction / direction_norm # origin, direction = origin.squeeze(0), direction.squeeze(0) # origin = origin.unsqueeze(1).repeat(1, direction.shape[1], 1).flatten(0, 1) # direction = direction.flatten(0, 1) # # update occupancy grid # self.radiance_field.pre_compute_density_color(representation) # def occ_eval_fn(x): # density = self.radiance_field.query_density(x) # # logger.info(f'mean of density: {density.mean()}') # return density * self.render_step_size # self.estimator._update( # step=0, # occ_eval_fn=occ_eval_fn, # occ_thre=1e-2, # ema_decay=0.) # # logger.info('number of nonzero grid: ' + str(self.estimator.binaries.sum())) # if self.estimator.binaries.sum() == 0: # logger.info(representation.mean()) # logger.info(self.radiance_field.density_net[-1].weight.mean()) # return {} @torch.cuda.amp.autocast(enabled=False) def forward_occ( self, representation, metas=None, **kwargs): if isinstance(representation, (tuple, list)): device = representation[0].device else: device = representation.device aabb = kwargs['aabb'] if 'aabb' in kwargs else self.aabb reso = kwargs['resolution'] if 'resolution' in kwargs else self.reso # update occupancy grid self.radiance_field.pre_compute_density_color(representation) if self.return_sem: sigma, sem, sem_logits, _ = self.get_uniform_sdf(aabb, reso, device=device) return {'sigma': sigma, 'rep': representation, 'sem': sem, 'logits': sem_logits} sigma, _ = self.get_uniform_sdf(aabb, reso, device=device) return {'sigma': sigma, 'rep': representation} def get_uniform_sdf(self, aabb, resolution, device, shift=False): xs = torch.linspace( aabb[0], aabb[3], int((aabb[3] - aabb[0]) / resolution), device=device) ys = torch.linspace( aabb[1], aabb[4], int((aabb[4] - aabb[1]) / resolution), device=device) zs = torch.linspace( aabb[2], aabb[5], int((aabb[5] - aabb[2]) / resolution), device=device) W, H, D = len(xs), len(ys), len(zs) xyzs = torch.stack([ xs[None, :, None].expand(H, W, D), ys[:, None, None].expand(H, W, D), zs[None, None, :].expand(H, W, D) ], dim=-1).flatten(0, 2) if shift: random_shift = torch.rand_like(xyzs) * resolution xyzs = xyzs + random_shift sigma, sem = self.radiance_field.forward_geo(xyzs) if self.return_sem: sigma = sigma.reshape(H, W, D) sem_logits = sem.reshape(H, W, D, -1) sem = torch.argmax(sem, dim=-1).reshape(H, W, D) return sigma, sem, sem_logits, xyzs.reshape(H, W, D, -1) else: sigma = sigma.reshape(H, W, D) return sigma, xyzs.reshape(H, W, D, -1) @torch.cuda.amp.autocast(enabled=False) def forward( self, representation, metas=None, **kwargs): if os.environ.get('eval', 'false') == 'true': ray_sampler = self.ray_sampler_eval else: ray_sampler = self.ray_sampler rays = ray_sampler() # HW, 2 origin, direction = self.img2lidar(metas, rays) num_cams, num_rays = direction.shape[1:3] direction_norm = torch.norm(direction, dim=-1, keepdim=True) direction = direction / direction_norm origin, direction = origin.squeeze(0), direction.squeeze(0) origin = origin.unsqueeze(1).repeat(1, direction.shape[1], 1).flatten(0, 1) direction = direction.flatten(0, 1) # update occupancy grid self.radiance_field.pre_compute_density_color(representation) def occ_eval_fn(x): density = self.radiance_field.query_density(x) # logger.info(f'mean of density: {density.mean()}') return density * self.render_step_size self.estimator._update( step=0, occ_eval_fn=occ_eval_fn, occ_thre=OCC_THRE, ema_decay=0.) # logger.info('number of nonzero grid: ' + str(self.estimator.binaries.sum())) if self.estimator.binaries.sum() == 0: logger.info(representation.mean()) logger.info(self.radiance_field.density_net[-1].weight.mean()) # render rgb, acc, depth, sem, n_rendering_samples, ray_indices, weights, ts = self.render_image_with_occgrid( Rays(origins=origin.float(), viewdirs=direction.float())) # turn depth to camera coordinate depth = depth / direction_norm.reshape(-1, 1) weights_for_cams, ts_for_cams, ray_idx_for_cams = [], [], [] for i in range(num_cams): curr_cam = torch.logical_and(ray_indices >= (i * num_rays), ray_indices < (i * num_rays + num_rays)) ray_idx_for_cam = ray_indices[curr_cam] - i * num_rays ray_idx_for_cams.append(ray_idx_for_cam) weights_for_cams.append(weights[curr_cam]) ts_for_curr_cam = ts[curr_cam] direction_norm_for_cam = direction_norm[0, i, ray_idx_for_cam, 0] ts_for_curr_cam = ts_for_curr_cam / direction_norm_for_cam ts_for_cams.append(ts_for_curr_cam) if self.two_split and self.img2lidar.two_split: depth = depth.reshape(1, -1, rays.shape[0])[:, :(num_cams//2), :] rgb = rgb.reshape(1, -1, rays.shape[0], 3)[:, (num_cams//2):, ...] sem = sem.reshape(1, num_cams, rays.shape[0], -1)[:, (num_cams//2):, ...] acc = acc.reshape(1, -1, rays.shape[0])[:, :(num_cams//2), :] ray_idx_for_cams = ray_idx_for_cams[:(num_cams // 2)] weights_for_cams = weights_for_cams[:(num_cams // 2)] ts_for_cams = ts_for_cams[:(num_cams // 2)] else: depth = depth.reshape(1, -1, rays.shape[0]) rgb = rgb.reshape(1, -1, rays.shape[0], 3) sem = sem.reshape(1, num_cams, rays.shape[0], -1) acc = acc.reshape(1, -1, rays.shape[0]) outputs = { 'ms_depths': [depth], 'ms_colors': [rgb], 'sem': [sem], 'ms_accs': [acc], 'ms_rays': rays, 'ray_indices': ray_idx_for_cams, 'weights': weights_for_cams, 'ts': ts_for_cams } return outputs ================================================ FILE: model/head/nerfacc_head/ray_sampler.py ================================================ import torch, torch.nn as nn import numpy as np class RaySampler(nn.Module): def __init__( self, ray_sample_mode='fixed', # fixed, cellular ray_number=[192, 400], # 192 * 400 ray_img_size=[768, 1600], ray_upper_crop=0, ray_x_dsr_max=None, ray_y_dsr_max=None): super().__init__() self.ray_sample_mode = ray_sample_mode self.ray_number = ray_number[0] * ray_number[1] self.ray_resize = ray_number self.ray_img_size = ray_img_size assert ray_sample_mode in ['fixed', 'cellular', 'random'] # TODO if ray_sample_mode == 'fixed': ray_x_dsr = 1.0 * ray_img_size[1] / ray_number[1] ray_y_dsr = 1.0 * ray_img_size[0] / ray_number[0] ray_x = torch.arange(ray_number[1], dtype=torch.float) * ray_x_dsr ray_y = torch.arange(ray_number[0], dtype=torch.float) * ray_y_dsr rays = torch.stack([ ray_x.unsqueeze(0).expand(ray_number[0], -1), ray_y.unsqueeze(1).expand(-1, ray_number[1])], dim=-1).flatten(0, 1) # HW, 2 self.register_buffer('rays', rays, False) elif ray_sample_mode == 'cellular': self.ray_upper_crop = ray_upper_crop self.ray_x_dsr_max = 1.0 * ray_img_size[1] / ray_number[1] self.ray_y_dsr_max = 1.0 * (ray_img_size[0] - ray_upper_crop) / ray_number[0] if ray_x_dsr_max is not None: self.ray_x_dsr_max = ray_x_dsr_max if ray_y_dsr_max is not None: self.ray_y_dsr_max = ray_y_dsr_max assert self.ray_x_dsr_max > 1 and self.ray_y_dsr_max > 1 ray_x = torch.arange(ray_number[1], dtype=torch.float) ray_y = torch.arange(ray_number[0], dtype=torch.float) rays = torch.stack([ ray_x.unsqueeze(0).expand(ray_number[0], -1), ray_y.unsqueeze(1).expand(-1, ray_number[1])], dim=-1) # H, W, 2 self.register_buffer('rays', rays, False) def forward(self): device = self.rays.device if self.ray_sample_mode == 'fixed': return self.rays elif self.ray_sample_mode == 'random': rays = torch.rand(self.ray_number, 2, device=device) rays[:, 0] = rays[:, 0] * self.ray_img_size[1] rays[:, 1] = rays[:, 1] * self.ray_img_size[0] return rays elif self.ray_sample_mode == 'cellular': ray_x_dsr = np.random.uniform() * (self.ray_x_dsr_max - 1) + 1 ray_y_dsr = np.random.uniform() * (self.ray_y_dsr_max - 1) + 1 ray_x_emp_max = self.ray_img_size[1] - self.ray_resize[1] * ray_x_dsr ray_y_emp_max = self.ray_img_size[0] - self.ray_upper_crop - self.ray_resize[0] * ray_y_dsr ray_x_emp = np.random.uniform() * ray_x_emp_max ray_y_emp = np.random.uniform() * ray_y_emp_max rays = self.rays.clone() # H, W, 2 rays[..., 0] = rays[..., 0] * ray_x_dsr + ray_x_emp rays[..., 1] = rays[..., 1] * ray_y_dsr + ray_y_emp + self.ray_upper_crop return rays.flatten(0, 1) ================================================ FILE: model/head/nerfacc_head/rendering.py ================================================ """ Modified from NeRFAcc. Copyright (c) 2022 Ruilong Li, UC Berkeley. """ from typing import Callable, Dict, Optional, Tuple import torch from torch import Tensor from nerfacc import render_weight_from_alpha, render_weight_from_density from nerfacc import accumulate_along_rays def custom_rendering( # ray marching results t_starts: Tensor, t_ends: Tensor, ray_indices: Optional[Tensor] = None, n_rays: Optional[int] = None, # radiance field rgb_sigma_fn: Optional[Callable] = None, rgb_alpha_fn: Optional[Callable] = None, # rendering options render_bkgd: Optional[Tensor] = None, ) -> Tuple[Tensor, Tensor, Tensor, Dict]: """Render the rays through the radience field defined by `rgb_sigma_fn`. This function is differentiable to the outputs of `rgb_sigma_fn` so it can be used for gradient-based optimization. It supports both batched and flattened input tensor. For flattened input tensor, both `ray_indices` and `n_rays` should be provided. Note: Either `rgb_sigma_fn` or `rgb_alpha_fn` should be provided. Warning: This function is not differentiable to `t_starts`, `t_ends` and `ray_indices`. Args: t_starts: Per-sample start distance. Tensor with shape (n_rays, n_samples) or (all_samples,). t_ends: Per-sample end distance. Tensor with shape (n_rays, n_samples) or (all_samples,). ray_indices: Ray indices of the flattened samples. LongTensor with shape (all_samples). n_rays: Number of rays. Only useful when `ray_indices` is provided. rgb_sigma_fn: A function that takes in samples {t_starts, t_ends, ray indices} and returns the post-activation rgb (..., 3) and density values (...,). The shape `...` is the same as the shape of `t_starts`. rgb_alpha_fn: A function that takes in samples {t_starts, t_ends, ray indices} and returns the post-activation rgb (..., 3) and opacity values (...,). The shape `...` is the same as the shape of `t_starts`. render_bkgd: Background color. Tensor with shape (3,). Returns: Ray colors (n_rays, 3), opacities (n_rays, 1), depths (n_rays, 1) and a dict containing extra intermediate results (e.g., "weights", "trans", "alphas") Examples: .. code-block:: python >>> t_starts = torch.tensor([0.1, 0.2, 0.1, 0.2, 0.3], device="cuda:0") >>> t_ends = torch.tensor([0.2, 0.3, 0.2, 0.3, 0.4], device="cuda:0") >>> ray_indices = torch.tensor([0, 0, 1, 1, 1], device="cuda:0") >>> def rgb_sigma_fn(t_starts, t_ends, ray_indices): >>> # This is a dummy function that returns random values. >>> rgbs = torch.rand((t_starts.shape[0], 3), device="cuda:0") >>> sigmas = torch.rand((t_starts.shape[0],), device="cuda:0") >>> return rgbs, sigmas >>> colors, opacities, depths, extras = rendering( >>> t_starts, t_ends, ray_indices, n_rays=2, rgb_sigma_fn=rgb_sigma_fn) >>> print(colors.shape, opacities.shape, depths.shape) torch.Size([2, 3]) torch.Size([2, 1]) torch.Size([2, 1]) >>> extras.keys() dict_keys(['weights', 'alphas', 'trans']) """ if ray_indices is not None: assert ( t_starts.shape == t_ends.shape == ray_indices.shape ), "Since nerfacc 0.5.0, t_starts, t_ends and ray_indices must have the same shape (N,). " if rgb_sigma_fn is None and rgb_alpha_fn is None: raise ValueError( "At least one of `rgb_sigma_fn` and `rgb_alpha_fn` should be specified." ) # Query sigma/alpha and color with gradients if rgb_sigma_fn is not None: if t_starts.shape[0] != 0: rgbs, sigmas, sems = rgb_sigma_fn(t_starts, t_ends, ray_indices) else: rgbs = torch.empty((0, 3), device=t_starts.device) sigmas = torch.empty((0,), device=t_starts.device) sems = torch.empty((0, 3), device=t_starts.device) assert rgbs.shape[-1] == 3 or rgbs.shape[-1] == 0, "rgbs must have 3 channels, got {}".format( rgbs.shape ) assert ( sigmas.shape == t_starts.shape ), "sigmas must have shape of (N,)! Got {}".format(sigmas.shape) # Rendering: compute weights. weights, trans, alphas = render_weight_from_density( t_starts, t_ends, sigmas, ray_indices=ray_indices, n_rays=n_rays, ) extras = { "weights": weights, "alphas": alphas, "trans": trans, "sigmas": sigmas, "rgbs": rgbs, 'sample_sems': sems } elif rgb_alpha_fn is not None: if t_starts.shape[0] != 0: rgbs, alphas = rgb_alpha_fn(t_starts, t_ends, ray_indices) else: rgbs = torch.empty((0, 3), device=t_starts.device) alphas = torch.empty((0,), device=t_starts.device) assert rgbs.shape[-1] == 3, "rgbs must have 3 channels, got {}".format( rgbs.shape ) assert ( alphas.shape == t_starts.shape ), "alphas must have shape of (N,)! Got {}".format(alphas.shape) # Rendering: compute weights. weights, trans = render_weight_from_alpha( alphas, ray_indices=ray_indices, n_rays=n_rays, ) extras = { "weights": weights, "trans": trans, "rgbs": rgbs, "alphas": alphas, } # Rendering: accumulate rgbs, opacities, and depths along the rays. colors = accumulate_along_rays( weights, values=rgbs, ray_indices=ray_indices, n_rays=n_rays ) sem = accumulate_along_rays( weights, values=sems, ray_indices=ray_indices, n_rays=n_rays ) opacities = accumulate_along_rays( weights, values=None, ray_indices=ray_indices, n_rays=n_rays ) depths = accumulate_along_rays( weights, values=(t_starts + t_ends)[..., None] / 2.0, ray_indices=ray_indices, n_rays=n_rays, ) depths = depths / opacities.clamp_min(torch.finfo(rgbs.dtype).eps) # opacity_on_ray = torch.gather(opacities.squeeze(-1), dim=0, index=ray_indices) ts = (t_starts + t_ends) / 2.0 #/ opacity_on_ray.clamp_min(torch.finfo(rgbs.dtype).eps) extras.update({"ts": ts}) # Background composition. if render_bkgd is not None and colors.shape[1] > 0: if render_bkgd == 'random': render_bkgd = torch.rand_like(colors).to(colors.device) colors = colors + render_bkgd * (1.0 - opacities) return colors, opacities, depths, sem, extras ================================================ FILE: model/head/neus_head/neus_head.py ================================================ import torch, torch.distributed as dist, torch.nn as nn, os from nerfstudio.models.neus_custom import NeuSCustomModelConfig from nerfstudio.fields.sdf_custom_field import SDFCustomFieldConfig from nerfstudio.data.scene_box import SceneBox from nerfstudio.cameras.rays import RayBundle from nerfstudio.field_components.field_heads import FieldHeadNames from ..base_head import BaseTaskHead from ..nerfacc_head.ray_sampler import RaySampler from ..nerfacc_head.img2lidar import Img2LiDAR from mmseg.models import HEADS from mmengine.logging import MMLogger import math logger = MMLogger.get_instance('selfocc') from utils.tb_wrapper import WrappedTBWriter if 'selfocc' in WrappedTBWriter._instance_dict: writer = WrappedTBWriter.get_instance('selfocc') else: writer = None @HEADS.register_module() class NeuSHead(BaseTaskHead): def __init__( self, roi_aabb, resolution=0.4, near_plane=0.0, far_plane=1e10, num_samples=64, num_samples_importance=64, num_up_sample_steps=4, base_variance=64, beta_init=0.1, beta_max=0.195, total_iters=3516 * 11, use_numerical_gradients=True, numerical_gradients_delta=0.01, use_uniform_gradient=False, nbr_gradient_points=128*128*16, calculate_online=False, sample_gradient=False, use_compact_2nd_grad=False, beta_hand_tune=False, return_uniform_sdf=False, estimate_flow=False, return_max_depth=False, return_surface_sdf=False, return_second_grad=False, return_sample_sdf=False, return_sem=False, disp_sampler=False, anneal_aabb=False, aabb_every_iters=3516, aabb_min_near=10., aabb_min_far_frac=0.25, # rays args ray_sample_mode='fixed', # fixed, cellular ray_number=[192, 400], # 192 * 400 ray_img_size=[768, 1600], ray_upper_crop=0, ray_x_dsr_max=None, ray_y_dsr_max=None, # img2lidar args trans_kw='img2lidar', trans_kw_eval=None, novel_view=None, # render args render_bkgd='white', # bev_inner = 128, # bev_outer = 32, # range_inner = 51.2, # range_outer = 51.2, # nonlinear_mode = "linear_upscale", # z_inner = 20, # z_outer = 10, # z_ranges = [-5.0, 3.0, 11.0], mapping_args=dict( nonlinear_mode="linear_upscale", h_size=[128, 32], h_range=[51.2, 28.8], h_half=False, w_size=[128, 32], w_range=[51.2, 28.8], w_half=False, d_size=[20, 10], d_range=[-4.0, 4.0, 12.0]), # mlp decoder embed_dims = 128, color_dims = 0, density_layers = 2, sh_deg = 2, sh_act = "relu", init_cfg=None, print_freq=50, two_split=True, tpv=False, using_2d_img_feats=False, **kwargs): super().__init__(init_cfg, **kwargs) self.ray_sampler = RaySampler( ray_sample_mode=ray_sample_mode, ray_number=ray_number, ray_img_size=ray_img_size, ray_upper_crop=ray_upper_crop, ray_x_dsr_max=ray_x_dsr_max, ray_y_dsr_max=ray_y_dsr_max) self.ray_sampler_eval = RaySampler( ray_sample_mode='fixed', ray_number=ray_number, ray_img_size=ray_img_size, ray_upper_crop=ray_upper_crop) self.img2lidar = Img2LiDAR( trans_kw=trans_kw, trans_kw_eval=trans_kw_eval, novel_view=novel_view) model_config = NeuSCustomModelConfig( enable_collider=True, near_plane=near_plane, far_plane=far_plane, background_color=render_bkgd, background_model='none', scene_contraction_activate=False, num_samples=num_samples, num_samples_importance=num_samples_importance, num_up_sample_steps=num_up_sample_steps, base_variance=base_variance, beta_hand_tune=beta_hand_tune, beta_min=beta_init, beta_max=beta_max, total_iters=total_iters, perturb=True, use_lpips=False, anneal_aabb=anneal_aabb, aabb_every_iters=aabb_every_iters, aabb_max_near=near_plane, aabb_min_near=aabb_min_near, aabb_min_far_frac=aabb_min_far_frac, aabb_original=roi_aabb, disp_sampler=disp_sampler, return_sem=return_sem, sdf_field=SDFCustomFieldConfig( beta_init = beta_init, # beta_hand_tune=beta_hand_tune, use_numerical_gradients = use_numerical_gradients, # bev_inner = bev_inner, # bev_outer = bev_outer, # range_inner = range_inner, # range_outer = range_outer, # nonlinear_mode = nonlinear_mode, # z_inner = z_inner, # z_outer = z_outer, # z_ranges = z_ranges, mapping_args = mapping_args, # mlp decoder embed_dims = embed_dims, color_dims = color_dims, density_layers = density_layers, sh_deg = sh_deg, sh_act = sh_act, beta_learnable = not beta_hand_tune, second_derivative= return_second_grad, tpv = tpv, use_uniform_gradient=use_uniform_gradient, nbr_gradient_points=nbr_gradient_points, calculate_online=calculate_online, sample_gradient=sample_gradient, use_compact_2nd_grad=use_compact_2nd_grad, using_2d_img_feats=using_2d_img_feats, return_sem=return_sem, ), ) self.model = model_config.setup( scene_box=SceneBox( aabb=torch.tensor([roi_aabb[:3], roi_aabb[3:]]), near=near_plane, far=far_plane, collider_type='box' ), num_train_data=0, ) self.model.field.set_numerical_gradients_delta(numerical_gradients_delta) self.print_freq = print_freq self.resolution = resolution self.aabb = roi_aabb self.return_uniform_sdf = return_uniform_sdf self.return_max_depth = return_max_depth self.return_surface_sdf = return_surface_sdf self.return_second_grad = return_second_grad self.return_sample_sdf = return_sample_sdf self.return_sem = return_sem self.estimate_flow = estimate_flow self.z_size = self.model.field.mapping.size_d self.bev_size = [self.model.field.mapping.size_h, self.model.field.mapping.size_w] self.two_split = two_split self.using_2d_img_feats = using_2d_img_feats if estimate_flow: # self.flow_net = nn.Sequential( # nn.Conv2d(embed_dims*2, embed_dims, 3, 1, 1, bias=False), # nn.BatchNorm2d(embed_dims), # nn.ReLU(True), # nn.Conv2d(embed_dims, embed_dims, 3, 1, 1, bias=False), # nn.BatchNorm2d(embed_dims), # nn.ReLU(True), # nn.Conv2d(embed_dims, self.z_size * 3, 1)) flow_net = [ nn.Conv2d(embed_dims*2, embed_dims, 3, 1, 1, bias=False), nn.BatchNorm2d(embed_dims), nn.ReLU(True), nn.Conv2d(embed_dims, embed_dims, 3, 1, 1, bias=False), nn.BatchNorm2d(embed_dims), nn.ReLU(True)] last_lin = nn.Conv2d(embed_dims, self.z_size * 3, 1) nn.init.normal_(last_lin.weight.data, 0., 1e-2) nn.init.constant_(last_lin.bias.data, 0.) flow_net.append(last_lin) self.flow_net = nn.Sequential(*flow_net) def forward_occ( self, representation, metas=None, **kwargs): if isinstance(representation, (tuple, list)): device = representation[0].device else: device = representation.device if not self.using_2d_img_feats: self.model.field.pre_compute_density_color(representation) else: self.model.field.pre_compute_density_color( representation, img_feats=kwargs['ms_img_feats'], img_metas=metas) aabb = kwargs['aabb'] if 'aabb' in kwargs else self.aabb reso = kwargs['resolution'] if 'resolution' in kwargs else self.resolution if self.return_sem: sdf, sem, sem_logits, _ = self.get_uniform_sdf(aabb, reso, device=device) # curr_s = self.model.field.deviation_network.get_variance().detach().item() # logger.info(f's: {curr_s}') return {'sdf': sdf, 'rep': representation, 'sem': sem, 'logits': sem_logits, 'xyz': _} sdf, _ = self.get_uniform_sdf(aabb, reso, device=device) # curr_s = self.model.field.deviation_network.get_variance().detach().item() # logger.info(f's: {curr_s}') return {'sdf': sdf, 'rep': representation, 'xyz': _} def get_uniform_sdf(self, aabb, resolution, device, shift=False): xs = torch.linspace( aabb[0], aabb[3], int((aabb[3] - aabb[0]) / resolution), device=device) ys = torch.linspace( aabb[1], aabb[4], int((aabb[4] - aabb[1]) / resolution), device=device) zs = torch.linspace( aabb[2], aabb[5], int((aabb[5] - aabb[2]) / resolution), device=device) W, H, D = len(xs), len(ys), len(zs) xyzs = torch.stack([ xs[None, :, None].expand(H, W, D), ys[:, None, None].expand(H, W, D), zs[None, None, :].expand(H, W, D) ], dim=-1).flatten(0, 2) if shift: random_shift = torch.rand_like(xyzs) * resolution xyzs = xyzs + random_shift if self.return_sem: h = self.model.field.forward_geonetwork(xyzs) sdf, sem = h[..., 0], h[..., 4:] sdf = sdf.reshape(H, W, D) sem_logits = sem.reshape(H, W, D, -1) sem = torch.argmax(sem, dim=-1).reshape(H, W, D) return sdf, sem, sem_logits, xyzs.reshape(H, W, D, -1) else: sdf = self.model.field.forward_sdfnetwork(xyzs) sdf = sdf.reshape(H, W, D) return sdf, xyzs.reshape(H, W, D, -1) def prepare( self, representation, metas=None, **kwargs): # self.model.field.pre_compute_density_color(representation)# , torch.float) if not self.using_2d_img_feats: self.model.field.pre_compute_density_color(representation) else: self.model.field.pre_compute_density_color( representation, img_feats=kwargs['ms_img_feats'], img_metas=metas) return {} def render( self, metas=None, batch=0, **kwargs): amp = 'amp' in os.environ and os.environ['amp'] == 'true' with torch.cuda.amp.autocast(enabled=amp): if os.environ.get('eval', 'false') == 'true': ray_sampler = self.ray_sampler_eval else: ray_sampler = self.ray_sampler rays = ray_sampler() origin, direction = self.img2lidar(metas, rays) # B, N, 3; B, N, R, 3 bs, num_cams, num_rays = direction.shape[:3] assert bs == 1, 'only support bs = 1 currently' origin = origin.unsqueeze(2).repeat(1, 1, num_rays, 1).flatten(0, 2) direction = direction.flatten(0, 2) direction_norm = torch.norm(direction, dim=-1, keepdim=True) direction = direction / direction_norm if batch > 0: output = { 'rgb': [], 'acc': [], 'depth': [], 'weights': [], 'ts': [], 'deltas': [], 'vis_normal': [], 'sem': [] # 'sdfs': [] } chunks = direction.shape[0] * 1.0 / batch chunks = int(math.ceil(chunks)) origins = torch.chunk(origin, chunks) directions = torch.chunk(direction, chunks) direction_norms = torch.chunk(direction_norm, chunks) for i_chunk in range(chunks): # torch.cuda.empty_cache() ray_bundle = RayBundle( origins=origins[i_chunk], directions=directions[i_chunk], directions_norm=direction_norms[i_chunk], pixel_area=torch.zeros_like(direction_norms[i_chunk])) output_i = self.model(ray_bundle) output['rgb'].append(output_i['rgb']) if self.return_sem: output['sem'].append(output_i['sem']) output['vis_normal'].append(output_i['normal_vis']) output['acc'].append(output_i['accumulation']) output['depth'].append(output_i['depth']) # output['sdfs'].append(output_i['field_outputs'][FieldHeadNames.SDF]) # depth_median = output['depth_median'].reshape(bs, num_cams, num_rays) output['weights'].append(output_i['weights']) ray_samples = output_i['ray_samples'] # there is bug here before 8.16, did not divide direction_norm ts = (ray_samples.frustums.starts + ray_samples.frustums.ends) / 2 # ts = ts.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1) ts = ts / direction_norms[i_chunk].unsqueeze(1) output['ts'].append(ts) # import pdb; pdb.set_trace() deltas = (ray_samples.frustums.ends - ray_samples.frustums.starts) # deltas = deltas.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1) deltas = deltas / direction_norms[i_chunk].unsqueeze(1) output['deltas'].append(deltas) rgb = torch.cat(output['rgb']).reshape(bs, num_cams, num_rays, -1) if self.return_sem: sem = torch.cat(output['sem']).reshape(bs, num_cams, num_rays, -1) vis_normal = torch.cat(output['vis_normal']).reshape(bs, num_cams, num_rays, -1) acc = torch.cat(output['acc']).reshape(bs, num_cams, num_rays) depth = torch.cat(output['depth']).reshape(bs, num_cams, num_rays) # sdfs = torch.cat(output['sdfs']).reshape(bs, num_cams, num_rays, -1) weights = torch.cat(output['weights']).reshape(bs, num_cams, num_rays, -1, 1) ts = torch.cat(output['ts']) deltas = torch.cat(output['deltas']) else: ray_bundle = RayBundle( origins=origin,#.float(), directions=direction,#.float(), directions_norm=direction_norm,#.float(), pixel_area=torch.zeros_like(direction_norm))#, dtype=torch.float)) output = self.model(ray_bundle) """ output: dict( "rgb": B, N, R, 3 "accumulation": B, N, R, 1 "depth": B, N, R, 1 "normal": B, N, R, 3 "weights": B, N, R, S, 1, "ray_points": B, N, R, S, 3 "directions_norm": B, N, R, 1 "normal_vis": while training "eik_grad": "points_norm": ) """ rgb = output['rgb'].reshape(bs, num_cams, num_rays, -1) if self.return_sem: sem = output['sem'].reshape(bs, num_cams, num_rays, -1) vis_normal = output['normal_vis'].reshape(bs, num_cams, num_rays, -1) acc = output['accumulation'].reshape(bs, num_cams, num_rays) depth = output['depth'].reshape(bs, num_cams, num_rays) sdfs = output['field_outputs'][FieldHeadNames.SDF].reshape(bs, num_cams, num_rays, -1) # depth_median = output['depth_median'].reshape(bs, num_cams, num_rays) weights = output['weights'].reshape(bs, num_cams, num_rays, -1, 1) ray_samples = output['ray_samples'] # there is bug here before 8.16, did not divide direction_norm ts = (ray_samples.frustums.starts + ray_samples.frustums.ends) / 2 # ts = ts.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1) ts = ts / direction_norm.unsqueeze(1) # import pdb; pdb.set_trace() deltas = (ray_samples.frustums.ends - ray_samples.frustums.starts) # deltas = deltas.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1) deltas = deltas / direction_norm.unsqueeze(1) if self.return_max_depth: eps = torch.finfo(deltas.dtype).eps deltas_ = deltas.reshape(bs, num_cams, num_rays, -1).cpu() weights_ = weights.reshape(bs, num_cams, num_rays, -1).cpu() # .clone() weights_[deltas_ < eps] = 0. w_per_d = weights_ / deltas_.clamp_min(eps) indices = w_per_d.argmax(dim=-1, keepdim=True) # bs, num_cams, num_rays, 1 max_depth = ts.reshape(bs, num_cams, num_rays, -1).cpu() max_depth = torch.gather(max_depth, -1, indices).squeeze(-1).to(rgb.device) # www = weights.squeeze(0).squeeze(-1).cpu() # ddd = deltas.reshape(bs, num_cams, num_rays, -1).squeeze(0).cpu() # sss = sdfs # sss = sss.reshape(bs, num_cams, num_rays, -1).squeeze(0).cpu() # ttt = ts.reshape(bs, num_cams, num_rays, -1).squeeze(0).cpu() # import matplotlib.pyplot as plt # while True: # plt.cla() # fig, ax = plt.subplots(3, 1) # cam = 0; ray=1643 # import pdb; pdb.set_trace() # ax[0].plot(ttt[cam, ray], sss[cam, ray]) # ax[0].plot(ttt[cam, ray], torch.zeros(256)) # ax[1].plot(ttt[cam, ray], www[cam, ray]) # ax[2].plot(ttt[cam, ray], www[cam, ray] / (ddd[cam, ray] + 1e-6)) # plt.savefig(f'wdelta_w_sdf_t_pro_{cam}_{ray}.png') # print(cam, ray) # import pdb; pdb.set_trace() outputs = { 'ms_depths': [depth], # 'ms_depths_median': [depth_median], 'ms_colors': [rgb], 'vis_normal': [vis_normal], 'ms_accs': [acc], 'ms_rays': rays} if self.return_max_depth: outputs.update({ 'ms_max_depths': [max_depth]}) if self.return_sem: outputs.update({'sem': [sem]}) return outputs def forward( self, representation, metas=None, **kwargs): estimate_flow = self.estimate_flow and (kwargs.get('prev_rep', None) is not None or self.training) # self.model.field.pre_compute_density_color(representation)#, torch.float) if not self.using_2d_img_feats: self.model.field.pre_compute_density_color(representation) else: self.model.field.pre_compute_density_color( representation, img_feats=kwargs['ms_img_feats'], img_metas=metas) if estimate_flow: assert not isinstance(representation, list) # TODO prev_rep = kwargs.get('prev_rep', None) next_rep = kwargs.get('next_rep', None) assert prev_rep is not None and \ next_rep is not None prev_rep = prev_rep.unflatten(1, self.bev_size).permute(0, 3, 1, 2) next_rep = next_rep.unflatten(1, self.bev_size).permute(0, 3, 1, 2) curr_rep = representation.unflatten(1, self.bev_size).permute(0, 3, 1, 2) prev_curr = torch.cat([prev_rep, curr_rep], dim=1) next_curr = torch.cat([next_rep, curr_rep], dim=1) curr_prev = torch.cat([curr_rep, prev_rep], dim=1) curr_next = torch.cat([curr_rep, next_rep], dim=1) curr2prev_flow = self.flow_net(prev_curr).unflatten(1, (3, self.z_size)).permute(0, 1, 3, 4, 2) # bs, 3, h, w, z curr2next_flow = self.flow_net(next_curr).unflatten(1, (3, self.z_size)).permute(0, 1, 3, 4, 2) prev2curr_flow = self.flow_net(curr_prev).unflatten(1, (3, self.z_size)).permute(0, 1, 3, 4, 2) next2curr_flow = self.flow_net(curr_next).unflatten(1, (3, self.z_size)).permute(0, 1, 3, 4, 2) global_iter = kwargs.get('global_iter', None) amp = 'amp' in os.environ and os.environ['amp'] == 'true' with torch.cuda.amp.autocast(enabled=amp): if os.environ.get('eval', 'false') == 'true': ray_sampler = self.ray_sampler_eval else: ray_sampler = self.ray_sampler rays = ray_sampler() origin, direction = self.img2lidar(metas, rays) # B, N, 3; B, N, R, 3 bs, num_cams, num_rays = direction.shape[:3] assert bs == 1, 'only support bs = 1 currently' origin = origin.unsqueeze(2).repeat(1, 1, num_rays, 1).flatten(0, 2) direction = direction.flatten(0, 2) direction_norm = torch.norm(direction, dim=-1, keepdim=True) direction = direction / direction_norm # camera_indices = torch.arange(bs * num_cams, device=origin.device).unsqueeze(-1).repeat(1, num_rays).reshape(-1, 1) ray_bundle = RayBundle( origins=origin,#.float(), directions=direction,#.float(), directions_norm=direction_norm,#.float(), # camera_indices=camera_indices, pixel_area=torch.zeros_like(direction_norm))#, dtype=torch.float) output = self.model(ray_bundle, iter=global_iter) uniform_sdf = None if self.return_uniform_sdf or estimate_flow: uniform_data = self.get_uniform_sdf(self.aabb, self.resolution, rays.device, True) if self.return_sem: uniform_sdf, _, _, uniform_xyz = uniform_data else: uniform_sdf, uniform_xyz = uniform_data """ output: dict( "rgb": B, N, R, 3 "accumulation": B, N, R, 1 "depth": B, N, R, 1 "normal": B, N, R, 3 "weights": B, N, R, S, 1, "ray_points": B, N, R, S, 3 "directions_norm": B, N, R, 1 "normal_vis": while training "eik_grad": "points_norm": ) """ rgb = output['rgb'].reshape(bs, num_cams, num_rays, -1) acc = output['accumulation'].reshape(bs, num_cams, num_rays) depth = output['depth'].reshape(bs, num_cams, num_rays) fars = output['fars'].reshape(bs, num_cams, num_rays) if self.return_surface_sdf: surface_sdf = self.model.field.forward_sdfnetwork(output['surface_points']) surface_sdf = surface_sdf.reshape(bs, num_cams, num_rays) if self.return_sample_sdf: sample_sdf = output['field_outputs'][FieldHeadNames.SDF] sample_sdf = sample_sdf.reshape(bs, num_cams, num_rays, -1) if self.return_sem: sem = output['sem'].reshape(bs, num_cams, num_rays, -1) # depth_median = output['depth_median'].reshape(bs, num_cams, num_rays) weights = output['weights'].reshape(bs, num_cams, num_rays, -1, 1) num_samples_per_ray = weights.shape[-2] ray_samples = output['ray_samples'] # there is bug here before 8.16, did not divide direction_norm ts = (ray_samples.frustums.starts + ray_samples.frustums.ends) / 2 # ts = ts.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1) ts = ts / direction_norm.unsqueeze(1) # import pdb; pdb.set_trace() deltas = (ray_samples.frustums.ends - ray_samples.frustums.starts) # deltas = deltas.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1) deltas = deltas / direction_norm.unsqueeze(1) if self.return_max_depth: eps = torch.finfo(deltas.dtype).eps deltas_ = deltas.reshape(bs, num_cams, num_rays, -1) weights_ = weights.reshape(bs, num_cams, num_rays, -1).clone() weights_[deltas_ < eps] = 0. w_per_d = weights_ / deltas_.clamp_min(eps) indices = w_per_d.argmax(dim=-1, keepdim=True) # bs, num_cams, num_rays, 1 max_depth = ts.reshape(bs, num_cams, num_rays, -1) max_depth = torch.gather(max_depth, -1, indices).squeeze(-1) # www = weights.squeeze(0).cpu() # ddd = deltas.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1).squeeze(0).cpu() # sss = output['field_outputs'][FieldHeadNames.SDF] # sss = sss.reshape(bs, num_cams, num_rays, num_samples_per_ray).squeeze(0).cpu() # ttt = ts.reshape(bs, num_cams, num_rays, num_samples_per_ray, 1).squeeze(0).cpu() # import matplotlib.pyplot as plt # while True: # plt.cla() # fig, ax = plt.subplots(3, 1) # cam = 0; ray=1643 # import pdb; pdb.set_trace() # ax[0].plot(ttt[cam, ray], sss[cam, ray]) # ax[0].plot(ttt[cam, ray], torch.zeros(256)) # ax[1].plot(ttt[cam, ray], www[cam, ray]) # ax[2].plot(ttt[cam, ray], www[cam, ray] / (ddd[cam, ray] + 1e-6)) # plt.savefig(f'wdelta_w_sdf_t_pro_{cam}_{ray}.png') # print(cam, ray) # import pdb; pdb.set_trace() if estimate_flow: positions = ray_samples.frustums.get_positions() # B * N * R, S, 3 positions = positions.reshape(bs, num_cams, num_rays, num_samples_per_ray, 3) grids = self.model.field.mapping.meter2grid(positions, True) # grids[..., :2] = grids[..., :2] / (self.bev_size - 1) # grids[..., 2:] = grids[..., 2:] / (self.z_size - 1) prev_sampled_flow = nn.functional.grid_sample( curr2prev_flow, grids[..., [2, 1, 0]] * 2 - 1, mode='bilinear', align_corners=True).permute(0, 2, 3, 4, 1) # bs, N, R, S, 3 next_sampled_flow = nn.functional.grid_sample( curr2next_flow, grids[..., [2, 1, 0]] * 2 - 1, mode='bilinear', align_corners=True).permute(0, 2, 3, 4, 1) prev_warp = positions + prev_sampled_flow # B, N, R, S, 3 next_warp = positions + next_sampled_flow if global_iter is not None and \ global_iter % self.print_freq == 0 and \ ((not dist.is_initialized()) or dist.get_rank() == 0): curr_s = output['inv_s'] logger.info(f'global iter {global_iter} s: {curr_s}, deltas=0: {(deltas == 0).sum().item()}') writer.add_scalar('inv_s', curr_s, global_iter) weights_for_cams = chunk_cams(weights, num_cams) ts_for_cams = chunk_cams(ts, num_cams) deltas_for_cams = chunk_cams(deltas, num_cams) ray_idx_for_cams = [ torch.arange(num_rays, device=rgb.device).unsqueeze(-1).repeat(1, num_samples_per_ray).flatten()] * num_cams eik_grad = output['eik_grad'] if estimate_flow: prev_warp_for_cams = chunk_cams(prev_warp, num_cams) next_warp_for_cams = chunk_cams(next_warp, num_cams) if self.return_sample_sdf: sample_sdf_for_cams = chunk_cams(sample_sdf, num_cams) if self.two_split and self.img2lidar.two_split: depth = depth[:, :(num_cams//2), :] # depth_median = depth_median[:, :(num_cams//2), :] rgb = rgb[:, (num_cams//2):, ...] acc = acc[:, :(num_cams//2), :] fars = fars[:, :(num_cams//2), :] ray_idx_for_cams = ray_idx_for_cams[:(num_cams // 2)] weights_for_cams = weights_for_cams[:(num_cams // 2)] ts_for_cams = ts_for_cams[:(num_cams // 2)] deltas_for_cams = deltas_for_cams[:(num_cams // 2)] if estimate_flow: prev_warp_for_cams = prev_warp_for_cams[:(num_cams // 2)] next_warp_for_cams = next_warp_for_cams[:(num_cams // 2)] if self.return_max_depth: max_depth = max_depth[:, :(num_cams//2), :] if self.return_sample_sdf: sample_sdf_for_cams = sample_sdf_for_cams[:(num_cams // 2)] if self.return_sem: sem = sem[:, (num_cams//2):, ...] outputs = { 'ms_depths': [depth], # 'ms_depths_median': [depth_median], 'ms_colors': [rgb], 'ms_accs': [acc], 'ms_fars': [fars], 'ms_rays': rays, 'origin': origin, 'direction': direction, 'direction_norm': direction_norm, 'ray_indices': ray_idx_for_cams, 'weights': weights_for_cams, 'ts': ts_for_cams, 'deltas': deltas_for_cams, 'eik_grad': eik_grad, 'uniform_sdf': uniform_sdf} if estimate_flow: outputs.update({ 'prev_warp': prev_warp_for_cams, 'next_warp': next_warp_for_cams, 'curr2prev_flow': curr2prev_flow, 'curr2next_flow': curr2next_flow, 'prev2curr_flow': prev2curr_flow, 'next2curr_flow': next2curr_flow, 'uniform_xyz': uniform_xyz, 'uniform_sdf_prev': kwargs['sdf_prev'], 'uniform_sdf_next': kwargs['sdf_next'] }) if self.return_max_depth: outputs.update({ 'ms_max_depths': [max_depth] }) if self.return_surface_sdf: outputs.update({ 'surface_sdf': surface_sdf }) if self.return_second_grad: outputs.update({ 'second_grad': output['field_outputs']['second_grad'] }) if self.return_sample_sdf: outputs.update({ 'sample_sdf': sample_sdf_for_cams }) if self.return_sem: outputs.update({'sem': [sem]}) return outputs def chunk_cams(tensor, num_cams): tensor_for_cams = torch.chunk( tensor.reshape(num_cams, -1), num_cams, dim=0) tensor_for_cams = [t.squeeze() for t in tensor_for_cams] return tensor_for_cams ================================================ FILE: model/head/utils/sh_render.py ================================================ import torch ################## sh function ################## C0 = 0.28209479177387814 C1 = 0.4886025119029199 C2 = [ 1.0925484305920792, -1.0925484305920792, 0.31539156525252005, -1.0925484305920792, 0.5462742152960396 ] C3 = [ -0.5900435899266435, 2.890611442640554, -0.4570457994644658, 0.3731763325901154, -0.4570457994644658, 1.445305721320277, -0.5900435899266435 ] C4 = [ 2.5033429417967046, -1.7701307697799304, 0.9461746957575601, -0.6690465435572892, 0.10578554691520431, -0.6690465435572892, 0.47308734787878004, -1.7701307697799304, 0.6258357354491761, ] def eval_sh_bases(deg, dirs): """ Evaluate spherical harmonics bases at unit directions, without taking linear combination. At each point, the final result may the be obtained through simple multiplication. :param deg: int SH max degree. Currently, 0-4 supported :param dirs: torch.Tensor (..., 3) unit directions :return: torch.Tensor (..., (deg+1) ** 2) """ assert deg <= 4 and deg >= 0 result = torch.empty((*dirs.shape[:-1], (deg + 1) ** 2), dtype=dirs.dtype, device=dirs.device) result[..., 0] = C0 if deg > 0: x, y, z = dirs.unbind(-1) result[..., 1] = -C1 * y result[..., 2] = C1 * z result[..., 3] = -C1 * x if deg > 1: xx, yy, zz = x * x, y * y, z * z xy, yz, xz = x * y, y * z, x * z result[..., 4] = C2[0] * xy result[..., 5] = C2[1] * yz result[..., 6] = C2[2] * (2.0 * zz - xx - yy) result[..., 7] = C2[3] * xz result[..., 8] = C2[4] * (xx - yy) if deg > 2: result[..., 9] = C3[0] * y * (3 * xx - yy) result[..., 10] = C3[1] * xy * z result[..., 11] = C3[2] * y * (4 * zz - xx - yy) result[..., 12] = C3[3] * z * (2 * zz - 3 * xx - 3 * yy) result[..., 13] = C3[4] * x * (4 * zz - xx - yy) result[..., 14] = C3[5] * z * (xx - yy) result[..., 15] = C3[6] * x * (xx - 3 * yy) if deg > 3: result[..., 16] = C4[0] * xy * (xx - yy) result[..., 17] = C4[1] * yz * (3 * xx - yy) result[..., 18] = C4[2] * xy * (7 * zz - 1) result[..., 19] = C4[3] * yz * (7 * zz - 3) result[..., 20] = C4[4] * (zz * (35 * zz - 30) + 3) result[..., 21] = C4[5] * xz * (7 * zz - 3) result[..., 22] = C4[6] * (xx - yy) * (7 * zz - 1) result[..., 23] = C4[7] * xz * (xx - 3 * yy) result[..., 24] = C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) return result def SHRender(xyz_sampled, viewdirs, features, deg=2, act='relu'): sh_mult = eval_sh_bases(deg, viewdirs)[:, None] rgb_sh = features.view(-1, 3, sh_mult.shape[-1]) raw = torch.sum(sh_mult * rgb_sh, dim=-1) if act == 'relu': rgb = torch.relu(raw + 0.5) elif act == 'sigmoid': rgb = torch.sigmoid(raw) else: raise NotImplementedError return rgb ================================================ FILE: model/lifter/__init__.py ================================================ from .bev_query_lifter import BEVQueryLifter from .tpv_query_lifter import TPVQueryLifter from .tpv_pos_lifter import TPVPositionLifter ================================================ FILE: model/lifter/base_lifter.py ================================================ from mmseg.registry import MODELS from mmengine.model import BaseModule @MODELS.register_module() class BaseLifter(BaseModule): """Base lifter class. image backbone -> neck -> lifter -> encoder -> segmentor Lift multi-scale image features to 3D representations, e.g. Voxels or TPV or BEV. """ def __init__(self, init_cfg=None, **kwargs) -> None: super().__init__(init_cfg) def forward( self, ms_img_feats, metas=None, **kwargs ): pass ================================================ FILE: model/lifter/bev_query_lifter.py ================================================ from .base_lifter import BaseLifter from mmseg.registry import MODELS import torch, torch.nn as nn @MODELS.register_module() class BEVQueryLifter(BaseLifter): def __init__( self, bev_h, bev_w, dim, init_cfg=None, **kwargs) -> None: super().__init__(init_cfg, **kwargs) self.bev_h = bev_h self.bev_w = bev_w self.dim = dim self.bev = nn.Parameter(torch.randn(1, bev_h * bev_w, dim)) def forward(self, ms_img_feats, *args, **kwargs): bs = ms_img_feats[0].shape[0] bev = self.bev.to(ms_img_feats[0].dtype).repeat(bs, 1, 1) return {'representation': bev} ================================================ FILE: model/lifter/tpv_pos_lifter.py ================================================ from .base_lifter import BaseLifter from mmseg.registry import MODELS import torch, torch.nn as nn from model.encoder.bevformer.mappings import GridMeterMapping def get_feat_from_meter(num_freqs, meter): freqs = torch.arange(-1, num_freqs - 1, dtype=torch.float) freqs = torch.pi * (2 ** freqs) meter_freq = meter.unsqueeze(-1) * freqs[None, None, None, ...] freq_feat = torch.stack([ torch.sin(meter_freq), torch.cos(meter_freq)], dim=-1) freq_feat = freq_feat.flatten(-3).flatten(0, 1) return freq_feat @MODELS.register_module() class TPVPositionLifter(BaseLifter): def __init__( self, embed_dims, tot_range, num_freqs, mapping_args, init_cfg=None, **kwargs) -> None: super().__init__(init_cfg, **kwargs) self.mapping = GridMeterMapping(**mapping_args) size_h = self.mapping.size_h size_w = self.mapping.size_w size_d = self.mapping.size_d hw_grid = torch.stack( [torch.arange(size_h, dtype=torch.float).unsqueeze(-1).expand(-1, size_w), torch.arange(size_w, dtype=torch.float).unsqueeze(0).expand(size_h, -1), torch.zeros(size_h, size_w)], dim=-1) hw_meter = self.mapping.grid2meter(hw_grid)[..., [0, 1]] zh_grid = torch.stack( [torch.arange(size_h, dtype=torch.float).unsqueeze(0).expand(size_d, -1), torch.zeros(size_d, size_h), torch.arange(size_d, dtype=torch.float).unsqueeze(-1).expand(-1, size_h)], dim=-1) zh_meter = self.mapping.grid2meter(zh_grid)[..., [1, 2]] wz_grid = torch.stack( [torch.zeros(size_w, size_d), torch.arange(size_w, dtype=torch.float).unsqueeze(-1).expand(-1, size_d), torch.arange(size_d, dtype=torch.float).unsqueeze(0).expand(size_w, -1)], dim=-1) wz_meter = self.mapping.grid2meter(wz_grid)[..., [0, 2]] assert isinstance(tot_range, list) and len(tot_range) == 6 pc_range = tot_range hw_meter = hw_meter.clone() hw_meter[..., 0] = (hw_meter[..., 0] - pc_range[0]) / (pc_range[3] - pc_range[0]) hw_meter[..., 1] = (hw_meter[..., 1] - pc_range[1]) / (pc_range[4] - pc_range[1]) hw_freq_feat = get_feat_from_meter(num_freqs[0], hw_meter) zh_meter = zh_meter.clone() zh_meter[..., 0] = (zh_meter[..., 0] - pc_range[1]) / (pc_range[4] - pc_range[1]) zh_meter[..., 1] = (zh_meter[..., 1] - pc_range[2]) / (pc_range[5] - pc_range[2]) zh_freq_feat = get_feat_from_meter(num_freqs[1], zh_meter) wz_meter = wz_meter.clone() wz_meter[..., 0] = (wz_meter[..., 0] - pc_range[0]) / (pc_range[3] - pc_range[0]) wz_meter[..., 1] = (wz_meter[..., 1] - pc_range[2]) / (pc_range[5] - pc_range[2]) wz_freq_feat = get_feat_from_meter(num_freqs[2], wz_meter) self.register_buffer('hw_freq_feat', hw_freq_feat, False) self.register_buffer('zh_freq_feat', zh_freq_feat, False) self.register_buffer('wz_freq_feat', wz_freq_feat, False) self.position_layer_hw = nn.Linear(4 * num_freqs[0], embed_dims) self.position_layer_zh = nn.Linear(4 * num_freqs[1], embed_dims) self.position_layer_wz = nn.Linear(4 * num_freqs[2], embed_dims) def forward(self, ms_img_feats, *args, **kwargs): bs = ms_img_feats[0].shape[0] tpv_hw = self.position_layer_hw(self.hw_freq_feat).unsqueeze(0).repeat(bs, 1, 1) tpv_zh = self.position_layer_zh(self.zh_freq_feat).unsqueeze(0).repeat(bs, 1, 1) tpv_wz = self.position_layer_wz(self.wz_freq_feat).unsqueeze(0).repeat(bs, 1, 1) return {'representation': [tpv_hw, tpv_zh, tpv_wz]} ================================================ FILE: model/lifter/tpv_query_lifter.py ================================================ from .base_lifter import BaseLifter from mmseg.registry import MODELS import torch, torch.nn as nn @MODELS.register_module() class TPVQueryLifter(BaseLifter): def __init__( self, tpv_h, tpv_w, tpv_z, dim, init_cfg=None, **kwargs) -> None: super().__init__(init_cfg, **kwargs) self.tpv_h = tpv_h self.tpv_w = tpv_w self.tpv_z = tpv_z self.dim = dim self.tpv_hw = nn.Parameter(torch.randn(1, tpv_h * tpv_w, dim)) self.tpv_zh = nn.Parameter(torch.randn(1, tpv_z * tpv_h, dim)) self.tpv_wz = nn.Parameter(torch.randn(1, tpv_w * tpv_z, dim)) def forward(self, ms_img_feats, *args, **kwargs): bs = ms_img_feats[0].shape[0] # dtype = ms_img_feats[0].dtype # tpv_hw = self.tpv_hw.to(dtype).repeat(bs, 1, 1) # tpv_zh = self.tpv_zh.to(dtype).repeat(bs, 1, 1) # tpv_wz = self.tpv_wz.to(dtype).repeat(bs, 1, 1) tpv_hw = self.tpv_hw.repeat(bs, 1, 1) tpv_zh = self.tpv_zh.repeat(bs, 1, 1) tpv_wz = self.tpv_wz.repeat(bs, 1, 1) return {'representation': [tpv_hw, tpv_zh, tpv_wz]} ================================================ FILE: model/neck/__init__.py ================================================ from mmseg.models.necks import * from mmdet3d.models.necks import SECONDFPN from .identity_neck import IdentityNeck ================================================ FILE: model/neck/identity_neck.py ================================================ from mmseg.registry import MODELS from mmengine.model import BaseModule @MODELS.register_module() class IdentityNeck(BaseModule): def __init__(self, init_cfg = None): super().__init__(init_cfg) def forward(self, inputs): assert isinstance(inputs, (list, tuple)) return inputs if __name__ == "__main__": from mmengine import Config from mmseg.models import builder cfg = Config(dict( type='IdentityNeck', )) model = builder.build_neck(cfg) # model = UNet2D(out_feature=256, use_decoder=True) pass ================================================ FILE: model/segmentor/__init__.py ================================================ from .tpv_segmentor import TPVSegmentor ================================================ FILE: model/segmentor/base_segmentor.py ================================================ from mmseg.models import SEGMENTORS, builder from mmdet3d.registry import MODELS from mmengine.model import BaseModule @SEGMENTORS.register_module() class CustomBaseSegmentor(BaseModule): def __init__( self, img_backbone=None, img_neck=None, lifter=None, encoder=None, head=None, init_cfg=None, **kwargs, ): super().__init__(init_cfg) if img_backbone is not None: self.img_backbone = builder.build_backbone(img_backbone) if img_neck is not None: try: self.img_neck = builder.build_neck(img_neck) except: self.img_neck = MODELS.build(img_neck) if lifter is not None: self.lifter = builder.build_head(lifter) if encoder is not None: self.encoder = builder.build_head(encoder) if head is not None: self.head = builder.build_head(head) def extract_img_feat(self, imgs, **kwargs): """Extract features of images.""" B = imgs.size(0) B, N, C, H, W = imgs.size() imgs = imgs.reshape(B * N, C, H, W) img_feats = self.img_backbone(imgs) if isinstance(img_feats, dict): img_feats = list(img_feats.values()) img_feats = self.img_neck(img_feats) img_feats_reshaped = [] for img_feat in img_feats: BN, C, H, W = img_feat.size() img_feats_reshaped.append(img_feat.view(B, int(BN / B), C, H, W)) return {'ms_img_feats': img_feats_reshaped} def forward( self, imgs, metas, **kwargs ): pass ================================================ FILE: model/segmentor/tpv_segmentor.py ================================================ import torch, torch.nn as nn import numpy as np from mmseg.models import SEGMENTORS from mmseg.models import build_backbone from mmengine.registry import MODELS from .base_segmentor import CustomBaseSegmentor @SEGMENTORS.register_module() class TPVSegmentor(CustomBaseSegmentor): def __init__( self, freeze_img_backbone=False, freeze_img_neck=False, img_backbone_out_indices=[1, 2, 3], extra_img_backbone=None, use_post_fusion=False, **kwargs, ): super().__init__(**kwargs) self.fp16_enabled = False self.freeze_img_backbone = freeze_img_backbone self.freeze_img_neck = freeze_img_neck self.img_backbone_out_indices = img_backbone_out_indices self.use_post_fusion = use_post_fusion if freeze_img_backbone: self.img_backbone.requires_grad_(False) if freeze_img_neck: self.img_neck.requires_grad_(False) if extra_img_backbone is not None: self.extra_img_backbone = build_backbone(extra_img_backbone) def extract_img_feat(self, imgs, metas, **kwargs): """Extract features of images.""" B = imgs.size(0) B, N, C, H, W = imgs.size() imgs = imgs.reshape(B * N, C, H, W) img_feats_backbone = self.img_backbone(imgs) if isinstance(img_feats_backbone, dict): img_feats_backbone = list(img_feats_backbone.values()) img_feats = [] for idx in self.img_backbone_out_indices: img_feats.append(img_feats_backbone[idx]) img_feats = self.img_neck(img_feats) img_feats_reshaped = [] for img_feat in img_feats: BN, C, H, W = img_feat.size() if metas[0]['flip']: # img_feat = torch.fliplr(img_feat) img_feat = torch.flip(img_feat, [-1]) if self.use_post_fusion: img_feats_reshaped.append(img_feat.unsqueeze(1)) else: img_feats_reshaped.append(img_feat.view(B, int(BN / B), C, H, W)) img_feats_backbone_reshaped = [] for img_feat_backbone in img_feats_backbone: BN, C, H, W = img_feat_backbone.size() if metas[0]['flip']: img_feat_backbone = torch.flip(img_feat_backbone, [-1]) img_feats_backbone_reshaped.append( img_feat_backbone.view(B, int(BN / B), C, H, W)) return { 'ms_img_feats_backbone': img_feats_backbone_reshaped, 'ms_img_feats': img_feats_reshaped} def forward_extra_img_backbone(self, imgs, **kwargs): """Extract features of images.""" B, N, C, H, W = imgs.size() imgs = imgs.reshape(B * N, C, H, W) img_feats_backbone = self.extra_img_backbone(imgs) if isinstance(img_feats_backbone, dict): img_feats_backbone = list(img_feats_backbone.values()) img_feats_backbone_reshaped = [] for img_feat_backbone in img_feats_backbone: BN, C, H, W = img_feat_backbone.size() img_feats_backbone_reshaped.append( img_feat_backbone.view(B, int(BN / B), C, H, W)) return img_feats_backbone_reshaped def forward(self, imgs=None, metas=None, points=None, img_feat_only=False, extra_backbone=False, occ_only=False, prepare=False, **kwargs, ): """Forward training function. """ if extra_backbone: return self.forward_extra_img_backbone(imgs=imgs) results = { 'imgs': imgs, 'metas': metas, 'points': points } results.update(kwargs) outs = self.extract_img_feat(**results) # outs['ms_img_feats'] = [feat.float() for feat in outs['ms_img_feats']] results.update(outs) if img_feat_only: return results['ms_img_feats_backbone'] # with torch.cuda.amp.autocast(enabled=False): outs = self.lifter(**results) results.update(outs) outs = self.encoder(**results) results.update(outs) if occ_only and hasattr(self.head, "forward_occ"): outs = self.head.forward_occ(**results) elif prepare and hasattr(self.head, "prepare"): outs = self.head.prepare(**results) else: outs = self.head(**results) results.update(outs) return results ================================================ FILE: train.py ================================================ import time, argparse, os.path as osp, os import torch, numpy as np import torch.distributed as dist from copy import deepcopy import mmcv from mmengine import Config from mmengine.runner import set_random_seed from mmengine.optim import build_optim_wrapper from mmengine.logging import MMLogger from mmengine.utils import symlink from mmseg.models import build_segmentor from timm.scheduler import CosineLRScheduler, MultiStepLRScheduler import warnings warnings.filterwarnings("ignore") def pass_print(*args, **kwargs): pass def main(local_rank, args): # global settings set_random_seed(args.seed) torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) if local_rank != 0: import builtins builtins.print = pass_print else: distributed = False world_size = 1 if local_rank == 0: # from torch.utils.tensorboard import SummaryWriter os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, osp.basename(args.py_config))) # writer = SummaryWriter(log_dir=osp.join(args.work_dir, 'tf')) from utils.tb_wrapper import WrappedTBWriter writer = WrappedTBWriter('selfocc', log_dir=osp.join(args.work_dir, 'tf')) WrappedTBWriter._instance_dict['selfocc'] = writer else: writer = None timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) MMLogger._instance_dict['selfocc'] = logger logger.info(f'Config:\n{cfg.pretty_text}') # build model import model from dataset import get_dataloader from loss import OPENOCC_LOSS from utils.feat_tools import multi2single_scale my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: if cfg.get('syncBN', True): my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) logger.info('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.cuda() raw_model = my_model logger.info('done ddp model') if args.dataset == 'nuscenes' and cfg.get('sem', False): from utils.openseed_utils import build_openseed_model, forward_openseed_model openseed_model = build_openseed_model() train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed, iter_resume=args.iter_resume) # get optimizer, loss, scheduler optimizer = build_optim_wrapper(my_model, cfg.optimizer) # cfg.loss.update({'writer': writer}) loss_func = OPENOCC_LOSS.build(cfg.loss).cuda() max_num_epochs = cfg.max_epochs if cfg.get('multisteplr', False): scheduler = MultiStepLRScheduler( optimizer, **cfg.multisteplr_config ) else: scheduler = CosineLRScheduler( optimizer, t_initial=len(train_dataset_loader) * max_num_epochs, lr_min=1e-6, warmup_t=cfg.get('warmup_iters', 500), warmup_lr_init=1e-6, t_in_epochs=False) amp = cfg.get('amp', False) if amp: scaler = torch.cuda.amp.GradScaler() os.environ['amp'] = 'true' else: os.environ['amp'] = 'false' # resume and load epoch = 0 global_iter = 0 last_iter = 0 cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from logger.info('resume from: ' + cfg.resume_from) logger.info('work dir: ' + args.work_dir) if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) print(raw_model.load_state_dict(ckpt['state_dict'], strict=False)) optimizer.load_state_dict(ckpt['optimizer']) scheduler.load_state_dict(ckpt['scheduler']) epoch = ckpt['epoch'] global_iter = ckpt['global_iter'] last_iter = ckpt['last_iter'] if 'last_iter' in ckpt else 0 if hasattr(train_dataset_loader.sampler, 'set_last_iter'): train_dataset_loader.sampler.set_last_iter(last_iter) print(f'successfully resumed from epoch {epoch}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # training print_freq = cfg.print_freq first_run = True grad_accumulation = args.gradient_accumulation # assert grad_accumulation * world_size == 8 grad_norm = 0 if args.depth_metric: from utils.metric_util import DepthMetric if args.dataset == 'kitti': camera_names = ['front'] elif args.dataset == 'nuscenes': camera_names = ['front', 'front_right', 'front_left', \ 'back', 'back_left', 'back_right'] depth_metric = DepthMetric( camera_names=camera_names).cuda() depth_metric._reset() while epoch < max_num_epochs: my_model.train() os.environ['eval'] = 'false' if hasattr(train_dataset_loader.sampler, 'set_epoch'): train_dataset_loader.sampler.set_epoch(epoch) loss_list = [] time.sleep(10) data_time_s = time.time() time_s = time.time() for i_iter, ( input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, img_metas, curr_aug, prev_aug, next_aug) in enumerate(train_dataset_loader): if first_run: i_iter = i_iter + last_iter input_imgs = input_imgs.cuda() curr_imgs = curr_imgs.cuda() prev_imgs = prev_imgs.cuda() next_imgs = next_imgs.cuda() color_imgs = color_imgs.cuda() data_time_e = time.time() with torch.cuda.amp.autocast(amp): # forward + backward + optimize if args.dataset == 'nuscenes' and cfg.get('sem', False): sem_map = forward_openseed_model(openseed_model, curr_imgs[0] * 256., cfg.img_size) img_metas[0].update({'sem': sem_map}) curr_feats, prev_feats, next_feats = curr_imgs, prev_imgs, next_imgs result_dict = my_model( imgs=input_imgs, metas=img_metas, global_iter=global_iter) loss_input = { 'curr_imgs': curr_imgs, 'prev_imgs': prev_imgs, 'next_imgs': next_imgs, 'curr_feats': curr_feats, 'prev_feats': prev_feats, 'next_feats': next_feats, 'metas': img_metas, 'color_imgs': color_imgs, } for loss_input_key, loss_input_val in cfg.loss_input_convertion.items(): loss_input.update({ loss_input_key: result_dict[loss_input_val]}) loss, loss_dict = loss_func(loss_input) loss = loss / grad_accumulation if not amp: loss.backward() if (global_iter + 1) % grad_accumulation == 0: grad_norm = torch.nn.utils.clip_grad_norm_(my_model.parameters(), cfg.grad_max_norm) optimizer.step() optimizer.zero_grad() else: scaler.scale(loss).backward() if (global_iter + 1) % grad_accumulation == 0: scaler.unscale_(optimizer) grad_norm = torch.nn.utils.clip_grad_norm_(my_model.parameters(), cfg.grad_max_norm) scaler.step(optimizer) scaler.update() optimizer.zero_grad() loss_list.append(loss.detach().cpu().item()) scheduler.step_update(global_iter) time_e = time.time() global_iter += 1 if i_iter % print_freq == 0 and local_rank == 0: lr = optimizer.param_groups[0]['lr'] logger.info('[TRAIN] Epoch %d Iter %5d/%d: Loss: %.3f (%.3f), grad_norm: %.3f, lr: %.7f, time: %.3f (%.3f)'%( epoch, i_iter, len(train_dataset_loader), loss.item(), np.mean(loss_list), grad_norm, lr, time_e - time_s, data_time_e - data_time_s)) detailed_loss = [] for loss_name, loss_value in loss_dict.items(): detailed_loss.append(f'{loss_name}: {loss_value:.5f}') detailed_loss = ', '.join(detailed_loss) logger.info(detailed_loss) loss_list = [] data_time_s = time.time() time_s = time.time() if args.iter_resume: if (i_iter + 1) % 50 == 0 and local_rank == 0: dict_to_save = { 'state_dict': raw_model.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), 'epoch': epoch, 'global_iter': global_iter, 'last_iter': i_iter + 1, } save_file_name = os.path.join(os.path.abspath(args.work_dir), 'iter.pth') torch.save(dict_to_save, save_file_name) dst_file = osp.join(args.work_dir, 'latest.pth') symlink(save_file_name, dst_file) logger.info(f'iter ckpt {i_iter + 1} saved!') # save checkpoint if local_rank == 0: dict_to_save = { 'state_dict': raw_model.state_dict(), 'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict(), 'epoch': epoch + 1, 'global_iter': global_iter, } save_file_name = os.path.join(os.path.abspath(args.work_dir), f'epoch_{epoch+1}.pth') torch.save(dict_to_save, save_file_name) dst_file = osp.join(args.work_dir, 'latest.pth') symlink(save_file_name, dst_file) epoch += 1 first_run = False # eval if epoch % cfg.get('eval_every_epochs', 1) != 0: continue my_model.eval() os.environ['eval'] = 'true' val_loss_list = [] with torch.no_grad(): for i_iter_val, (input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, \ img_metas, curr_aug, prev_aug, next_aug) in enumerate(val_dataset_loader): input_imgs = input_imgs.cuda() curr_imgs = curr_imgs.cuda() prev_imgs = prev_imgs.cuda() next_imgs = next_imgs.cuda() color_imgs = color_imgs.cuda() with torch.cuda.amp.autocast(amp): if args.dataset == 'nuscenes' and cfg.get('sem', False): sem_map = forward_openseed_model(openseed_model, curr_imgs[0] * 256., cfg.img_size) img_metas[0].update({'sem': sem_map}) curr_feats, prev_feats, next_feats = curr_imgs, prev_imgs, next_imgs result_dict = my_model(imgs=input_imgs, metas=img_metas) loss_input = { 'curr_imgs': curr_imgs, 'prev_imgs': prev_imgs, 'next_imgs': next_imgs, 'curr_feats': curr_feats, 'prev_feats': prev_feats, 'next_feats': next_feats, 'metas': img_metas, 'color_imgs': color_imgs, } for loss_input_key, loss_input_val in cfg.loss_input_convertion.items(): loss_input.update({ loss_input_key: result_dict[loss_input_val] }) loss, loss_dict = loss_func(loss_input) if args.depth_metric: ms_depths = result_dict['ms_depths'][0] ms_depths = ms_depths.unflatten(-1, cfg.num_rays) depth_loc = ms_depths.new_tensor(img_metas[0]['depth_loc']) depth_gt = ms_depths.new_tensor(img_metas[0]['depth_gt']) depth_mask = torch.from_numpy(img_metas[0]['depth_mask']).cuda() # depth_pred = ms_depths[0, :(ms_depths.shape[1] // 2)] depth_pred = ms_depths[0] depth_metric._after_step(depth_loc, depth_gt, depth_mask, depth_pred) val_loss_list.append(loss.detach().cpu().numpy()) if i_iter_val % print_freq == 0 and local_rank == 0: logger.info('[EVAL] Epoch %d Iter %5d: Loss: %.3f (%.3f)'%( epoch, i_iter_val, loss.item(), np.mean(val_loss_list))) detailed_loss = [] for loss_name, loss_value in loss_dict.items(): detailed_loss.append(f'{loss_name}: {loss_value:.5f}') detailed_loss = ', '.join(detailed_loss) logger.info(detailed_loss) if args.depth_metric: depth_metric._after_epoch() depth_metric._reset() logger.info('Current val loss is %.3f' % (np.mean(val_loss_list))) if writer is not None: writer.close() if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--iter-resume', action='store_true', default=False) parser.add_argument('--seed', type=int, default=42) parser.add_argument('--gradient-accumulation', type=int, default=1) parser.add_argument('--depth-metric', action='store_true', default=False) parser.add_argument('--dataset', type=str, default='nuscenes') args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: utils/__init__.py ================================================ ================================================ FILE: utils/config_tools.py ================================================ NUM_RAYS = { # 'nuscenes': [96, 200], # 'nuscenes': [384, 800], 'nuscenes': [450, 800], 'kitti': [176, 608], 'kitti_raw': [176, 608], # 'kitti_raw': [55, 190], } def modify_for_eval(cfg, dataset='nuscenes', novel_depth=False, args=None): num_rays = NUM_RAYS[dataset] if args is not None and hasattr(args, "num_rays"): num_rays = args.num_rays cfg.num_rays = num_rays if dataset == 'nuscenes': cfg.train_dataset_config.update(dict( return_color_imgs = False, return_temporal_depth = False, return_depth = True, strict = False, cur_prob = 1.0 )) cfg.val_dataset_config.update(dict( return_color_imgs = False, return_temporal_depth = False, return_depth = True, strict = False, cur_prob = 1.0 )) elif dataset == 'kitti': cfg.train_dataset_config.update(dict( return_temporal = False, return_depth = True, strict = False, cur_prob = 1.0 )) cfg.val_dataset_config.update(dict( return_temporal = False, return_depth = True, strict = False, cur_prob = 1.0 )) elif dataset == 'kitti_raw': cfg.train_dataset_config.update(dict( cur_prob = 1.0, return_depth = True, strict = False )) cfg.val_dataset_config.update(dict( cur_prob = 1.0, return_depth = True, strict = False )) cfg.train_wrapper_config['phase'] = 'val' cfg.train_wrapper_config['use_flip'] = False cfg.loss['loss_cfgs'][0]['ray_resize'] = num_rays cfg.loss['loss_cfgs'][1]['ray_resize'] = num_rays cfg.model.head.update(dict( ray_sample_mode = 'fixed', ray_number = num_rays, trans_kw = 'img2lidar' )) if novel_depth and dataset == 'kitti': data_path = cfg.train_dataset_config['root'] img_size = cfg.train_dataset_config['crop_size'] cfg.train_dataset_config = dict( _delete_=True, type='Kitti_Novel_View_Eval', split = 'train', root = data_path, preprocess_root = data_path + 'preprocess', crop_size = img_size, ) cfg.val_dataset_config = dict( _delete_=True, type='Kitti_Novel_View_Eval', split = 'val', root = data_path, preprocess_root = data_path + 'preprocess', crop_size = img_size, ) cfg.model.head.update(dict( trans_kw = 'render_img2lidar' )) if novel_depth and dataset == 'nuscenes': data_path = cfg.train_dataset_config['data_path'] img_size = cfg.train_dataset_config['crop_size'] cfg.train_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Eval', data_path = data_path, imageset = 'data/nuscenes_infos_train_temporal_v2.pkl', crop_size = img_size, ) cfg.val_dataset_config = dict( _delete_=True, type='nuScenes_One_Frame_Eval', data_path = data_path, imageset = 'data/nuscenes_infos_val_temporal_v2.pkl', crop_size = img_size, ) cfg.model.head.update(dict( trans_kw = 'render_img2lidar' )) return cfg ================================================ FILE: utils/feat_tools.py ================================================ import torch import torch.nn.functional as F def multi2single_scale( ms_feats, imgs=None, max_shape=None, use_scales='all', normalize=None, scale_factor=None): num_level = len(ms_feats) if use_scales == 'all': scales = range(num_level) else: assert isinstance(use_scales, list) scales = use_scales used_feats = [ms_feats[i] for i in scales] if imgs is not None: used_feats.append(imgs) first2dims = used_feats[0].shape[:2] if max_shape is None: max_shape = used_feats[0].shape[-2:] reshaped = [] for i, feat in enumerate(used_feats): if not all([s1 == s2 for s1, s2 in zip(feat.shape[-2:], max_shape)]): feat = F.interpolate( feat.flatten(0, 1), size=max_shape, mode='bilinear', align_corners=True).unflatten(0, first2dims) if normalize is not None and (imgs is None or i < len(used_feats) - 1): feat = F.normalize(feat, p=normalize, dim=2) if scale_factor is not None and (imgs is None or i < len(used_feats) - 1): feat = feat * scale_factor reshaped.append(feat) return torch.cat(reshaped, dim=2) ================================================ FILE: utils/metric_util.py ================================================ import numpy as np # from mmseg.utils import get_root_logger from mmengine import MMLogger logger = MMLogger.get_instance('selfocc') import torch, torch.nn as nn import torch.distributed as dist import torch.nn.functional as F def cityscapes2semantickitti(sem): lut_table = torch.tensor([ 9, # 0: road -> road 11, # 1: sidewalk -> sidewalk 13, # 2: building -> building 13, # 3: wall -> building 14, # 4: fence -> fence 18, # 5: pole -> pole 19, # 6: traffic light -> traffic sign 19, # 7: traffic sign -> traffic sign 15, # 8: vegetation -> vegetation 17, # 9: terrain -> terrain 0, # 10: sky -> unlabeled 6, # 11: person -> person 7, # 12: rider -> bicyclist 1, # 13: car -> car 4, # 14: truck -> truck 5, # 15: bus -> other vehicle 5, # 16: train -> other vehicle 3, # 17: motorcycle -> motorcycle 2, # 18: bicycle -> bicycle # "motorcyclist", "parking", "other-ground", "trunk" ], dtype=sem.dtype, device=sem.device) sem_shape = sem.shape sem = lut_table[sem.flatten()].reshape(*sem_shape) return sem def openseed2nuscenes(sem): lut_table = torch.tensor([ 1, # 0: barrier -> barrier 2, # 1: bicycle -> bicycle 3, # 2: bus -> bus 4, # 3: car -> car 5, # 4: construction_vehicle -> construction_vehicle 5, # 5: crane -> construction_vehicle 6, # 6: motorcycle -> motorcycle 7, # 7: person -> person 8, # 8: traffic_cone -> traffic_cone 9, # 9: trailer -> trailer 9, # 10: trailer_truck -> trailer_truck 10, # 11: truck -> truck 11, # 12: road -> road 12, # 13: other_flat -> other_flat 13, # 14: sidewalk -> sidewalk 14, # 15: terrain -> terrain 14, # 16: grass -> terrain 15, # 17: building -> building 15, # 18: wall -> building 16, # 19: tree -> vegetation 0, # 20: sky -> unlabeled # "motorcyclist", "parking", "other-ground", "trunk" ], dtype=sem.dtype, device=sem.device) sem_shape = sem.shape sem = lut_table[sem.flatten()].reshape(*sem_shape) return sem class MeanIoU: def __init__(self, class_indices, # ignore_label: int, empty_label, label_str, use_mask=False, dataset_empty_label=17, name = 'none'): self.class_indices = class_indices self.num_classes = len(class_indices) # self.ignore_label = ignore_label self.empty_label = empty_label self.dataset_empty_label = dataset_empty_label self.label_str = label_str self.use_mask = use_mask self.name = name def reset(self) -> None: self.total_seen = torch.zeros(self.num_classes+1).cuda() self.total_correct = torch.zeros(self.num_classes+1).cuda() self.total_positive = torch.zeros(self.num_classes+1).cuda() def _after_step(self, outputs, targets, mask=None): # outputs = outputs[targets != self.ignore_label] # targets = targets[targets != self.ignore_label] if not isinstance(targets, (torch.Tensor, np.ndarray)): assert mask is None labels = torch.from_numpy(targets['semantics']).cuda() masks = torch.from_numpy(targets['mask_camera']).bool().cuda() targets = labels targets[targets == self.dataset_empty_label] = self.empty_label max_z = (targets != self.empty_label).nonzero()[:, 2].max() min_z = (targets != self.empty_label).nonzero()[:, 2].min() outputs[..., (max_z + 1):] = self.empty_label outputs[..., :min_z] = self.empty_label if self.use_mask: outputs = outputs[masks] targets = targets[masks] else: if mask is not None: outputs = outputs[mask] targets = targets[mask] for i, c in enumerate(self.class_indices): self.total_seen[i] += torch.sum(targets == c).item() self.total_correct[i] += torch.sum((targets == c) & (outputs == c)).item() self.total_positive[i] += torch.sum(outputs == c).item() self.total_seen[-1] += torch.sum(targets != self.empty_label).item() self.total_correct[-1] += torch.sum((targets != self.empty_label) & (outputs != self.empty_label)).item() self.total_positive[-1] += torch.sum(outputs != self.empty_label).item() def _after_epoch(self): if dist.is_initialized(): dist.all_reduce(self.total_seen) dist.all_reduce(self.total_correct) dist.all_reduce(self.total_positive) dist.barrier() ious = [] precs = [] recas = [] for i in range(self.num_classes): if self.total_positive[i] == 0: precs.append(0.) else: cur_prec = self.total_correct[i] / self.total_positive[i] precs.append(cur_prec.item()) if self.total_seen[i] == 0: ious.append(1) recas.append(1) else: cur_iou = self.total_correct[i] / (self.total_seen[i] + self.total_positive[i] - self.total_correct[i]) cur_reca = self.total_correct[i] / self.total_seen[i] ious.append(cur_iou.item()) recas.append(cur_reca) miou = np.mean(ious) # logger = get_root_logger() logger.info(f'Validation per class iou {self.name}:') for iou, prec, reca, label_str in zip(ious, precs, recas, self.label_str): logger.info('%s : %.2f%%, %.2f, %.2f' % (label_str, iou * 100, prec, reca)) logger.info(self.total_seen.int()) logger.info(self.total_correct.int()) logger.info(self.total_positive.int()) occ_iou = self.total_correct[-1] / (self.total_seen[-1] + self.total_positive[-1] - self.total_correct[-1]) # logger.info(f'iou: {occ_iou}') return miou * 100, occ_iou * 100 class IoU(nn.Module): def __init__(self, use_mask=False): super().__init__() self.class_indices = [0] self.num_classes = 1 self.label_str = ['occupied'] self.use_mask = use_mask xx = torch.linspace(-40.0, 40.0, 200) yy = torch.linspace(-40.0, 40.0, 200) zz = torch.linspace(-1.0, 5.4, 16) xyz = torch.stack([ xx[:, None, None].expand(-1, 200, 16), yy[None, :, None].expand(200, -1, 16), zz[None, None, :].expand(200, 200, -1) ], dim=-1) self.register_buffer('xyz', xyz, persistent=False) def reset(self) -> None: self.total_seen = torch.zeros(self.num_classes).cuda() self.total_correct = torch.zeros(self.num_classes).cuda() self.total_positive = torch.zeros(self.num_classes).cuda() def _after_step(self, outputs, targets, occ3d=False): if occ3d: self._after_step_occ3d(outputs, targets) return seen = targets.shape[0] correct = outputs[targets.transpose(0, 1).tolist()].sum() positive = outputs.sum() self.total_seen[0] += seen self.total_correct[0] += correct self.total_positive[0] += positive def _after_step_occ3d(self, outputs, targets): mask = torch.from_numpy(targets['mask_camera']).cuda() label = torch.from_numpy(targets['semantics']).cuda() label = label != 17 if self.use_mask: label = label[mask] label = torch.nonzero(label) if self.use_mask: outputs = outputs[mask] seen = label.shape[0] correct = outputs[label.transpose(0, 1).tolist()].sum() positive = outputs.sum() self.total_seen[0] += seen self.total_correct[0] += correct self.total_positive[0] += positive def _after_epoch(self): dist.all_reduce(self.total_seen) dist.all_reduce(self.total_correct) dist.all_reduce(self.total_positive) ious = [] for i in range(self.num_classes): if self.total_seen[i] == 0: ious.append(1) else: cur_iou = self.total_correct[i] / (self.total_seen[i] + self.total_positive[i] - self.total_correct[i]) ious.append(cur_iou.item()) miou = np.mean(ious) # logger = get_root_logger() logger.info(f'Validation per class iou:') for iou, label_str in zip(ious, self.label_str): logger.info('%s : %.2f%%' % (label_str, iou * 100)) logger.info(f'Final iou: {miou * 100}') return miou * 100 def cal_depth_metric(depth_pred, depth_gt): depth_pred = torch.clamp(depth_pred, 1e-3, 80) thresh = torch.maximum((depth_gt / depth_pred), (depth_pred / depth_gt)) a1 = (thresh < 1.25).to(torch.float) a2 = (thresh < 1.25 ** 2).to(torch.float) a3 = (thresh < 1.25 ** 3).to(torch.float) a1 = a1.mean() a2 = a2.mean() a3 = a3.mean() rmse = (depth_gt - depth_pred) ** 2 rmse = rmse.mean() ** .5 rmse_log = (torch.log(depth_gt) - torch.log(depth_pred)) ** 2 rmse_log = rmse_log.mean() ** .5 abs_rel = torch.abs(depth_gt - depth_pred) / depth_gt abs_rel = abs_rel.mean() sq_rel = ((depth_gt - depth_pred) ** 2) / depth_gt sq_rel = sq_rel.mean() metrics_dict = { "abs_rel": abs_rel, "sq_rel": sq_rel, "rmse": rmse, "rmse_log": rmse_log, "a1": a1, "a2": a2, "a3": a3 } return metrics_dict class DepthMetric(nn.Module): def __init__(self, camera_names=['front'], eval_types=['raw', 'median']): super().__init__() self.num_cams = len(camera_names) self.camera_names = camera_names self.num_types = len(eval_types) self.eval_types = eval_types self.register_buffer('abs_rel', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('sq_rel', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('rmse', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('rmse_log', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('a1', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('a2', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('a3', torch.zeros(self.num_types, self.num_cams)) self.register_buffer('count', torch.zeros(1)) self.register_buffer('scaling', torch.zeros(self.num_types, self.num_cams)) def _reset(self): self.abs_rel.zero_() self.sq_rel.zero_() self.rmse.zero_() self.rmse_log.zero_() self.a1.zero_() self.a2.zero_() self.a3.zero_() self.count.zero_() self.scaling.zero_() def _after_step(self, depth_loc, depth_gt, depth_mask, depth_pred): # depth_loc: N, n, 2 # depth_gt: N, n # depth_mask: N, n # depth_pred: N, h, w num_cams, num_points = depth_gt.shape depth_pred = F.grid_sample( depth_pred.unsqueeze(1), depth_loc.unsqueeze(1) * 2 - 1, mode='bilinear', padding_mode='border', align_corners=True) # N, 1, 1, n depth_pred = depth_pred.reshape(num_cams, num_points) for cam, (depth_gt_i, depth_pred_i, depth_mask_i) in enumerate( zip(depth_gt, depth_pred, depth_mask)): depth_gt_i_mask = depth_gt_i[depth_mask_i] depth_pred_i_mask = depth_pred_i[depth_mask_i] for type_idx, type in enumerate(self.eval_types): if type == 'raw': depth_pred_i_cal = depth_pred_i_mask self.scaling[type_idx, cam] += 1.0 elif type == 'median': scaling = torch.median(depth_gt_i_mask) / \ torch.median(depth_pred_i_mask) depth_pred_i_cal = scaling * depth_pred_i_mask self.scaling[type_idx, cam] += scaling else: raise NotImplementedError metrics = cal_depth_metric(depth_pred_i_cal, depth_gt_i_mask) self.abs_rel[type_idx, cam] += metrics['abs_rel'] self.sq_rel[type_idx, cam] += metrics['sq_rel'] self.rmse[type_idx, cam] += metrics['rmse'] self.rmse_log[type_idx, cam] += metrics['rmse_log'] self.a1[type_idx, cam] += metrics['a1'] self.a2[type_idx, cam] += metrics['a2'] self.a3[type_idx, cam] += metrics['a3'] self.count += 1 def _after_epoch(self): dist.barrier() dist.all_reduce(self.count) dist.all_reduce(self.abs_rel) dist.all_reduce(self.sq_rel) dist.all_reduce(self.rmse) dist.all_reduce(self.rmse_log) dist.all_reduce(self.a1) dist.all_reduce(self.a2) dist.all_reduce(self.a3) dist.all_reduce(self.scaling) dist.barrier() abs_rel = self.abs_rel / self.count sq_rel = self.sq_rel / self.count rmse = self.rmse / self.count rmse_log = self.rmse_log / self.count a1 = self.a1 / self.count a2 = self.a2 / self.count a3 = self.a3 / self.count scaling = self.scaling / self.count if not dist.is_initialized() or dist.get_rank() == 0: logger.info(f'Averaging over {self.count.item()} samples.') for type_idx, type in enumerate(self.eval_types): logger.info("{} evaluation:".format(type)) logger.info(("{:>12} | " * 9).format("cam_name", "abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3", "scale")) for cam, cam_name in enumerate(self.camera_names): logger.info((f"{cam_name:>12} | " + "&{: 12.3f} " * 8).format( abs_rel[type_idx, cam], sq_rel[type_idx, cam], rmse[type_idx, cam], rmse_log[type_idx, cam], a1[type_idx, cam], a2[type_idx, cam], a3[type_idx, cam], scaling[type_idx, cam]) + "\\\\") logger.info(("{:>12} | " + "&{: 12.3f} " * 8).format( "All", abs_rel[type_idx].mean(), sq_rel[type_idx].mean(), rmse[type_idx].mean(), rmse_log[type_idx].mean(), a1[type_idx].mean(), a2[type_idx].mean(), a3[type_idx].mean(), scaling[type_idx].mean()) + "\\\\") def compute_depth_errors(gt, pred, min_depth=1e-3, max_depth=80): """Computation of error metrics between predicted and ground truth depths """ pred[pred < min_depth] = min_depth pred[pred > max_depth] = max_depth thresh = np.maximum((gt / pred), (pred / gt)) a1 = (thresh < 1.25 ).mean() a2 = (thresh < 1.25 ** 2).mean() a3 = (thresh < 1.25 ** 3).mean() rmse = (gt - pred) ** 2 rmse = np.sqrt(rmse.mean()) rmse_log = (np.log(gt) - np.log(pred)) ** 2 rmse_log = np.sqrt(rmse_log.mean()) abs_rel = np.mean(np.abs(gt - pred) / gt) sq_rel = np.mean(((gt - pred) ** 2) / gt) return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3 def compute_depth_errors_torch(gt, pred, min_depth=1e-3, max_depth=80): """Computation of error metrics between predicted and ground truth depths """ pred[pred < min_depth] = min_depth pred[pred > max_depth] = max_depth thresh = torch.maximum((gt / pred), (pred / gt)) a1 = (thresh < 1.25 ).float().mean() a2 = (thresh < 1.25 ** 2).float().mean() a3 = (thresh < 1.25 ** 3).float().mean() rmse = (gt - pred) ** 2 rmse = torch.sqrt(rmse.mean()) rmse_log = (torch.log(gt) - torch.log(pred)) ** 2 rmse_log = torch.sqrt(rmse_log.mean()) abs_rel = torch.mean(torch.abs(gt - pred) / gt) sq_rel = torch.mean(((gt - pred) ** 2) / gt) return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3 ================================================ FILE: utils/misc.py ================================================ import os.path as osp import torch, numpy as np import torch.nn.functional as F from mmengine import Config from mmengine.registry import MODELS from mmengine.config.utils import MODULE2PACKAGE from mmengine.infer import BaseInferencer from mmengine.registry import init_default_scope from mmengine.runner.checkpoint import _load_checkpoint, _load_checkpoint_to_model def _load_model_from_metafile(model: str, scope: str = 'mmseg'): """Load config and weights from metafile. Args: model (str): model name defined in metafile. Returns: Tuple[Config, str]: Loaded Config and weights path defined in metafile. """ init_default_scope(scope if scope else 'mmseg') model = model.lower() assert scope is not None, ( 'scope should be initialized if you want ' 'to load config from metafile.') assert scope in MODULE2PACKAGE, ( f'{scope} not in {MODULE2PACKAGE}!,' 'please pass a valid scope.') repo_or_mim_dir = BaseInferencer._get_repo_or_mim_dir(scope) for model_cfg in BaseInferencer._get_models_from_metafile( repo_or_mim_dir): model_name = model_cfg['Name'].lower() model_aliases = model_cfg.get('Alias', []) if isinstance(model_aliases, str): model_aliases = [model_aliases.lower()] else: model_aliases = [alias.lower() for alias in model_aliases] if (model_name == model or model in model_aliases): cfg = Config.fromfile( osp.join(repo_or_mim_dir, model_cfg['Config'])) weights = model_cfg['Weights'] weights = weights[0] if isinstance(weights, list) else weights return cfg, weights raise ValueError(f'Cannot find model: {model} in {scope}') def _init_model( cfg, weights, device: str = 'cpu', ): """Initialize the model with the given config and checkpoint on the specific device. Args: cfg (ConfigType): Config containing the model information. weights (str, optional): Path to the checkpoint. device (str, optional): Device to run inference. Defaults to 'cpu'. Returns: nn.Module: Model loaded with checkpoint. """ checkpoint = None if weights is not None: checkpoint = _load_checkpoint(weights, map_location='cpu') # Delete the `pretrained` field to prevent model from loading the # the pretrained weights unnecessarily. if cfg.model.get('pretrained') is not None: del cfg.model.pretrained model = MODELS.build(cfg.model) model.cfg = cfg _load_checkpoint_to_model(model, checkpoint) model.to(device) model.eval() return model nuscenes_to_cityscapes = [ 11, 13, 15, 15, 15, 15, 15, 15, 16, 14, 17, 7, 7, 4, 10, 3, 0, 6, 2 ] def generate_segmentation_map_2d(model_2d, inputs): bs, num_cams = inputs.shape[:2] outputs = model_2d(inputs.flatten(0, 1)) # bs*num_cams, c, h, w logits = torch.softmax(outputs, dim=1) nusc_logits = torch.zeros( (logits.shape[0], 18, *logits.shape[2:]), device=logits.device, dtype=logits.dtype) index = logits.new_tensor(nuscenes_to_cityscapes, dtype=torch.long) nusc_logits.index_add_(dim=1, index=index, source=logits) return nusc_logits.unflatten(0, (bs, num_cams)) # bs, num_cams, c, h, w # ['barrier', 'bicycle', 'bus', 'car', 'construction_vehicle', # 'motorcycle', 'pedestrian', 'traffic_cone', 'trailer', 'truck', # 'driveable_surface', 'other_flat', 'sidewalk', 'terrain', 'manmade', # 'vegetation'], nuscenes_to_openseed = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0 ] nuscenes_to_openseed = [ 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, 9, 10, 11, 12, 12, 12, 12, 13, 14, 14, 14, 15, 15, 16, 0 ] def read_segmentation_map_2d(map_path, img_metas): labelss = [] for img_meta in img_metas: token = img_meta['token'] labels = [] for cam in ['CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT']: label_path = osp.join(map_path, cam, token + '.npy') label = np.load(label_path).astype(np.int64) label = label[:768, :1600] label = torch.from_numpy(label).cuda() label = F.one_hot(label, num_classes=len(nuscenes_to_openseed)).float() labels.append(label.permute(2, 0, 1)) labels = torch.stack(labels) labelss.append(labels) labelss = torch.stack(labelss) # b, n, c, h, w nusc_logits = torch.zeros( (*labelss.shape[:2], 17, *labelss.shape[3:]), device=labelss.device, dtype=labelss.dtype) index = labelss.new_tensor(nuscenes_to_openseed, dtype=torch.long) nusc_logits.index_add_(dim=2, index=index, source=labelss) return nusc_logits ================================================ FILE: utils/openseed_utils.py ================================================ import os, numpy as np from openseed.BaseModel import BaseModel from openseed import build_model from openseed.utils.arguments import load_opt_command from detectron2.data import MetadataCatalog from detectron2.utils.colormap import random_color import torch.nn.functional as F import torch def build_openseed_model(): args = 'evaluate --conf_files config/openseed/openseed_swint_lang.yaml --overrides WEIGHT ckpts/openseed_model_state_dict_swint_51.2ap.pt' opt, cmdline_args = load_opt_command(args.split()) pretrained_pth = os.path.join(opt['WEIGHT']) model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda() model.requires_grad_(False) for param in model.parameters(): param.requires_grad_(False) stuff_classes = [ 'barrier', 'bicycle', 'bus', 'car', # 'wagon', 'van', 'minivan', 'SUV', 'jeep', 'construction_vehicle', 'crane', 'motorcycle', # 'vespa', 'Scooter', 'person', 'traffic_cone', 'trailer', 'trailer_truck', 'truck', 'road', 'other_flat', # 'rail track', 'lake', 'river', 'sidewalk', 'terrain', 'grass', # 'hill', # 'sand', 'gravel', 'building', 'wall', # 'guard rail', 'fence', # 'pole', 'tree', # 'plant', 'sky'] stuff_colors = [random_color(rgb=True, maximum=255).astype(np.int).tolist() for _ in range(len(stuff_classes))] stuff_dataset_id_to_contiguous_id = {x:x for x in range(len(stuff_classes))} MetadataCatalog.get("demo").set( stuff_colors=stuff_colors, stuff_classes=stuff_classes, stuff_dataset_id_to_contiguous_id=stuff_dataset_id_to_contiguous_id, ) model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(stuff_classes, is_eval=True) metadata = MetadataCatalog.get('demo') model.model.metadata = metadata model.model.sem_seg_head.num_classes = len(stuff_classes) return model def forward_openseed_model(model, img, size): img = F.interpolate(img, [512, int(1.0 * size[1] / size[0] * 512)], mode='bicubic', align_corners=True) batch_inputs = [{'image': img[i], 'height': size[0], 'width': size[1]} for i in range(img.shape[0])] with torch.no_grad(): outputs = model.forward(batch_inputs, inference_task="sem_seg") sem_seg = [outputs[i]['sem_seg'].max(0)[1] for i in range(img.shape[0])] sem_seg = torch.stack(sem_seg, dim=0) torch.cuda.empty_cache() return sem_seg ================================================ FILE: utils/scenerf_metric.py ================================================ """ Part of the code is taken from https://github.com/waterljwant/SSC/blob/master/sscMetrics.py """ import numpy as np import torch, torch.distributed as dist def get_iou(iou_sum, cnt_class): _C = iou_sum.shape[0] # 12 iou = np.zeros(_C, dtype=np.float32) # iou for each class for idx in range(_C): iou[idx] = iou_sum[idx] / cnt_class[idx] if cnt_class[idx] else 0 mean_iou = np.sum(iou[1:]) / np.count_nonzero(cnt_class[1:]) return iou, mean_iou def get_accuracy(predict, target, weight=None): # 0.05s _bs = predict.shape[0] # batch size _C = predict.shape[1] # _C = 12 target = np.int32(target) target = target.reshape(_bs, -1) # (_bs, 60*36*60) 129600 predict = predict.reshape(_bs, _C, -1) # (_bs, _C, 60*36*60) predict = np.argmax( predict, axis=1 ) # one-hot: _bs x _C x 60*36*60 --> label: _bs x 60*36*60. correct = predict == target # (_bs, 129600) if weight: # 0.04s, add class weights weight_k = np.ones(target.shape) for i in range(_bs): for n in range(target.shape[1]): idx = 0 if target[i, n] == 255 else target[i, n] weight_k[i, n] = weight[idx] correct = correct * weight_k acc = correct.sum() / correct.size return acc class SSCMetrics: def __init__(self, n_classes): self.n_classes = n_classes self.reset() def hist_info(self, n_cl, pred, gt): assert pred.shape == gt.shape k = (gt >= 0) & (gt < n_cl) # exclude 255 labeled = np.sum(k) correct = np.sum((pred[k] == gt[k])) return ( np.bincount( n_cl * gt[k].astype(int) + pred[k].astype(int), minlength=n_cl ** 2 ).reshape(n_cl, n_cl), correct, labeled, ) @staticmethod def compute_score(hist, correct, labeled): iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist)) mean_IU = np.nanmean(iu) mean_IU_no_back = np.nanmean(iu[1:]) freq = hist.sum(1) / hist.sum() freq_IU = (iu[freq > 0] * freq[freq > 0]).sum() mean_pixel_acc = correct / labeled if labeled != 0 else 0 return iu, mean_IU, mean_IU_no_back, mean_pixel_acc def add_batch(self, y_pred, y_true, nonempty=None, nonsurface=None): # self.count += 1 mask = y_true != 255 if nonempty is not None: mask = mask & nonempty if nonsurface is not None: mask = mask & nonsurface tp, fp, fn = self.get_score_completion(y_pred, y_true, mask) self.completion_tp += tp self.completion_fp += fp self.completion_fn += fn mask = y_true != 255 if nonempty is not None: mask = mask & nonempty tp_sum, fp_sum, fn_sum = self.get_score_semantic_and_completion( y_pred, y_true, mask ) self.tps += tp_sum self.fps += fp_sum self.fns += fn_sum def get_stats(self): # print(self.completion_tp, self.completion_fp) dist.barrier() dist.all_reduce(self.completion_tp) dist.all_reduce(self.completion_fn) dist.all_reduce(self.completion_fp) dist.all_reduce(self.tps) dist.all_reduce(self.fps) dist.all_reduce(self.fns) dist.barrier() if self.completion_tp != 0: precision = self.completion_tp / (self.completion_tp + self.completion_fp) recall = self.completion_tp / (self.completion_tp + self.completion_fn) iou = self.completion_tp / ( self.completion_tp + self.completion_fp + self.completion_fn ) else: precision, recall, iou = 0, 0, 0 iou_ssc = self.tps / (self.tps + self.fps + self.fns + 1e-5) return { "precision": precision, "recall": recall, "iou": iou, "iou_ssc": iou_ssc, "iou_ssc_mean": torch.mean(iou_ssc[1:]), } def reset(self): self.completion_tp = torch.zeros(1, dtype=torch.float).cuda() self.completion_fp = torch.zeros(1, dtype=torch.float).cuda() self.completion_fn = torch.zeros(1, dtype=torch.float).cuda() self.tps = torch.zeros(self.n_classes, dtype=torch.float).cuda() self.fps = torch.zeros(self.n_classes, dtype=torch.float).cuda() self.fns = torch.zeros(self.n_classes, dtype=torch.float).cuda() # self.hist_ssc = torch.zeros((self.n_classes, self.n_classes)).cuda() # self.labeled_ssc = 0 # self.correct_ssc = 0 # self.precision = 0 # self.recall = 0 # self.iou = 0 # self.count = 1e-8 # self.iou_ssc = np.zeros(self.n_classes, dtype=np.float32) # self.cnt_class = np.zeros(self.n_classes, dtype=np.float32) def get_score_completion(self, predict, target, nonempty=None): predict = predict.clone() target = target.clone() """for scene completion, treat the task as two-classes problem, just empty or occupancy""" _bs = predict.shape[0] # batch size # ---- ignore predict[target == 255] = 0 target[target == 255] = 0 # ---- flatten target = target.reshape(_bs, -1) # (_bs, 129600) predict = predict.reshape(_bs, -1) # (_bs, _C, 129600), 60*36*60=129600 # ---- treat all non-empty object class as one category, set them to label 1 b_pred = torch.zeros(predict.shape).cuda() b_true = torch.zeros(target.shape).cuda() b_pred[predict > 0] = 1 b_true[target > 0] = 1 tp_sum, fp_sum, fn_sum = 0, 0, 0 for idx in range(_bs): y_true = b_true[idx, :] # GT y_pred = b_pred[idx, :] if nonempty is not None: nonempty_idx = nonempty[idx, :].reshape(-1) y_true = y_true[nonempty_idx == 1] y_pred = y_pred[nonempty_idx == 1] tp = torch.logical_and(y_true == 1, y_pred == 1).sum() fp = torch.logical_and(y_true != 1, y_pred == 1).sum() fn = torch.logical_and(y_true == 1, y_pred != 1).sum() tp_sum += tp fp_sum += fp fn_sum += fn # print(tp_sum, fp_sum, fn_sum) return tp_sum, fp_sum, fn_sum def get_score_semantic_and_completion(self, predict, target, nonempty=None): predict = predict.clone() target = target.clone() _bs = predict.shape[0] # batch size _C = self.n_classes # _C = 12 # ---- ignore predict[target == 255] = 0 target[target == 255] = 0 # ---- flatten target = target.reshape(_bs, -1) # (_bs, 129600) predict = predict.reshape(_bs, -1) # (_bs, 129600), 60*36*60=129600 # cnt_class = np.zeros(_C, dtype=np.int32) # count for each class # iou_sum = np.zeros(_C, dtype=np.float32) # sum of iou for each class tp_sum = torch.zeros(_C, dtype=torch.int32).cuda() # tp fp_sum = torch.zeros(_C, dtype=torch.int32).cuda() # fp fn_sum = torch.zeros(_C, dtype=torch.int32).cuda() # fn for idx in range(_bs): y_true = target[idx, :] # GT y_pred = predict[idx, :] if nonempty is not None: nonempty_idx = nonempty[idx, :].reshape(-1) y_pred = y_pred[ torch.logical_and(nonempty_idx == 1, y_true != 255) ] y_true = y_true[ torch.logical_and(nonempty_idx == 1, y_true != 255) ] for j in range(_C): # for each class tp = torch.logical_and(y_true == j, y_pred == j).sum() fp = torch.logical_and(y_true != j, y_pred == j).sum() fn = torch.logical_and(y_true == j, y_pred != j).sum() tp_sum[j] += tp fp_sum[j] += fp fn_sum[j] += fn return tp_sum, fp_sum, fn_sum ================================================ FILE: utils/tb_wrapper.py ================================================ from torch.utils.tensorboard import SummaryWriter from mmengine.utils import ManagerMixin class WrappedTBWriter(SummaryWriter, ManagerMixin): def __init__(self, name, **kwargs): SummaryWriter.__init__(self, **kwargs) ManagerMixin.__init__(self, name) ================================================ FILE: utils/temporal_pkl.py ================================================ import pickle import argparse def arange_according_to_scene(infos, nusc): scenes = dict() for i, info in enumerate(infos): scene_token = nusc.get('sample', info['token'])['scene_token'] scene_meta = nusc.get('scene', scene_token) scene_name = scene_meta['name'] if not scene_name in scenes: scenes[scene_name] = [info] else: scenes[scene_name].append(info) sorted_scenes = [] cnt = 0 for k, v in scenes.items(): sorted_v = sorted(v, key=lambda x:x['timestamp']) for i, sample in enumerate(sorted_v): sample['prev'] = cnt + i - 1 sample['next'] = cnt + i + 1 sorted_v[0]['prev'] = -1 sorted_v[-1]['next'] = -1 cnt += len(sorted_v) # sorted_scenes.append(sorted_v) sorted_scenes.extend(sorted_v) valid_idx = [] for i, sample in enumerate(sorted_scenes): if sample['prev'] != -1 and sample['next'] != -1: valid_idx.append(i) return sorted_scenes, valid_idx if __name__ == "__main__": parse = argparse.ArgumentParser('') parse.add_argument('--src-path', type=str, default='', help='path of the original pkl file') parse.add_argument('--dst-path', type=str, default='', help='path of the output pkl file') parse.add_argument('--data-path', type=str, default='', help='path of the nuScenes dataset') parse.add_argument('--hfai', action='store_true', default=False) args = parse.parse_args() with open(args.src_path, 'rb') as f: data = pickle.load(f) if args.hfai: from ..dataset.custom_nuscenes import CustomNuScenes as NuScenes else: from nuscenes import NuScenes nusc = NuScenes('v1.0-trainval', args.data_path) data['infos'], data['valid_idx'] = arange_according_to_scene(data['infos'], nusc) with open(args.dst_path, 'wb') as f: pickle.dump(data, f) ================================================ FILE: vis_3d.py ================================================ import os offscreen = False if os.environ.get('DISP', 'f') == 'f': from pyvirtualdisplay import Display display = Display(visible=False, size=(2560, 1440)) display.start() offscreen = True from mayavi import mlab import mayavi mlab.options.offscreen = offscreen print("Set mlab.options.offscreen={}".format(mlab.options.offscreen)) import os, time, argparse, math, os.path as osp, numpy as np import torch import torch.distributed as dist import torch.nn.functional as F import matplotlib.pyplot as plt import matplotlib; matplotlib.use('agg') import mmcv from mmengine import Config from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") import model from dataset import get_dataloader from dataset.dataset_wrapper_temporal import custom_collate_fn_temporal from utils.config_tools import modify_for_eval from utils.metric_util import cityscapes2semantickitti, openseed2nuscenes from vis_pics import create_label_colormap from PIL import Image def pass_print(*args, **kwargs): pass KITTI_ROOT = 'data/kitti' import dataset.kitti.io_data as SemanticKittiIO def read_semantic_kitti(metas, idx): label_path = os.path.join( KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.label".format(metas['token'])) invalid_path = os.path.join( KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.invalid".format(metas['token'])) remap_lut = SemanticKittiIO.get_remap_lut("dataset/kitti/semantic-kitti.yaml") LABEL = SemanticKittiIO._read_label_SemKITTI(label_path) INVALID = SemanticKittiIO._read_invalid_SemKITTI(invalid_path) LABEL = remap_lut[LABEL.astype(np.uint16)].astype( np.float32 ) # Remap 20 classes semanticKITTI SSC LABEL[ np.isclose(INVALID, 1) ] = 255 # Setting to unknown all voxels marked on invalid mask... LABEL = LABEL.reshape(256, 256, 32) LABEL = np.flip(LABEL, 1) save_info = f"{metas['sequence']}_{metas['token']}_{idx}.png" return LABEL, save_info def read_occ3d_label(metas, nusc, idx): token = metas['token'] scene_token = nusc.get('sample', token)['scene_token'] scene_name = nusc.get('scene', scene_token)['name'] label_file = f'data/occ3d/gts/{scene_name}/{token}/labels.npz' label = np.load(label_file) save_info = f"{scene_name}_{token}_{idx}.png" return label, save_info def get_grid_coords(dims, resolution): """ :param dims: the dimensions of the grid [x, y, z] (i.e. [256, 256, 32]) :return coords_grid: is the center coords of voxels in the grid """ g_xx = np.arange(0, dims[0]) # [0, 1, ..., 256] # g_xx = g_xx[::-1] g_yy = np.arange(0, dims[1]) # [0, 1, ..., 256] # g_yy = g_yy[::-1] g_zz = np.arange(0, dims[2]) # [0, 1, ..., 32] # Obtaining the grid with coords... xx, yy, zz = np.meshgrid(g_xx, g_yy, g_zz) coords_grid = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T coords_grid = coords_grid.astype(np.float32) resolution = np.array(resolution, dtype=np.float32).reshape([1, 3]) coords_grid = (coords_grid * resolution) + resolution / 2 return coords_grid def draw( voxels, # semantic occupancy predictions pred_pts, # lidarseg predictions vox_origin, voxel_size=0.2, # voxel size in the real world grid=None, # voxel coordinates of point cloud pt_label=None, # label of point cloud save_dir=None, cam_positions=None, focal_positions=None, timestamp=None, mode=0, sem=False, dataset='nuscenes', save_path=None ): w, h, z = voxels.shape # grid = grid.astype(np.int) # Compute the voxels coordinates grid_coords = get_grid_coords( [voxels.shape[0], voxels.shape[1], voxels.shape[2]], voxel_size ) + np.array(vox_origin, dtype=np.float32).reshape([1, 3]) if mode == 0: grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T elif mode == 1: indexes = grid[:, 0] * h * z + grid[:, 1] * z + grid[:, 2] indexes, pt_index = np.unique(indexes, return_index=True) pred_pts = pred_pts[pt_index] grid_coords = grid_coords[indexes] grid_coords = np.vstack([grid_coords.T, pred_pts.reshape(-1)]).T elif mode == 2: indexes = grid[:, 0] * h * z + grid[:, 1] * z + grid[:, 2] indexes, pt_index = np.unique(indexes, return_index=True) gt_label = pt_label[pt_index] grid_coords = grid_coords[indexes] grid_coords = np.vstack([grid_coords.T, gt_label.reshape(-1)]).T else: raise NotImplementedError # Get the voxels inside FOV fov_grid_coords = grid_coords # Remove empty and unknown voxels fov_voxels = fov_grid_coords[ (fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 100) ] print(len(fov_voxels)) figure = mlab.figure(size=(2560, 1440), bgcolor=(1, 1, 1)) # Draw occupied inside FOV voxels voxel_size = sum(voxel_size) / 3 if not sem: plt_plot_fov = mlab.points3d( # fov_voxels[:, 1], # fov_voxels[:, 0], fov_voxels[:, 0], fov_voxels[:, 1], fov_voxels[:, 2], fov_voxels[:, 3], colormap="jet", # colormap="hot", scale_factor=1.0 * voxel_size, mode="cube", opacity=1.0, ) else: plt_plot_fov = mlab.points3d( # fov_voxels[:, 1], # fov_voxels[:, 0], fov_voxels[:, 0], fov_voxels[:, 1], fov_voxels[:, 2], fov_voxels[:, 3], scale_factor=1.0 * voxel_size, mode="cube", opacity=1.0, vmin=1, vmax=19 if dataset == 'kitti' else 16, # 16 ) plt_plot_fov.glyph.scale_mode = "scale_by_vector" if sem: if dataset == 'kitti': colors = create_label_colormap() colors = np.concatenate([colors, np.ones_like(colors[:, :1]) * 255], axis=-1) else: colors = np.array( [ [255, 120, 50, 255], # barrier orange [255, 192, 203, 255], # bicycle pink [255, 255, 0, 255], # bus yellow [ 0, 150, 245, 255], # car blue [ 0, 255, 255, 255], # construction_vehicle cyan [255, 127, 0, 255], # motorcycle dark orange [255, 0, 0, 255], # pedestrian red [255, 240, 150, 255], # traffic_cone light yellow [135, 60, 0, 255], # trailer brown [160, 32, 240, 255], # truck purple [255, 0, 255, 255], # driveable_surface dark pink # [175, 0, 75, 255], # other_flat dark red [139, 137, 137, 255], [ 75, 0, 75, 255], # sidewalk dard purple [150, 240, 80, 255], # terrain light green [230, 230, 250, 255], # manmade white [ 0, 175, 0, 255], # vegetation green ] ).astype(np.uint8) plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors scene = figure.scene if dataset == 'nuscenes': scene.camera.position = [118.7195754824976, 118.70290907014409, 120.11124225247899] scene.camera.focal_point = [0.008333206176757812, -0.008333206176757812, 1.399999976158142] scene.camera.view_angle = 30.0 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [114.42016931210819, 320.9039783052695] scene.camera.compute_view_plane_normal() scene.render() scene.camera.azimuth(-5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(5) scene.render() scene.camera.azimuth(-5) scene.render() scene.camera.position = [-138.7379881436844, -0.008333206176756428, 99.5084646673331] scene.camera.focal_point = [0.008333206176757812, -0.008333206176757812, 1.399999976158142] scene.camera.view_angle = 30.0 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [104.37185230017721, 252.84608651497263] scene.camera.compute_view_plane_normal() scene.render() scene.camera.position = [-114.65804807470022, -0.008333206176756668, 82.48137575398867] scene.camera.focal_point = [0.008333206176757812, -0.008333206176757812, 1.399999976158142] scene.camera.view_angle = 30.0 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [75.17498702830105, 222.91192666552377] scene.camera.compute_view_plane_normal() scene.render() scene.camera.position = [-94.75727115818437, -0.008333206176756867, 68.40940144543957] scene.camera.focal_point = [0.008333206176757812, -0.008333206176757812, 1.399999976158142] scene.camera.view_angle = 30.0 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [51.04534630774225, 198.1729515833347] scene.camera.compute_view_plane_normal() scene.render() scene.camera.elevation(5) scene.camera.orthogonalize_view_up() scene.render() scene.camera.position = [-107.15500034628069, -0.008333206176756742, 92.16667026873841] scene.camera.focal_point = [0.008333206176757812, -0.008333206176757812, 1.399999976158142] scene.camera.view_angle = 30.0 scene.camera.view_up = [0.6463156430702276, -6.454925414290924e-18, 0.7630701733934554] scene.camera.clipping_range = [78.84362692774403, 218.2948716014858] scene.camera.compute_view_plane_normal() scene.render() scene.camera.position = [-107.15500034628069, -0.008333206176756742, 92.16667026873841] scene.camera.focal_point = [0.008333206176757812, -0.008333206176757812, 1.399999976158142] scene.camera.view_angle = 30.0 scene.camera.view_up = [0.6463156430702277, -6.4549254142909245e-18, 0.7630701733934555] scene.camera.clipping_range = [78.84362692774403, 218.2948716014858] scene.camera.compute_view_plane_normal() scene.render() scene.camera.elevation(5) scene.camera.orthogonalize_view_up() scene.render() scene.camera.elevation(5) scene.camera.orthogonalize_view_up() scene.render() scene.camera.elevation(-5) scene.camera.orthogonalize_view_up() scene.render() else: # camera view scene.camera.position = cam_positions[0] # - np.array([0.7, 1.3, 0.]) scene.camera.focal_point = focal_positions[0] # - np.array([0.7, 1.3, 0.]) scene.camera.view_angle = 41 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [0.01, 300.] scene.camera.compute_view_plane_normal() scene.render() if offscreen: mlab.savefig(save_path) else: mlab.show() mlab.close() def main(local_rank, args): if args.dataset == 'nuscenes': from nuscenes import NuScenes nusc = NuScenes(version='v1.0-trainval', dataroot='data/nuscenes') # global settings torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, args.dataset) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank ) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) local_rank = dist.get_rank() if dist.get_rank() != 0: import builtins builtins.print = pass_print else: distributed = False local_rank = 0 # configure logger if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval-{timestamp}.log') logger = MMLogger(name='selfocc', log_file=log_file, log_level='INFO') # build model my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) print('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.to(args.device) raw_model = my_model print('done ddp model') cfg.train_wrapper_config.phase = 'val' if args.scene_name: cfg.val_dataset_config.update({ 'type': 'nuScenes_One_Frame', 'imageset': 'data/nuscenes_infos_val_temporal_v1_scene_1.pkl', 'scene_name': args.scene_name, 'strict': False}) train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed, iter_resume=False, val_only=True) if args.vis_train: dataset = train_dataset_loader.dataset else: dataset = val_dataset_loader.dataset cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from print('resume from: ', cfg.resume_from) print('work dir: ', args.work_dir) epoch = 'last' if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt if 'epoch' in ckpt: epoch = ckpt['epoch'] print(raw_model.load_state_dict(state_dict, strict=False)) print(f'successfully resumed from {cfg.resume_from}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) xx = torch.linspace(-40.0, 40.0, 200) yy = torch.linspace(-40.0, 40.0, 200) zz = torch.linspace(-1.0, 5.4, 16) xyz = torch.stack([ xx[:, None, None].expand(-1, 200, 16), yy[None, :, None].expand(200, -1, 16), zz[None, None, :].expand(200, 200, -1), torch.ones(200, 200, 16) ], dim=-1) # 200, 200, 16, 4 xyz = xyz.to(args.device) # eval my_model.eval() vis_save_path = osp.join(args.work_dir, args.dir_name, f'epoch{epoch}') if args.scene_name: vis_save_path = osp.join(vis_save_path, args.scene_name) os.makedirs(vis_save_path, exist_ok=True) with torch.no_grad(): num_iters = len(val_dataset_loader) if args.scene_name else len(args.frame_idx) if args.scene_name: loader_iter = iter(val_dataset_loader) for idx in range(num_iters): if args.scene_name: input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, img_metas, _, _, _ = next(loader_iter) real_idx = img_metas[0]['timestamp'] else: real_idx = args.frame_idx[idx] one_batch = dataset[real_idx] input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, img_metas, _, _, _ = custom_collate_fn_temporal([one_batch]) input_imgs = input_imgs.to(args.device) if 'kitti' in args.dataset: point_cloud_range = [-25.6, 0.0, -2.0, 25.6, 51.2, 4.4] resolution = 0.2 else: if args.scene_size == 0: point_cloud_range = [-51.2, -51.2, -4, 51.2, 51.2, 4] resolution = 0.4 elif args.scene_size == 1: point_cloud_range = [-40.0, -40.0, -2.8, 40.0, 40.0, 3.6] resolution = 0.4 elif args.scene_size == 2: point_cloud_range = [-40.0, -40.0, -3.1, 40.0, 40.0, 3.9] resolution = 0.4 elif args.scene_size == 3: point_cloud_range = [-40.0, -40.0, -3.2, 40.0, 40.0, 4.0] resolution = 0.3 elif args.scene_size == 4: point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4] resolution = 0.4 if args.model_pred: if cfg.get('estimate_pose'): assert curr_aug is not None and prev_aug is not None and next_aug is not None assert img_metas[0]['input_imgs_path'] == img_metas[0]['curr_imgs_path'] curr_aug, prev_aug, next_aug = curr_aug.cuda(), prev_aug.cuda(), next_aug.cuda() pose_dict = my_model(pose_input=[curr_aug, prev_aug, next_aug], metas=img_metas, predict_pose=True) for i_meta, meta in enumerate(img_metas): meta.update(pose_dict[i_meta]) result_dict = my_model( imgs=input_imgs, metas=img_metas, occ_only=True, aabb=point_cloud_range, resolution=resolution) if args.transform: ego2lidar = img_metas[0]['ego2lidar'] ego2lidar = xyz.new_tensor(ego2lidar) # 4, 4 lidar_points = torch.matmul(ego2lidar.unsqueeze(0), xyz.reshape(-1, 4, 1)) lidar_points = lidar_points.squeeze(-1)[:, :3] # 200*200*16, 3 lidar_points[:, 0] = (lidar_points[:, 0] - point_cloud_range[0]) / (point_cloud_range[3] - point_cloud_range[0]) lidar_points[:, 1] = (lidar_points[:, 1] - point_cloud_range[1]) / (point_cloud_range[4] - point_cloud_range[1]) lidar_points[:, 2] = (lidar_points[:, 2] - point_cloud_range[2]) / (point_cloud_range[5] - point_cloud_range[2]) lidar_points = lidar_points.reshape(1, 200, 200, 16, 3) sampled_sdf = F.grid_sample( result_dict['sdf'][None, None, ...], # 1, 1, H, W, D lidar_points[..., [2, 0, 1]] * 2 - 1, mode='bilinear', align_corners=True, padding_mode='border') # 1, 1, 200, 200, 16 sdf = sampled_sdf.squeeze().permute(1, 0, 2).cpu() else: sdf = result_dict['sdf'].cpu() grid_size = sdf.shape voxel_density = sdf predict_vox = (voxel_density <= args.thresh).to(torch.int) predict_vox[:6, ...] = 0 predict_vox[-6:, ...] = 0 predict_vox[:, :6, :] = 0 predict_vox[:, -6:, :] = 0 print((voxel_density > 1.0).sum()) print((voxel_density > 2.0).sum()) print((voxel_density > 3.0).sum()) if args.dataset == 'nuscenes': gt_occ, save_info = read_occ3d_label(img_metas[0], nusc, real_idx) camera_mask = torch.from_numpy(gt_occ['mask_camera']).bool()#.cuda() lidar_mask = torch.from_numpy(gt_occ['mask_lidar']).bool()#.cuda() gt_vox = torch.from_numpy(gt_occ['semantics'])#.cuda() gt_vox[gt_vox == 17] = 0 gt_vox = gt_vox.permute(1, 0, 2) camera_mask = camera_mask.permute(1, 0, 2) lidar_mask = lidar_mask.permute(1, 0, 2) else: gt_vox, save_info = read_semantic_kitti(img_metas[0], real_idx) lidar_mask = gt_vox != 255 if not args.model_pred: predict_vox = gt_vox grid_size = predict_vox.shape if args.use_lidar_mask: predict_vox[~lidar_mask] = 0 elif args.use_cam_mask and args.dataset == 'nuscenes': predict_vox[~camera_mask] = 0 predict_vox[..., (grid_size[2] - args.cap):] = 0 if args.sem: if args.model_pred: if args.transform: sem = result_dict['logits'].permute(3, 0, 1, 2) # C, H, W, D sampled_sem = F.grid_sample( sem[None, ...], # 1, C, H, W, D lidar_points[..., [2, 0, 1]] * 2 - 1, mode='bilinear', align_corners=True) # 1, C, 200, 200, 16 sampled_sem = torch.argmax(sampled_sem, dim=1).squeeze() sem = sampled_sem.permute(1, 0, 2) else: sem = result_dict['sem'] if args.dataset == 'nuscenes': sem = openseed2nuscenes(sem) elif args.dataset == 'kitti': sem = cityscapes2semantickitti(sem) else: raise NotImplementedError sem = sem.cpu() predict_vox = predict_vox * sem else: for z in range(grid_size[2]-args.cap): mask = (predict_vox > 0)[..., z] predict_vox[..., z][mask] = z + 1 # grid_size[2] - z if args.dataset == 'nuscenes': voxel_origin = [-40.0, -40.0, -1.0] voxel_max = [40.0, 40.0, 5.4] resolution = 0.4 else: voxel_origin = point_cloud_range[:3] voxel_max = point_cloud_range[3:] # visualization if args.save_rgb: frame_dir = os.path.join(vis_save_path, str(real_idx)) os.makedirs(frame_dir, exist_ok=True) gt_imgs = curr_imgs.squeeze(0).permute(0, 2, 3, 1).cpu().numpy() * 256 gt_imgs = gt_imgs[..., [2, 1, 0]] gt_imgs = gt_imgs.astype(np.uint8) for i in range(gt_imgs.shape[0]): img = Image.fromarray(gt_imgs[i]) img.save(osp.join(frame_dir, f'gtimg{i}.png')) save_path = os.path.join(vis_save_path, save_info) draw(predict_vox, None, # predict_pts, voxel_origin, [resolution] * 3, None, # grid.squeeze(0).cpu().numpy(), None, # pt_label.squeeze(-1), None, img_metas[0]['cam_positions'] if 'cam_positions' in img_metas[0] else None, img_metas[0]['focal_positions'] if 'focal_positions' in img_metas[0] else None, timestamp=timestamp, mode=args.mode, sem=args.sem, dataset=args.dataset, save_path=save_path) logger.info(f'done processing frame idx{idx}') if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--dir-name', type=str, default='vis_3d') parser.add_argument('--frame-idx', type=int, nargs='+', default=[0]) parser.add_argument('--vis-train', action='store_true', default=False) parser.add_argument('--thresh', type=float, default=0) parser.add_argument('--mode', type=int, default=0, help='0: occupancy, 1: predicted point cloud, 2: gt point cloud') parser.add_argument('--cap', type=int, default=0) parser.add_argument('--dataset', type=str, default='nuscenes') parser.add_argument('--device', type=str, default='cuda') parser.add_argument('--model-pred', action='store_true', default=False) parser.add_argument('--scene-size', type=int, default=0) parser.add_argument('--sem', action='store_true', default=False) parser.add_argument('--use-lidar-mask', action='store_true', default=False) parser.add_argument('--use-cam-mask', action='store_true', default=False) parser.add_argument('--transform', action='store_true', default=False) parser.add_argument('--save-rgb', action='store_true', default=False) parser.add_argument('--scene-name', type=str, default='') args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: vis_3d_scene.py ================================================ import os offscreen = False if os.environ.get('DISP', 'f') == 'f': from pyvirtualdisplay import Display display = Display(visible=False, size=(2560, 1440)) display.start() offscreen = True from mayavi import mlab import mayavi mlab.options.offscreen = offscreen print("Set mlab.options.offscreen={}".format(mlab.options.offscreen)) import os, time, argparse, math, os.path as osp, numpy as np import torch import torch.distributed as dist import torch.nn.functional as F import matplotlib.pyplot as plt import matplotlib; matplotlib.use('agg') import shutil import mmcv from mmengine import Config from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") import model from dataset import get_dataloader from dataset.dataset_wrapper_temporal import custom_collate_fn_temporal from utils.config_tools import modify_for_eval from utils.metric_util import cityscapes2semantickitti, openseed2nuscenes from vis_pics import create_label_colormap from PIL import Image def pass_print(*args, **kwargs): pass KITTI_ROOT = 'data/kitti' import dataset.kitti.io_data as SemanticKittiIO def read_semantic_kitti(metas, idx): label_path = os.path.join( KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.label".format(metas['token'])) invalid_path = os.path.join( KITTI_ROOT, "dataset/sequences", metas['sequence'], "voxels", "{}.invalid".format(metas['token'])) remap_lut = SemanticKittiIO.get_remap_lut("dataset/kitti/semantic-kitti.yaml") LABEL = SemanticKittiIO._read_label_SemKITTI(label_path) INVALID = SemanticKittiIO._read_invalid_SemKITTI(invalid_path) LABEL = remap_lut[LABEL.astype(np.uint16)].astype( np.float32 ) # Remap 20 classes semanticKITTI SSC LABEL[ np.isclose(INVALID, 1) ] = 255 # Setting to unknown all voxels marked on invalid mask... LABEL = LABEL.reshape(256, 256, 32) LABEL = np.flip(LABEL, 1) save_info = f"{metas['sequence']}_{metas['token']}_{idx}.png" return LABEL, save_info def read_occ3d_label(metas, nusc, idx): token = metas['token'] scene_token = nusc.get('sample', token)['scene_token'] scene_name = nusc.get('scene', scene_token)['name'] label_file = f'data/occ3d/gts/{scene_name}/{token}/labels.npz' label = np.load(label_file) save_info = f"{scene_name}_{token}_{idx}.png" return label, save_info def draw( voxels, # semantic occupancy predictions grid_coords, voxel_size=0.2, # voxel size in the real world save_dirs=None, cam_positions=None, focal_positions=None, timestamp=None, mode=0, sem=False, dataset='nuscenes', ): if mode == 0: grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T # draw a simple car at the middle # car_vox_range = np.array([ # [w//2 - 2 - 4, w//2 - 2 + 4], # [h//2 - 2 - 4, h//2 - 2 + 4], # [z//2 - 2 - 3, z//2 - 2 + 3] # ], dtype=np.int) # car_x = np.arange(car_vox_range[0, 0], car_vox_range[0, 1]) # car_y = np.arange(car_vox_range[1, 0], car_vox_range[1, 1]) # car_z = np.arange(car_vox_range[2, 0], car_vox_range[2, 1]) # car_xx, car_yy, car_zz = np.meshgrid(car_x, car_y, car_z) # car_label = np.zeros([8, 8, 6], dtype=np.int) # car_label[:3, :, :2] = 17 # car_label[3:6, :, :2] = 18 # car_label[6:, :, :2] = 19 # car_label[:3, :, 2:4] = 18 # car_label[3:6, :, 2:4] = 19 # car_label[6:, :, 2:4] = 17 # car_label[:3, :, 4:] = 19 # car_label[3:6, :, 4:] = 17 # car_label[6:, :, 4:] = 18 # car_grid = np.array([car_xx.flatten(), car_yy.flatten(), car_zz.flatten()]).T # car_indexes = car_grid[:, 0] * h * z + car_grid[:, 1] * z + car_grid[:, 2] # grid_coords[car_indexes, 3] = car_label.flatten() fov_grid_coords = grid_coords # Remove empty and unknown voxels fov_voxels = fov_grid_coords[ (fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 100) ] print(len(fov_voxels)) figure = mlab.figure(size=(2560, 1440), bgcolor=(1, 1, 1)) # Draw occupied inside FOV voxels voxel_size = sum(voxel_size) / 3 if not sem: plt_plot_fov = mlab.points3d( fov_voxels[:, 1], fov_voxels[:, 0], fov_voxels[:, 2], fov_voxels[:, 3], colormap="jet", scale_factor=1.0 * voxel_size, mode="cube", opacity=1.0, ) else: plt_plot_fov = mlab.points3d( fov_voxels[:, 1], fov_voxels[:, 0], fov_voxels[:, 2], fov_voxels[:, 3], scale_factor=1.0 * voxel_size, mode="cube", opacity=1.0, vmin=1, vmax=19 if dataset == 'kitti' else 16, # 16 ) plt_plot_fov.glyph.scale_mode = "scale_by_vector" if sem: if dataset == 'kitti': colors = create_label_colormap() colors = np.concatenate([colors, np.ones_like(colors[:, :1]) * 255], axis=-1) else: colors = np.array( [ [255, 120, 50, 255], # barrier orange [255, 192, 203, 255], # bicycle pink [255, 255, 0, 255], # bus yellow [ 0, 150, 245, 255], # car blue [ 0, 255, 255, 255], # construction_vehicle cyan [255, 127, 0, 255], # motorcycle dark orange [255, 0, 0, 255], # pedestrian red [255, 240, 150, 255], # traffic_cone light yellow [135, 60, 0, 255], # trailer brown [160, 32, 240, 255], # truck purple [255, 0, 255, 255], # driveable_surface dark pink # [175, 0, 75, 255], # other_flat dark red [139, 137, 137, 255], [ 75, 0, 75, 255], # sidewalk dard purple [150, 240, 80, 255], # terrain light green [230, 230, 250, 255], # manmade white [ 0, 175, 0, 255], # vegetation green # [ 0, 255, 127, 255], # ego car dark cyan # [255, 99, 71, 255], # ego car # [ 0, 191, 255, 255] # ego car ] ).astype(np.uint8) plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors scene = figure.scene for i, save_dir in enumerate(save_dirs): if i < 6: scene.camera.position = cam_positions[i] - np.array([0.7, 1.3, -1.]) scene.camera.focal_point = focal_positions[i] - np.array([0.7, 1.3, -1.]) scene.camera.view_angle = 35 if i != 3 else 60 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [0.01, 300.] scene.camera.compute_view_plane_normal() scene.render() elif i == 6: # scene.camera.position = [-4.69302904, -52.74874688, 19.16181492] # scene.camera.focal_point = [-4.52985313, -51.8233303, 18.81979477] # scene.camera.view_angle = 40.0 # scene.camera.view_up = [0.0, 0.0, 1.0] # scene.camera.clipping_range = [0.01, 300.] # scene.camera.compute_view_plane_normal() # scene.render() scene.camera.position = [ 0.75131739, -35.08337438, 16.71378558] scene.camera.focal_point = [ 0.75131739, -34.21734897, 16.21378558] scene.camera.view_angle = 40.0 scene.camera.view_up = [0.0, 0.0, 1.0] scene.camera.clipping_range = [0.01, 300.] scene.camera.compute_view_plane_normal() scene.render() else: # scene.camera.position = [91.84365261779985, 87.2356528161641, 86.90232146965226] # scene.camera.focal_point = [4.607997894287109, -1.9073486328125e-06, -0.33333325386047363] # scene.camera.view_angle = 30.0 # scene.camera.view_up = [0.0, 0.0, 1.0] # scene.camera.clipping_range = [33.458354318473965, 299.5433372220855] # scene.camera.compute_view_plane_normal() # scene.render() scene.camera.position = [ 0.75131739, 0.78265103, 93.21378558] scene.camera.focal_point = [ 0.75131739, 0.78265103, 92.21378558] scene.camera.view_angle = 40.0 scene.camera.view_up = [0., 1., 0.] scene.camera.clipping_range = [0.01, 400.] scene.camera.compute_view_plane_normal() scene.render() mlab.savefig(os.path.join(save_dir, f'vis_{timestamp}.png')) mlab.close() def main(local_rank, args): if args.dataset == 'nuscenes': from nuscenes import NuScenes nusc = NuScenes(version='v1.0-trainval', dataroot='data/nuscenes') for scene_meta in nusc.scene: if scene_meta['name'] == args.scene_name: scene_token = scene_meta['token'] # global settings torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, args.dataset) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank ) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) local_rank = dist.get_rank() if dist.get_rank() != 0: import builtins builtins.print = pass_print else: distributed = False local_rank = 0 # configure logger if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval-{timestamp}.log') logger = MMLogger(name='selfocc', log_file=log_file, log_level='INFO') # logger.info(f'Config:\n{cfg.pretty_text}') # build model my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) print('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.to(args.device) raw_model = my_model print('done ddp model') cfg.train_wrapper_config.phase = 'val' if args.scene_name: cfg.val_dataset_config.update({ 'type': 'nuScenes_One_Frame_Sweeps_Dist_Vis', 'imageset': 'data/nuscenes_infos_val_sweeps_lid.pkl', 'scene_name': args.scene_name, 'scene_token': scene_token}) cfg.val_wrapper_config.update({ 'type': 'tpvformer_dataset_nuscenes_vis', }) train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed, iter_resume=False, val_only=True) if args.vis_train: dataset = train_dataset_loader.dataset else: dataset = val_dataset_loader.dataset cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from print('resume from: ', cfg.resume_from) print('work dir: ', args.work_dir) epoch = 'last' if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt if 'epoch' in ckpt: epoch = ckpt['epoch'] print(raw_model.load_state_dict(state_dict, strict=False)) print(f'successfully resumed from {cfg.resume_from}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) xx = torch.linspace(-40.0, 40.0, 200) yy = torch.linspace(-40.0, 40.0, 200) zz = torch.linspace(-1.0, 5.4, 16) xyz = torch.stack([ xx[:, None, None].expand(-1, 200, 16), yy[None, :, None].expand(200, -1, 16), zz[None, None, :].expand(200, 200, -1), torch.ones(200, 200, 16) ], dim=-1) # 200, 200, 16, 4 xyz = xyz.to(args.device) # eval my_model.eval() vis_save_path = osp.join(args.work_dir, args.dir_name, f'epoch{epoch}') if args.scene_name: vis_save_path = osp.join(vis_save_path, args.scene_name) clip_dirs = [] for video_clip in range(8): clip_dir = os.path.join(vis_save_path, str(video_clip)) clip_dirs.append(clip_dir) os.makedirs(clip_dir, exist_ok=True) os.makedirs(vis_save_path, exist_ok=True) # args.frame_idx = range(0, len(dataset), 10) # args.frame_idx = range(6000, 3000, -10) # args.frame_idx = [2400, 2420, 2490, 2530, 2670, 2680, 2780, 2820, 2950, 3210, 3450, 3510, 3560, 3610, 3730, 4150, 4320, 4330, 4450, 4530, 4580, 4640, 4890, 5040, 5120, 5220, 5370, 5550] # args.frame_idx = [0,110,190,270,350,360,380,420,440,480,500,550,560,600,650,710,720,750,770,840,880,930,940,990,1090,1130,1200,1210,1300,1560,1600,1850,1930,1980,2000,2050,2040,2090,2120,2400,2490,2550,2670,2820,3200,3510,3560,3690,3800,4100,4150,4320,4530,4580,4890,5220,5370] # args.frame_idx = [2000, 1300, 990, 1600, 1980] #kitti # args.frame_idx = [640, 0, 130, 260, 330, 470, 770, 730, 750, 570, 600, 400, 510, 100] if args.scene_name: args.frame_idx = list(range(args.start_idx, len(dataset))) with torch.no_grad(): # num_iters = len(val_dataset_loader) if args.scene_name else len(args.frame_idx) # if args.scene_name: # loader_iter = iter(val_dataset_loader) # for idx in range(num_iters): # if args.scene_name: # input_imgs, img_metas = next(loader_iter) # real_idx = img_metas[0]['timestamp'] # else: # real_idx = args.frame_idx[idx] # one_batch = dataset[real_idx] # input_imgs, img_metas = custom_collate_fn_temporal([one_batch]) for idx in args.frame_idx: one_batch = dataset[idx] input_imgs, img_metas = custom_collate_fn_temporal([one_batch]) real_idx = img_metas[0]['timestamp'] input_imgs = input_imgs.to(args.device) if 'kitti' in args.dataset: point_cloud_range = [-25.6, 0.0, -2.0, 25.6, 51.2, 4.4] resolution = 0.2 else: if args.scene_size == 0: point_cloud_range = [-51.2, -51.2, -4, 51.2, 51.2, 4] resolution = 0.4 elif args.scene_size == 1: point_cloud_range = [-40.0, -40.0, -2.8, 40.0, 40.0, 3.6] resolution = 0.4 elif args.scene_size == 2: point_cloud_range = [-40.0, -40.0, -3.1, 40.0, 40.0, 3.9] resolution = 0.4 elif args.scene_size == 3: point_cloud_range = [-40.0, -40.0, -3.2, 40.0, 40.0, 4.0] resolution = 0.4 elif args.scene_size == 4: point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4] resolution = 0.4 if args.model_pred: result_dict = my_model( imgs=input_imgs, metas=img_metas, occ_only=True, aabb=point_cloud_range, resolution=resolution) if args.transform: ego2lidar = img_metas[0]['ego2lidar'] ego2lidar = xyz.new_tensor(ego2lidar) # 4, 4 lidar_points = torch.matmul(ego2lidar.unsqueeze(0), xyz.reshape(-1, 4, 1)) lidar_points = lidar_points.squeeze(-1)[:, :3] # 200*200*16, 3 # ori_lidar_points = lidar_points.clone() lidar_points[:, 0] = (lidar_points[:, 0] - point_cloud_range[0]) / (point_cloud_range[3] - point_cloud_range[0]) lidar_points[:, 1] = (lidar_points[:, 1] - point_cloud_range[1]) / (point_cloud_range[4] - point_cloud_range[1]) lidar_points[:, 2] = (lidar_points[:, 2] - point_cloud_range[2]) / (point_cloud_range[5] - point_cloud_range[2]) lidar_points = lidar_points.reshape(1, 200, 200, 16, 3) sampled_sdf = F.grid_sample( result_dict['sdf'][None, None, ...], # 1, 1, H, W, D lidar_points[..., [2, 0, 1]] * 2 - 1, mode='bilinear', align_corners=True, padding_mode='border') # 1, 1, 200, 200, 16 sdf = sampled_sdf.squeeze().permute(1, 0, 2).cpu() else: sdf = result_dict['sdf'].cpu() grid_size = sdf.shape voxel_density = sdf predict_vox = (voxel_density <= args.thresh).to(torch.int) predict_vox[:6, ...] = 0 predict_vox[-6:, ...] = 0 predict_vox[:, :6, :] = 0 predict_vox[:, -6:, :] = 0 print((voxel_density > 1.0).sum()) print((voxel_density > 2.0).sum()) print((voxel_density > 3.0).sum()) # if args.dataset == 'nuscenes': # gt_occ, save_info = read_occ3d_label(img_metas[0], nusc, real_idx) # camera_mask = torch.from_numpy(gt_occ['mask_camera']).bool()#.cuda() # lidar_mask = torch.from_numpy(gt_occ['mask_lidar']).bool()#.cuda() # gt_vox = torch.from_numpy(gt_occ['semantics'])#.cuda() # gt_vox[gt_vox == 17] = 0 # gt_vox = gt_vox.permute(1, 0, 2) # camera_mask = camera_mask.permute(1, 0, 2) # lidar_mask = lidar_mask.permute(1, 0, 2) # else: # gt_vox, save_info = read_semantic_kitti(img_metas[0], real_idx) # lidar_mask = gt_vox != 255 # if not args.model_pred: # predict_vox = gt_vox grid_size = predict_vox.shape # if args.use_lidar_mask: # predict_vox[~lidar_mask] = 0 # elif args.use_cam_mask and args.dataset == 'nuscenes': # predict_vox[~camera_mask] = 0 predict_vox[..., (grid_size[2] - args.cap):] = 0 if args.sem: if args.model_pred: if args.transform: sem = result_dict['logits'].permute(3, 0, 1, 2) # C, H, W, D sampled_sem = F.grid_sample( sem[None, ...], # 1, C, H, W, D lidar_points[..., [2, 0, 1]] * 2 - 1, mode='bilinear', align_corners=True) # 1, C, 200, 200, 16 sampled_sem = torch.argmax(sampled_sem, dim=1).squeeze() sem = sampled_sem.permute(1, 0, 2) else: sem = result_dict['sem'] if args.dataset == 'nuscenes': sem = openseed2nuscenes(sem) elif args.dataset == 'kitti': sem = cityscapes2semantickitti(sem) else: raise NotImplementedError sem = sem.cpu() predict_vox = predict_vox * sem else: for z in range(grid_size[2]-args.cap): mask = (predict_vox > 0)[..., z] predict_vox[..., z][mask] = z + 1 # grid_size[2] - z if args.dataset == 'nuscenes': # ego2lidar = img_metas[0]['ego2lidar'] # ego2lidar = xyz.new_tensor(ego2lidar) # 4, 4 # lidar_points = torch.matmul(ego2lidar.unsqueeze(0), xyz.reshape(-1, 4, 1)) # lidar_points = lidar_points.squeeze(-1)[:, :3] # 200*200*16, 3 # lidar_points = lidar_points[..., [1, 0, 2]] # gird_coords = lidar_points.cpu().numpy() grid_coords = result_dict['xyz'].reshape(-1, 3).cpu().numpy() voxel_origin = [-40.0, -40.0, -1.0] voxel_max = [40.0, 40.0, 5.4] else: voxel_origin = point_cloud_range[:3] voxel_max = point_cloud_range[3:] # visualization if args.save_rgb: # frame_dir = os.path.join(vis_save_path, str(real_idx)) # os.makedirs(frame_dir, exist_ok=True) # gt_imgs = input_imgs.squeeze(0).permute(0, 2, 3, 1).cpu().numpy() * 256 # gt_imgs = gt_imgs[..., [2, 1, 0]] # gt_imgs = gt_imgs.astype(np.uint8) for i in range(6): # img = Image.fromarray(gt_imgs[i]) # img.save(osp.join(clip_dirs[i], 'img_'+str(real_idx)+'.jpg')) # if video_clip < 6: filename = img_metas[0]['input_imgs_path'][i] shutil.copy(filename, os.path.join(clip_dirs[i], 'img_'+str(real_idx)+'.jpg')) # save_path = os.path.join(vis_save_path, save_info) draw(predict_vox, grid_coords, [resolution] * 3, clip_dirs, img_metas[0]['cam_positions'], img_metas[0]['focal_positions'], timestamp=real_idx, mode=args.mode, sem=args.sem, dataset=args.dataset) logger.info(f'done processing frame idx{idx}') if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--dir-name', type=str, default='vis_scene') parser.add_argument('--frame-idx', type=int, nargs='+', default=[0]) parser.add_argument('--vis-train', action='store_true', default=False) parser.add_argument('--thresh', type=float, default=0) parser.add_argument('--mode', type=int, default=0, help='0: occupancy, 1: predicted point cloud, 2: gt point cloud') parser.add_argument('--cap', type=int, default=0) parser.add_argument('--dataset', type=str, default='nuscenes') parser.add_argument('--device', type=str, default='cuda') parser.add_argument('--model-pred', action='store_true', default=False) parser.add_argument('--scene-size', type=int, default=0) parser.add_argument('--sem', action='store_true', default=False) parser.add_argument('--use-lidar-mask', action='store_true', default=False) parser.add_argument('--use-cam-mask', action='store_true', default=False) parser.add_argument('--transform', action='store_true', default=False) parser.add_argument('--save-rgb', action='store_true', default=False) parser.add_argument('--scene-name', type=str, default='') parser.add_argument('--start-idx', type=int, default=0) args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args) ================================================ FILE: vis_pics.py ================================================ import os import time, argparse, math, os.path as osp, numpy as np import torch import torch.distributed as dist import matplotlib.pyplot as plt import matplotlib; matplotlib.use('agg') from pathlib import Path import PIL.Image as pil import matplotlib as mpl import matplotlib.cm as cm import mmcv from mmengine import Config from mmengine.logging import MMLogger from mmseg.models import build_segmentor import warnings warnings.filterwarnings("ignore") import model from dataset import get_dataloader from dataset.dataset_wrapper_temporal import custom_collate_fn_temporal from utils.config_tools import modify_for_eval def pass_print(*args, **kwargs): pass def create_label_colormap(): """Creates a label colormap used in CITYSCAPES segmentation benchmark. Returns: A colormap for visualizing segmentation results. """ colormap = np.zeros((19, 3), dtype=np.uint8) colormap[0] = [128, 64, 128] # road colormap[1] = [244, 35, 232] # colormap[2] = [70, 70, 70] colormap[3] = [102, 102, 156] colormap[4] = [190, 153, 153] colormap[5] = [153, 153, 153] colormap[6] = [250, 170, 30] colormap[7] = [220, 220, 0] colormap[8] = [107, 142, 35] colormap[9] = [152, 251, 152] colormap[10] = [70, 130, 180] colormap[11] = [220, 20, 60] colormap[12] = [255, 0, 0] colormap[13] = [0, 0, 142] colormap[14] = [0, 0, 70] colormap[15] = [0, 60, 100] colormap[16] = [0, 80, 100] colormap[17] = [0, 0, 230] colormap[18] = [119, 11, 32] return colormap def save_depth_map(disp_resized_np, save_path): vmax = np.percentile(disp_resized_np, 95) normalizer = mpl.colors.Normalize(vmin=disp_resized_np.min(), vmax=vmax) mapper = cm.ScalarMappable(norm=normalizer, cmap='magma') colormapped_im = (mapper.to_rgba(disp_resized_np)[:, :, :3] * 255).astype(np.uint8) im = pil.fromarray(colormapped_im) im.save(save_path) return def main(local_rank, args): # global settings torch.backends.cudnn.benchmark = True # load config cfg = Config.fromfile(args.py_config) cfg = modify_for_eval(cfg, args.dataset, False, args) cfg.work_dir = args.work_dir # init DDP if args.gpus > 1: distributed = True ip = os.environ.get("MASTER_ADDR", "127.0.0.1") port = os.environ.get("MASTER_PORT", "20506") hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes rank = int(os.environ.get("RANK", 0)) # node id gpus = torch.cuda.device_count() # gpus per node print(f"tcp://{ip}:{port}") dist.init_process_group( backend="nccl", init_method=f"tcp://{ip}:{port}", world_size=hosts * gpus, rank=rank * gpus + local_rank ) world_size = dist.get_world_size() cfg.gpu_ids = range(world_size) torch.cuda.set_device(local_rank) local_rank = dist.get_rank() if dist.get_rank() != 0: import builtins builtins.print = pass_print else: distributed = False local_rank = 0 # configure logger if local_rank == 0: os.makedirs(args.work_dir, exist_ok=True) cfg.dump(osp.join(args.work_dir, 'eval' + osp.basename(args.py_config))) timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(args.work_dir, f'eval-{timestamp}.log') logger = MMLogger('selfocc', log_file=log_file) # build model cfg.model.head.ray_sample_mode = 'fixed' cfg.model.head.return_second_grad = False cfg.model.head.return_max_depth = True if len(args.novel_view) > 0: cfg.model.head.novel_view = args.novel_view my_model = build_segmentor(cfg.model) my_model.init_weights() n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad) logger.info(f'Number of params: {n_parameters}') if distributed: my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model) print('converted sync bn.') find_unused_parameters = cfg.get('find_unused_parameters', False) ddp_model_module = torch.nn.parallel.DistributedDataParallel my_model = ddp_model_module( my_model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False, find_unused_parameters=find_unused_parameters) raw_model = my_model.module else: my_model = my_model.to(args.device) raw_model = my_model print('done ddp model') cfg.train_wrapper_config.phase = 'val' train_dataset_loader, val_dataset_loader = get_dataloader( cfg.train_dataset_config, cfg.val_dataset_config, cfg.train_wrapper_config, cfg.val_wrapper_config, cfg.train_loader, cfg.val_loader, cfg.nusc, dist=distributed, iter_resume=False, val_only=True) dataset = val_dataset_loader.dataset print('length of dataset: ', len(dataset)) cfg.resume_from = '' if osp.exists(osp.join(args.work_dir, 'latest.pth')): cfg.resume_from = osp.join(args.work_dir, 'latest.pth') if args.resume_from: cfg.resume_from = args.resume_from print('resume from: ', cfg.resume_from) print('work dir: ', args.work_dir) epoch = 'last' if cfg.resume_from and osp.exists(cfg.resume_from): map_location = 'cpu' ckpt = torch.load(cfg.resume_from, map_location=map_location) if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt if 'epoch' in ckpt: epoch = ckpt['epoch'] # revise ckpt if 'head.rays' in state_dict: del state_dict['head.rays'] print(raw_model.load_state_dict(state_dict, strict=False)) print(f'successfully resumed from {cfg.resume_from}') elif cfg.load_from: ckpt = torch.load(cfg.load_from, map_location='cpu') if 'state_dict' in ckpt: state_dict = ckpt['state_dict'] else: state_dict = ckpt print(raw_model.load_state_dict(state_dict, strict=False)) # eval my_model.eval() amp = cfg.get('amp', False) with torch.no_grad(): for idx in args.frame_idx: vis_save_path = osp.join(args.work_dir, args.dir_name, f'epoch{epoch}', f'{idx}') os.makedirs(vis_save_path, exist_ok=True) one_batch = dataset[idx] input_imgs, curr_imgs, prev_imgs, next_imgs, color_imgs, img_metas, curr_aug, prev_aug, next_aug = \ custom_collate_fn_temporal([one_batch]) input_imgs = input_imgs.to(args.device) with torch.cuda.amp.autocast(amp): if cfg.get('estimate_pose'): assert curr_aug is not None and prev_aug is not None and next_aug is not None assert img_metas[0]['input_imgs_path'] == img_metas[0]['curr_imgs_path'] curr_aug, prev_aug, next_aug = curr_aug.cuda(), prev_aug.cuda(), next_aug.cuda() pose_dict = my_model(pose_input=[curr_aug, prev_aug, next_aug], metas=img_metas, predict_pose=True) for i_meta, meta in enumerate(img_metas): meta.update(pose_dict[i_meta]) my_model(imgs=input_imgs, metas=img_metas, prepare=True) if distributed: result_dict = my_model.module.head.render(metas=img_metas, batch=args.batch) else: result_dict = my_model.head.render(metas=img_metas, batch=args.batch) # visualization gt_imgs = curr_imgs.squeeze(0).permute(0, 2, 3, 1).cpu().numpy() gt_imgs = (gt_imgs * 256).astype(np.uint8) gt_imgs = gt_imgs[..., [2, 1, 0]] for i in range(gt_imgs.shape[0]): gt_imgs_tmp = pil.fromarray(gt_imgs[i]) gt_imgs_tmp.save(osp.join(vis_save_path, f'i{idx}_gtimg{i}.png')) plt.imshow(gt_imgs[i]) plt.savefig(osp.join(vis_save_path, f'i{idx}_gtimg{i}.png')) if args.vis_2d_depth: to_vis = result_dict[args.vis_2d_depth] if not isinstance(to_vis, list): to_vis = [to_vis] for scale, to_vis_i in enumerate(to_vis): to_vis_i = to_vis_i.cpu().squeeze() num_cams = to_vis_i.shape[0] for i in range(num_cams): plt.imshow(to_vis_i[i]) plt.savefig(osp.join( vis_save_path, f'i{idx}_{args.vis_2d_depth}{i}_s{scale}.png')) if len(args.vis_nerf_depth) > 0: for curr_kw in args.vis_nerf_depth: to_vis = result_dict[curr_kw] if not isinstance(to_vis, list): to_vis = [to_vis] for scale, to_vis_i in enumerate(to_vis): to_vis_i = to_vis_i.cpu().squeeze(0) num_cams = to_vis_i.shape[0] for i in range(num_cams): to_vis_tmp = to_vis_i[i].reshape(*cfg.num_rays).numpy() to_vis_tmp = 80.0 / to_vis_tmp save_depth_map( to_vis_tmp, osp.join(vis_save_path, f'i{idx}_{curr_kw}{i}_s{scale}.png')) if args.vis_nerf_rgb: rgb_gt_imgs = color_imgs.squeeze(0).permute(0, 2, 3, 1).cpu().numpy() rgb_gt_imgs = (rgb_gt_imgs * 256).astype(np.uint8) rgb_gt_imgs = rgb_gt_imgs[..., [2, 1, 0]] for i in range(rgb_gt_imgs.shape[0]): rgb_gt_img = pil.fromarray(rgb_gt_imgs[i]) rgb_gt_img.save(osp.join(vis_save_path, f'i{idx}_rgb_gtimg{i}.png')) to_vis = result_dict[args.vis_nerf_rgb] if not isinstance(to_vis, list): to_vis = [to_vis] for scale, to_vis_i in enumerate(to_vis): to_vis_i = to_vis_i.cpu().squeeze(0) num_cams = to_vis_i.shape[0] to_vis_i = torch.clamp(to_vis_i, 0., 1.) * 255 to_vis_i = to_vis_i.numpy().astype(np.uint8) to_vis_i = to_vis_i[..., [2, 1, 0]] to_vis_i = to_vis_i.reshape(num_cams, *cfg.num_rays, 3) for i in range(num_cams): to_vis_i_tmp = pil.fromarray(to_vis_i[i]) to_vis_i_tmp.save(osp.join( vis_save_path, f'i{idx}_{args.vis_nerf_rgb}{i}_s{scale}.png')) if args.vis_nerf_sem: colormap = create_label_colormap() to_vis = result_dict[args.vis_nerf_sem] if not isinstance(to_vis, list): to_vis = [to_vis] for scale, to_vis_i in enumerate(to_vis): to_vis_i = torch.argmax(to_vis_i, dim=-1) # B, N, R to_vis_i = to_vis_i.cpu().squeeze(0).numpy() # N, R num_cams = to_vis_i.shape[0] to_vis_i = colormap[to_vis_i] for i in range(num_cams): plt.imshow(to_vis_i[i].reshape(*cfg.num_rays, 3)) plt.savefig(osp.join( vis_save_path, f'i{idx}_{args.vis_nerf_sem}{i}_s{scale}.png')) logger.info(f'done processing frame idx{idx}') if __name__ == '__main__': # Training settings parser = argparse.ArgumentParser(description='') parser.add_argument('--py-config', default='config/tpv_lidarseg.py') parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg') parser.add_argument('--resume-from', type=str, default='') parser.add_argument('--hfai', action='store_true', default=False) parser.add_argument('--vis-2d-depth', type=str, default='') parser.add_argument('--vis-nerf-depth', type=str, default=['ms_depths', 'ms_max_depths'], nargs='+') parser.add_argument('--vis-nerf-rgb', type=str, default='') parser.add_argument('--vis-nerf-sem', type=str, default='') parser.add_argument('--dir-name', type=str, default='vis') parser.add_argument('--frame-idx', type=int, nargs='+', default=[0, 100, 300, 200, 500]) parser.add_argument('--vis-train', action='store_true', default=False) parser.add_argument('--novel-view', type=float, nargs='+', default=[]) parser.add_argument('--device', type=str, default='cuda') parser.add_argument('--dataset', type=str, default='nuscenes') parser.add_argument('--batch', type=int, default=0) parser.add_argument('--num-rays', type=int, nargs='+', default=[24, 50]) args = parser.parse_args() ngpus = torch.cuda.device_count() args.gpus = ngpus print(args) if args.hfai: os.environ['HFAI'] = 'true' if ngpus > 1: torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus) else: main(0, args)