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_base_ = ['../../../configs/_base_/default_runtime.py'] |
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custom_imports = dict(imports=['projects.NeRF-Det.nerfdet']) |
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prior_generator = dict( |
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type='AlignedAnchor3DRangeGenerator', |
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ranges=[[-3.2, -3.2, -1.28, 3.2, 3.2, 1.28]], |
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rotations=[.0]) |
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model = dict( |
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type='NerfDet', |
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data_preprocessor=dict( |
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type='NeRFDetDataPreprocessor', |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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bgr_to_rgb=True, |
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pad_size_divisor=10), |
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backbone=dict( |
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type='mmdet.ResNet', |
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depth=101, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=False), |
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norm_eval=True, |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'), |
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style='pytorch'), |
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neck=dict( |
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type='mmdet.FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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num_outs=4), |
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neck_3d=dict( |
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type='IndoorImVoxelNeck', |
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in_channels=256, |
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out_channels=128, |
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n_blocks=[1, 1, 1]), |
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bbox_head=dict( |
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type='NerfDetHead', |
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bbox_loss=dict(type='AxisAlignedIoULoss', loss_weight=1.0), |
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n_classes=18, |
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n_levels=3, |
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n_channels=128, |
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n_reg_outs=6, |
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pts_assign_threshold=27, |
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pts_center_threshold=18, |
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prior_generator=prior_generator), |
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prior_generator=prior_generator, |
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voxel_size=[.16, .16, .2], |
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n_voxels=[40, 40, 16], |
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aabb=([-2.7, -2.7, -0.78], [3.7, 3.7, 1.78]), |
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near_far_range=[0.2, 8.0], |
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N_samples=64, |
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N_rand=2048, |
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nerf_mode='image', |
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depth_supervise=True, |
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use_nerf_mask=True, |
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nerf_sample_view=20, |
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squeeze_scale=4, |
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nerf_density=True, |
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train_cfg=dict(), |
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test_cfg=dict(nms_pre=1000, iou_thr=.25, score_thr=.01)) |
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dataset_type = 'MultiViewScanNetDataset' |
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data_root = 'data/scannet/' |
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class_names = [ |
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'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', |
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'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', |
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'toilet', 'sink', 'bathtub', 'garbagebin' |
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] |
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metainfo = dict(CLASSES=class_names) |
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file_client_args = dict(backend='disk') |
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input_modality = dict( |
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use_camera=True, |
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use_depth=True, |
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use_lidar=False, |
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use_neuralrecon_depth=False, |
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use_ray=True) |
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backend_args = None |
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train_collect_keys = [ |
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'img', 'gt_bboxes_3d', 'gt_labels_3d', 'depth', 'lightpos', 'nerf_sizes', |
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'raydirs', 'gt_images', 'gt_depths', 'denorm_images' |
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] |
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test_collect_keys = [ |
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'img', |
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'depth', |
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'lightpos', |
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'nerf_sizes', |
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'raydirs', |
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'gt_images', |
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'gt_depths', |
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'denorm_images', |
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] |
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train_pipeline = [ |
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dict(type='LoadAnnotations3D'), |
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dict( |
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type='MultiViewPipeline', |
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n_images=48, |
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transforms=[ |
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dict(type='LoadImageFromFile', file_client_args=file_client_args), |
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dict(type='Resize', scale=(320, 240), keep_ratio=True), |
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], |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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margin=10, |
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depth_range=[0.5, 5.5], |
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loading='random', |
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nerf_target_views=10), |
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dict(type='RandomShiftOrigin', std=(.7, .7, .0)), |
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dict(type='PackNeRFDetInputs', keys=train_collect_keys) |
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] |
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test_pipeline = [ |
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dict(type='LoadAnnotations3D'), |
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dict( |
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type='MultiViewPipeline', |
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n_images=101, |
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transforms=[ |
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dict(type='LoadImageFromFile', file_client_args=file_client_args), |
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dict(type='Resize', scale=(320, 240), keep_ratio=True), |
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], |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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margin=10, |
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depth_range=[0.5, 5.5], |
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loading='random', |
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nerf_target_views=1), |
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dict(type='PackNeRFDetInputs', keys=test_collect_keys) |
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] |
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train_dataloader = dict( |
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batch_size=1, |
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num_workers=1, |
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persistent_workers=True, |
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sampler=dict(type='DefaultSampler', shuffle=True), |
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dataset=dict( |
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type='RepeatDataset', |
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times=6, |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='scannet_infos_train_new.pkl', |
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pipeline=train_pipeline, |
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modality=input_modality, |
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test_mode=False, |
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filter_empty_gt=True, |
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box_type_3d='Depth', |
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metainfo=metainfo))) |
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val_dataloader = dict( |
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batch_size=1, |
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num_workers=5, |
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persistent_workers=True, |
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drop_last=False, |
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sampler=dict(type='DefaultSampler', shuffle=False), |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='scannet_infos_val_new.pkl', |
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pipeline=test_pipeline, |
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modality=input_modality, |
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test_mode=True, |
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filter_empty_gt=True, |
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box_type_3d='Depth', |
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metainfo=metainfo)) |
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test_dataloader = val_dataloader |
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val_evaluator = dict(type='IndoorMetric') |
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test_evaluator = val_evaluator |
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1) |
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test_cfg = dict() |
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val_cfg = dict() |
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optim_wrapper = dict( |
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type='OptimWrapper', |
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optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.0001), |
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paramwise_cfg=dict( |
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custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}), |
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clip_grad=dict(max_norm=35., norm_type=2)) |
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param_scheduler = [ |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=12, |
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by_epoch=True, |
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milestones=[8, 11], |
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gamma=0.1) |
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] |
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default_hooks = dict( |
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=12)) |
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find_unused_parameters = True |
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