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* add fast configs for 3 x sunrgbd and scannet * add new model links toreadme * remove extra prints
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configs/imvoxelnet/imvoxelnet_perspective_sunrgbd_fast.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| model = dict( | ||
| type='ImVoxelNet', | ||
| pretrained='torchvision://resnet50', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| frozen_stages=1, | ||
| norm_cfg=dict(type='BN', requires_grad=False), | ||
| norm_eval=True, | ||
| style='pytorch'), | ||
| neck=dict( | ||
| type='FPN', | ||
| in_channels=[256, 512, 1024, 2048], | ||
| out_channels=256, | ||
| num_outs=4), | ||
| neck_3d=dict( | ||
| type='FastIndoorImVoxelNeck', | ||
| in_channels=256, | ||
| out_channels=128, | ||
| n_blocks=[1, 1, 1]), | ||
| bbox_head=dict( | ||
| type='SunRgbdImVoxelHeadV2', | ||
| n_classes=30, | ||
| n_channels=128, | ||
| n_reg_outs=7, | ||
| n_scales=3, | ||
| limit=27, | ||
| centerness_topk=18), | ||
| n_voxels=(40, 40, 16), | ||
| voxel_size=(.16, .16, .16)) | ||
| train_cfg = dict() | ||
| test_cfg = dict( | ||
| nms_pre=1000, | ||
| nms_thr=.15, | ||
| use_rotate_nms=True, | ||
| score_thr=.01) | ||
| img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
|
|
||
| dataset_type = 'SunRgbdPerspectiveMultiViewDataset' | ||
| data_root = 'data/sunrgbd/' | ||
| class_names = ('recycle_bin', 'cpu', 'paper', 'toilet', 'stool', 'whiteboard', 'coffee_table', 'picture', | ||
| 'keyboard', 'dresser', 'painting', 'bookshelf', 'night_stand', 'endtable', 'drawer', 'sink', | ||
| 'monitor', 'computer', 'cabinet', 'shelf', 'lamp', 'garbage_bin', 'box', 'bed', 'sofa', | ||
| 'sofa_chair', 'pillow', 'desk', 'table', 'chair') | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadAnnotations3D'), | ||
| dict( | ||
| type='MultiViewPipeline', | ||
| n_images=1, | ||
| transforms=[ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Resize', img_scale=[(512, 384), (768, 576)], multiscale_mode='range', keep_ratio=True), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32)]), | ||
| dict(type='SunRgbdRandomFlip'), | ||
| dict(type='DefaultFormatBundle3D', class_names=class_names), | ||
| dict(type='Collect3D', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='MultiViewPipeline', | ||
| n_images=1, | ||
| transforms=[ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='Resize', img_scale=(640, 480), keep_ratio=True), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32)]), | ||
| dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False), | ||
| dict(type='Collect3D', keys=['img'])] | ||
| data = dict( | ||
| samples_per_gpu=4, | ||
| workers_per_gpu=4, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=2, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'sunrgbd_perspective_infos_train.pkl', | ||
| pipeline=train_pipeline, | ||
| classes=class_names, | ||
| filter_empty_gt=True, | ||
| box_type_3d='Depth')), | ||
| val=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'sunrgbd_perspective_infos_val.pkl', | ||
| pipeline=test_pipeline, | ||
| classes=class_names, | ||
| test_mode=True, | ||
| box_type_3d='Depth'), | ||
| test=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'sunrgbd_perspective_infos_val.pkl', | ||
| pipeline=test_pipeline, | ||
| classes=class_names, | ||
| test_mode=True, | ||
| box_type_3d='Depth')) | ||
|
|
||
| optimizer = dict( | ||
| type='AdamW', | ||
| lr=0.0001, | ||
| weight_decay=0.0001, | ||
| paramwise_cfg=dict( | ||
| custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) | ||
| optimizer_config = dict(grad_clip=dict(max_norm=35., norm_type=2)) | ||
| lr_config = dict(policy='step', step=[8, 11]) | ||
| total_epochs = 12 | ||
|
|
||
| checkpoint_config = dict(interval=1, max_keep_ckpts=1) | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| dict(type='TensorboardLoggerHook') | ||
| ]) | ||
| evaluation = dict(interval=1) | ||
| dist_params = dict(backend='nccl') | ||
| find_unused_parameters = True # todo: fix number of FPN outputs | ||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| model = dict( | ||
| type='ImVoxelNet', | ||
| pretrained='torchvision://resnet50', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(0, 1, 2, 3), | ||
| frozen_stages=1, | ||
| norm_cfg=dict(type='BN', requires_grad=False), | ||
| norm_eval=True, | ||
| style='pytorch'), | ||
| neck=dict( | ||
| type='FPN', | ||
| in_channels=[256, 512, 1024, 2048], | ||
| out_channels=256, | ||
| num_outs=4), | ||
| neck_3d=dict( | ||
| type='FastIndoorImVoxelNeck', | ||
| in_channels=256, | ||
| out_channels=128, | ||
| n_blocks=[1, 1, 1]), | ||
| bbox_head=dict( | ||
| type='ScanNetImVoxelHeadV2', | ||
| loss_bbox=dict(type='AxisAlignedIoULoss', loss_weight=1.0), | ||
| n_classes=18, | ||
| n_channels=128, | ||
| n_reg_outs=6, | ||
| n_scales=3, | ||
| limit=27, | ||
| centerness_topk=18), | ||
| voxel_size=(.16, .16, .16), | ||
| n_voxels=(40, 40, 16)) | ||
| train_cfg = dict() | ||
| test_cfg = dict( | ||
| nms_pre=1000, | ||
| iou_thr=.25, | ||
| score_thr=.01) | ||
| img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
|
|
||
| dataset_type = 'ScanNetMultiViewDataset' | ||
| data_root = 'data/scannet/' | ||
| class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', | ||
| 'bookshelf', 'picture', 'counter', 'desk', 'curtain', | ||
| 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', | ||
| 'garbagebin') | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadAnnotations3D'), | ||
| dict( | ||
| type='MultiViewPipeline', | ||
| n_images=20, | ||
| transforms=[ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='Resize', img_scale=(640, 480), keep_ratio=True), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size=(480, 640)) | ||
| ]), | ||
| dict(type='RandomShiftOrigin', std=(.7, .7, .0)), | ||
| dict(type='DefaultFormatBundle3D', class_names=class_names), | ||
| dict(type='Collect3D', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='MultiViewPipeline', | ||
| n_images=50, | ||
| transforms=[ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='Resize', img_scale=(640, 480), keep_ratio=True), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size=(480, 640)) | ||
| ]), | ||
| dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False), | ||
| dict(type='Collect3D', keys=['img']) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=1, | ||
| workers_per_gpu=1, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=3, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'scannet_infos_train.pkl', | ||
| pipeline=train_pipeline, | ||
| classes=class_names, | ||
| filter_empty_gt=True, | ||
| box_type_3d='Depth')), | ||
| val=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'scannet_infos_val.pkl', | ||
| pipeline=test_pipeline, | ||
| classes=class_names, | ||
| test_mode=True, | ||
| box_type_3d='Depth'), | ||
| test=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'scannet_infos_val.pkl', | ||
| pipeline=test_pipeline, | ||
| classes=class_names, | ||
| test_mode=True, | ||
| box_type_3d='Depth') | ||
| ) | ||
|
|
||
| optimizer = dict( | ||
| type='AdamW', | ||
| lr=0.0001, | ||
| weight_decay=0.0001, | ||
| paramwise_cfg=dict( | ||
| custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) | ||
| optimizer_config = dict(grad_clip=dict(max_norm=35., norm_type=2)) | ||
| lr_config = dict(policy='step', step=[8, 11]) | ||
| total_epochs = 12 | ||
|
|
||
| checkpoint_config = dict(interval=1, max_keep_ckpts=1) | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| dict(type='TensorboardLoggerHook') | ||
| ]) | ||
| evaluation = dict(interval=1) | ||
| dist_params = dict(backend='nccl') | ||
| find_unused_parameters = True # todo: fix number of FPN outputs | ||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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