| _base_ = ['./nerfdet_res50_2x_low_res_depth.py'] | |
| model = dict(depth_supervise=False) | |
| dataset_type = 'MultiViewScanNetDataset' | |
| data_root = 'data/scannet/' | |
| class_names = [ | |
| 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', | |
| 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', | |
| 'toilet', 'sink', 'bathtub', 'garbagebin' | |
| ] | |
| metainfo = dict(CLASSES=class_names) | |
| file_client_args = dict(backend='disk') | |
| input_modality = dict(use_depth=False) | |
| backend_args = None | |
| train_collect_keys = [ | |
| 'img', 'gt_bboxes_3d', 'gt_labels_3d', 'lightpos', 'nerf_sizes', 'raydirs', | |
| 'gt_images', 'gt_depths', 'denorm_images' | |
| ] | |
| test_collect_keys = [ | |
| 'img', | |
| 'lightpos', | |
| 'nerf_sizes', | |
| 'raydirs', | |
| 'gt_images', | |
| 'gt_depths', | |
| 'denorm_images', | |
| ] | |
| train_pipeline = [ | |
| dict(type='LoadAnnotations3D'), | |
| dict( | |
| type='MultiViewPipeline', | |
| n_images=50, | |
| transforms=[ | |
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | |
| dict(type='Resize', scale=(320, 240), keep_ratio=True), | |
| ], | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| margin=10, | |
| depth_range=[0.5, 5.5], | |
| loading='random', | |
| nerf_target_views=10), | |
| dict(type='RandomShiftOrigin', std=(.7, .7, .0)), | |
| dict(type='PackNeRFDetInputs', keys=train_collect_keys) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadAnnotations3D'), | |
| dict( | |
| type='MultiViewPipeline', | |
| n_images=101, | |
| transforms=[ | |
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | |
| dict(type='Resize', scale=(320, 240), keep_ratio=True), | |
| ], | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| margin=10, | |
| depth_range=[0.5, 5.5], | |
| loading='random', | |
| nerf_target_views=1), | |
| dict(type='PackNeRFDetInputs', keys=test_collect_keys) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=1, | |
| num_workers=1, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=dict( | |
| type='RepeatDataset', | |
| times=6, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='scannet_infos_train_new.pkl', | |
| pipeline=train_pipeline, | |
| modality=input_modality, | |
| test_mode=False, | |
| filter_empty_gt=True, | |
| box_type_3d='Depth', | |
| metainfo=metainfo))) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=1, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='scannet_infos_val_new.pkl', | |
| pipeline=test_pipeline, | |
| modality=input_modality, | |
| test_mode=True, | |
| filter_empty_gt=True, | |
| box_type_3d='Depth', | |
| metainfo=metainfo)) | |
| test_dataloader = val_dataloader | |