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[Feature] Add center sampling for indoor datasets (#23)
* try centerness topk configs * add perspective_ and imvoxelnet_sunrgbd configs with centerness top18 * fix typo in perspective config * try topk=27 for sunrgbd and scannet * try more regress ranges for sunrgbd * try less regress ranges for scannet * rename indoor configs to _top27 * add resnet 18 and 34 experiments * ready to merge center sampling to master * fix typo in readme * fix topk parameter to 28
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configs/imvoxelnet/imvoxelnet_perspective_sunrgbd_top27.py
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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=64, | ||
num_outs=4), | ||
neck_3d=dict( | ||
type='ImVoxelNeck', | ||
channels=[64, 128, 256, 512], | ||
out_channels=64, | ||
down_layers=[1, 2, 3, 4], | ||
up_layers=[3, 2, 1], | ||
conditional=False), | ||
bbox_head=dict( | ||
type='SunRgbdImVoxelHead', | ||
n_classes=30, | ||
n_channels=64, | ||
n_convs=0, | ||
n_reg_outs=7, | ||
centerness_topk=28), | ||
n_voxels=(80, 80, 32), | ||
voxel_size=(.08, .08, .08)) | ||
train_cfg = dict() | ||
test_cfg = dict( | ||
nms_pre=1000, | ||
nms_thr=.15, | ||
use_rotate_nms=True, | ||
score_thr=.0) | ||
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') | ||
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||
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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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=64, | ||
num_outs=4), | ||
neck_3d=dict( | ||
type='ImVoxelNeck', | ||
channels=[64, 128, 256, 512], | ||
out_channels=64, | ||
down_layers=[1, 2, 3, 4], | ||
up_layers=[3, 2, 1], | ||
conditional=False), | ||
bbox_head=dict( | ||
type='ScanNetImVoxelHead', | ||
loss_bbox=dict(type='AxisAlignedIoULoss', loss_weight=1.0), | ||
n_classes=18, | ||
n_channels=64, | ||
n_convs=0, | ||
n_reg_outs=6, | ||
centerness_topk=28), | ||
voxel_size=(.08, .08, .08), | ||
n_voxels=(80, 80, 32)) | ||
train_cfg = dict() | ||
test_cfg = dict( | ||
nms_pre=1000, | ||
iou_thr=.15, | ||
score_thr=.0) | ||
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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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,126 @@ | ||
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=64, | ||
num_outs=4), | ||
neck_3d=dict( | ||
type='ImVoxelNeck', | ||
channels=[64, 128, 256, 512], | ||
out_channels=64, | ||
down_layers=[1, 2, 3, 4], | ||
up_layers=[3, 2, 1], | ||
conditional=False), | ||
bbox_head=dict( | ||
type='SunRgbdImVoxelHead', | ||
n_classes=10, | ||
n_channels=64, | ||
n_convs=0, | ||
n_reg_outs=7, | ||
centerness_topk=28), | ||
n_voxels=(80, 80, 32), | ||
voxel_size=(.08, .08, .08)) | ||
train_cfg = dict() | ||
test_cfg = dict( | ||
nms_pre=1000, | ||
nms_thr=.15, | ||
use_rotate_nms=True, | ||
score_thr=.0) | ||
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
|
||
dataset_type = 'SunRgbdMultiViewDataset' | ||
data_root = 'data/sunrgbd/' | ||
class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser', | ||
'night_stand', 'bookshelf', 'bathtub') | ||
|
||
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_imvoxelnet_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_imvoxelnet_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_imvoxelnet_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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