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mv-grounding_8xb12_embodiedscan-vg-9dof_fcaf-coder.py
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mv-grounding_8xb12_embodiedscan-vg-9dof_fcaf-coder.py
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_base_ = ['../default_runtime.py']
n_points = 100000
backend_args = None
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/scannet/':
# 's3://openmmlab/datasets/detection3d/scannet_processed/',
# 'data/scannet/':
# 's3://openmmlab/datasets/detection3d/scannet_processed/'
# }))
metainfo = dict(classes='all')
model = dict(
type='SparseFeatureFusion3DGrounder',
num_queries=256,
voxel_size=0.01,
data_preprocessor=dict(type='Det3DDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='mmdet.ResNet',
depth=50,
base_channels=16, # to make it consistent with mink resnet
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
style='pytorch'),
backbone_3d=dict(type='MinkResNet', in_channels=3, depth=34),
use_xyz_feat=True,
# change due to no img feature fusion
neck_3d=dict(type='MinkNeck',
num_classes=1,
in_channels=[128, 256, 512, 1024],
out_channels=256,
voxel_size=0.01,
pts_prune_threshold=1000),
decoder=dict(
num_layers=6,
return_intermediate=True,
layer_cfg=dict(
# query self attention layer
self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
# cross attention layer query to text
cross_attn_text_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
# cross attention layer query to image
cross_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
ffn_cfg=dict(embed_dims=256,
feedforward_channels=2048,
ffn_drop=0.0)),
post_norm_cfg=None),
bbox_head=dict(type='GroundingHead',
num_classes=256,
box_coder='FCAF',
sync_cls_avg_factor=True,
decouple_bbox_loss=True,
decouple_groups=4,
share_pred_layer=True,
decouple_weights=[0.2, 0.2, 0.2, 0.4],
contrastive_cfg=dict(max_text_len=256,
log_scale='auto',
bias=True),
loss_cls=dict(type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='BBoxCDLoss',
mode='l1',
loss_weight=1.0,
group='g8')),
coord_type='DEPTH',
# training and testing settings
train_cfg=dict(assigner=dict(type='HungarianAssigner3D',
match_costs=[
dict(type='BinaryFocalLossCost',
weight=1.0),
dict(type='BBox3DL1Cost', weight=2.0),
dict(type='IoU3DCost', weight=2.0)
]), ),
test_cfg=None)
dataset_type = 'MultiView3DGroundingDataset'
data_root = 'data'
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(type='MultiViewPipeline',
n_images=20,
transforms=[
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadDepthFromFile', backend_args=backend_args),
dict(type='ConvertRGBDToPoints', coord_type='CAMERA'),
dict(type='PointSample', num_points=n_points // 10),
dict(type='Resize', scale=(480, 480), keep_ratio=False)
]),
dict(type='AggregateMultiViewPoints', coord_type='DEPTH'),
dict(type='PointSample', num_points=n_points),
dict(type='GlobalRotScaleTrans',
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[.9, 1.1],
translation_std=[.1, .1, .1],
shift_height=False),
dict(type='Pack3DDetInputs',
keys=['img', 'points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadAnnotations3D'),
dict(type='MultiViewPipeline',
n_images=50,
ordered=True,
transforms=[
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadDepthFromFile', backend_args=backend_args),
dict(type='ConvertRGBDToPoints', coord_type='CAMERA'),
dict(type='PointSample', num_points=n_points // 10),
dict(type='Resize', scale=(480, 480), keep_ratio=False)
]),
dict(type='AggregateMultiViewPoints', coord_type='DEPTH'),
dict(type='PointSample', num_points=n_points),
dict(type='Pack3DDetInputs',
keys=['img', 'points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
# TODO: to determine a reasonable batch size
train_dataloader = dict(
batch_size=12,
num_workers=12,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(type='RepeatDataset',
times=1,
dataset=dict(type=dataset_type,
data_root=data_root,
ann_file='embodiedscan_infos_train.pkl',
vg_file='embodiedscan_train_mini_vg.json',
metainfo=metainfo,
pipeline=train_pipeline,
test_mode=False,
filter_empty_gt=True,
box_type_3d='Euler-Depth',
tokens_positive_rebuild=True)))
val_dataloader = dict(batch_size=12,
num_workers=12,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(type=dataset_type,
data_root=data_root,
ann_file='embodiedscan_infos_val.pkl',
vg_file='embodiedscan_val_mini_vg.json',
metainfo=metainfo,
pipeline=test_pipeline,
test_mode=True,
filter_empty_gt=True,
box_type_3d='Euler-Depth',
tokens_positive_rebuild=True))
test_dataloader = val_dataloader
val_evaluator = dict(type='GroundingMetric')
test_evaluator = val_evaluator
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=3)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# optimizer
lr = 5e-4
optim_wrapper = dict(type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, weight_decay=0.0005),
paramwise_cfg=dict(
custom_keys={
'text_encoder': dict(lr_mult=0.0),
'decoder': dict(lr_mult=0.1, decay_mult=1.0)
}),
clip_grad=dict(max_norm=10, norm_type=2))
# learning rate
param_scheduler = dict(type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
# hooks
default_hooks = dict(
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
# vis_backends = [
# dict(type='TensorboardVisBackend'),
# dict(type='LocalVisBackend')
# ]
# visualizer = dict(
# type='Det3DLocalVisualizer',
# vis_backends=vis_backends, name='visualizer')
find_unused_parameters = True
load_from = '/mnt/petrelfs/wangtai/EmbodiedScan/work_dirs/mv-3ddet-challenge/epoch_12.pth' # noqa