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2024-09-21 17:32:30,446 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: NVIDIA RTX A5000
CUDA_HOME: /data/home/hanbo/cuda-11.6
NVCC: Cuda compilation tools, release 11.6, V11.6.55
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 1.12.1+cu116
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.6
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- CuDNN 8.3.2 (built against CUDA 11.5)
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.13.1+cu116
OpenCV: 4.10.0
MMCV: 1.7.2
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.6
MMDetection: 2.28.2+d592e33
------------------------------------------------------------
2024-09-21 17:32:31,819 - mmdet - INFO - Distributed training: True
2024-09-21 17:32:33,172 - mmdet - INFO - Config:
norm_cfg = dict(
type='BN',
requires_grad=False,
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True)
model = dict(
type='FasterRCNNRelAfford',
backbone=dict(
type='mmdet.ResNet',
depth=101,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')),
rpn_head=dict(
type='mmdet.RPNHead',
in_channels=1024,
feat_channels=1024,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8, 16, 32],
ratios=[0.33, 0.5, 1.0, 2.0, 3.0],
strides=[16]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='mmdet.L1Loss', loss_weight=1.0)),
roi_head=None,
child_head=dict(
type='invigorate.PairedRoIHead',
shared_head=dict(
type='invigorate.PairedResLayer',
depth=50,
stage=3,
stride=1,
style='caffe',
norm_eval=False,
share_weights=False),
paired_roi_extractor=dict(
type='invigorate.VMRNPairedRoIExtractor',
roi_layer=dict(type='RoIPool', output_size=7),
out_channels=1024,
featmap_strides=[16]),
relation_head=dict(
type='invigorate.BBoxPairHead',
with_avg_pool=True,
roi_feat_size=7,
in_channels=2048,
num_relations=1,
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0))),
leaf_head=dict(
type='mmdet.StandardRoIHead',
shared_head=dict(
type='mmdet.ResLayer',
depth=50,
stage=3,
stride=1,
style='caffe',
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True),
bbox_roi_extractor=dict(
type='mmdet.SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=1024,
featmap_strides=[16]),
bbox_head=dict(
type='mmdet.BBoxHead',
with_avg_pool=True,
with_reg=False,
roi_feat_size=7,
in_channels=2048,
num_classes=2,
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=12000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
child_head=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.5,
min_pos_iou=0.7,
match_low_quality=False,
ignore_iof_thr=-1),
relation_sampler=dict(
type='RandomRelationSampler',
num=32,
pos_fraction=0.5,
cls_ratio_ub=1.0,
add_gt_as_proposals=True,
num_relation_cls=1,
neg_id=0),
pos_weight=-1,
online_data=True,
online_start_iteration=0),
leaf_head=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=64,
pos_fraction=0.25,
neg_pos_ub=3.0,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=6000,
max_per_img=300,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.3),
max_per_img=100),
child_head=dict(
bbox_score_thr=0.5, verbose_relation=False, average_scores=False),
leaf_head=dict(score_thr=0.5, nms=None, max_per_img=100)))
dataset_type = 'REGRADAffordDataset'
data_root = 'data/regrad/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[1.0, 1.0, 1.0], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='RandomCrop', crop_type='random_keep', allow_negative_crop=False),
dict(type='Expand', mean=[123.675, 116.28, 103.53], ratio_range=(1, 2)),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves']),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
]
data = dict(
train=dict(
_delete_=True,
type='ConcatDataset',
datasets=[
dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
meta_info_file='dataset_train_5k/meta_infos.json',
ann_file='dataset_train_5k/objects.json',
img_prefix='dataset_train_5k/RGBImages',
seg_prefix='dataset_train_5k/SegmentationImages',
depth_prefix='dataset_train_5k/DepthImages',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=[
'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='RandomCrop',
crop_type='random_keep',
allow_negative_crop=False),
dict(type='Expand', mean=[123.675, 116.28, 103.53]),
dict(
type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
])
],
min_pos_relation=1,
class_agnostic=True),
dict(
type='MetaGraspNetAffordDataset',
data_root='data/metagraspnet/sim/',
meta_info_file='meta_infos_train.json',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=[
'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='RandomCrop',
crop_type='random_keep',
allow_negative_crop=False),
dict(
type='Expand',
mean=[123.675, 116.28, 103.53],
ratio_range=(1, 2)),
dict(
type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
])
],
min_pos_relation=1,
class_agnostic=True),
dict(
type='VMRDAffordDataset',
ann_file='data/vmrd/ImageSets/Main/trainval.txt',
img_prefix='data/vmrd/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=[
'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Expand', mean=[123.675, 116.28, 103.53]),
dict(
type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
])
],
class_agnostic=True),
dict(
type='VRDAffordDataset',
data_root='data/vrd/',
ann_file='sg_dataset/sg_train_annotations.json',
img_prefix='sg_dataset/sg_train_images/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=[
'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
])
],
class_agnostic=True),
dict(
type='VGAffordDataset',
data_root='data/vg/downloads',
ann_file='relationships.json',
img_prefix='',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=[
'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves'
])
],
class_agnostic=True)
],
separate_eval=True,
class_agnostic=True),
val=dict(
_delete_=True,
type='ConcatDataset',
datasets=[
dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='dataset_seen_val_1k/meta_infos.json',
ann_file='dataset_seen_val_1k/objects.json',
img_prefix='dataset_seen_val_1k/RGBImages',
seg_prefix='dataset_seen_val_1k/SegmentationImages',
depth_prefix='dataset_seen_val_1k/DepthImages',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img', 'relation_proposals'])
])
],
class_agnostic=True,
max_sample_num=1000),
dict(
type='VMRDAffordDataset',
ann_file='data/vmrd/ImageSets/Main/test.txt',
img_prefix='data/vmrd/',
using_gt_proposals=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img', 'relation_proposals'])
])
],
class_agnostic=True)
],
separate_eval=True,
class_agnostic=True),
test=dict(
_delete_=True,
type='ConcatDataset',
datasets=[
dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='dataset_seen_val_1k/meta_infos.json',
ann_file='dataset_seen_val_1k/objects.json',
img_prefix='dataset_seen_val_1k/RGBImages',
seg_prefix='dataset_seen_val_1k/SegmentationImages',
depth_prefix='dataset_seen_val_1k/DepthImages',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img', 'relation_proposals'])
])
],
class_agnostic=True,
max_sample_num=1000),
dict(
type='VMRDAffordDataset',
ann_file='data/vmrd/ImageSets/Main/test.txt',
img_prefix='data/vmrd/',
using_gt_proposals=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img', 'relation_proposals'])
])
],
class_agnostic=True)
],
separate_eval=True,
class_agnostic=True),
samples_per_gpu=4,
workers_per_gpu=2)
evaluation = dict(interval=1, metric=['mAP', 'ImgAcc'])
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=100, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=4000,
warmup_ratio=0.001,
step=[12, 18])
runner = dict(type='EpochBasedRunner', max_epochs=20)
checkpoint_config = dict(interval=1, max_keep_ckpts=5)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
mmdet = None
mmdet_root = '/data/home/hanbo/projects/cloud_services/service/vmrn/vmrn_models/mmdetection/mmdet'
test_with_object_detector = False
test_crop_config = (174, 79, 462, 372)
kinect_img_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='FixedCrop',
crop_type='absolute',
top_left=(174, 79),
bottom_right=(462, 372)),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
]
seen_val_dataset = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='dataset_seen_val_1k/meta_infos.json',
ann_file='dataset_seen_val_1k/objects.json',
img_prefix='dataset_seen_val_1k/RGBImages',
seg_prefix='dataset_seen_val_1k/SegmentationImages',
depth_prefix='dataset_seen_val_1k/DepthImages',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True,
max_sample_num=1000)
unseen_val_dataset = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='dataset_unseen_val_1k/meta_infos.json',
ann_file='dataset_unseen_val_1k/objects.json',
img_prefix='dataset_unseen_val_1k/RGBImages',
seg_prefix='dataset_unseen_val_1k/SegmentationImages',
depth_prefix='dataset_unseen_val_1k/DepthImages',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True,
max_sample_num=1000)
real_val_dataset = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='real/meta_infos.json',
ann_file='real/objects.json',
img_prefix='real/RGBImages',
img_suffix='png',
depth_prefix='real/DepthImages',
test_mode=True,
test_gt_bbox_offset=(174, 79),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='FixedCrop',
crop_type='absolute',
top_left=(174, 79),
bottom_right=(462, 372)),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True)
regrad_datatype = 'REGRADAffordDataset'
regrad_root = 'data/regrad/'
vmrd_datatype = 'VMRDAffordDataset'
vmrd_root = 'data/vmrd/'
vmrd_train = dict(
type='VMRDAffordDataset',
ann_file='data/vmrd/ImageSets/Main/trainval.txt',
img_prefix='data/vmrd/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Expand', mean=[123.675, 116.28, 103.53]),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
])
],
class_agnostic=True)
regrad_train = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
meta_info_file='dataset_train_5k/meta_infos.json',
ann_file='dataset_train_5k/objects.json',
img_prefix='dataset_train_5k/RGBImages',
seg_prefix='dataset_train_5k/SegmentationImages',
depth_prefix='dataset_train_5k/DepthImages',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='RandomCrop',
crop_type='random_keep',
allow_negative_crop=False),
dict(type='Expand', mean=[123.675, 116.28, 103.53]),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
])
],
min_pos_relation=1,
class_agnostic=True)
metagraspnet_sim_train = dict(
type='MetaGraspNetAffordDataset',
data_root='data/metagraspnet/sim/',
meta_info_file='meta_infos_train.json',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='RandomCrop',
crop_type='random_keep',
allow_negative_crop=False),
dict(
type='Expand', mean=[123.675, 116.28, 103.53], ratio_range=(1, 2)),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
])
],
min_pos_relation=1,
class_agnostic=True)
vgvrd_train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds',
'gt_relleaves']),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'])
]
vrd_train = dict(
type='VRDAffordDataset',
data_root='data/vrd/',
ann_file='sg_dataset/sg_train_annotations.json',
img_prefix='sg_dataset/sg_train_images/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
])
],
class_agnostic=True)
vg_train = dict(
type='VGAffordDataset',
data_root='data/vg/downloads',
ann_file='relationships.json',
img_prefix='',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotationsCustom',
keys=['gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves']),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(
type='DefaultFormatBundleCustom',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
]),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_relchilds', 'gt_relleaves'
])
],
class_agnostic=True)
real_test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='FixedCrop',
crop_type='absolute',
top_left=(174, 79),
bottom_right=(462, 372)),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
]
regrad_seen_val_dataset = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='dataset_seen_val_1k/meta_infos.json',
ann_file='dataset_seen_val_1k/objects.json',
img_prefix='dataset_seen_val_1k/RGBImages',
seg_prefix='dataset_seen_val_1k/SegmentationImages',
depth_prefix='dataset_seen_val_1k/DepthImages',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True,
max_sample_num=1000)
regrad_unseen_val_dataset = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='dataset_unseen_val_1k/meta_infos.json',
ann_file='dataset_unseen_val_1k/objects.json',
img_prefix='dataset_unseen_val_1k/RGBImages',
seg_prefix='dataset_unseen_val_1k/SegmentationImages',
depth_prefix='dataset_unseen_val_1k/DepthImages',
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True,
max_sample_num=1000)
regrad_real_val_dataset = dict(
type='REGRADAffordDataset',
data_root='data/regrad/',
using_depth=False,
using_gt_proposals=True,
meta_info_file='real/meta_infos.json',
ann_file='real/objects.json',
img_prefix='real/RGBImages',
img_suffix='png',
depth_prefix='real/DepthImages',
test_mode=True,
test_gt_bbox_offset=(174, 79),
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='FixedCrop',
crop_type='absolute',
top_left=(174, 79),
bottom_right=(462, 372)),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True)
vmrd_val_dataset = dict(
type='VMRDAffordDataset',
ann_file='data/vmrd/ImageSets/Main/test.txt',
img_prefix='data/vmrd/',
using_gt_proposals=True,
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadRelationProposals'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1.0, 1.0, 1.0],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img', 'relation_proposals'])
])
],
class_agnostic=True)
train_sampler = dict(
type='DistributedWeightedSampler',
weights=[0.1, 0.1, 0.05, 0.05, 0.7],
sample_per_epoch=150000,
shuffle=True)
work_dir = './work_dirs/relation_afford_r101_caffe_c4_1x_regrad_vmrd_metagraspnet_vrd_vg_class_agnostic'
gpu_ids = range(0, 8)
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